Biomarker combination and model for predicting calendar age and application of biomarker combination and model

Abstract

The invention discloses a biomarker combination for predicting calendar age, a model and application of the biomarker combination. 57 kinds of DNA methylated CpG sites screened from many elderly subjects are creatively introduced, through multiple detection and large-scale parallel DNA sequencing, operation is easy, the result is highly repeatable, and the age prediction model constructed based on the method is good in stability and accuracy and high in sensitivity and has good application prospects. Calendar age can be predicted from trace blood samples associated with forensic cases.

Description

Technical Field

[0001] This invention belongs to the field of biological detection technology, specifically relating to a combination of biomarkers, a model, and their applications for predicting calendar age. Background Technology

[0002] In the context of forensic medicine, age estimation is particularly valuable. Calendar age (also known as chronological age or true age) can be predicted by assessing DNA methylation status, although the accuracy of different models may vary.

[0003] Forensic DNA phenotyping not only utilizes genetic polymorphism but also epigenetic modifications, namely DNA methylation, to characterize the biological features of unknown individuals. [1] DNA methylation refers to the attachment of a guanine (G) methyl group after cytosine (C), commonly known as a CpG site, where p represents the phosphodiester bond between the two nucleotides. Recent studies have reported models for estimating age through DNA methylation status, based on the ratio between methylated and unmethylated CpG forms. However, their predictive accuracy varies greatly, typically differing from actual age by 3 to 5 years or more. [2–4] Furthermore, these models analyze DNA methylation using low-throughput assays such as pyrosequencing or PCR-based SNaPshot, thus limiting their ability to process large numbers of samples. [5,6] .

[0004] Mounting evidence suggests that aging and disease are associated with a loss of fidelity in DNA methylation epigenetic drift, i.e., random changes in methylation status. [7–9] Currently available age prediction models involve the use of five to eight selected CpG sites, therefore their practical performance may be affected by aging and pathophysiological conditions. [4,5,10] In fact, previous models have already documented a significant decline in predictive accuracy in older populations. [5] It is noteworthy that, regardless of human race and applicable bodily fluids, these models are concentrated on CpG islands of the ELOVL2, FHL2, KLF14, miR29B2C, and TRIM59 genes. [4,5,11–13] This suggests that these regions may contain DNA methylation changes largely consistent with age progression, and that other age-related CpG sites within these regions could be used to improve the accuracy of age prediction.

[0005] References

[0006] [1]M.Kayser,Forensic DNAPhenotyping:Predicting human appearance fromcrime scene material for investigative purposes,Forensic Sci Int Genet 18(2015)33–48.

[0007] https: / / doi.org / 10.1016 / J.FSIGEN.2015.02.003.

[0008] [2]B.Bekaert,A.Kamalandua,S.C.Zapico,W.Van de Voorde,R.Decorte,Aselective set of DNA-methylation markers for age determination of blood,teeth and buccal samples,Forensic Sci Int Genet Suppl Ser 5(2015)e144–e145.

[0009] https: / / doi.org / 10.1016 / j.fsigss.2015.09.058.

[0010] [3]B.Bekaert,A.Kamalandua,S.C.Zapico,W.Van De Voorde,R.Decorte,Improved age determination of blood and teeth samples using a selected set ofDNA methylation markers,Epigenetics 10(2015)922–930.https: / / doi.org / 10.1080 / 15592294.2015.1080413.

[0011] [4]SEJung, SMLim, SRHong, EHLee, KJShin, HYLee, DNA methylationof the ELOVL2, FHL2, KLF14, C1orf132 / MIR29B2C, and TRIM59 genes for age prediction from blood, saliva, and buccal swab samples, Forensic Sci Int Genes38(2019)1–8.

[0012] [5]R. M.Spolnicka,T.Kupiec,A.Parys-Proszek,Z.Makowska,A.

[0013] K.Kucharczyk,R. W.Branicki,Development of a forensicallyuseful age prediction method based on DNA methylation analysis,Forensic SciInt Genet 17(2015)173–179.

[0014] [6]HYLee, SEJung, YNOh, A. Choi, WIYang, KJShin, Epigenetic age signatures in the forensically relevant body fluid of semen: a preliminarystudy, Forensic Sci Int Genet 19(2015)28–34.https: / / doi.org / 10.1016 / J.phys.

[0015] [7]M.Alimohammadi,S.Makaremi,A.Rahimi,V.Asghariazar,M.Taghadosi,E.Safarzadeh,DNA methylation changes and inflammation in aging-associateddiseases,Epigenomics 14(2022)965–986.https: / / doi.org / 10.2217 / EPI-2022-0143.

[0016] [8]Y.Salameh,Y.Bejaoui,N.El Hajj,DNA Methylation Biomarkers in Agingand Age-Related Diseases,Front Genet 11(2020).https: / / doi.org / 10.3389 / FGENE.2020.00171 / FULL.

[0017] [9] J. Wu, L. Lin Liu, M. Cao, A. Hu, D. Hu, Y. Luo, H. Wang, J. Ning Zhong, Exp EyeRes 211(2021).

[0018]

[10] A.Freire-Plows,C.Phillips,A.Mosquera-Michael,L. A.Gómez-Tato,M.Lime Marriages,J. J.Ansede-Bermejo,M. PMSchneider,E. W.Branicki,Carracedo,M.V.Lareu,Development of amethylation marker set for forensic age estimation using analysis of publicmethylation data and the Agena Bioscience EpiTYPER system,Forensic Sci IntGenet 24(2016)65–74.https: / / doi.org / 10.1016 / J.FSIGEN.2016.06.005.

[0019]

[11] S.Cho,S.E.Jung,S.R.Hong,E.H.Lee,J.H.Lee,S.D.Lee,H.Y.Lee,Independent validation of DNA-based approaches for age prediction in blood,Forensic Sci Int Genet 29(2017)250–256.https: / / doi.org / 10.1016 / j.fsigen.2017.04.020.

[0020]

[12] A.Freire-Aradas,C.Phillips,L. A.Mosquera-Miguel,A.Gómez-Tato, Casares de Cal,J. M.V.Lareu,Tracking age-correlatedDNA methylation markers in the young,Forensic Sci Int Genet 36(2018)50–59.

[0021] https: / / doi.org / 10.1016 / j.fsigen.2018.06.011.

[0022]

[13] Summary of the Invention

[0023] Due to medical history and age, older adults may be coupled with epigenetic drift that affects DNA methylation status; therefore, current models can only select CpG sites that are largely age-related. This invention aims to improve the accuracy of age prediction by reducing the impact of epigenetic drift, providing a combination of biomarkers, a model, and its applications for predicting calendrical age. A discovery cohort was carefully designed during model construction to recruit more older subjects than other age groups; and the model's performance was evaluated using trace blood samples obtained directly from forensic case work through independent validation cohorts and single-blind tests. This invention demonstrates that when multiplexing of target regions is combined with massively parallel DNA sequencing, calendrical age can be predicted from blood samples with unprecedented accuracy, sensitivity, batch processing convenience, and low cost.

[0024] Specifically,

[0025] A first aspect of the present invention is to provide a combination of biomarkers for predicting calendar age, the combination of biomarkers comprising one or more of the following DNA methylation sites: chr1:207823637, chr1:207823657, chr1:207823660, chr1:207823672, chr1:207823675, chr1:207823681, chr1:207823702, chr1:207823705, chr1:207823715, chr1:207823723, chr2:105399282. chr2:105399288, chr2:105399291, chr2:105399297, chr2:105399300, chr2:105399310, chr2:105399316, chr2:105399323, chr2:10 5399327, chr2:105399338, chr2:105399340, chr2:105399360, chr2:105399363, chr2:105399388, chr3:160450172, chr3:160450174, chr3:160450179, chr3:160450184, chr3:160450189, chr3:160450192, chr3:160450199, chr3:160450202, chr3:160450231, chr3:16 0450238、chr3:160450243、chr3:160450268、chr3:160450283、chr6:11044628、chr6:11044631、chr6:11044634、chr6:11044640、chr6 :11044642, chr6:11044644, chr6:11044647, chr6:11044655, chr6:11044661, chr6:11044683, chr6:11044695, chr6:11044702, chr6:11044710, chr6:11044726, chr6:11044741, chr7:130734355, chr7:130734357, chr7:130734375, chr7:130734398 and chr7:130734412.

[0026] A second aspect of the invention is to provide a reagent for detecting the combination of biomarkers described in the first aspect of the invention.

[0027] In some embodiments, the reagents include reagents for detecting the ELOVL2, FHL2, KLF14, miR29B2C, and / or TRIM59 genes.

[0028] In some preferred embodiments, the reagent includes primers for detecting the ELOVL2, FHL2, KLF14, miR29B2C, and / or TRIM59 genes, which can be designed using conventional software in the art. In some specific embodiments, the reagent includes primers with sequences as shown in SEQ ID NO:1–10.

[0029] A third aspect of the present invention is to provide a kit comprising the reagents described in the second aspect of the present invention; preferably, it further comprises a probe that specifically binds to the biomarker combination described in the first aspect of the present invention and / or the biomarker combination described in the first aspect of the present invention.

[0030] A fourth aspect of the present invention is to provide the use of the biomarker combination described in the first aspect of the present invention, the reagent described in the second aspect of the present invention, or the kit described in the third aspect of the present invention in the preparation of products for predicting calendar age.

[0031] A fifth aspect of the present invention is to provide a method for constructing a calendar age prediction model, comprising:

[0032] The calendar age prediction model is constructed by using the DNA methylation level data of the biomarker combination described in the first aspect of the present invention through elastic network regression.

[0033] Preferably, the construction method includes hyperparameter tuning, wherein the optimal hyperparameters determined by the hyperparameter tuning are preferably α = 0.01 and λ = 1.2; and / or, the methylation level data is obtained by bismark extraction.

[0034] A sixth aspect of the present invention is to provide a calendar age prediction model, which is constructed by the construction method described in the fifth aspect of the present invention.

[0035] A seventh aspect of the present invention is to provide a method for predicting calendar age, wherein biomarker data of a sample to be tested is input into the calendar age prediction model described in the sixth aspect of the present invention to obtain the calendar age prediction result of the individual sample to be tested; wherein the biomarker data includes methylation level data in the biomarker combination described in the first aspect of the present invention;

[0036] Preferably, the sample to be tested is a blood sample or a swab sample;

[0037] More preferably, the method is not for diagnostic purposes.

[0038] An eighth aspect of the present invention is to provide a system for predicting calendar age, comprising:

[0039] A data receiving module is used to receive or input biomarker data of a sample to be tested, wherein the biomarker data includes methylation level data in the biomarker combination described in the first aspect of the present invention;

[0040] The judgment and output module is used to, after the reception or input is completed, output the calendar age prediction result of the individual in the test sample according to the calendar age prediction model described in the sixth aspect of the present invention; the calendar age prediction result is obtained by the following calculation formula: Predicted age (years) = -31.78052433 + 0.0150860653912567 * meth_C1orf132_C1 + 0.0370236766736256 * meth_C1orf132_C2 + 0.00519730532636727 * meth_C1orf132_C3 - 0.0666874453401467 * meth_C1orf13 2_C4-0.0312715497589939*meth_C1orf132_C5-0.0418141103700104*meth_C1orf132_C6-0.0129505545870815*meth_C1orf132_C7-0.04217659 76570936*meth_C1orf132_C8+0.110942889396701*meth_C1orf132_C9-0.0434390670182746*meth_C1orf132_C10+0.115187654225682*meth_FH L2_C1+0.121095331770076*meth_FHL2_C2-0.000776827565128392*meth_FHL2_C3+0.0583308602939788*meth_FHL2_C4+0.0979638121567077*m eth_FHL2_C5-0.0348383417317097*meth_FHL2_C6-0.0710963975147093*meth_FHL2_C7+0.0421765976570936*meth_FHL2_C8-0.2318710846545 91*meth_FHL2_C9-0.0034801475857648*meth_FHL2_C10-0.190250111317891*meth_FHL2_C11-0.580991175082793*meth_FHL2_C12-0.06925610 31841335*meth_FHL2_C13+0.105071719329159*meth_FHL2_C14-0.140249060504829*meth_TRIM59_C1-0.094402413301233*meth_TRIM59_C2+0.116398296522688*meth_TRIM59_C3+0.0515119879390015*meth_TRIM59_C4+0.115271347258114*meth_TRI M59_C5+0.0624591881492509*meth_TRIM59_C6+0.100362625352148*meth_TRIM59_C7+0.054443692101716*meth_TRIM59_C8-0.00812296487786016*meth_TRIM59_C9+0.0224429733600165*meth_TRIM59_C10+0.248359413351082*meth_TRIM59_C11+0.0203210868696916*meth_TRIM59_C12+0.096369899778472*meth_TRIM59_C13+0.141459984636301*meth_ELOVL2_C1-0.0377448398680573*meth_ELOVL2_C2+0.232612236618917*meth_ELOVL2_C3+0.280794808203567*meth_ELOVL2_C4+0.050745509888902*meth_ELOV L2_C5+0.000231987731572179*meth_ELOVL2_C6+0.0197998557002511*me th_ELOVL2_C7-0.000169612298385955*meth_ELOVL2_C8+0.23184952572738*meth_ELOVL2_C9-0.0741419471439054*meth_ELOVL2_C10+0.071139048523776*meth_ELOVL2_C11-0.06415924131288*meth_ELOVL2_C12+0.0661337244642001*meth_ELOVL2_C13-0.0410039539300835*meth_ELOVL2_C14+0.0248273034704668*meth_ELOVL2_C15+0.0831682981701047*meth_KL F14_C1+0.175155550258806*meth_KLF14_C2+0.0728269428045073*meth_KLF14_C3+0.452157943034236*meth_KLF14_C4-0.145945555503622*meth_KLF14_C5, where "meth_" refers to the methylation level of the corresponding DNA methylation site.

[0041] Preferably, the system further includes a data processing module for collecting the biomarker data.

[0042] A ninth aspect of the present invention is to provide a computer-aided method for predicting calendar age, comprising the following steps:

[0043] Step 1: Receive or input biomarker data from the sample to be tested, wherein the biomarker data includes methylation level data in the biomarker combination described in the first aspect of the present invention;

[0044] Step 2: Input the biomarker data received or input in Step 1 into the calendar age prediction model described in the sixth aspect of this invention to obtain the calendar age prediction result of the individual in the test sample; the calendar age prediction result is obtained by the following calculation formula: Predicted age (years) = -31.78052433 + 0.0150860653912567 * meth_C1orf132_C1 + 0.0370236766736256 * meth_C1orf132_C2 + 0.00519730532636727 * meth_C1orf132_C3 - 0.0666874453401467 * met h_C1orf132_C4-0.0312715497589939*meth_C1orf132_C5-0.0418141103700104*meth_C1orf132_C6-0.01295 05545870815*meth_C1orf132_C7-0.0421765976570936*meth_C1orf132_C8+0.110942889396701*meth_C1orf 132_C9-0.0434390670182746*meth_C1orf132_C10+0.115187654225682*meth_FHL2_C1+0.121095331770076* meth_FHL2_C2-0.000776827565128392*meth_FHL2_C3+0.0583308602939788*meth_FHL2_C4+0.0979638121567 077*meth_FHL2_C5-0.0348383417317097*meth_FHL2_C6-0.0710963975147093*meth_FHL2_C7+0.0421765976 570936*meth_FHL2_C8-0.231871084654591*meth_FHL2_C9-0.0034801475857648*meth_FHL2_C10-0.19025011 1317891*meth_FHL2_C11-0.580991175082793*meth_FHL2_C12-0.0692561031841335*meth_FHL2_C13+0.1050 71719329159*meth_FHL2_C14-0.140249060504829*meth_TRIM59_C1-0.094402413301233*meth_TRIM59_C2+0.116398296522688*meth_TRIM59_C3+0.0515119879390015*meth_TRIM59_C4+0.115271347258114*meth_TRIM59_C5+0.0624591881492509*meth_TRIM59_C6+0.100362625352148*meth_TRI M59_C7+0.054443692101716*meth_TRIM59_C8-0.00812296487786016*meth_TRIM59_C9+0.0224429733600165*meth_TRIM59_C10+0.248359413351082*meth_TRIM59_C11+0.0203210868696916*meth_TRIM59_C12+0.096369899778472*meth_TRIM59_C13+0.141459984636301*meth_ELOVL2_C1-0.0377448398680573*meth_ELOVL2_C2+0.232612236618917*meth_ELOVL2_C3+0.280794808203567*meth_ELOVL2_C4+0.050745509888902*meth_ELOVL2_C5+0.000231987731572179*meth_ELOVL2_C6+0.0197998557002511*meth_EL OVL2_C7-0.000169612298385955*meth_ELOVL2_C8+0.23184952572738*me th_ELOVL2_C9-0.0741419471439054*meth_ELOVL2_C10+0.071139048523776*meth_ELOVL2_C11-0.06415924131288*meth_ELOVL2_C12+0.0661337244642001*meth_ELOVL2_C13-0.0410039539300835*meth_ELOVL2_C14+0.0248273034704668*meth_ELOVL2_C15+0.0831682981701047*meth_KLF14_C1+0.175155550258806*meth_KLF14_C2+0.0728269428045073*meth_KLF14_C3+0.452157943034236*meth_KLF14_C4-0.145945555503622*meth_KLF14_C5, where "meth_" refers to the methylation level of the corresponding DNA methylation site.

[0045] The tenth aspect of the present invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the functions of the system described in the eighth aspect of the present invention, or implement the steps of the computer-aided method for predicting calendar age described in the ninth aspect of the present invention.

[0046] The eleventh aspect of the present invention is to provide an electronic device comprising a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the functions of the system described in the eighth aspect of the present invention, or the steps of the computer-aided method for predicting calendar age described in the ninth aspect of the present invention.

[0047] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of the present invention.

[0048] The reagents and raw materials used in this invention are all commercially available.

[0049] The positive and progressive effects of this invention are as follows:

[0050] This invention creatively incorporates a large number of elderly subjects to screen 57 DNA methylation CpG sites. Through multiplex detection and massively parallel DNA sequencing, the operation is simple and the results are highly reproducible. The age prediction model constructed based on this has good stability, accuracy and sensitivity, and can predict calendar age from trace blood samples related to forensic cases. Attached Figure Description

[0051] Figure 1 Basic information about the participants in the training cohort of the age prediction model is displayed.

[0052] Figure 2 The results of PCR monomeric and multiplex reactions targeting the C1orf132, FHL2, TRIM59, ELOVL2, and KLF14 gene regions are shown.

[0053] Figure 3 The methylation levels of 60 methylation sites were shown in the training cohort participants, where the methylation level was greater than 0 in more than 70% of the population.

[0054] Figure 4 This demonstrates the age prediction model based on the training cohort on the training set ( Figure 4 Part A) and validation set ( Figure 4The performance test results of Part B).

[0055] Figure 5 The performance validation results of the age prediction model based on the training queue are shown.

[0056] Figure 6 Basic information about the participants in the validation cohort of the age prediction model is displayed.

[0057] Figure 7 The data shows the methylation levels of 60 methylation sites in the validation cohort participants whose methylation levels were greater than 0 in more than 70% of the population.

[0058] Figure 8 The performance validation results of the age prediction model based on the validation queue are shown.

[0059] Figure 9 The accuracy of the age prediction model across different age groups in the test population is shown.

[0060] Figure 10 The results show the performance of the age prediction model validated using a single-blind test.

[0061] Figure 11 Zbiec-Piekarska et al. showed a training queue-based approach. Figure 11 Part A) and Jung et al. Figure 11 (Part B) Performance test results of previously published age prediction models.

[0062] Figure 12 The results show the results of three age predictions for a subject of known age using an age prediction model.

[0063] Figure 13 The results show the age prediction of a known age subject based on three different blood sample volumes using an age prediction model.

[0064] Figure 14 This is a schematic diagram of the system for predicting calendar age; where 51: data processing module, 52: data receiving module, 53: judgment and output module, and 61: the system for predicting calendar age.

[0065] Figure 15 This is a schematic diagram of an electronic device; where 9: electronic device, 91: processor, 92: memory, 921: random access memory (RAM), 922: cache memory, 923: read-only memory (ROM), 924: program module, 925: program tool, 93: bus, 94: external device, 95: input / output interface, 96: network adapter. Detailed Implementation

[0066] The present invention is further illustrated below by way of embodiments, but the invention is not limited to the scope of the embodiments described herein. Experimental methods in the following embodiments that do not specify specific conditions were performed according to conventional methods and conditions, or as selected according to the product instructions.

[0067] In this invention, "year" and "age" are used interchangeably as units of age.

[0068] Example 1 Data Collection

[0069] I. Participants

[0070] Training cohort: Includes 191 Han Chinese volunteers from communities in Baoshan District, Shanghai. Basic information of these 191 participants is as follows: Figure 1 As shown.

[0071] II. Methods

[0072] 1. Blood extraction (MagPure Uninversal DNA Kit B, Guangzhou Meiji Biotechnology Co., Ltd., D6310-03B)

[0073] 1.1) Preparation of the sample to be tested

[0074] A: Sample type - Blood: Add 50-300 μl of body fluid sample and 20 μl of Proteinase K Solution to a 1.5 ml centrifuge tube;

[0075] B: Sample type - swab card: Cut the swab card into pieces and place it into the corresponding numbered 1.5ml centrifuge tube. Add 600μl ATL and 20μl Proteinase K, incubate at 55℃ for 90min, centrifuge briefly at 1200rpm, and transfer the supernatant to a new 1.5ml centrifuge tube.

[0076] 1.2) Add 10 μl of RNase Solution to the digestion solution, mix well, and let stand at room temperature for 15 min;

[0077] 1.3) Add 500 μl Buffer MLA and 30 μl MagPure Particles to the sample, invert and mix 15 times, let stand at room temperature for 10 min, invert and mix several times during the process;

[0078] 1.4) Transfer to a magnetic rack for adsorption for 1 minute, then discard or aspirate the solution;

[0079] 1.5) Add 500 μl Buffer DW1, vortex for 15 s, transfer to a magnetic rack for adsorption for 1 min, and discard or aspirate the solution;

[0080] 1.6) Add 500 μl Buffer DW1, vortex for 15 s, transfer to a magnetic rack for adsorption for 1 min, and discard or aspirate the solution;

[0081] 1.7) Add 500 μl of Buffer EW, vortex for 15 s, transfer to a magnetic rack for adsorption for 1 min, and discard or aspirate the solution;

[0082] 1.8) Slowly add 500 μl of Buffer BW3, without breaking the magnetic beads, let stand for 50 seconds, carefully aspirate all the solution, briefly centrifuge to collect the droplets on the tube wall, and aspirate the remaining liquid (the centrifuge tube does not need to be removed from the magnetic field).

[0083] 1.9) Add 80 μl of Elution Buffer, vortex to break up the magnetic beads, and incubate at 55°C with high-speed oscillation for 10 min;

[0084] 1.10) Transfer the DNA to a magnetic rack for 2 min to adsorb, then transfer the DNA to a new centrifuge tube, measure the concentration with NanoDrop, and then store it in a -80°C freezer.

[0085] 2. Bisulfite Conversion (Methylation Detection Sample Pretreatment Kit, Jiangsu Kunyuan Biotechnology Co., Ltd., EP110192)

[0086] 2.1) Add 500-1000 ng of DNA to a 0.2 mL PCR reaction tube. If the volume of DNA added is less than 60 μl, make up the difference to 60 μl with nuclease-free water.

[0087] 2.2) Add 100 μl of pre-mixed conversion buffer and 20 μl of protective buffer to a 0.2 mL PCR reaction tube containing DNA, vortex to mix and centrifuge (take an appropriate amount of conversion buffer B and 4 times the volume of conversion buffer A according to the experimental amount, mix well, and use the conversion buffer fresh).

[0088] 2.3) Place the 0.2 mL PCR reaction tube on the PCR instrument and run the program shown in Table 1:

[0089] Table 1 Reaction Procedure

[0090]

[0091] 2.4) Add 600 μl of binding solution to the adsorption column, add all the above products (about 180 μl) to the adsorption column containing the binding solution, invert and mix at least ten times, centrifuge at 12000g for 30s, and discard the filtrate in the collection tube.

[0092] 2.5) Add 300 μl of washing solution to the adsorption column and centrifuge at 12000g for 30s;

[0093] 2.6) Add 200 μl of desulfurization reagent to the adsorption column (Note: Do not centrifuge immediately), incubate at room temperature for 20 min, and then centrifuge at 12000g for 30 s;

[0094] 2.7) Add 300 μl of washing solution to the adsorption column, centrifuge at 12000g for 30s, and discard the filtrate in the collection tube;

[0095] 2.8) Add 300 μl of washing solution to the adsorption column and centrifuge at 12000g for 30s;

[0096] 2.9) Add 300 μl of anhydrous ethanol to the adsorption column, centrifuge at 12000g for 30s, and discard the filtrate in the collection tube.

[0097] 2.10) Centrifuge at 12000g for 2 min, discard the collection tube, carefully transfer the adsorption column to a clean 1.5ml centrifuge tube, open the cap and let stand for 3 min (Note: leave the collection tube in the centrifuge when removing the adsorption column to avoid liquid splashing);

[0098] 2.11) Add 17 μl of eluent to the adsorption column, incubate at room temperature for 2 min, and centrifuge at 12000g for 2 min;

[0099] 2.12) Take 1 μl of sample, use Qubit to quantify the concentration, write the concentration on the side wall of the centrifuge tube, and then store it in a -20℃ refrigerator.

[0100] 3. PCR amplification and library construction

[0101] 3.1) Preparation of primer mixture

[0102] The primer information used is shown in Table 2 below.

[0103] Table 2. Primers used for PCR amplification and the length of the amplified fragments.

[0104]

[0105] The concentration of each primer was 100 μM. Take 4 μl of ELOVL2-F / R, 3 μl of C1orf132-F / R, 2.9 μl of TRIM59-F / R, 13.5 μl of KLF14-F / R, and 8.5 μl of FHL2-F / R, and add 36.2 μl of ddH2O to make a primer mixture of 100 μl.

[0106] 3.2) Library preconstruction was performed using KAPA2G Fast Multiplex Mix (Roche, catalog number: 2GFMPXKB). The total PCR reaction volume was 25 μl, containing 10 ng template DNA, 1 μl primer mixture, 12.5 μl 2X KAPA2G Fast Multiplex Mix, and ddH2O. The PCR program included: initial denaturation at 95°C for 5 minutes; 25 cycles of amplification (95°C denaturation for 15 seconds, 58°C annealing for 15 seconds, 72°C extension for 30 seconds); and a final extension at 72°C for 5 minutes.

[0107] Simultaneously, monomeric PCR reactions were performed using these primers, and the results were as follows: Figure 2 As shown. By Figure 2 It is evident that each target region amplified by multiplex PCR is as demonstrated by a single PCR product.

[0108] 3.3) Purification

[0109] The amplified prehybridized library was purified using VAHTS DNA Clean Beads (Vazyme, catalog number: N411-02). The volume ratio for the first round of screening was 0.9 (DNA Clean Beads: DNA), and the volume ratio for the second round of screening was 0.3.

[0110] 3.4) Secondary amplification

[0111] The purified product was amplified a second time using the PCR enzyme TaKaRa Ex Taq (TaKaRa, catalog number: RR53A) and specific adapter primers (F: AATGATACGGCGACCACCGAGATCTACAC AGCGCTAGACACTCTTTCCCTACACGACGCTCTTCCGATCT (SEQ ID NO:11); R: CAAGCAGAAGACGGCATACGAGATAACCGCGGGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT (SEQ ID NO:12)). The total volume of the PCR reaction was 25 μl, containing 10 μl of purified product, 2.5 μl of 10×Ex Taq Buffer, 2 μl of dNTPs, 1 μl of TaKaRa Ex Taq, 1 μl each of adapter-specific primers F and R, and 8.4 μl of ddH2O. The PCR program includes: an initial denaturation step at 95°C for 5 minutes; 13 cycles of amplification (denaturation at 95°C for 15 seconds, annealing at 58°C for 15 seconds, extension at 72°C for 30 seconds); and a final extension at 72°C for 5 minutes.

[0112] 3.5) Purification was performed using VAHTS DNA Clean Beads. The volume ratio for the first round of screening was 0.7 (DNAClean Beads:DNA), and the volume ratio for the second round of screening was 0.3. Qubit was used.

[0113] The final library concentration was determined using Invitrogen 2.0, and then sequenced using an Illumina NovaSeq 6000 system (paired end; 150bp).

[0114] Example 2 Data Analysis

[0115] The raw sample data were processed using Bismark software.

[0116] High-throughput sequencing data analysis: All sequencing reads were processed using Trim Galore (v0.6.6) with the parameter "--nextseq 30--paired" to remove adapter sequences (AGATCGGAAGAGC) from the NovaSeq platform, retaining reads longer than 20 bp. Reads that passed the quality control procedure were retained and mapped to the human genome (GRCh38) using bismark (v0.24.1) with default parameters. Uniquely mapped read pairs were extracted using samtools (v1.17). Methylation levels were extracted using bismark.

[0117] Example 3: Construction of an Age Prediction Model

[0118] After obtaining methylation data, methylation sites were screened, identifying 60 methylation sites with methylation levels greater than 0 in individuals with methylation levels above 70%. These sites are: chr1:207823637, chr1:207823657, chr1:207823660, chr1:207823672, chr1:207823675, chr1:207823681, chr1:207823702, chr1:207823705, chr1:207823715, chr1:207823723, chr2:105399282, and chr2:105399288. chr2:105399291, chr2:105399297, chr2:105399300, chr2:105399310, chr2:105399314, chr2:105399316, chr2:105399323, chr2:1053993 27. chr2:105399338, chr2:105399340, chr2:105399360, chr2:105399363, chr2:105399388, chr3:160450172, chr3:160450174, chr3:16045 0179, chr3:160450184, chr3:160450189, chr3:160450192, chr3:160450199, chr3:160450202, chr3:160450231, chr3:160450238, chr3:16 0450243, chr3:160450268, chr3:160450283, chr6:11044628, chr6:11044631, chr6:11044634, chr6:11044640, chr6:11044642, chr6:11044 644, chr6:11044647, chr6:11044655, chr6:11044661, chr6:11044683, chr6:11044695, chr6:11044702, chr6:11044710, chr6:11044726, chr6:11044735, chr6:11044741, chr7:130734355, chr7:130734357, chr7:130734372, chr7:130734375, chr7:130734398, chr7:130734412, such as Figure 3 As shown.

[0119] To develop an age prediction model, a resilient network regression method was employed. On the discovery cohort (N=191), the model was constructed by using time-series age as the dependent variable and the methylation level of the aforementioned methylation sites as the independent variable, and training regression was performed. The prediction accuracy of each optimal model with 1 to 60 CpG sites was calculated, and the prediction performance under different CpG site numbers was compared. First, the discovery cohort was randomly divided into a training set (70%) and a test set (30%), maintaining a balanced age distribution. Model hyperparameter optimization was performed based on the training set data using a grid search incorporating leave-one-out cross-validation (LOOCV). Model performance was evaluated on the test set using multiple statistical metrics, including median absolute error (MAE), Pearson correlation coefficient, and its associated p-value. Furthermore, this study utilized a cross-validation strategy to obtain a minimum bias estimate of the accuracy of the aging clock. This strategy iteratively predicted on all samples in the discovery cohort, removing one sample at a time and predicting the age of that sample.

[0120] Ultimately, based on the principles of having the lowest MAE, the largest number of feature loci, and a validation set MAE greater than the test set MAE, the age prediction model constructed with 57 CpG loci was selected: Calendar Age (Years) = -31.78052433 + 0.0150860653912567*meth_C1orf132_C1 + 0.0370236766736256*meth_C1orf132_C2 + 0.00519730532636727*meth_C1orf132_C3 - 0.0666874453401467*meth_C1orf132_C4 - 0.0312715497589939*meth _C1orf132_C5-0.0418141103700104*meth_C1orf132_C6-0.0129505545870815*meth_C1orf132_C7-0.0421765976570936*meth_C1orf132_C8+0.1 10942889396701*meth_C1orf132_C9-0.0434390670182746*meth_C1orf132_C10+0.115187654225682*meth_FHL2_C1+0.121095331770076*meth_F HL2_C2-0.000776827565128392*meth_FHL2_C3+0.0583308602939788*meth_FHL2_C4+0.0979638121567077*meth_FHL2_C5-0.0348383417317097 *meth_FHL2_C6-0.0710963975147093*meth_FHL2_C7+0.0421765976570936*meth_FHL2_C8-0.231871084654591*meth_FHL2_C9-0.0034801475857 648*meth_FHL2_C10-0.190250111317891*meth_FHL2_C11-0.580991175082793*meth_FHL2_C12-0.0692561031841335*meth_FHL2_C13+0.1050717 19329159*meth_FHL2_C14-0.140249060504829*meth_TRIM59_C1-0.094402413301233*meth_TRIM59_C2+0.116398296522688*meth_TRIM59_C3+0.0515119879390015*meth_TRIM59_C4+0.115271347258114*meth_TRIM59_C5+0.0624591881492509*meth_TRIM59_C6+0.100362625352148*meth_TRIM59_C7+0.054443692101716*meth_TRIM59_C8-0.00812296487786016*meth_TRIM59_C9+0.0224429733600165*meth_TRIM59_C10+0.248359413351082*meth_TRIM59_C11+0.0203210868696916*meth_TRIM59_C12+0.096369899778472*meth_TRIM59_C13+0.141459984636301*meth_ELOVL 2_C1-0.0377448398680573*meth_ELOVL2_C2+0.232612236618917*meth_EL OVL2_C3+0.280794808203567*meth_ELOVL2_C4+0.050745509888902*meth_ELOVL2_C5+0.000231987731572179*meth_ELOVL2_C6+0.0197998557002511*meth_ELOVL2_C7-0.000169612298385955*meth_ELOVL2_C8+0.23184952572738*meth_ELOVL2_C9-0.0741419471439054*meth_ELOVL2_C10+0.071139048523776*meth_ELOVL2_C11-0.06415924131288*meth_ELOVL2_C12+0.0661337244642001*meth_ELOVL2_C13-0.0410039539300835*meth_ELO VL2_C14+0.0248273034704668*meth_ELOVL2_C15+0.0831682981701047*meth_KLF14_C1+0.175155550258806*meth_KLF14_C2+0.0728269428045073*meth_KLF14_C3+0.452157943034236*meth_KLF14_C4-0.145945555503622*meth_KLF14_C5; where "meth_" refers to the methylation level of the corresponding DNA methylation site.

[0121] The results are as follows Figure 4 As shown, in the training set, MAE = 1.984, R = 0.909, p < 2. -16 (See Figure 4 (Part A); In the validation set, MAE = 2.482, R = 0.923, p < 2 -16 (See Figure 4 Part B of the model); model performance is as follows Figure 5 As shown, MAE = 2.117, R = 0.916, p < 2 -16 .

[0122] The optimally tuned hyperparameters α and λ are 0.01 and 1.2, respectively. The model training and hyperparameter tuning were performed using the R packages caret (v6.0-93) and glmnet (v4.1-4).

[0123] The specific information for the 57 loci is shown in Table 3 below:

[0124] Table 3. Basic information on 57 CpG sites

[0125]

[0126]

[0127]

[0128] Example 4: Evaluation of the Age Prediction Model

[0129] 1. Includes 63 human subjects (their age distribution is as follows) Figure 6 As shown, methylation levels are as follows Figure 7 The validation queue (shown) confirmed the model's accuracy (MAE = 2.078, R = 0.974, p < 2). -16 See also Figure 8 When the maximum difference between predicted age and actual age was set at 5 years, the success rate of prediction for this cohort was 87.3%, with a 100% success rate in the 30-40 age group (see [link to article]). Figure 9 ).

[0130] 2. A single-blind test was conducted using 11 samples (trace amounts of blood). The results are shown in Table 4 below. Figure 10 When the deviation between predicted age and actual age is set to 3 years, the prediction success rate from bloodstains and trace amounts of blood fluid can reach 100%; and the age prediction model can also achieve accurate prediction of calendar age using trace peripheral blood samples.

[0131] Table 4 Results of Single-Blind Test

[0132]

[0133]

[0134] 3. Using Zbiec-Piekarska et al. ( Figure 11 Part A) and Jung et al. Figure 11 (Part B) The previously published age prediction model based on CpG loci predicted the age of the training cohort population, and the mean absolute error (MAE = 3.258 and 3.021) was significantly larger than the above model (MAE = 2.117 and 2.078). See [link to relevant documentation]. Figure 11 It is evident that the accuracy of the prediction model obtained by this invention is far higher than that of the prediction models in the prior art.

[0135] 4. Model stability test

[0136] To verify the model's stability, three independent biological replicates, from blood sampling to methylation level detection, were performed on a known-age subject (male, 48 years old). The results are as follows: Figure 12 As shown, when the deviation between the predicted age and the actual age is set to 3 years, the prediction success rate can reach 100%, with high repeatability, reflecting the robustness of the model.

[0137] 5. Model micro-sample testing

[0138] Blood samples were collected from subjects of known age (male, 48 years old). DNA extraction and subsequent methylation level determination were performed using 200, 100, and 50 μl samples, respectively. An age prediction model was then used for prediction. The results showed that the age prediction model could successfully predict DNA levels in blood samples as low as 50 μl (approximately 200 ng of total DNA). Figure 13 As shown.

[0139] Example 5: System for Predicting Calendar Age

[0140] System 61 for predicting calendar age: data receiving module 52 and judgment and output module 53, preferably also including data processing module 51 (see Figure 14 ).

[0141] The data processing module 51 is used to collect biomarker data from the participants' blood samples, namely the methylation level data of the aforementioned 57 methylation sites, and transmit it to the data receiving module 52.

[0142] The data receiving module 52 is used to receive or input biomarker data of the sample to be tested. The biomarker data can be collected by the data collection module 51, or the biomarker combination data can be obtained from other sources.

[0143] The judgment and output module 53 is used to output the calendar age prediction result of the individual of the test sample after the reception or input is completed, through the calendar age prediction model as described in Embodiment 3.

[0144] The predicted calendar age is obtained using the following formula: Predicted age (years) = -31.78052433 + 0.0150860653912567 * meth_C1orf132_C1 + 0.0370236766736256 * meth_C1orf132_C2 + 0.00519730532636727 * meth_C1orf132_C3 - 0.0666874453401467 * meth_C1orf132_C4 - 0.0312715497589939 * meth_C1orf132_C5 - 0.0418141103700104 * meth _C1orf132_C6-0.0129505545870815*meth_C1orf132_C7-0.0421765976570936*meth_C1orf132_C8+0.110942889396701*meth_C1orf132_C9-0.0 434390670182746*meth_C1orf132_C10+0.115187654225682*meth_FHL2_C1+0.121095331770076*meth_FHL2_C2-0.000776827565128392*meth_FH L2_C3+0.0583308602939788*meth_FHL2_C4+0.0979638121567077*meth_FHL2_C5-0.0348383417317097*meth_FHL2_C6-0.0710963975147093*me th_FHL2_C7+0.0421765976570936*meth_FHL2_C8-0.231871084654591*meth_FHL2_C9-0.0034801475857648*meth_FHL2_C10-0.190250111317891 *meth_FHL2_C11-0.580991175082793*meth_FHL2_C12-0.0692561031841335*meth_FHL2_C13+0.105071719329159*meth_FHL2_C14-0.140249060 504829*meth_TRIM59_C1-0.094402413301233*meth_TRIM59_C2+0.116398296522688*meth_TRIM59_C3+0.0515119879390015*meth_TRIM59_C4+0.115271347258114*meth_TR IM59_C5+0.0624591881492509*meth_TRIM59_C6+0.100362625352148*meth_TRIM59_C7+0.054443692101716*meth_TRIM59_C8-0.00812296487786016*meth_TRIM59_C9+0.0224429733600165*meth_TRIM59_C10+0.248359413351082*meth_TRIM59_C11+0.0203210868696916*meth_TRIM59_C12+0.096369899778472*meth_TRIM59_C13+0.141459984636301*meth_ELOVL2_C1-0.0377448398680573*meth_ELOVL2_C2+0.232612236618917*meth_ELOVL2_C3+0.280794808203567*meth_ELOVL2_C4+0.050745509888902*meth_ELOV L2_C5+0.000231987731572179*meth_ELOVL2_C6+0.0197998557002511*me th_ELOVL2_C7-0.000169612298385955*meth_ELOVL2_C8+0.23184952572738*meth_ELOVL2_C9-0.0741419471439054*meth_ELOVL2_C10+0.071139048523776*meth_ELOVL2_C11-0.06415924131288*meth_ELOVL2_C12+0.0661337244642001*meth_ELOVL2_C13-0.0410039539300835*meth_ELOVL2_C14+0.0248273034704668*meth_ELOVL2_C15+0.0831682981701047*meth_KL F14_C1+0.175155550258806*meth_KLF14_C2+0.0728269428045073*meth_KLF14_C3+0.452157943034236*meth_KLF14_C4-0.145945555503622*meth_KLF14_C5, where "meth_" refers to the methylation level of the corresponding DNA methylation site.

[0145] Example 6 Electronic device

[0146] This embodiment provides an electronic device, which can be represented in the form of a computing device (e.g., a server device), including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it can implement the steps of the age prediction method in Embodiment 3 of the present invention or implement the functions of the system described in Embodiment 5.

[0147] Figure 15 A schematic diagram of the hardware structure of this embodiment is shown. The electronic device 9 specifically includes:

[0148] At least one processor 91, at least one memory 92, and a bus 93 for connecting different system components (including processor 91 and memory 92), wherein:

[0149] Bus 93 includes a data bus, an address bus, and a control bus.

[0150] The memory 92 includes volatile memory, such as random access memory (RAM) 921 and / or cache memory 922, and may further include read-only memory (ROM) 923.

[0151] The memory 92 also includes a program / utility 925 having a set (at least one) of program modules 924, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0152] The processor 91 performs various functional applications and data processing by running computer programs stored in the memory 92.

[0153] Electronic device 9 can further communicate with one or more external devices 94 (e.g., keyboard, pointing device, etc.). This communication can be performed via input / output (I / O) interface 95. Furthermore, electronic device 9 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public network, such as the Internet) via network adapter 96. Network adapter 96 communicates with other modules of electronic device 9 via bus 93. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 9, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.

[0154] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0155] Example 7: Computer-readable storage medium

[0156] This invention provides a computer-readable storage medium storing a computer program thereon. When the program is executed by a processor, it implements the steps of the age prediction method in embodiment 3 of this invention or implements the functions of the system described in embodiment 5.

[0157] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0158] In a possible implementation, the present invention can also be implemented as a program product comprising program code, which, when the program product is run on a terminal device, is used to cause the terminal device to perform the steps of the method for predicting age in Embodiment 3 of the present invention or to perform the functions of the system described in Embodiment 5.

[0159] The program code for executing the present invention can be written in any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

Claims

1. A combination of biomarkers for predicting calendar age, characterized in that, The biomarker combination includes one or more of the following DNA methylation sites: chr1:207823637, chr1:207823657, chr1:207823660, chr1:207823672, chr1:207823675, chr1:207823681, chr1:207823702, chr1:207823705, chr1:207823715, chr1:207823723, chr2:105399282, chr2:105399288, chr2:105 399291, chr2:105399297, chr2:105399300, chr2:105399310, chr2:105399316, chr2:105399323, chr2:105399327, chr2:105399 338. chr2:105399340, chr2:105399360, chr2:105399363, chr2:105399388, chr3:160450172, chr3:160450174, chr3:160450179, chr3:160450184、chr3:160450189、chr3:160450192、chr3:160450199、chr3:160450202、chr3:160450231、chr3:160450238、chr 3:160450243, chr3:160450268, chr3:160450283, chr6:11044628, chr6:11044631, chr6:11044634, chr6:11044640, chr6:110446 42. chr6:11044644, chr6:11044647, chr6:11044655, chr6:11044661, chr6:11044683, chr6:11044695, chr6:11044702, chr6:11044710, chr6:11044726, chr6:11044741, chr7:130734355, chr7:130734357, chr7:130734375, chr7:130734398 and chr7:130734412.

2. A reagent for detecting the combination of biomarkers as described in claim 1; Preferably, the reagents include reagents for detecting the ELOVL2, FHL2, KLF14, miR29B2C and / or TRIM59 genes; More preferably, the reagent includes primers for detecting the ELOVL2, FHL2, KLF14, miR29B2C and / or TRIM59 genes; More preferably, the reagent comprises primers with sequences as shown in SEQ ID NO:1-10.

3. A reagent kit, characterized in that, The kit includes the reagents as described in claim 2; preferably, it also includes probes that specifically bind to the biomarker combination as described in claim 1 and / or the biomarker combination as described in claim 1.

4. The use of the biomarker combination of claim 1, the reagent of claim 2, or the kit of claim 3 in the preparation of products for predicting calendar age.

5. A method for constructing a calendar age prediction model, characterized in that, The construction method includes: The calendar age prediction model is constructed by using the DNA methylation level data of the biomarker combination as described in claim 1 through elastic network regression. Preferably, the construction method includes hyperparameter tuning, wherein the optimal hyperparameters determined by the hyperparameter tuning are preferably α = 0.01 and λ = 1.2; and / or, the methylation level data is obtained by bismark extraction.

6. A calendar age prediction model, characterized in that, The calendar age prediction model is constructed using the construction method described in claim 5.

7. A method for predicting calendar age, characterized in that, The biomarker data of the sample to be tested is input into the calendar age prediction model as described in claim 6 to obtain the age prediction result of the individual sample to be tested; the biomarker data includes the methylation level data in the biomarker combination as described in claim 1; Preferably, the sample to be tested is a blood sample or a swab sample.

8. A system for predicting calendar age, characterized in that, The system includes: A data receiving module is used to receive or input biomarker data of a sample to be tested, wherein the biomarker data includes methylation level data in the biomarker combination as described in claim 1; The judgment and output module is used to, after the reception or input is completed, output the calendar age prediction result of the individual in the test sample according to the calendar age prediction model as described in claim 6; the calendar age prediction result is obtained by the following calculation formula: Predicted age (years) = -31.78052433 + 0.0150860653912567 * meth_C1orf132_C1 + 0.0370236766736256 * meth_C1orf132_C2 + 0.00519730532636727 * meth_C1orf132_C3 - 0.0666874453401467 * meth_C1orf13 2_C4-0.0312715497589939*meth_C1orf132_C5-0.0418141103700104*meth_C1orf132_C6-0.0129505545870815*meth_C1orf132_C7-0.04217659 76570936*meth_C1orf132_C8+0.110942889396701*meth_C1orf132_C9-0.0434390670182746*meth_C1orf132_C10+0.115187654225682*meth_FH L2_C1+0.121095331770076*meth_FHL2_C2-0.000776827565128392*meth_FHL2_C3+0.0583308602939788*meth_FHL2_C4+0.0979638121567077*m eth_FHL2_C5-0.0348383417317097*meth_FHL2_C6-0.0710963975147093*meth_FHL2_C7+0.0421765976570936*meth_FHL2_C8-0.2318710846545 91*meth_FHL2_C9-0.0034801475857648*meth_FHL2_C10-0.190250111317891*meth_FHL2_C11-0.580991175082793*meth_FHL2_C12-0.06925610 31841335*meth_FHL2_C13+0.105071719329159*meth_FHL2_C14-0.140249060504829*meth_TRIM59_C1-0.094402413301233*meth_TRIM59_C2+0.116398296522688*meth_TRIM59_C3+0.0515119879390015*meth_TRIM59_C4+0.115271347258114*meth_TRIM59_C5+0.0624591881492509*meth_TRIM59_C6+0.100362625352148*meth_TRIM59_C7+0.054443692101716*meth_TRIM59_C8-0.00812296487786016*meth_TRIM59_C9+0.0224429733600165*meth_TRIM59_C10+0.248359413351082*meth_TRIM59_C11+0.0203210868696916*meth_TRIM59_C12+0.096369899778472*meth_TRIM59_C13+0.141459984636301*meth_ELOVL2_C1-0.0377448398680573*meth_ELOVL2_C2+0.232612236618917*meth_ELOVL2_C3+0.280794808203567*meth_ELOVL2_C4+0.050745509888902*meth_ELOVL2_C5+0.000231987731572179*meth_ELOVL2_C6+0.0197998557002511*meth_ELOVL2_C7-0.000169612298385955*meth_ELOVL2_C8+0.23184952572738*meth_ELOVL2_C9-0.0741419471439054*meth_ELOVL2_C10+0.071139048523776*meth_ELOVL2_C11-0.06415924131288*meth_ELOVL2_C12+0.0661337244642001*meth_ELOVL2_C13-0.0410039539300835*meth_ELOVL2_C14+0.0248273034704668*meth_ELOVL2_C15+0.0831682981701047*meth_KLF14_C1+0.175155550258806*meth_KLF14_C2+0.0728269428045073*meth_KLF14_C3+0.452157943034236*meth_KLF14_C4-0.145945555503622*meth_KLF14_C5, where "meth_" refers to the methylation level of the corresponding DNA methylation site. Preferably, the system further includes a data processing module for collecting the biomarker data.

9. A computer-aided method for predicting calendar age, characterized in that, The method includes the following steps: Step 1: Receive or input biomarker data from the sample to be tested, wherein the biomarker data includes methylation level data in the biomarker combination as described in claim 1; Step 2: Input the biomarker data received or input in Step 1 into the calendar age prediction model as described in claim 6 to obtain the calendar age prediction result of the individual in the test sample; the calendar age prediction result is obtained by the following calculation formula: Predicted age (years) = -31.78052433 + 0.0150860653912567 * meth_C1orf132_C1 + 0.0370236766736256 * meth_C1orf132_C2 + 0.00519730532636727 * meth_C1orf132_C3 - 0.0666874453401467 * meth_C1orf132 _C4-0.0312715497589939*meth_C1orf132_C5-0.0418141103700104*meth_C1orf132_C6-0.0129505545870815*meth_C1orf132_C7-0.042176597 6570936*meth_C1orf132_C8+0.110942889396701*meth_C1orf132_C9-0.0434390670182746*meth_C1orf132_C10+0.115187654225682*meth_FHL 2_C1+0.121095331770076*meth_FHL2_C2-0.000776827565128392*meth_FHL2_C3+0.0583308602939788*meth_FHL2_C4+0.0979638121567077*m eth_FHL2_C5-0.0348383417317097*meth_FHL2_C6-0.0710963975147093*meth_FHL2_C7+0.0421765976570936*meth_FHL2_C8-0.2318710846545 91*meth_FHL2_C9-0.0034801475857648*meth_FHL2_C10-0.190250111317891*meth_FHL2_C11-0.580991175082793*meth_FHL2_C12-0.06925610 31841335*meth_FHL2_C13+0.105071719329159*meth_FHL2_C14-0.140249060504829*meth_TRIM59_C1-0.094402413301233*meth_TRIM59_C2+0.116398296522688*meth_TRIM59_C3+0.0515119879390015*meth_TRIM59_C4+0.115271347258114*meth_TRIM59_C5+0.0624591881492509*meth_TRIM59_C6+0.100362625352148*meth_TRIM59_C7+0.054443692101716*meth_TRIM59_C8-0.00812296487786016*meth_TRIM59_C9+0.0224429733600165*meth_TRIM59_C10+0.248359413351082*meth_TRIM59_C11+0.0203210868696916*meth_TRIM59_C12+0.096369899778472*meth_TRIM59_C13+0.141459984636301*meth_ELOVL2_C1-0.0377448398680573*meth_ELOVL2_C2+0.232612236618917*meth_ELOVL2_C3+0.280794808203567*meth_ELOVL2_C4+0.050745509888902*meth_ELOVL2_C5+0.000231987731572179*meth_ELOVL2_C6+0.0197998557002511*meth_ELOVL2_C7-0.000169612298385955*meth_ELOVL2_C8+0.23184952572738*meth_ELOVL2_C9-0.0741419471439054*meth_ELOVL2_C10+0.071139048523776*meth_ELOVL2_C11-0.06415924131288*meth_ELOVL2_C12+0.0661337244642001*meth_ELOVL2_C13-0.0410039539300835*meth_ELOVL2_C14+0.0248273034704668*meth_ELOVL2_C15+0.0831682981701047*meth_KLF14_C1+0.175155550258806*meth_KLF14_C2+0.0728269428045073*meth_KLF14_C3+0.452157943034236*meth_KLF14_C4-0.145945555503622*meth_KLF14_C5, where "meth_" refers to the methylation level of the corresponding DNA methylation site.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it can implement the functions of the system as described in claim 8, or implement the steps of the computer-aided method for predicting calendar age as described in claim 9.

11. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor executes the computer program to implement the functions of the system as described in claim 8, or the steps of the computer-aided method for predicting calendar age as described in claim 9.

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