Application of CpG site combination methylation level in inferring individual physiological age

Abstract

The invention provides an application of the methylation level of a CpG site combination in deducing the physiological age of an individual. The CpG site combination is selected from at least two of the group consisting of cg25410668, cg16867657, cg22454769, cg08097417, cg21572722, cg17268658, cg23606718, cg03607117, cg24724428, cg13552692, cg04875128, cg14692377, cg00481951, cg07547549, cg06639320 and cg07553761. The invention further provides a kit for detecting Individual physiological age deduction can be realized based on the CpG site combination, and a solution is provided for quick, convenient and accurate physiological age deduction in forensic actual combat application.

Description

Technical Field

[0001] The present invention relates to the field of biochemical technology, and in particular to an application of a CpG site combination methylation level in inferring an individual's physiological age. Background Art

[0002] Age information is a significant marker of personal identity, and the inference of individual physiological age has attracted much attention in the field of forensic evidence. Inferring individual physiological age based on biological evidence found at crime or disaster scenes plays an important role in narrowing the scope of investigation, improving the timeliness of investigation, and identifying victims in various disaster cases. In recent years, the inference of individual physiological age has developed from the initial morphological method to the biomolecular detection method, among which DNA methylation is currently a biomarker with high accuracy and stability in the field of physiological age inference. DNA methylation refers to the process of adding a methyl group (-CH3) to the 5th carbon atom of the cytosine residue of the CpG dinucleotide under the catalysis of DNA methyltransferase, provided by S-adenosylmethionine (SAM) as a methyl donor, to form 5-methylcytosine (5mC). Studies have shown that DNA methylation is closely related to aging, and the methylation level of a specific DNA methylation site contains rich information related to the individual's physiological age. Therefore, based on DNA methylation sites related to physiological age, the construction of a corresponding physiological age inference model through the determination of DNA methylation levels has important practical application value for forensic medicine.

[0003] At present, hundreds of methylation sites related to physiological age have been found in various studies, and DNA methylation levels vary among different populations and tissues. Saliva and blood are common sample types at crime scenes. Therefore, how to determine a set of methylation site combinations that can accurately infer the physiological age of Chinese individuals in both body fluids is one of the hot topics of concern to technicians in this field. Summary of the invention

[0004] The technical problem to be solved by the present invention is how to accurately infer the physiological age of an individual based on a saliva sample or a blood sample of an individual in the Chinese population.

[0005] In a first aspect, the present invention provides an application of a substance for detecting a combined methylation level of CpG sites in a biological sample from an individual to be tested, wherein the application is selected from at least one of A1) to A2):

[0006] A1) Application in estimating individual physiological age;

[0007] A2) Use in the preparation of products for estimating the physiological age of an individual;

[0008] The CpG site combination is selected from at least two of the following CpG sites:

[0009] CpG sites Chromosome number Chromosome location cg25410668 chr1 28241577 cg16867657 chr6 11044877 cg22454769 chr2 106015767 cg08097417 chr7 130419133 cg21572722 chr6 11044894 cg17268658 chr2 106015745 cg23606718 chr2 131513927 cg03607117 chr3 53080440 cg24724428 chr6 11044888 cg13552692 chr18 66389447 cg04875128 chr15 31775895 cg14692377 chr17 28562685 cg00481951 chr3 187387650 cg07547549 chr20 44658225 cg06639320 chr2 106015739 cg07553761 chr3 160167977

[0010] The CpG site position is numbered according to the human reference genome GRCh37 version, and the chromosome position refers to the position of C in the CpG site.

[0011] In the application described above, the individual to be tested is specifically an Asian, and more specifically an East Asian, such as a Chinese.

[0012] In the application described above, the biological sample is a saliva sample and / or a blood sample from the individual to be tested.

[0013] For the above-mentioned application, the required CpG site combination is different according to the different sources of biological samples and the different algorithms used in the inference model. Specifically, the CpG site combination is selected from any one of B1)-B5):

[0014] B1) a combination of 16 CpG sites including cg25410668, cg16867657, cg22454769, cg08097417, cg21572722, cg17268658, cg23606718, cg03607117, cg24724428, cg13552692, cg04875128, cg14692377, cg00481951, cg07547549, cg06639320 and cg07553761;

[0015] B2) a combination of 8 CpG sites including cg07547549, cg06639320, cg25410668, cg16867657, cg17268658, cg03607117, cg04875128 and cg14692377;

[0016] B3) a combination of 13 CpG sites including cg07547549, cg25410668, cg16867657, cg17268658, cg03607117, cg04875128, cg14692377, cg00481951, cg07553761, cg22454769, cg08097417, cg21572722 and cg24724428;

[0017] B4) a combination of 7 CpG sites including cg06639320, cg25410668, cg16867657, cg21572722, cg03607117, cg24724428 and cg13552692;

[0018] B5) A combination of 13 CpG sites including cg00481951, cg07547549, cg06639320, cg07553761, cg25410668, cg16867657, cg08097417, cg21572722, cg17268658, cg23606718, cg24724428, cg13552692 and cg14692377.

[0019] Furthermore, the CpG site combination is preferably a combination of 16 CpG sites shown in B1).

[0020] As described above, the substance for detecting the methylation level of the CpG site combination in the biological sample from the individual to be tested includes a primer probe composition, specifically including a primer composition and a probe composition, the primer composition includes a forward primer and a reverse primer, the probe composition includes a methylation detection probe and an unmethylation detection probe, and the nucleotide sequences of the primer composition and the probe composition corresponding to the methylation level of each CpG site are specifically shown in Table 2:

[0021] Table 2 Nucleotide sequences of amplification primers and probe combinations corresponding to the detection of methylation levels at various CpG sites

[0022]

[0023] In the above probe composition, Y represents T or C, and R represents A or G.

[0024] In the above-mentioned probe composition, the probe with an odd number in the sequence list corresponding to each CpG site is used to detect the methylation of the corresponding CpG site, and the other probe is used to detect the unmethylation of the corresponding CpG site. Therefore, the 5' ends of the two probes corresponding to each CpG site are labeled with different fluorescent groups; specifically, the fluorescent group used at the 5' end of each probe is independently selected from conventional fluorescent groups in the art, such as one of FAM, VIC, ROX, Cy5, and Q705, and can also be selected from enhanced alternative fluorescence of conventional fluorescent groups, such as one of SX610, SX670, and Cy5.5.

[0025] Furthermore, the fluorescent groups at the 5' end of the probe corresponding to at least two CpG sites are different. During the droplet digital PCR (ddPCR) detection process, the amplification primer composition and the probe primer composition corresponding to the two CpG sites can be mixed and the biological sample from the individual to be tested can be tested, and the methylation status of the CpG site can be determined based on the fluorescent signals of different fluorescent groups.

[0026] In the above probe composition, the 3' end of each probe is labeled with a fluorescent quencher group, and the fluorescent quencher group used at the 3' end of each probe is independently selected from one of BHQ1, BHQ2, BHQ3, and DBQ1.

[0027] In the above-mentioned probe composition, the probe may also be modified to improve the stability of the probe. Specifically, at least part of the nucleotides in the probe may be modified to locked nucleic acid (LNA) to increase the Tm value of the probe and improve thermal stability.

[0028] In a second aspect, the present invention provides a substance for detecting the combined methylation level of CpG sites in the above application.

[0029] The substance as described above is specifically a primer-probe combination required in the process of detecting the methylation level of the CpG site combination, as shown in Table 2.

[0030] The substance described above, depending on the different CpG sites included in the CpG site combination, the substance for detecting the methylation level of the CpG site combination in the above application is also different. Specifically, the substance is any one of C1)-C5):

[0031] C1) comprising a primer-probe combination for detecting 16 CpG sites, namely, cg25410668, cg16867657, cg22454769, cg08097417, cg21572722, cg17268658, cg23606718, cg03607117, cg24724428, cg13552692, cg04875128, cg14692377, cg00481951, cg07547549, cg06639320 and cg07553761;

[0032] C2) comprising a primer-probe combination for detecting a total of 8 CpG sites, namely, cg07547549, cg06639320, cg25410668, cg16867657, cg17268658, cg03607117, cg04875128 and cg14692377;

[0033] C3) comprising a primer-probe combination for detecting 13 CpG sites, namely, cg07547549, cg25410668, cg16867657, cg17268658, cg03607117, cg04875128, cg14692377, cg00481951, cg07553761, cg22454769, cg08097417, cg21572722 and cg24724428;

[0034] C4) comprising a primer-probe combination for detecting a total of 7 CpG sites, namely, cg06639320, cg25410668, cg16867657, cg21572722, cg03607117, cg24724428 and cg13552692;

[0035] C5) A primer-probe combination for detecting a total of 13 CpG sites, namely, cg00481951, cg07547549, cg06639320, cg07553761, cg25410668, cg16867657, cg08097417, cg21572722, cg17268658, cg23606718, cg24724428, cg13552692 and cg14692377.

[0036] In a third aspect, the present invention also provides a product for inferring the physiological age of an individual to be tested, comprising the above-mentioned substance.

[0037] The product as described above may be a kit.

[0038] The product as described above may also include substances for bisulfite conversion of DNA samples and other reagents required for droplet digital PCR (ddPCR) technology.

[0039] In a fourth aspect, the present invention provides a method for inferring the physiological age of an individual to be tested, comprising:

[0040] Obtaining a biological sample from an individual to be tested, and extracting genomic DNA from the biological sample;

[0041] Obtaining the methylation level of the CpG site combination in the above application of the individual to be tested according to the DNA sample;

[0042] Inputting the CpG site combination methylation level of the individual to be tested into the individual physiological age target inference model to obtain the physiological age of the individual to be tested;

[0043] The individual physiological age target inference model is obtained by training the target individual's physiological age and the methylation level of the CpG site combination.

[0044] In the above method, the individual physiological age target inference model can be obtained by performing regression analysis based on the physiological age of the target individual and the methylation level of the CpG site combination, and the regression analysis is selected from a multiple linear regression (MLR) model or a support vector regression (SVR) model. The target individual refers to an individual whose physiological age and the methylation level of the CpG site combination are known.

[0045] For the multivariate linear regression (MLR) model, the individual physiological age target inference model can be the product of the weight coefficient corresponding to the methylation rate of each CpG site obtained by regression analysis and the methylation rate of each optimal CpG site, that is, the individual physiological age target inference expression. Depending on the weight coefficient and the optimal CpG site, the individual physiological age target inference expression can be selected from any one of D1)-D4):

[0046] D1)Y=-13.703+26.591×X1+40.960×X2+11.604×X3+4.539×X4+22.479×X5+32.475×X6+9.510×X7+4.788×X 8+13.368×X9+13.588×X10-5.279×X11+53.797×X12-19.957×X13-1.940×X14+218.629×X15+67.570×X16;

[0047] D2)Y=-14.675+48.553×X2+18.652×X3+27.152×X5+32.754×X6+13.974×X10+75.981×X12+218.239×X15+71.013×X16;

[0048] D3)Y=10.910-7.889×X1-3.778×X2+34.000×X3+1.505×X4+14.848×X5+28.849×X6-5.738×X7+124.781×X 8-28.012×X9+7.308×X10+60.417×X11+127.052×X12+63.015×X13-43.134×X14-9.347×X15+13.616×X16;

[0049] D4)Y=8.583+39.032×X3+19.417×X5+35.504×X6-36.270×X9+166.439×X12+71.870×X13-42.754×X14;

[0050] In D1)-D4), Y represents the physiological age of the individual to be tested, X1 represents the methylation rate of cg00481951, X2 represents the methylation rate of cg07547549, X3 represents the methylation rate of cg06639320, X4 represents the methylation rate of cg07553761, X5 represents the methylation rate of cg25410668, X6 represents the methylation rate of cg16867657, X7 represents the methylation rate of cg22454769, and X8 represents the methylation rate of cg080974 X17 represents the methylation rate of cg17, X9 represents the methylation rate of cg21572722, X10 represents the methylation rate of cg17268658, X11 represents the methylation rate of cg23606718, X12 represents the methylation rate of cg03607117, X13 represents the methylation rate of cg24724428, X14 represents the methylation rate of cg13552692, X15 represents the methylation rate of cg04875128, and X16 represents the methylation rate of cg14692377.

[0051] As described above, the methylation rate of each CpG site can be calculated by droplet digital PCR (ddPCR) after testing the biological sample of the individual to be tested. Specifically, according to the test results of ddPCR, it is calculated according to methylation rate (%) = methylation probe fluorescence copy number / (methylation probe fluorescence copy number + unmethylated probe fluorescence copy number) * 100%, and the methylation rate of each CpG site ranges from 0 to 100%.

[0052] Furthermore, the individual physiological age target inference model is preferably constructed according to a support vector regression (SVR) model. Furthermore, the individual physiological age target inference model is constructed according to an SVR algorithm using the methylation rate of 16 CpG site combinations as input data, which helps to improve the prediction accuracy, and the SVR model parameters are 'kernel="radial", cost=10, gamma=0.01, type="eps-regression", seed=1234'.

[0053] In a fifth aspect, the present invention provides a device for inferring the physiological age of an individual to be tested, comprising:

[0054] A data acquisition module, used to obtain the methylation level data of the CpG site combination in the above application of the individual to be tested;

[0055] A data processing module, used for inputting the methylation level data of the CpG site combination of the individual to be tested into the individual physiological age target inference model to obtain the physiological age of the individual to be tested;

[0056] The individual physiological age target inference model is obtained by training the target individual's physiological age and the methylation level of the CpG site combination.

[0057] In a sixth aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for inferring the physiological age of an individual to be tested as described above when executing the program.

[0058] In a seventh aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for inferring the physiological age of an individual to be tested as described above.

[0059] The present invention constructs an application of CpG site combination methylation level in the inference of individual physiological age. By obtaining the CpG site combination methylation level of the individual to be tested, the individual physiological age can be inferred based on saliva and blood samples from the Chinese population. The inference accuracy within ±5 years can reach 88.92% in saliva and 88.79% in blood. The present invention provides a solution to the demand for fast, convenient and accurate age prediction in practical forensic applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1a The detection results of droplet digital PCR detection of the cg04875128 site of the methylation standard using the amplification primers and probe combinations provided in Table 2;

[0061] Figure 1b The test results of droplet digital PCR detection of the cg08097417 site of the methylation standard using the amplification primers and probe combinations provided in Table 2;

[0062] Figure 1c The detection results of droplet digital PCR detection of the cg21572722 site of the methylation standard using the amplification primers and probe combinations provided in Table 2;

[0063] Figure 1d The detection results of droplet digital PCR detection of the cg24724428 site of the methylation standard using the amplification primers and probe combinations provided in Table 2;

[0064] Figure 1e The detection results of droplet digital PCR detection of the cg06639320 site of the methylation standard using the amplification primers and probe combinations provided in Table 2;

[0065] Figure 1fThe test results of droplet digital PCR detection of the cg00481951 site of the methylation standard using the amplification primers and probe combinations provided in Table 2;

[0066] Figure 1g The detection results of droplet digital PCR detection of the cg25410668 site of the methylation standard using the amplification primer and probe combination provided in Table 2;

[0067] Figure 1h The detection results of droplet digital PCR detection of the cg17268658 site of the methylation standard using the amplification primer and probe combination provided in Table 2;

[0068] Figure 1i The detection results of droplet digital PCR detection of the cg16867657 site of the methylation standard using the amplification primers and probe combinations provided in Table 2;

[0069] Figure 1j The detection results of droplet digital PCR detection of the cg14692377 site of the methylation standard using the amplification primers and probe combinations provided in Table 2;

[0070] Figure 1k The detection results of droplet digital PCR detection of the cg07553761 site of the methylation standard using the amplification primers and probe combinations provided in Table 2;

[0071] Figure 1l The test results of droplet digital PCR detection of the cg03607117 site of the methylation standard using the amplification primers and probe combinations provided in Table 2;

[0072] Figure 1m The results of droplet digital PCR detection of the cg07547549 site of the methylation standard using the amplification primers and probe combinations provided in Table 2;

[0073] Figure 1n The detection results of droplet digital PCR detection of the cg23606718 site of the methylation standard using the amplification primers and probe combinations provided in Table 2;

[0074] Figure 1o The detection results of droplet digital PCR detection of the cg22454769 site of the methylation standard using the amplification primer and probe combination provided in Table 2;

[0075] Figure 1pThe detection results of droplet digital PCR detection of the cg13552692 site of the methylation standard using the amplification primers and probe combinations provided in Table 2;

[0076] Figure 2 The results of the correlation analysis between the methylation rates of 16 CpG sites in 325 saliva samples and the physiological age of individuals;

[0077] Figure 3 The methylation rates of 16 CpG sites in 325 saliva samples were input as training set data into the 16-CpGsMLR model, with the predicted age (Predicted age) as the ordinate and the individual's actual age (chronological age) as the abscissa to construct a curve; the left figure is the curve constructed by using 235 saliva samples as training set data, and the right figure is the curve constructed by using 90 saliva samples as validation set data;

[0078] Figure 4 The methylation rates of 8 CpG sites in 325 saliva samples were input as training set data into the 8-CpGsMLR model, and the predicted age (Predicted age) was output as the vertical axis, and the individual's actual age (chronological age) was constructed as the horizontal axis. The left figure is the curve constructed by using 235 saliva samples as training set data, and the right figure is the curve constructed by using 90 saliva samples as validation set data.

[0079] Figure 5 The methylation rates of 16 CpG sites in 325 saliva samples were input as training set data into the 16-CpGsSVR model, and the predicted age (Predicted age) was output as the vertical axis, and the individual's actual age (chronological age) was constructed as the horizontal axis. The left figure is the curve constructed by using 235 saliva samples as training set data, and the right figure is the curve constructed by using 90 saliva samples as validation set data.

[0080] Figure 6 The methylation rates of 13 CpG sites in 325 saliva samples were input as training set data into the 13-CpGsSVR model, and the predicted age (Predicted age) was output as the vertical axis, and the individual's actual age (chronological age) was constructed as the horizontal axis. The left figure is the curve constructed by using 235 saliva samples as training set data, and the right figure is the curve constructed by using 90 saliva samples as validation set data.

[0081] Figure 7The methylation rates of 16 CpG sites in 107 blood samples were input as training set data into the 16-CpGsMLR model, and the predicted age (Predicted age) was output as the vertical axis, and the individual's actual age (chronological age) was constructed as the horizontal axis. The left figure is the curve constructed with 74 blood samples as training set data, and the right figure is the curve constructed with 33 blood samples as validation set data.

[0082] Figure 8 The methylation rates of 7 CpG sites in 107 blood samples were input as training set data into the 7-CpGsMLR model, and the predicted age (Predicted age) was output as the vertical axis and the individual's actual age (chronological age) was constructed as the horizontal axis. The left figure is the curve constructed with 74 blood samples as training set data, and the right figure is the curve constructed with 33 blood samples as validation set data.

[0083] Fig. 9 The methylation rates of 16 CpG sites in 107 blood samples were input as training set data into the 16-CpGsSVR model, and the predicted age (Predicted age) was output as the vertical axis, and the individual's actual age (chronological age) was constructed as the horizontal axis. The left figure is the curve constructed by using 74 blood samples as training set data, and the right figure is the curve constructed by using 33 blood samples as validation set data.

[0084] Fig.10 A curve constructed by inputting the methylation rates of 13 CpG sites in 107 blood samples as training set data into the 13-CpGsSVR model, with the predicted age (Predicted age) as the vertical axis and the individual's actual age (chronological age) as the horizontal axis; the left figure is a curve constructed by using 74 blood samples as training set data, and the right figure is a curve constructed by using 33 blood samples as validation set data. DETAILED DESCRIPTION

[0085] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments, and they should not be understood as limitations on the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In the description of the present invention, it should be understood that the terms used are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0086] The experimental methods in the following examples, unless otherwise specified, are all conventional methods, and are performed according to the techniques or conditions described in the literature in the field or according to the product instructions. The materials, reagents, etc. used in the following examples, unless otherwise specified, can all be obtained from commercial channels.

[0087] Example 1. Combination of CpG sites associated with physiological age and substances for detecting methylation levels of each CpG site

[0088] 1.1. The age-related CpG sites (AR-CpGs) combinations provided in this example are shown in Table 1.

[0089] Table 1 Combinations of 16 CpG sites provided in this example

[0090] CpG sites Chromosome number Chromosome location cg25410668 chr1 28241577 cg16867657 chr6 11044877 cg22454769 chr2 106015767 cg08097417 chr7 130419133 cg21572722 chr6 11044894 cg17268658 chr2 106015745 cg23606718 chr2 131513927 cg03607117 chr3 53080440 cg24724428 chr6 11044888 cg13552692 chr18 66389447 cg04875128 chr15 31775895 cg14692377 chr17 28562685 cg00481951 chr3 187387650 cg07547549 chr20 44658225 cg06639320 chr2 106015739 cg07553761 chr3 160167977

[0091] The above-mentioned CpG site positions are numbered according to the human reference genome GRCh37 version, and the chromosome position refers to the position of C in the CpG site.

[0092] 1.2. Synthesis of amplification primers and probes for detecting the methylation levels of the above 16 AR-CpGs site combinations

[0093] MethPrimer software (http: / / www.urogene.org / methprimer / ) was used to perform bisulfite conversion on the 200 bp nucleotide sequences upstream and downstream of the 16 AR-CpGs sites shown in Table 1 to obtain the converted sequence information; amplification primers and probes were designed based on the converted sequence information, and the Tm values, dimers and other conditions of amplification primers and probes were evaluated using IDT online software (https: / / sg.idtdna.com / ) to determine the final sequences of amplification primers and probes; the 5' end of the probe was labeled with a fluorescent group, and the 3' end of the probe was labeled with a fluorescent quenching group to obtain the probes shown in Table 2. All amplification primers and probes were synthesized by Sangon (Shanghai) Co., Ltd.

[0094] Table 2: Substances for detecting the combined methylation levels of the above 16 CpG sites

[0095]

[0096] In Table 2, F represents forward primer, R represents reverse primer, Y represents T or C, and R represents A or G; the first line of probes is a probe for detecting methylation, and the second line is a probe for detecting unmethylation; + represents the addition of a locked nucleic acid to the base after +; FAM is carboxyfluorescein; VIC is phosphoramidite; Cy5 is Cyanine5; Q705 is Quasar 705; ROX is 6-Carboxy-X-rhodamine, SX610 is ROX alternative enhanced fluorescence, SX670 is Cy5 alternative enhanced fluorescence, Cy5.5 is Q705 alternative enhanced fluorescence, and BHQ1, BHQ2, and BHQ3 are all fluorescence quenching groups.

[0097] 1.3. Verify the accuracy of the detection system shown in Table 2 using fully methylated, fully unmethylated and 50% methylated standards

[0098] Human DNA methylation standards with fully methylated, fully unmethylated, and 50% methylation levels (purchased from Zymo Research) were used as DNA samples and the DNA was methylated using the EZ DNA Methylation-Lightning TM Kit (purchased from Zymo Research) was used to perform bisulfite conversion on the DNA sample, with the DNA input amount being 200 ng, to obtain a bisulfite-converted DNA sample.

[0099] The primer probes shown in Table 2 were prepared into a solution with a concentration of 10 μmol / L.

[0100] The 16 sites were double-site compounded, and 8 wells after compounding were double-site detection. The amplification primers and probes for detecting the cg08097417 and cg04875128 sites were mixed; the amplification primers and probes for detecting the cg21572722 and cg24724428 sites were mixed; the amplification primers and probes for detecting the cg00481951 and cg06639320 sites were mixed; the amplification primers and probes for detecting the cg25410668 and cg17268658 sites were mixed; The amplification primers and probes for detecting cg16867657 and cg14692377 sites were mixed; the amplification primers and probes for detecting cg07553761 and cg03607117 sites were mixed; the amplification primers and probes for detecting cg07547549 and cg23606718 sites were mixed; the amplification primers and probes for detecting cg22454769 and cg13552692 sites were mixed.

[0101] 15 μL of Methyl Supermix A, 15 μL of Methyl Supermix B (purchased from Xinyi Manufacturing Technology (Beijing) Co., Ltd.), 1 μL of bisulfite-converted DNA, 1.5-2.7 μL of primers, 0.3-0.6 μL of probes were mixed and filled to a final volume of 30 μL with DEPC water. After the reaction system was configured, it was transferred to the water phase well in the droplet generation chip (purchased from Xinyi Manufacturing Technology (Beijing) Co., Ltd.), 160 μL of droplet generation oil was added to the oil phase well, the chip sealing gasket was covered, the chip was placed in the chip mechanical mold of the sample preparation instrument, and the mold with the chip was placed in the Drop Maker M1 sample preparation instrument (purchased from Xinyi Manufacturing Technology (Beijing) Co., Ltd.) to generate droplets. After the droplets were generated, PCR amplification was performed in an A300 gene amplifier (purchased from Hangzhou Langji Scientific Instrument Co., Ltd.) under the following conditions: 95°C pre-denaturation for 10 min; 94°C denaturation for 30 s, 54°C-59°C annealing for 1 min, 40 cycles; 12°C for 5 min. The heating and cooling rate of the entire PCR amplification process was 1.5°C / s. The amplified reaction system (8 rows) was loaded into the droplet detection instrument mold, and 430 μL and 500 μL of droplet detection oil were added to the large and small oil holes of the droplet detection chip, respectively. The detection chip sealing pad was covered and placed in the Chip Reader R2 biochip analyzer (purchased from Xinyi Manufacturing Technology (Beijing) Co., Ltd.) for fluorescence signal detection. The methylation level (0-1) of each CpG site was calculated by the copy number of the fluorescence signal, and the formula was:

[0102]

[0103] At the same time, a blank control was set up, and the methylation standard test of each site was repeated 3 times. The methylation rate obtained from the 3 repeated experiments was expressed as the mean plus or minus the standard deviation (Mean±SD). When the standard deviation of each site was within 5%, it was considered accurate. The test results of each site were as follows: Figure 1a-1p As shown, the statistical results of the three standards are shown in Table 3-10. It can be seen that the detection system provided by the present invention is accurate.

[0104] Table 3 Methylation statistics of cg08097417 and cg04875128 sites

[0105]

[0106] Table 4 Methylation statistics of cg21572722 and cg24724428 sites

[0107]

[0108] Table 5 Methylation statistics of cg00481951 and cg06639320 sites

[0109]

[0110]

[0111] Table 6 Methylation statistics of cg25410668-cg17268658 sites

[0112]

[0113] Table 7 Methylation statistics of cg16867657 and cg14692377 sites

[0114]

[0115] Table 8 Methylation statistics of cg07553761 and cg03607117 sites

[0116]

[0117] Table 9 Methylation statistics of cg07547549 and cg23606718 sites

[0118]

[0119]

[0120] Table 10 Methylation statistics of cg22454769 and cg13552692 sites

[0121]

[0122] Example 2: Constructing an individual physiological age inference model based on different biological samples

[0123] 2.1 Sources of individual samples

[0124] Saliva samples were collected from 325 East Asian Chinese aged 2-89 years old, and blood samples were collected from 107 individuals aged 10-80 years old. The age and gender distribution of the individuals are detailed in Table 11. All individuals signed informed consent forms before sample collection and passed the review of the Ethics Committee of the Public Security Identification Center (No.: 2021-030). The collected saliva samples were stored at room temperature, and the blood samples were stored at -20°C.

[0125] Table 11 Age and gender distribution of individuals collected in this example

[0126]

[0127] use DNA was extracted from saliva and blood samples using the M48 DNA Manual Kit (purchased from QIAGEN) and the Invitrogen Qubit TM 4 (purchased from Thermo Fisher Scientific) for quantification. After DNA extraction, EZ DNA Methylation-Lightning TM The DNA was bisulfite converted using the DNA Extraction Kit (purchased from Zymo Research), with a DNA input of 200 ng and an elution volume of 15 μl. The DNA extraction and bisulfite conversion procedures were performed according to the instructions of the kit.

[0128] 2.2 Correlation analysis between CpG sites and physiological age based on saliva samples

[0129] The ddPCR multiple methylation detection system established in Example 1 was used to detect the DNA of 325 saliva samples to obtain the methylation rates of 16 CpG sites of the 325 individuals. The methylation rates of the 16 CpG sites of the 325 individuals were correlated with physiological age. The analysis results are as follows: Figure 2 As shown, it can be seen that the methylation rates of the 16 CpG sites are strongly correlated with age, among which cg16867657 has the highest correlation with physiological age (r=0.9294), and cg13552692 has the lowest correlation with physiological age (r=-0.5535). Among all 16 CpG sites, 4 CpG sites are extremely strongly correlated with physiological age (0.9<|r|<1.0), and 10 CpG sites are strongly correlated with physiological age (0.7<|r|<0.9).

[0130] 2.3. Construction of individual physiological age inference model based on saliva samples

[0131] 2.3.1. The 325 saliva samples were divided into a training set (N=235) and a validation set (N=90) in a ratio of 7:3. The multivariate linear regression (MLR) model was established using the methylation rates of all 16 AR-CpGs sites in the training set and the physiological age of the individuals corresponding to the samples. The constructed MLR model is shown in Table 12.

[0132] The methylation rates of all 16 AR-CpGs sites in the training set samples were input into the constructed MLR model for calculation, and the predicted age was output. A curve was constructed based on the predicted age calculated by the MLR model and the individual's actual age, as shown in Figure 3 As shown in the figure on the left and Table 12, the salivary MLR model constructed by 16-CpGs explained 96.90% of the individual age variation (adjusted R 2 =0.969), the mean absolute deviation MAD is 3.0101 years, and the root mean square error RMSE is 3.8876 years.

[0133] On this basis, the stepwise regression method (step(AIC)) was used to further analyze the data, and the optimal CpGs site combination with the smallest AIC value and Pr(>|t|)<0.05 was selected. The optimal CpGs site combination screened out included 8 CpGs sites, namely cg07547549, cg06639320, cg25410668, cg16867657, cg17268658, cg03607117, cg04875128, and cg14692377; and an MLR model was constructed based on the combination of 8 CpGs sites. The constructed MLR model is shown in Table 12.

[0134] The methylation rate data of the 8 CpGs sites of the test set samples were input into the MLR model to calculate the predicted age, and a curve was constructed based on the predicted age calculated by the MLR model and the individual's actual age, as shown in Figure 2. Figure 4 As shown in the left figure and Table 12, it can be seen that the combination of 8 CpG sites explains 96.75% of the individual physiological age changes (adjusted R 2 =0.9675), MAD is 3.1299 years, and RMSE is 3.9811 years.

[0135] 2.3.2, 325 saliva samples were divided into training set (N = 235) and validation set (N = 90) in a ratio of 7:3, and a support vector regression (SVR) model was established using the methylation rates of all 16 AR-CpGs sites in the training set and the physiological age of the individuals corresponding to the samples. The SVR model parameters were 'kernel = "radial", cost = 10, gamma = 0.01, type = "eps-regression", seed = 1234'.

[0136] The methylation rates of all 16 AR-CpGs sites in the training set samples are input into the constructed SVR model for calculation, and the predicted age is output. A curve is constructed based on the predicted age calculated by the SVR model and the individual's actual age, as shown in Figure 5 As shown in the left figure, it can be seen that the salivary SVR model constructed by 16-CpGs explains 97.99% of the individual age variation (adjusted R 2 =0.9799), MAD is 2.424 years, and RMSE is 3.1357 years.

[0137] On this basis, the wrapper method was used to screen out the optimal CpG site combination in the SVR algorithm. The control parameters of the wrapper method were 'method = "repeatedcv", number = 10, repeats = 5', and the RMSE value was used as the evaluation index. The selected CpG site combinations included cg07547549, cg25410668, cg16867657, cg17268658, cg03607117, cg04875128, cg14692377, cg00481951, cg07553761, cg22454769, cg08097417, cg21572722, and cg24724428, a total of 13 CpGs site combinations; and the SVR model was constructed based on the 13 CpGs site combinations.

[0138] The SVR model was constructed based on the combination of 13 CpG sites. The methylation rates of all 13 AR-CpGs sites in the training set were input into the constructed SVR model for calculation, and the predicted age was output. A curve was constructed based on the predicted age calculated by the SVR model and the individual's actual age, as shown in Figure 1. Figure 6 As shown in the left figure, it can be seen that the salivary SVR model constructed by 13-CpGs explains 97.64% of the individual age variation (adjusted R 2 =0.9764), MAD is 2.5759 years, and RMSE is 3.3951 years.

[0139] 2.4. Construction of an individual physiological age inference model based on blood samples

[0140] 2.4.1. Using the modeling method in 2.3, the 107 blood samples were divided into a training set (n=74) and a validation set (n=33) in a ratio of 7:3. The MLR model was first established using all 16 loci. The constructed model is shown in Table 12.

[0141] The methylation rates of all 16 AR-CpGs sites in the training set samples were input into the constructed MLR model for calculation, and the predicted age was output. A curve was constructed based on the predicted age calculated by the MLR model and the individual's actual age, as shown in Figure 7 As shown in the left figure, the blood MLR model constructed by 16-CpGs explained 97.37% of individual age changes (adjusted R2=0.9737), MAD=2.5911 years, and RMSE=3.1388 years.

[0142] Using the optimal CpG site combination screening method for the MLR model in 2.3, the optimal CpG site combination screened out included 7 CpGs sites, namely cg06639320, cg25410668, cg16867657, cg21572722, cg03607117, cg24724428, and cg13552692; a blood MLR model was constructed based on the screened optimal 7 CpG site combinations, and the constructed MLR model is shown in Table 12.

[0143] The methylation rates of all 7 AR-CpGs sites in the training set samples were input into the constructed MLR model for calculation, and the predicted age was output. A curve was constructed based on the predicted age calculated by the MLR model and the individual's actual age, as shown in Figure 8 As shown in the left figure, the blood MLR model constructed by 7-CpGs explained 97.11% of individual age changes (adjusted R2=0.9711), MAD=2.7439 years, and RMSE=3.2872 years.

[0144] 2.4.2. Using the modeling method in 2.3, the 107 blood samples were divided into a training set (n=74) and a validation set (n=33) according to a ratio of 7:3, and all 16 sites were used to establish the SVR model. The methylation rates of all 16 AR-CpGs sites in the training set were input into the constructed SVR model for calculation, and the predicted age was output. A curve was constructed based on the predicted age calculated by the MLR model and the individual's actual age (chronological age), as shown in Figure 2. Fig. 9 As shown in the left figure, the blood SVR model constructed by 16-CpGs explained 98.46% of individual age variation (adjusted R2=0.9846), MAD=2.0394 years, and RMSE=2.4033 years.

[0145] The optimal CpG site combination screening method for the SVR model in 2.3 was used, and the optimal CpG site combination screened out included 13 CpGs sites, including cg00481951, cg07547549, cg06639320, cg07553761, cg25410668, cg16867657, cg08097417, cg21572722, cg17268658, cg23606718, cg24724428, cg13552692, and cg14692377; a blood SVR model was constructed based on the optimal 13 CpG site combinations screened out.

[0146] The methylation rates of all 13 AR-CpGs sites in the training set samples were input into the constructed SVR model for calculation, and the predicted age was output. A curve was constructed based on the predicted age calculated by the SVR model and the individual's actual age, as shown in Figure 2. Fig.10 As shown in the left figure, the blood SVR model constructed by 13-CpGs explained 98.1% of individual age variation (adjusted R2=0.981), MAD=2.1545 years, and RMSE=2.6761 years.

[0147] Table 12 MLR models constructed based on saliva samples and blood samples

[0148]

[0149]

[0150] In Table 12, Y represents the physiological age of the individual to be tested, X1 represents the methylation rate of cg00481951, X2 represents the methylation rate of cg07547549, X3 represents the methylation rate of cg06639320, X4 represents the methylation rate of cg07553761, X5 represents the methylation rate of cg25410668, X6 represents the methylation rate of cg16867657, X7 represents the methylation rate of cg22454769, and X8 represents the methylation rate of cg08097417 , X9 represents the methylation rate of cg21572722, X10 represents the methylation rate of cg17268658, X11 represents the methylation rate of cg23606718, X12 represents the methylation rate of cg03607117, X13 represents the methylation rate of cg24724428, X14 represents the methylation rate of cg13552692, X15 represents the methylation rate of cg04875128, and X16 represents the methylation rate of cg14692377.

[0151] Example 4: Study on the performance of MLR and SVR age inference models in saliva and blood

[0152] 4.1. The methylation rates of all 16 AR-CpGs sites of 92 validation set samples divided from 325 saliva samples were input into the constructed MLR model for calculation, and the predicted age (Predicted age) was output. A curve was constructed based on the predicted age (Predicted age) calculated by the MLR model and the individual's actual age (chronological age), as shown in the figure. Figure 3 As shown in the figure on the right, the salivary MLR model constructed by 16-CpGs explained 95.99% of the individual age variation (adjusted R 2 =0.9599), the mean absolute deviation MAD is 3.3104 years, and the root mean square error RMSE is 4.2355 years.

[0153] The methylation rates of the 8 AR-CpGs sites of the 92 validation set samples divided from the 325 saliva samples were input into the MLR model constructed by the combination of the 8 CpG sites to calculate the predicted age, and a curve was constructed based on the predicted age calculated by the MLR model and the individual's actual age, as shown in Figure 2. Figure 4 As shown in the figure on the right, it can be seen that the combination of 8 CpG sites explains 96.01% of the individual physiological age changes (adjusted R 2=0.9601), MAD is 3.3536 years, and RMSE is 4.2394 years.

[0154] The methylation rates of 16 AR-CpGs sites of 92 validation set samples divided from 325 saliva samples were input into the constructed SVR model for calculation, and the predicted age was output. A curve was constructed based on the predicted age calculated by the SVR model and the individual's actual age, as shown in Figure 2. Figure 5 As shown in the figure on the right, it can be seen that the salivary SVR model constructed by 16-CpGs explains 96.24% of the individual age variation (adjusted R 2 =0.9624), MAD is 2.8119 years, and RMSE is 3.9451 years.

[0155] The methylation rates of 13 AR-CpGs sites of 92 validation set samples divided from 325 saliva samples were input into the constructed SVR model for calculation, and the predicted age was output. A curve was constructed based on the predicted age calculated by the SVR model and the individual's actual age, as shown in Figure 2. Figure 6 As shown in the right figure, it can be seen that the salivary SVR model constructed by 13-CpGs explains 95.89% of individual age changes (adjusted R2=0.9589), with a MAD of 3.2753 years and a RMSE of 4.3344 years.

[0156] 4.2. The methylation rates of 16 AR-CpGs sites of 33 validation set samples divided from 107 blood samples were input into the constructed MLR model for calculation, and the predicted age was output. A curve was constructed based on the predicted age calculated by the MLR model and the individual's actual age, as shown in Figure 4. Figure 7 As shown in the right figure, the blood MLR model constructed by 16-CpGs explained 96.28% of individual age changes (adjusted R2=0.9628), MAD=3.1636 years, and RMSE=4.1413 years.

[0157] The methylation rates of the 7 AR-CpGs sites of the 33 validation set samples divided from the 107 blood samples were input into the constructed MLR model for calculation, and the predicted age was output. A curve was constructed based on the predicted age calculated by the MLR model and the individual's actual age, as shown in Figure 8 As shown in the right figure, the blood MLR model constructed by 7-CpGs explained 95% of individual age changes (adjusted R2=0.95), MAD=3.6382 years, and RMSE=4.9676 years.

[0158] The methylation rates of 16 AR-CpGs sites of 33 validation set samples divided from 107 blood samples were input into the constructed SVR model for calculation, and the predicted age was output. A curve was constructed based on the predicted age calculated by the SVR model and the individual's actual age, as shown in Figure 2. Fig. 9 As shown in the right figure, the blood SVR model constructed by 16-CpGs explained 96.92% of individual age variation (adjustedR2=0.9692), MAD=2.6791 years, and RMSE=3.6086 years.

[0159] The methylation rates of 13 AR-CpGs sites of 33 validation set samples divided from 107 blood samples were input into the constructed SVR model for calculation, and the predicted age was output. A curve was constructed based on the predicted age calculated by the SVR model and the individual's actual age, as shown in Figure 2. Fig.10 As shown in the right figure, the blood MLR model constructed by 13-CpGs explained 96.64% of individual age changes (adjusted R2=0.9664), MAD=2.7967 years, and RMSE=3.7949 years.

[0160] 4.3. According to the actual age of the individuals corresponding to the 325 saliva samples, the 325 saliva samples were divided into 8 categories (Ⅰ: 2-9 years old, Ⅱ: 10-19 years old, Ⅲ: 20-29 years old, Ⅳ: 30-39 years old, Ⅴ: 40-49 years old, Ⅵ: 50-59 years old, Ⅶ: 60-69 years old, Ⅷ: 70-89 years old). The predicted physiological age output by each model was within the range of ±5 years of the actual physiological age, and the accuracy of different categories under different models was statistically analyzed. The statistical results are shown in Table 13.

[0161] Table 13 Statistical results of the accuracy of 325 saliva samples under different inference models

[0162]

[0163] According to Table 13, in the MLR model, the prediction accuracy of groups I to VI was significantly higher than that of groups VII and VIII, while the accuracy of each age group in the SVR model was relatively stable, and the age prediction accuracy of groups VII and VIII in the SVR model was significantly higher than that of samples in groups VII and VIII in the MLR model.

[0164] 4.4. Similarly, according to the actual age of the individuals corresponding to the 107 blood samples, the 107 blood samples were divided into 7 categories (Ⅰ: 12-19 years old, Ⅱ: 20-29 years old, Ⅲ: 30-39 years old, Ⅳ: 40-49 years old, Ⅴ: 50-59 years old, Ⅵ: 60-69 years old, Ⅶ: 70-79 years old). The predicted physiological age output by each model was within the range of ±5 years of the actual physiological age. The accuracy of different categories under different models was statistically analyzed. The statistical results are shown in Table 14.

[0165] Table 14 Statistical results of the accuracy of 107 blood samples under different inference models

[0166]

[0167]

[0168] According to Table 14, in the 16-CpGs MLR model and the 16-CpGs SVR model, the age predictions of 92 samples (85.98%) and 95 samples (88.79%) were correct, respectively, while in the 7-CpGs MLR model and the 13-CpGs SVR model, the age predictions of 87 samples (81.31%) and 95 samples (88.79%) were correct.

[0169] In summary, the present invention provides 16 AR-CpGs combinations that can predict the physiological age of saliva and blood samples of the Chinese population. The prediction accuracy within ±5 years can reach 88.92% in saliva and 88.79% in blood, which is expected to meet the needs of fast, convenient and accurate age prediction in forensic practical applications. In addition, the SVR model is superior to the MLR model in terms of age prediction accuracy and accuracy, and the age prediction accuracy of the optimal AR-CpGs combination screened out based on the 16 AR-CpGs combination is slightly lower than the prediction accuracy of all 16 CpG sites, and there is no significant difference.

[0170] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. The use of a substance for detecting the methylation level of a combination of CpG sites in a biological sample of an individual to be tested, characterized in that: The application is selected from at least one of A1)-A2): A1) application in estimating the physiological age of an individual; A2) Use in the preparation of products for estimating the physiological age of an individual; The CpG site combination is selected from at least two of the following CpG sites: The CpG site position is numbered according to the human reference genome GRCh37 version, and the chromosome position refers to the position of C in the CpG site.

2. The use according to claim 1, characterized in that: The CpG site combination is selected from any one of B1)-B5): B1) a combination of 16 CpG sites including cg25410668, cg16867657, cg22454769, cg08097417, cg21572722, cg17268658, cg23606718, cg03607117, cg24724428, cg13552692, cg04875128, cg14692377, cg00481951, cg07547549, cg06639320 and cg07553761; B2) a combination of 8 CpG sites including cg07547549, cg06639320, cg25410668, cg16867657, cg17268658, cg03607117, cg04875128 and cg14692377; B3) a combination of 13 CpG sites including cg07547549, cg25410668, cg16867657, cg17268658, cg03607117, cg04875128, cg14692377, cg00481951, cg07553761, cg22454769, cg08097417, cg21572722 and cg24724428; B4) a combination of 7 CpG sites, including cg06639320, cg25410668, cg16867657, cg21572722, cg03607117, cg24724428 and cg13552692; B5) a combination of 13 CpG sites, including cg00481951, cg07547549, cg06639320, cg07553761, cg25410668, cg16867657, cg08097417, cg21572722, cg17268658, cg23606718, cg24724428, cg13552692 and cg14692377.

3. The use according to claim 1 or 2, characterized in that: The substance comprises a primer-probe combination for detecting the methylation level of a CpG site in a CpG site combination, wherein: The primer-probe composition for detecting the cg25410668 site includes four single-stranded DNA molecules whose nucleotide sequences are SEQ ID NOs: 1-4 respectively; The primer-probe composition for detecting the cg16867657 site includes four single-stranded DNA molecules whose nucleotide sequences are SEQ ID NOs: 5-8 respectively; The primer-probe composition for detecting the cg22454769 site includes four single-stranded DNA molecules whose nucleotide sequences are SEQ ID NOs: 9-12 respectively; The primer-probe composition for detecting the cg08097417 site includes four single-stranded DNA molecules whose nucleotide sequences are SEQ ID NOs: 13-16 respectively; The primer-probe composition for detecting the cg21572722 site includes four single-stranded DNA molecules whose nucleotide sequences are SEQ ID NOs: 17-20 respectively; The primer-probe composition for detecting the cg17268658 site includes four single-stranded DNA molecules whose nucleotide sequences are SEQ ID NOs: 21-24, respectively; The primer-probe composition for detecting the cg23606718 site includes four single-stranded DNA molecules whose nucleotide sequences are SEQ ID NOs: 25-28 respectively; The primer-probe composition for detecting the cg03607117 site includes four single-stranded DNA molecules whose nucleotide sequences are SEQ ID NOs: 29-32, respectively; The primer-probe composition for detecting the cg24724428 site includes four single-stranded DNA molecules whose nucleotide sequences are SEQ ID NOs: 33-36, respectively; The primer-probe composition for detecting the cg13552692 site includes four single-stranded DNA molecules whose nucleotide sequences are SEQ ID NOs: 37-40 respectively; The primer-probe composition for detecting the cg04875128 site includes four single-stranded DNA molecules whose nucleotide sequences are SEQ ID NOs: 41-44, respectively; The primer-probe composition for detecting the cg14692377 site includes four single-stranded DNA molecules whose nucleotide sequences are SEQ ID NOs: 45-48, respectively; The primer-probe composition for detecting the cg00481951 site includes four single-stranded DNA molecules whose nucleotide sequences are SEQ ID NOs: 49-52, respectively; The primer-probe composition for detecting the cg07547549 site includes four single-stranded DNA molecules whose nucleotide sequences are SEQ ID NOs: 53-56, respectively; The primer-probe composition for detecting the cg06639320 site includes four single-stranded DNA molecules whose nucleotide sequences are SEQ ID NOs: 57-60, respectively; The primer-probe composition for detecting the cg07553761 site includes four single-stranded DNA molecules whose nucleotide sequences are SEQ ID NOs: 61-64, respectively.

4. The use according to any one of claims 1 to 3, characterized in that: The biological sample is a blood sample and / or a saliva sample from the individual to be tested.

5. A substance for detecting the methylation level of a combination of CpG sites in the application according to any one of claims 1 to 4.

6. A product for inferring the physiological age of an individual to be tested, characterized in that: Comprising the substance described in claim 5.

7. A method for inferring the physiological age of an individual to be tested, characterized in that: include: Obtaining a biological sample from an individual to be tested, and extracting genomic DNA from the biological sample; Obtaining the methylation level of the CpG site combination in the application of any one of claims 1 to 4 of the individual to be tested according to the genomic DNA; Inputting the methylation level of the CpG site combination of the individual to be tested into the individual physiological age target inference model to obtain the physiological age of the individual to be tested; The individual physiological age target inference model is obtained by training the target individual's physiological age and the methylation level of the CpG site combination.

8. A device for inferring the physiological age of an individual to be tested, characterized in that: include: A data acquisition module, used to obtain methylation level data of a CpG site combination in the application of any one of claims 1 to 4 of a tested individual; A data processing module, used for inputting the methylation level data of the CpG site combination of the individual to be tested into the individual physiological age target inference model to obtain the physiological age of the individual to be tested; The individual physiological age target inference model is obtained by training the target individual's physiological age and the methylation level of the CpG site combination.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for inferring the physiological age of the individual to be tested as claimed in claim 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for inferring the physiological age of an individual to be tested as claimed in claim 7 is implemented.

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