Age estimation method based on methylation group sequencing

Abstract

The invention relates to an age estimation method based on methylation chip sequencing, in particular to a DNA methylation level data set, a data set establishing method, an age estimation model training method, an age estimation method, an age estimation model or device, electronic equipment, a nonvolatile computer readable storage medium and application thereof. By screening the Beta value of the methylation site related to the time sequence age and / or the biological age, the time sequence age and / or the biological age of the testee can be accurately predicted by utilizing an elastic network machine learning model.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine, and in particular relates to an age estimation method based on methylome sequencing. Background Art

[0002] Aging is a complex process influenced by multiple factors, including genetics and the environment, and is considered to be the root cause of many chronic diseases (such as cardiovascular, neurodegenerative diseases, diabetes, or chronic obstructive pulmonary disease). Therefore, quantifying the aging rate will help evaluate disease risk and health levels, and provide a basis for personalized medicine. Currently, a variety of aging intervention strategies have been reported to slow the aging rate to a certain extent, such as dietary restrictions and exercise. Converting the above interventions into clinical practice requires measuring an individual's biological age and biological aging rate. However, whether in the population or within an individual, aging is a highly heterogeneous process, and there is no absolute consistency with chronological age (i.e., the year from birth to the time of recording). In other words, human biological age is not equal to chronological age. [2] In addition, aging is heterogeneous in different tissues, organs or systems, and the aging rate at different levels of the same individual may be different. [1] Therefore, accurate evaluation of aging has become an emerging key scientific issue in aging research, and the development of tools and methods that can effectively evaluate the degree of aging is of great significance for understanding and intervening in aging.

[0003] Large-scale omics data has brought new opportunities for the analysis and interpretation of aging. The aging clock is a machine learning model that can learn molecular features or clinical features that change with aging in samples, such as CpG methylation levels at specific genomic sites in blood cells, protein concentrations in plasma, or gene expression levels, to estimate the age of the sample. [2] Among them, DNA methylation is a stable biomarker of aging, and the clock constructed using it can accurately predict chronological age. There are also studies that replace chronological age with phenotypic age predicted by other biomarkers when constructing the model, and use the aging clock to predict. The age predicted by this method may be more directly related to biological processes. [2,3] Establishing a model that accurately measures the degree of aging in the population will help evaluate aging interventions and translate basic aging research into clinical practice. Summary of the Invention

[0004] DNA methylation refers to the covalent attachment of a methyl group to the 5' carbon position of cytosine in genomic CpG dinucleotides by DNA methyltransferases. In normal cells, CpG dinucleotides comprise 10% of the human genome. Methylation and demethylation of CpG sites regulate the expression of downstream genes. DNA methylation is one of the earliest discovered epigenetic modifications and plays a crucial role in maintaining normal cell function, chromosome structure, X chromosome inactivation, gene imprinting, embryonic development, aging, and disease.

[0005] Through rigorous health screening and a uniform diet before testing, the present invention established a cohort of naturally aging people in Quzhou, Zhejiang, and collected blood samples from the cohort volunteers. By sequencing the methylome of peripheral blood, a DNA methylation profile of the aging population in China was established, and a series of methylation sites correlated with age were identified. Based on the Beta values of these methylation sites, the present invention used an elastic network machine learning model to predict the actual age of the subjects and established a methylation age model for the aging Chinese population. The results showed that the model has good predictive performance.

[0006] In a first aspect, the present invention provides a DNA methylation level dataset, comprising quantitative values of the methylation levels of one or more gene loci as follows:

[0007] cg16867657,cg22454769,cg07553761,cg26921969,cg02669012,cg18933331,cg07323488,cg16476710,cg11935615,cg07184080,cg01499479,cg08553327,cg25427880,cg16540789,cg14200127,cg08231710,cg09445550,cg13927769,cg20665157,cg11025681,cg01034094,cg01720616,cg23754382,cg03032497,cg22156456,cg21762610,cg03473532,cg21548231,cg25410668,cg13221458,cg12379720,cg13416486,cg21126595,cg00792799,cg10283268,cg22206517,cg02838877,cg23776628,cg15110296,cg16969368,cg09899215,cg07897508,cg25584771,cg27517999,cg24429836,cg00698382,cg24012925,cg12650870,cg11793423,cg12754260,cg12183225,cg22720431,cg16440532,cg03621974,cg07895657,cg23995459,cg12110659,cg11463113,cg19937979,cg00075507,cg16322747,cg16308533,cg18380497,cg08121845,cg02968445,cg18882457,cg21283066,cg18622896,cg09317508,cg12008779,cg04795774,cg20983625,cg05758220,cg25950625,cg15140557,cg06512316,cg04231094,cg05306173,cg20060108,cg01718065,cg15074709,cg01409259,cg16782161,

[0008] cg05852568,cg00442507,cg10929690,cg27311590,cg17107388,cg12456927,

[0009] cg05615996,cg04265523,cg22943590,cg05923226,cg11807280,cg16323641,

[0010] cg09401099,cg18057497,cg27192248,cg18635670,cg00740914,cg13039251,

[0011] cg25336746,cg06413398,cg23704995,cg03991512,cg09082066,cg12919873,

[0012] cg25690897,cg02001279,cg12959488,cg10804656,cg03566001,cg18598117,

[0013] cg25799109,cg24840300,cg21811021,cg08087275,cg15627188,cg01052636,

[0014] cg23060047,cg26964651,cg23530239,cg19479373,cg02191230,cg03534662,

[0015] cg25199254,cg08276645,cg03770646,cg02495518,cg02163378,cg11347033,

[0016] cg11747462,cg00449347,cg13943760,cg17906168,cg03486986,cg06213635,

[0017] cg11106156,cg07518714,cg01394339,cg04028695,cg24428144,cg16101574,

[0018] cg20016095,cg15490784,cg24158931,cg07548283,cg11298844,cg15028525,

[0019] cg18675951,cg13287296,cg12211471,cg07497042,cg17373256,cg00048759,

[0020] cg06865772,cg18045815,cg27162435,cg19439768,cg25021026,cg19545701,

[0021] cg19560781,cg23448505,cg07024568,cg13108341,cg23565569,cg25940196,

[0022] cg20816447,cg07975200,cg02597337,cg11215901,cg04873577,cg07738234,

[0023] cg18902238,cg10665321,cg20249566,cg18091225,cg19344626,cg08993878,

[0024] cg19965693,cg15393702,cg07582229,cg05106770,cg19784428,cg08975875,

[0025] cg25212397,cg03018753,cg09199338,cg05657694,cg01062621,cg05202858,

[0026] cg07485775,cg25150953,cg23563656,cg08928145,cg18815647,cg07320140,

[0027] cg13585080,cg25352836,cg08790320,cg19878479,cg04199077,cg04189678,

[0028] cg14395060,cg01901084,cg15726426,cg15846506,cg14643892,cg05157098,

[0029] cg00537045,cg15951188,cg14282632,cg07592361,cg08867461,cg21184711,

[0030] cg00563824,cg12594502,cg26910511,cg25912474,cg13138147,cg08247321,

[0031] cg00002033,cg21734651,cg22554913,cg23669081,cg06306128,cg11126410,

[0032] cg04135377,cg05574272,cg03551561,cg03294458,cg14944696,cg16316394,

[0033] cg23928763,cg24690094,cg24969147,cg06394621,cg23574427,cg01471572,

[0034] cg15500907,cg11811828,cg24724428,cg21572722,cg06639320,cg00481951,

[0035] cg17110586,cg08097417,cg19991948,cg23500537,cg01021271,cg11218872,

[0036] cg19505546,cg07191657,cg14829814,cg13001142,cg21020378,cg11415498,

[0037] cg20692569,cg00305797,cg19045573,cg03763300,cg22493216,cg23893806,

[0038] cg12990614,cg25693132,cg03732728,cg13339563,cg05301470,cg09213502,

[0039] cg09884851,cg13518852,cg03743982,cg05863502,cg21907521,cg04826368,cg02673352,cg25175009,cg14077898,cg07016145,cg08946781,cg05119200,cg10472919,cg11651896,cg16655765,cg13417066,cg08774009,cg08871495,cg05213896,cg11180750,cg27321460,cg05380920,cg01898573,cg17200669,cg00907668,cg17268658,cg14692377,cg11649376,cg07082267,cg22083892,cg13206721,cg15894389,cg06737494,cg25078444,cg14556683,cg05823563,cg25129960,cg07544187,cg04940570,cg18230220,cg03054277,cg23395688,cg22320795,cg10149600,cg08468401,cg03864443,cg14510445,cg03905236,cg06723863,cg06735626,cg22554456,cg13523510,cg13949829,cg26059468,cg22652747,cg00498942,cg10716444,cg17804348,cg23538901,cg19536810,cg03996822,cg19904058,cg13719901,cg01278873,cg13356175,cg24706980,cg10097598,cg06752955,cg10781276,cg07894352,cg06352730,cg03825335,cg26316885,cg17796960,cg27048095,cg12419863,cg16547529,cg25274915,cg00687714,cg04904561,cg25818583,cg01735357,cg24647428,cg06119452,cg09253736,cg26846030,cg24309739,cg04091563,cg20768358,cg20947470,cg22907979,cg08389814,cg14339214,cg27211295,cg03251378,cg07503069,cg160 08966,cg21213853,cg01949324,cg14812628,cg08100129,cg17437939, cg11288218,cg16545019,cg09878971,cg26882438,cg03994651,cg115 03396,cg01828742,cg09988805,cg21288889,cg09822170,cg16686396, cg22455642,cg05412615,cg19663246,cg07660627,cg19683655,cg182 25577,cg11586189,cg14370847,cg00464640,cg06041150,cg12542255, cg19603903,cg21397540,cg18921954,cg01966334,cg22545206,cg160 34541,cg23936410,cg15052665,cg01531605,cg13552692,cg12642568, cg21620282,cg15366841,cg18071806,cg22736354,cg11693709,cg002 77334,cg00143474,cg23078123,cg09472506,cg01119503,cg04453050, cg03763997,cg05727665,cg14933832,cg23737927,cg10616795,cg003 03541,cg27367526,cg07872519,cg06279276,cg17508941,cg10092685, cg26343883,cg07433723,cg17471939,cg10676327,cg15773296,cg24393673,cg11207143,cg18329187,cg06862636,cg11415496,cg03371609,cg19271162,cg25325636,cg26679004,cg25218220,cg00074818,cg01517680,cg11661000,cg23445321,cg24083051,cg01124132 and cg13714644. ,

[0040] In some embodiments, the dataset includes any one of the following (1)-(11):

[0041] (1) Includes quantitative values of methylation levels of one or more of the following gene loci: cg16867657,cg13552692,cg17268658,cg22454769,cg08097417,cg07553761,cg12642568,cg23500537,cg18933331,cg03473532,cg21620282,cg15366841,cg18071806,cg22736354,cg116 93709,cg00277334,cg00143474,cg11298844,cg23078123,cg09472506,cg16008966,cg01119503,cg2542788 0,cg04453050,cg13585080,cg03763997,cg05727665,cg14933832,cg23737927,cg10616795,cg17804348,cg0 5213896,cg00303541,cg27367526,cg07872519,cg06279276,cg17508941,cg10092685,cg26343883,cg19878 479,cg06865772,cg07433723,cg17471939,cg10676327,cg13949829,cg15773296,cg24393673,cg11207143,c g18329187,cg06862636,cg11415496,cg03371609,cg19271162,cg25325636,cg26679004,cg25218220,cg0007 4818,cg01517680,cg11661000,cg06213635,cg23445321,cg24083051,cg15052665,cg01124132,cg13714644;

[0042] (2) Includes quantitative values of methylation levels of one or more of the following gene loci: cg16867657, cg22454769, cg07553761, cg26921969, cg02669012, cg18933331, cg07323488, cg16476710, cg11935615, cg07184080, cg0 1499479,cg08553327,cg25427880,cg16540789,cg14200127,cg08231710,cg09445 550,cg13927769,cg20665157,cg11025681,cg01034094,cg01720616,cg23754382;

[0043] (3) Includes quantitative values of methylation levels of one or more of the following gene loci: cg03032497,cg22156456,cg21762610,cg07323488,cg03473532,cg21548231,cg25410668,cg13221458,cg12379720,cg13416486,cg21126595,cg00792799,cg10283268,cg22206517,cg02838877,cg237766 28,cg15110296,cg16969368,cg09899215,cg07897508,cg25584771,cg27517999,cg24429836,cg00698382,cg240129 25,cg12650870,cg11793423,cg12754260,cg12183225,cg22720431,cg16440532,cg03621974,cg07895657,cg2399545 9,cg12110659,cg11463113,cg19937979,cg00075507,cg16322747,cg16308533,cg18380497,cg08121845,cg0296844 5,cg18882457,cg21283066,cg18622896,cg09317508,cg12008779,cg04795774,cg20983625,cg05758220,cg25950625 ,cg23754382,cg15140557,cg06512316,cg04231094,cg05306173,cg20060108,cg01718065,cg15074709,cg01409259 ,cg16782161,cg05852568,cg00442507,cg10929690,cg27311590,cg17107388,cg12456927,cg05615996,cg04265523;

[0044] (4) Includes quantitative values of methylation levels of one or more of the following gene loci: cg26921969,cg22943590,cg05923226,cg11807280,cg16323641,cg09401099,cg18057497,cg27192248,cg18635670,cg00740914,cg13039251,cg25336746,cg064133 98,cg23704995,cg03991512,cg09082066,cg12919873,cg24012925,cg25690897,cg02001279,cg1 2959488,cg10804656,cg03566001,cg18598117,cg25799109,cg24840300,cg21811021,cg0808727 5,cg15627188,cg01052636,cg23060047,cg26964651,cg23530239,cg19479373,cg02191230,cg03 534662,cg25199254,cg08276645,cg03770646,cg02495518,cg02163378,cg11347033,cg11747462 ,cg00449347,cg13943760,cg17906168,cg03486986,cg06213635,cg11106156,cg07518714,cg013 94339,cg04028695,cg24428144,cg16101574,cg20016095,cg15490784,cg24158931,cg07548283;

[0045] (5) Includes quantitative values of methylation levels of one or more of the following gene loci: cg11298844,cg15028525,cg18675951,cg13287296,cg12211471,cg07497042,cg17373256,cg24012925,cg00048759,cg12650870,cg06865772,cg18045815,cg27162435,cg19439768,cg25021026,cg19545701,cg19560781,cg23448505;

[0046] (6) Includes quantitative values of methylation levels of one or more of the following gene loci: cg07024568,cg13108341,cg23565569,cg25940196,cg20816447,cg02669012,cg07975200,cg02597337,cg11215901,cg04873577,cg16476710,cg07738234,cg18902238,cg25410668,cg10665321,cg01499479,cg20249566,cg18091225,cg16323641,cg19344626,cg 08993878,cg19965693,cg15393702,cg16540789,cg07582229,cg0510677 0,cg19784428,cg08975875,cg25212397,cg03018753,cg09199338,cg142 00127,cg05657694,cg01062621,cg05202858,cg07485775,cg25150953,c g23563656,cg08928145,cg09445550,cg18815647,cg07320140,cg1358508 0,cg25352836,cg09899215,cg08790320,cg19878479,cg04199077,cg244 29836,cg20665157,cg04189678,cg14395060,cg12754260,cg01901084,c g15726426,cg15846506,cg14643892,cg05157098,cg00537045,cg159511 88,cg14282632,cg16308533,cg07592361,cg08867461,cg21184711,cg18 882457,cg00563824,cg12594502,cg26910511,cg01720616,cg25912474, cg13138147,cg08247321,cg00002033,cg21734651,cg22554913,cg23669 081,cg06306128,cg11126410,cg04135377,cg05574272,cg03551561,cg0 3294458,cg14944696,cg16316394,cg23928763,cg24690094,cg24969147,cg06394621,cg12456927,cg23574427,cg01471572,cg15500907,cg11811828;,

[0047] (7) Includes quantitative values of methylation levels of one or more of the following gene loci: cg16867657,cg24724428,cg22454769,cg21572722,cg06639320,cg00481951,cg17110586,cg08097417,cg03032497,cg19991948,cg23500537,cg02669012,cg01021271,cg11218872,cg19505546,cg07191657 ,cg14829814,cg13221458,cg15028525,cg13001142,cg21020378,cg25427880,cg08993878,cg16540789,cg09401099,c g11415498,cg20692569,cg00305797,cg19045573,cg03763300,cg22493216,cg23893806,cg00740914,cg12990614,cg2 5693132,cg03732728,cg13339563,cg08231710,cg07320140,cg09899215,cg05301470,cg09213502,cg09884851,cg135 18852,cg03743982,cg17373256,cg24429836,cg05863502,cg21907521,cg04826368,cg02673352,cg15726426,cg25175 009,cg14077898,cg07016145,cg18045815,cg11463113,cg08946781,cg00075507,cg15951188,cg05119200,cg1047291 9,cg18882457,cg11651896,cg16655765,cg13417066,cg05758220,cg11126410,cg08774009,cg00442507,cg08871495;

[0048] (8) Include quantitative values of methylation levels of one or more of the following gene loci: cg16867657,cg07553761,cg02669012,cg07323488,cg16476710,cg11935615,cg07184080,cg05213896,cg11180750,cg16540789,cg27321460,cg05380920,cg01898573,cg14200127,cg09445550,cg09082066,cg21907521,cg02673352,cg17200669,cg00907668;

[0049] (9) Includes quantitative values of methylation levels of one or more of the following gene loci: cg16867657,cg24724428,cg17268658,cg22454769,cg07024568,cg03032497,cg07553761,cg14692377,cg11649376,cg07082267,cg26921969,cg02669012,cg18933331,cg07323488,cg03473532,cg11218872,cg16476710,cg22943590,cg22083892,cg13206721,cg 15894389,cg21548231,cg06737494,cg11935615,cg05923226,cg1180728 0,cg13221458,cg15028525,cg25078444,cg14556683,cg05213896,cg058 23563,cg25129960,cg25427880,cg11180750,cg07544187,cg04940570,c g18230220,cg03054277,cg09401099,cg23395688,cg05380920,cg2069256 9,cg22320795,cg10149600,cg08468401,cg03864443,cg14510445,cg039 05236,cg06723863,cg06735626,cg00740914,cg22554456,cg13523510,c g13339563,cg13949829,cg23776628,cg26059468,cg16969368,cg226527 47,cg00498942,cg10716444,cg13518852,cg17804348,cg23538901,cg19 536810,cg03996822,cg19904058,cg13719901,cg01278873,cg13356175, cg24706980,cg10097598,cg06752955,cg10781276,cg07894352,cg06352 730,cg03825335,cg26316885,cg17796960,cg27048095,cg12419863,cg1 6547529,cg25274915,cg00687714,cg14282632,cg04904561,cg25818583,cg17200669,cg01735357,cg24647428,cg06119452,cg09253736,cg26846030,cg24309739,cg04091563,cg23754382,cg2076 8358,cg20947470,cg22907979,cg08389814,cg17906168,cg23448505,cg14339214,cg27211295,cg03251378,cg24428144;,

[0050] (10) Includes quantitative values of methylation levels of one or more of the following gene loci: cg03032497,cg07553761,cg25940196,cg23500537,cg26921969,cg04873577,cg07503069,cg16008966,cg05923226,cg21213853,cg01949324,cg07582229,cg14812628,cg g07497042,cg08100129,cg13039251,cg17437939,cg11288218,cg16545019,cg09878971,cg26882438 ,cg20665157,cg03994651,cg11503396,cg25690897,cg00048759,cg01828742,cg09988805,cg212888 89,cg09822170,cg16686396,cg18598117,cg22455642,cg05412615,cg01720616,cg24647428,cg2366 9081,cg19663246,cg07660627,cg23754382,cg06512316,cg11347033,cg19683655,cg18225577,cg11 586189,cg14370847,cg00464640,cg06041150,cg12542255,cg19603903,cg21397540,cg18921954,cg 01966334,cg22545206,cg16034541,cg23574427,cg20016095,cg23936410,cg15052665,cg01531605;

[0051] (11) Includes quantitative values of methylation levels of one or more of the following gene loci: cg16867657, cg22454769, cg07553761, cg08097417, cg23500537, cg22736354, cg03473532.

[0052] The above-mentioned DNA methylation level datasets (1)-(11) are particularly advantageous for constructing the following age estimation model:

[0053] Group (1) (Time Series) Age Estimation Model Group (2) Composite age estimation model Group (3) Facial age estimation model Group (4) Hormone age estimation model Group (5) Immune age estimation model Group (6) Lipid age estimation model Group (7) Metabolic age estimation model Group (8) Phenotypic age estimation model Group (9) Protein age estimation model Group (10) Transcriptional age estimation model Group (11) (Time Series) Age Estimation Model .

[0054] In some embodiments, the genomic methylation sequencing is based on bisulfite sequencing technology. The principle of bisulfite sequencing technology is: after bisulfite treatment, specific primers are designed for the altered DNA sequence and polymerase chain reaction (PCR) is performed. The previously unmethylated cytosine sites in the PCR product are replaced by thymine, while the methylated cytosine sites remain unchanged. The PCR products are cloned and sequenced. Through this method, the methylation status of specific sites in each genomic DNA molecule can be obtained.

[0055] To estimate the methylation status, the Illumina Infinium analysis uses a pair of probes (methylated probe and unmethylated probe) to measure the intensity of the methylated and unmethylated alleles of the CpG site being queried. The methylation level is then estimated based on the measured intensity of the pair of probes. There are currently two methods to measure methylation levels. The first is called the beta value (β value), which ranges from 0 to 1 and is widely used to measure the percentage of methylation. This is the method currently recommended by Illumina. The second method is the log2 ratio of the intensity of the methylated probe relative to the unmethylated probe, i.e., the M-value method, which is widely used in mRNA expression microarray analysis. In some embodiments, the quantitative value of the DNA methylation level is selected from the beta value and the M value, preferably the beta value.

[0056] In a second aspect, the present invention provides a method for establishing a DNA methylation level dataset, comprising:

[0057] (1) collecting biological samples from multiple subjects, performing DNA methylation sequencing on the biological samples, and screening for quantitative values of methylation levels of one or more gene loci described in any one of the first aspects;

[0058] (2) Using chronological age or biological age as training labels, the dataset is constructed for training an age estimation model.

[0059] In some embodiments, in step (1), the quantitative value of the methylation level of one or more gene sites described in the aforementioned group (11) is screened.

[0060] In some embodiments, the biological sample is selected from blood (eg, whole blood, plasma, or serum), urine, saliva, extracts of cells and tissues, etc. In some embodiments, the biological sample is whole blood.

[0061] In some embodiments, the subjects are Asian (eg, East Asian or Southeast Asian), such as Chinese; preferably, the subjects are ≥100, eg, ≥500, ≥1000, or ≥10000.

[0062] In this article, "chronological age" refers to the actual age of an individual, that is, the time an individual has experienced since birth. "Biological age" refers to the degree of aging of an individual's tissues, organs, and systems reflected by one or more biological indicators. Known biological indicators that can be used to measure the true degree of aging of the body include facial features, hormone levels, immunity, lipids, metabolite abundance, clinical phenotype, protein abundance, gene expression, and their combinations. Predicting biological age through the above biological indicators is known in the art. For example, by using quantifiable detection indicators and artificial intelligence algorithms, the biological age of an individual in a certain dimension can be calculated. Li, J. et al. (2023). Determining a multimodal aging clock in a cohort of Chinese women. Med. 2023 Nov 10; 4(11): 825-848. [1]It is disclosed that a series of age-related markers, including transcripts, proteins, metabolites, microorganisms and clinical laboratory values, were identified based on multimodal measurement results, based on which composite aging clocks and customized aging clocks were developed. In some embodiments, the biological age described herein includes composite age, facial age, hormone age, immune age, lipid age, metabolic age, phenotype age, protein age, and transcript age, etc. "Facial age" is the biological age defined based on individual facial measurements; "transcription age" is the biological age reflected by individual transcriptome expression; "hormonal age" is the biological age reflected by hormone-related protein, metabolite, gene expression levels and phenotypic measurements; "protein age" is the biological age reflected by individual plasma protein; "phenotype age" is the biological age defined by clinical phenotype measurements; "metabolic age" is the biological age reflected by individual metabolite levels; "immune age" is the biological age reflected by immune-related protein, metabolite, gene expression levels and phenotypic measurements; "lipid age" is the biological age reflected by lipid-related protein, metabolite, gene expression levels and phenotypic measurements. These ages correspond to different levels of aging, and evaluating them can more comprehensively calculate the degree of aging of an individual from multiple levels. The biological age was calculated by the method disclosed by Li J et al. (Med. 2023 Nov 10; 4 (11): 825-848). As disclosed by Li J, the standardized matrix of the multi-omics data of each biological indicator was z-score transformed and merged row by row (with elements as rows and individuals as columns). The merged matrix was then subjected to k-means clustering. The joint pathway function of MetaboAnalyst (version 5.0) was used to extract the molecular features (genes, proteins and metabolites) of the two clusters with smaller variance in age trajectory for pathway annotation analysis. The characteristics of the three main categories (immune, lipid, hormone) were used together with the phenotypic measurements associated with the three categories for composite age prediction.

[0063] In some preferred embodiments, the DNA methylation level dataset is established by the following method:

[0064] i) collecting whole blood samples from multiple subjects;

[0065] ii) performing methylation chip sequencing on the sample;

[0066] iii) obtaining the methylation beta value of the methylation site in the whole blood based on the methylation chip sequencing results;

[0067] Preferably, the method further comprises standardizing the Beta value, for example, using a BMIQ conversion method to perform the standardization.

[0068] In some embodiments, the method further comprises performing a correlation analysis between the methylation beta value and age to screen for age-related methylation sites;

[0069] Preferably, a Pearson correlation analysis is performed on the methylation beta value and age, with BMI and hemocyte ratio as covariates, the P value is corrected by the BH method, and the results with P < 0.01 and the absolute value of the methylation beta value change per year > 0.002 are selected as age-related methylation sites.

[0070] In some embodiments, the hematocrit refers to the ratio of NK cells, B cells, monocytes, CD4 T cells, CD8 T cells, and neutrophils.

[0071] In another aspect, the present invention provides a method for training an age estimation model, comprising:

[0072] (1) establishing the DNA methylation level dataset according to any one of the methods described in the second aspect;

[0073] (2) calculating, based on the data set, the estimated age (e.g., chronological age or biological age) of the subject corresponding to the data using the age estimation model;

[0074] (3) Training the age estimation model based on the difference between the estimation result and the age training label of the data.

[0075] In some embodiments, the training is performed using the glmnet package.

[0076] In some embodiments, the estimated age of the subject is

[0077]

[0078] A x Represents the methylation beta value of the gene site mentioned above, a x Represents the corresponding A x The coefficient of , b represents a constant;

[0079] Through the training, determine A x The coefficient a x and the constant b.

[0080] In some embodiments, when A x When selected from the groups (1) to (11) above, the coefficient a x And the constant b is corresponding to the following 1)-10): 1)：

[0082]

[0083] 2)：

[0085]

[0086] 3)：

[0088]

[0089]

[0090] 4)：

[0092]

[0093] 5)：

[0095]

[0096] 6)：

[0098]

[0099]

[0100] 7)：

[0102]

[0103]

[0104] 8)：

[0106]

[0107] 9)：

[0109]

[0110]

[0111] 10):

[0113]

[0114]

[0115]

[0116] In another aspect, the present invention provides a simplified method for training an age estimation model, comprising:

[0117] (1) establishing the DNA methylation level dataset according to the method described above;

[0118] (2) performing stepwise linear regression on the data to construct a simplified age estimation model, wherein the simplified age estimation model includes the quantitative values of the methylation levels of the following gene loci: cg16867657, cg22454769, cg07553761, cg08097417, and cg03473532.

[0119] In some embodiments, the method for establishing the DNA methylation level dataset comprises the following steps:

[0120] (1) collecting biological samples from multiple subjects, performing DNA methylation sequencing on the biological samples, and screening the quantitative values of the methylation levels of the one or more gene sites in group (11) of the first aspect;

[0121] (2) Using age or biological age as training labels, construct the dataset for training the age estimation model.

[0122] In some embodiments, the estimated age of the subject is:

[0123]

[0124] A x Represents the methylation beta value of the gene site, a x Represents the corresponding A x The coefficient of , b represents a constant;

[0125] Through the training, determine A x The coefficient a x and the constant b.

[0126] In some embodiments, the coefficient a x And the constant b is:

[0127] Coefficient a Methylation sites Coefficient a Methylation sites 0.9463 b 3.9463 cg07553761 1.9463 cg16867657 4.9463 cg08097417 2.9463 cg22454769 5.9463 cg03473532 .

[0128] In another aspect, the present invention provides an age estimation model, which is trained by the method described in any one of the third aspects.

[0129] In another aspect, the present invention provides an age estimation device comprising:

[0130] Input unit, used to input A x , where A x As defined above;

[0131] The estimation unit is used to use the age estimation model described in this article to estimate the age of the x , estimate the estimated age of the subjects;

[0132] and an optional output unit for outputting the estimated age of the subject.

[0133] In another aspect, the present invention provides an age estimation method, comprising the step of estimating the age of a subject using the age estimation model or age estimation device described herein.

[0134] In another aspect, the present invention provides an electronic device comprising:

[0135] Memory; and

[0136] A processor coupled to the memory, the processor being configured to execute any of the aforementioned age estimation model training methods or age estimation methods based on instructions stored in the memory.

[0137] In another aspect, the present invention provides a non-volatile computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the age estimation model training method or age estimation method described in any of the above items.

[0138] Use of the DNA methylation level dataset, method for establishing a dataset, method for training an age estimation model, age estimation model, age estimation model or device, age estimation method, electronic device, or non-volatile computer-readable storage medium described in any of the foregoing items in preparing a kit, wherein the kit is used for one or more of the following:

[0139] 1) Estimation of the subject's chronological age and / or biological age;

[0140] 2) Evaluate or assist in evaluating the aging rate or degree of a subject;

[0141] 3) Evaluate or assist in evaluating the effectiveness of aging interventions;

[0142] 4) Evaluate or assist in the evaluation of the risk of aging-related diseases (such as cardiovascular diseases, neurodegenerative diseases, diabetes, or chronic obstructive pulmonary disease);

[0143] 5) Evaluate or assist in evaluating the therapeutic effects of aging-related diseases;

[0144] 6) Evaluate or assist in evaluating the risk of age-related death;

[0145] 7) Assisted diagnosis of aging-related diseases (e.g., hypertension, cytomegalovirus infection);

[0146] 8) Evaluate or assist in evaluating the rate or degree of composite age aging;

[0147] 9) Evaluate or assist in evaluating the rate or degree of facial aging;

[0148] 10) Evaluate or assist in evaluating the rate or extent of transcriptional aging;

[0149] 11) Evaluate or assist in evaluating the rate or extent of hormonal aging;

[0150] 12) Evaluate or assist in evaluating the rate or extent of protein aging;

[0151] 13) Evaluate or assist in evaluating the rate or extent of phenotypic aging;

[0152] 14) Evaluate or assist in evaluating the rate or degree of metabolic aging;

[0153] 15) Evaluate or assist in evaluating the rate or degree of immune age aging;

[0154] 16) Evaluate or assist in evaluating the rate or extent of lipid age aging.

[0155] In some embodiments, the subject is a mammal, preferably a human, more preferably an Asian, such as an East Asian or Southeast Asian, and further such as a Chinese.

[0156] Definition of terms

[0157] Unless otherwise indicated, scientific and technical terms used herein have the meanings commonly understood by those skilled in the art. Furthermore, the molecular genetics, nucleic acid chemistry, immunology, and bioinformatics laboratory procedures used herein are conventional procedures widely used in the respective fields. To facilitate a better understanding of the present invention, definitions and explanations of relevant terms are provided below.

[0158] As used herein, the term "aging" has the meaning commonly understood by those skilled in the art and is a common biological phenomenon in the body during growth and development. Aging can be considered a process of functional decline associated with aging, which is often accompanied by a continuous decline in physical health and an increased risk of aging-related diseases, functional impairment, and death.

[0159] As used herein, the term "aging clock" refers to a chronological age predictor, which is a model that interprets omics data in the context of aging. The aging clock is a machine learning model that learns patterns of molecular features in a large number of samples, such as CpG methylation levels at specific gene sites in blood cells or protein concentrations in plasma, which can be used to estimate the age of the sample source. The age estimate of the sample source can be used as a measure of the individual's biological age, and the difference between the estimate and the model fitting curve is called "Δ age" or "age gap", which reflects the individual's past aging rate. Individuals with a positive age gap (age acceleration) face a greater risk of death and some aging-related diseases (e.g., heart disease, metabolic syndrome, and certain cancers).

[0160] As used herein, the term "aging-related diseases" refers to aging-related diseases whose risk of occurrence increases significantly with aging of the body. In certain embodiments, the aging-related diseases include but are not limited to neurodegenerative diseases (such as Alzheimer's disease), diabetes, heart disease, cardiovascular and cerebrovascular diseases, hypertension, hyperlipidemia, osteoporosis, osteoarthritis, chronic kidney disease, cancer and vision loss. In certain embodiments, the aging-related diseases are cardiovascular diseases, neurodegenerative diseases, diabetes or chronic obstructive pulmonary disease. The age-related diseases described in the present invention are selected from cardiovascular diseases (such as hypertension, coronary heart disease, heart failure or stroke), neurodegenerative diseases (such as Alzheimer's disease or Parkinson's disease), diabetes or chronic obstructive pulmonary disease.

[0161] As used herein, the term "gene" refers to a segment of DNA involved in producing a polypeptide chain. It can include regions preceding and following the coding region (leader and non-transcribed trailer), as well as intervening sequences (introns) between individual coding segments (exons). The term gene is used interchangeably with nucleic acid, cDNA, mRNA, small non-coding RNA, microRNA (miRNA), Piwi-interacting RNA, and short hairpin RNA (shRNA) encoded by a gene or locus. The terms "nucleic acid" and "polynucleotide" refer to deoxyribonucleic acid (DNA) or ribonucleic acid "RNA" and polymers thereof in single-stranded or double-stranded form. Unless otherwise specified, the term includes nucleic acids containing known analogs of natural nucleotides that have similar binding properties to the reference nucleic acid and are metabolized in a manner similar to naturally occurring nucleotides. Unless otherwise specified, a specific nucleic acid sequence also implicitly includes conservatively modified variants thereof (such as degenerate codon substitutions), alleles, orthologs, SNPs, and complementary sequences, as well as explicitly indicated sequences. Specifically, degenerate codon substitution can be achieved by generating sequences in which the third position of one or more selected (or all) codons is substituted with mixed bases and / or deoxyinosine residues (Batzer et al., Nucleic Acid Res. 19:5081 (1991); Ohtsuka et al., J. Biol. Chem. 260:2605-2608 (1985); and Rossolini et al., Mol. Cell. Probes 8:91-98 (1994)). "Genome" as used herein refers to the sum of all DNA in an organism.

[0162] As used herein, the term "protein" or "proteins" is a biological molecule composed of amino acids that is essential for life and has many roles, including structure, metabolism, transport, immunity, signaling, and regulation. The term "proteome" is an umbrella term that refers to all proteins that can be expressed by an organism, a tissue, or a cell.

[0163] As used herein, the term "metabolite" refers to a substance produced or consumed by metabolic processes. Small molecule compounds generated or converted by enzymes during metabolism are also called metabolites. The term "metabolome" refers to the collection of all metabolites in an organism, tissue, or cell.

[0164] In this article, the methylation sites are defined by the numbering of CpG sites in the Illumina chip.

[0165] Advantageous Effects of the Invention

[0166] This invention is based on a high-quality cohort of naturally aging healthy people in China, which eliminates the influence of factors other than aging on the results to the greatest extent. By performing methylation sequencing on easily accessible blood cells and establishing a methylation age model, it helps to accurately measure the degree of aging in the Chinese population, assist in the diagnosis of aging-related diseases (such as hypertension and cytomegalovirus infection), and evaluate aging intervention measures. It is an important tool for the transformation of basic aging research into clinical practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0167] Figure 1 : Age-related differential methylation sites (BH-corrected P value less than 0.01 and |ΔBeta / year|>0.002, corrected by BMI and hematocrit). The upper bar graph shows individual age, and the right column shows the location information of the methylation sites.

[0168] Figure 2 : Pathway enrichment analysis results of genes corresponding to differentially methylated sites. The left side shows differentially methylated sites that are upregulated with aging, and the right side shows differentially methylated sites that are downregulated with aging.

[0169] Figure 3 : The results of age prediction using the iCAS methylation clock. R is the Pearson correlation coefficient, and MAE is the mean absolute error.

[0170] Figure 4 : Simplified version of the clock prediction result, R is the Pearson correlation coefficient, MAE is the mean absolute error.

[0171] Figure 5 : Comparison of age prediction results between iCAS methylation clock and other clocks.

[0172] Figure 6 : Methylation aging clock predicts multimodal age results, R is the Pearson correlation coefficient, MAE is the mean absolute error.

[0173] Figure 7 : The methylation composite age clock predicts the difference in aging rates between healthy people and hypertensive patients. R is the Pearson correlation coefficient, and MAE is the mean absolute error.

[0174] Figure 8 : Differences in cytomegalovirus IgG antibody concentrations between the accelerated aging group and the decelerated aging group in terms of methylation composite age and methylation transcription age. DETAILED DESCRIPTION

[0175] The embodiments of the present invention will be described in detail below with reference to the examples, but it will be understood by those skilled in the art that the following examples are merely illustrative of the present invention and should not be construed as limiting the scope of the invention. Where specific conditions are not specified in the examples, the methods were performed according to conventional conditions or the conditions recommended by the manufacturer. Where the manufacturers of the reagents or instruments are not specified, they are all conventional products that can be obtained commercially.

[0176] The technical route of the present invention mainly involves seven steps: cohort establishment, data collection, data processing, age-related analysis, aging clock establishment, and evaluation and application of the aging clock.

[0177] 1. Volunteer recruitment and sample collection

[0178] We published a "Preliminary Screening Questionnaire" through the WeChat mini-program to collect basic information of potential volunteers interested in joining this study based on the inclusion criteria. This information was used to screen volunteers who met the inclusion criteria. The inclusion criteria were as follows:

[0179] 1) Enrollment criteria

[0180] a) Aged 18 or above, with no major bad habits;

[0181] b) The applicant must be in good health and have not undergone any major surgery within the past six months. Routine conditions associated with the normal aging process are permitted, including:

[0182] i. Hypertension, well-controlled (taking no more than 3 medications);

[0183] ii. Osteoporosis, osteopenia and / or osteoarthritis;

[0184] iii. Benign prostatic hypertrophy;

[0185] iv. Cataracts, glaucoma, macular degeneration;

[0186] v.Dyslipidemia;

[0187] vi. Hypothyroidism;

[0188] vii. Prediabetes or impaired fasting blood glucose (fasting blood glucose 100-126 mg / dL).

[0189] c) The subjects can communicate well with the researchers and cooperate with all tasks during the study;

[0190] i. Subjects must voluntarily sign a written informed consent.

[0191] 2) Exclusion criteria

[0192] a) Age does not meet the inclusion criteria;

[0193] b) Participants are currently undergoing other clinical trials or using any investigational drugs or devices for treatment within 30 days prior to enrollment;

[0194] c) The investigator reports a history or current diagnosis of a significant chronic disease, including:

[0195] i. Any cancer (except polycythemia vera, basal cell or squamous cell skin cancer);

[0196] ii. Coronary artery disease / myocardial infarction / clinically significant congestive heart failure;

[0197] iii. Stroke / transient ischemic attack;

[0198] iv. Deep vein thrombosis / pulmonary embolism;

[0199] v. Serum creatinine >1.5 mg / dL (male);

[0200] vi. Poorly controlled hypertension (significant hypertension (systolic blood pressure > 160 mmHg, or diastolic blood pressure > 100 mmHg) despite treatment);

[0201] vii. History of active liver disease or metabolic acidosis;

[0202] viii. Chronic kidney disease / hemodialysis treatment, history of severe renal impairment and / or eGFR ≤ 45 ml / min / 1.73 m2;

[0203] ix. Severe autoimmune / inflammatory diseases, such as rheumatoid arthritis, lupus, Crohn's disease, etc.;

[0204] x. Neurological diseases such as dementia, such as Alzheimer's / Parkinson's disease;

[0205] xi. Diabetes mellitus (hemoglobin A1C > 6.5% or fasting blood glucose > 126 mg / dL or taking diabetes medication or insulin);

[0206] xii. Recent (within 3 months) cardiovascular disease events (myocardial infarction, percutaneous coronary intervention, coronary artery bypass grafting);

[0207] xiii. Infectious diseases such as HIV, hepatitis, tuberculosis, etc.

[0208] d) Currently taking the following medications regularly:

[0209] i. Chemotherapy drugs (such as tamoxifen, doxorubicin, mitoxantrone, bleomycin);

[0210] ii. Antiplatelet drugs (e.g., clopidogrel / Plavix, dipyridamole / Angeleno, ticlopidine / Ticlopidine, excluding aspirin);

[0211] iii. Cholinesterase inhibitors for Alzheimer's disease (donepezil / alicept).

[0212] e) Continuous alcohol or drug abuse;

[0213] f) Unable to provide informed consent;

[0214] g) The researcher believes that the physical factors of the participants may adversely affect the research process or results.

[0215] 3) Termination criteria

[0216] a) Failure to follow the dietary, work and rest schedule and physical examination requirements as required by the research content;

[0217] b) Experiencing abnormal physical conditions such as colds or fever during the study;

[0218] c) Accidental injury or physical discomfort during the research process;

[0219] d) Comply with the subject’s subjective desire to terminate the program.

[0220] Volunteers who met the inclusion criteria and were confirmed for enrollment signed an informed consent form and joined a volunteer WeChat group to facilitate management and communication. For one month prior to the program, volunteers were required to maintain a normal sleep and sleep schedule, eat a balanced diet, avoid strenuous exercise, and refrain from excessive smoking or alcohol. The day before the program, volunteers received a standardized diet (see Table 1 for the menu) at the implementing hospital and were not permitted to consume any other food.

[0221] On the day of sample collection and physical examination, volunteers must wear clothes and shoes that are convenient for movement, are not allowed to wear makeup, and bring identification documents (such as ID cards, etc.) to the hospital for physical examination and biological sample collection.

[0222] Table 1 Unified and standardized diet menu

[0223]

[0224] 2. Data Collection

[0225] 1) Biological sample processing

[0226] Blood samples were obtained from volunteers after an overnight fast. The collected blood was placed in two EDTA-coated anticoagulant tubes (for plasma and peripheral mononuclear cell separation, respectively) and immediately transported to the laboratory in a 4°C refrigerator for subsequent processing. One tube of blood sample (approximately 5 ml) was centrifuged (400 g, 15 minutes, 4°C) to separate the plasma sample, which was stored in a cryovial and placed in a -80°C refrigerator for long-term storage.

[0227] 2) Peripheral blood methylome sequencing

[0228] a) Take 200 μl of peripheral blood, place it in a glass homogenizer, add 20 μl of proteinase K solution, and mix thoroughly.

[0229] b) Add 200 μl of Buffer GB, mix thoroughly by inversion, and incubate at 70°C for 10 min. The solution should become clear. Centrifuge briefly to remove any water droplets from the inner wall of the tube cap.

[0230] c) Add 200 μl of anhydrous ethanol, shake thoroughly for 15 seconds, and briefly centrifuge to remove water droplets on the inner wall of the tube cap.

[0231] d) Add the solution and flocculent precipitate obtained in the previous step to an adsorption column CB3 (place the adsorption column in a collection tube), centrifuge at 12,000 rpm (~13,400 × g) for 30 seconds, discard the waste liquid, and return the adsorption column CB3 to the collection tube.

[0232] e) Add 500 μl of buffer GD to the adsorption column CB3, centrifuge at 12,000 rpm (~13,400×g) for 30 s, discard the waste liquid, and place the adsorption column CB3 in a collection tube.

[0233] f) Add 600 μl of rinse solution PW to the adsorption column CB3, centrifuge at 12,000 rpm (~13,400×g) for 30 s, discard the waste liquid, place the adsorption column CB3 in a collection tube, and repeat the operation once.

[0234] g) Place the adsorption column CB3 back into the collection tube and centrifuge at 12,000 rpm (~13,400 × g) for 2 minutes. Discard the waste liquid. Leave the adsorption column CB3 at room temperature for several minutes to completely dry any remaining rinse solution from the adsorption material.

[0235] h) Transfer the adsorption column CB3 to a clean centrifuge tube. Add 50-200 μl of elution buffer TE dropwise to the middle of the adsorption membrane. Incubate at room temperature for 2-5 minutes. Centrifuge at 12,000 rpm (~13,400 × g) for 2 minutes and collect the solution in a centrifuge tube.

[0236] i) Bisulfite treatment of DNA.

[0237] j) DNA methylation sequencing was performed using the Infinium Methylation EPIC BeadChip platform.

[0238] 3. Data Preprocessing

[0239] 1) Peripheral blood methylation group data

[0240] a) ChAMP software (version 2.21.1) was used to read and perform QC analysis on the original idat files of the sequencing to remove low-quality sites;

[0241] b) Correcting the probe data using the BMIQ method;

[0242] c) Batch correction of the data was performed using the champ.combat function in ChAMP software (version 2.21.1);

[0243] d) The estimateCellProp function in Enmix software (version 1.34.2) was used to calculate the blood cell proportions (NK cells, B cells, monocytes, CD4 T cells, CD8 T cells, and neutrophils).

[0244] e) Remove probes related to sex chromosomes and SNP sites;

[0245] f) Construct a DNA methylation Beta value matrix with individuals as columns and methylation sites as rows;

[0246] 4. Age-related feature screening

[0247] a) First, an age-related analysis was performed on the DNA methylation beta value matrix and the blood cell ratio matrix. This involved performing a linear regression analysis between the beta value of each gene and the individual's age, with BMI and cell ratio as covariates.

[0248] b) The results were screened and only those with a BH-corrected P value less than 0.01 and |Δbeta / year|>0.002 were selected as age-related methylation sites. The results showed that the methylation levels of these methylation sites increased or decreased with age ( Figure 2 );

[0249] c) Regarding age-related methylation sites, further annotation using the Metascape tool revealed that these upregulated genes were related to brain development and cell fate determination pathways, while the downregulated pathways mainly involved cell-cell interactions ( Figure 3 );

[0250] 5. Establishment of Methylation Age Model

[0251] a) First, the methylation site Beta value matrix is screened for features, retaining only age-related features;

[0252] b) Split the filtered matrix into training and test sets according to the pre-randomly assigned 1:1 ratio;

[0253] c) Using age or multimodal age as training labels, the glmnet package (version 4.1.4) was used to construct the model (iCAS methylation clock and 5CpG-methylation clock (simplified version clock) use age as training labels, and multimodal age methylation clock uses biological age of each modality as training labels). The multimodal age (composite age, facial age, hormone age, immune age, lipid age, metabolic age, phenotypic age, protein age, transcriptional age) is the biological age of the corresponding level. [1] For example, facial age is the biological age predicted using a neural network, hormone age is the biological age predicted based on hormone-related marker levels, immune age is the biological age predicted based on immune-related marker levels, and composite age is a combination of these multimodal age models. Nine alpha values between 0.1 and 0.9 were used to model the model and tested on the test set. The alpha with the highest accuracy was selected as the parameter for the final model. The final model is shown in Table 2.

[0254] Table 2 Final model of methylation age

[0255]

[0256]

[0257]

[0258]

[0259]

[0260]

[0261]

[0262]

[0263]

[0264]

[0265]

[0266]

[0267]

[0268]

[0269]

[0270]

[0271]

[0272]

[0273]

[0274]

[0275]

[0276]

[0277]

[0278]

[0279]

[0280]

[0281]

[0282]

[0283]

[0284]

[0285] d) Calculate the residual between the model-predicted age and the model (i.e., the difference between the predicted age and the fitted curve) to obtain the aging rate of the individuals in the test set.

[0286] 6. Measurement of Giant Virus IgG Antibody Concentration

[0287] At Quzhou Hospital, the Alinity i chemiluminescence detection instrument and kit were used to detect the concentration of giant virus IgG antibodies in human blood samples using a chemiluminescent microparticle immunoassay.

[0288] 7. Evaluation and Application of Methylation Age Model

[0289] The final model was applied to the test set to obtain the predicted age of the test set individuals and compared them with their corresponding actual age. The results showed that the correlation coefficient between iCAS predicted age and actual age reached 0.97 (Pearson correlation), and the average absolute error was 3.45 years ( Figure 3 ), compared to other clocks [3-5] With higher accuracy ( Figure 5), the correlation coefficient between the simplified clock-predicted age and the actual age reached 0.95, and the average absolute error was 3.90 years ( Figure 4 ), also with high accuracy. In addition, the multimodal methylation clock has a strong correlation with both chronological age and the corresponding phenotypic age ( Figure 6 ).

[0290] Hypertension patients have persistently elevated systolic or diastolic blood pressure, and their risk of developing the disease increases with aging. [6] The methylation composite age of the present invention can assist in the diagnosis of hypertension. The subjects were divided into a hypertension group and a healthy group according to the blood pressure test results (systolic pressure>160mmHg, or diastolic pressure>100mmHg), and the methylation age of the hypertension group and the healthy group was calculated. [7] The results showed that the methylation composite age aging rate of the hypertension group was significantly higher than that of the healthy group ( Figure 7 ).

[0291] The 20% individuals (11 people) with the highest aging rate in the Quzhou population cohort were defined as the accelerated aging group, and the 20% individuals (11 people) with the lowest aging rate were defined as the decelerated aging group. Methylation composite age and methylation transcription age can assist in the diagnosis of cytomegalovirus infection. Cytomegalovirus concentration increases with age. [8] , the concentration of cytomegalovirus IgG antibodies in individuals with accelerated aging was significantly higher than that in individuals with decelerated aging ( Figure 8 ).

[0292] References

[0293] [1]Li J., Xiong M.,Fu

[0294] [2]Rutledge J.,Oh H.,and Wyss-Coray T.(2022).Measuring biological ageusing omics data.Nat Rev Genet 23,715-727.

[0295] [3]Levine M.E.,Lu A.T.,Quach A.,Chen B.H.,Assimes T.L.,Bandinelli S.,Hou L.,Baccarelli A.A.,Stewart J.D.,Li Y.,et al.(2018).An epigeneticbiomarker of aging for lifespan and healthspan.Aging(Albany NY)10,573-591.

[0296] [4]Horvath S.(2013).DNA methylation age of human tissues and celltypes.Genome Biol 2013;14:R115.

[0297] [5]Hannum G.,Guinney J.,Zhao L.,Zhang L.,Hughes G.,Sadda S.,KlotzleB.,Bibikova M.,Fan J.B.,Gao Y.,et al.(2013).Genome-wide methylation profilesreveal quantitative views of human aging rates.Mol Cell49,359-367.

[0298] [6]Sun Z(2015).Aging,arterial stiffness,and hypertension.Hypertension65(2):252-6.

[0299] [7]Du Z,Ma L,Qu H,Chen W,Zhang B,Lu X,et al.(2019).Whole GenomeAnalyses of Chinese Population and De Novo Assembly of A Northern HanGenome.Genomics Proteomics Bioinformatics 17(3):229-247.

[0300] [8]Weymouth LA, Gomolin IH, Brennan T., Sirpenski SP and Mayo DR (1990). Cytomegalovirus antibody in the elderly. Intervirology 31(2-4):223-9.

[0301] Although the specific embodiments of the present invention have been described in detail, it will be understood by those skilled in the art that various modifications and substitutions may be made to those details based on all the teachings disclosed, and these changes are all within the scope of protection of the present invention. The full scope of the present invention is given by the appended claims and any equivalents thereof.

Claims

1. A DNA methylation level dataset, comprising quantitative values of methylation levels of one or more gene loci as follows: cg16867657,cg22454769,cg07553761,cg26921969,cg02669012,cg18933331,cg07323488,cg16476710,cg11935615,cg07184080,cg01499479,cg08553327,cg25427880,cg16540789,cg14200127,cg08231710,cg09445550,cg13927769,cg20665157,cg11025681,cg01034094,cg01720616,cg23754382,cg03032497,cg22156456,cg21762610,cg03473532,cg21548231,cg25410668,cg13221458,cg12379720,cg13416486,cg21126595,cg00792799,cg10283268,cg22206517,cg02838877,cg23776628,cg15110296,cg16969368,cg09899215,cg07897508,cg25584771,cg27517999,cg24429836,cg00698382,cg24012925,cg12650870,cg11793423,cg12754260,cg12183225,cg22720431,cg16440532,cg03621974,cg07895657,cg23995459,cg12110659,cg11463113,cg19937979,cg00075507,cg16322747,cg16308533,cg18380497,cg08121845,cg02968445,cg18882457,cg21283066,cg18622896,cg09317508,cg12008779,cg04795774,cg20983625,cg05758220,cg25950625,cg15140557,cg06512316,cg04231094,cg05306173,cg20060108,cg01718065,cg15074709,cg01409259,cg16782161,cg05852568,cg00442507,cg10929690,cg27311590,cg17107388,cg12456927,cg05615996,cg04265523,cg22943590,cg05923226,cg11807280,cg16323641,cg09401099,cg18057497,cg27192248,cg18635670,cg00740914,cg13039251,cg25336746,cg06413398,cg23704995,cg03991512,cg09082066,cg12919873,cg25690897,cg02001279,cg12959488,cg10804656,cg03566001,cg18598117,cg25799109,cg24840300,cg21811021,cg08087275,cg15627188,cg01052636,cg23060047,cg26964651,cg23530239,cg19479373,cg02191230,cg03534662,cg25199254,cg08276645,cg03770646,cg02495518,cg02163378,cg11347033,cg11747462,cg00449347,cg13943760,cg17906168,cg03486986,cg06213635,cg11106156,cg07518714,cg01394339,cg04028695,cg24428144,cg16101574,cg20016095,cg15490784,cg24158931,cg07548283,cg11298844,cg15028525,cg18675951,cg13287296,cg12211471,cg07497042,cg17373256,cg00048759,cg06865772,cg18045815,cg27162435,cg19439768,cg25021026,cg19545701,cg19560781,cg23448505,cg07024568,cg13108341,cg23565569,cg25940196,cg20816447,cg07975200,cg02597337,cg11215901,cg04873577,cg07738234,cg18902238,cg10665321,cg20249566,cg18091225,cg19344626,cg08993878,cg19965693,cg15393702,cg07582229,cg05106770,cg19784428,cg08975875,cg25212397,cg03018753,cg09199338,cg05657694,cg01062621,cg05202858,cg07485775,cg25150953,cg23563656,cg08928145,cg18815647,cg07320140,cg13585080,cg25352836,cg08790320,cg19878479,cg04199077,cg04189678,cg14395060,cg01901084,cg15726426,cg15846506,cg14643892,cg05157098,cg00537045,cg15951188,cg14282632,cg07592361,cg08867461,cg21184711,cg00563824,cg12594502,cg26910511,cg25912474,cg13138147,cg08247321,cg00002033,cg21734651,cg22554913,cg23669081,cg06306128,cg11126410,cg04135377,cg05574272,cg03551561,cg03294458,cg14944696,cg16316394,cg23928763,cg24690094,cg24969147,cg06394621,cg23574427,cg01471572,cg15500907,cg11811828,cg24724428,cg21572722,cg06639320,cg00481951,cg17110586,cg08097417,cg19991948,cg23500537,cg01021271,cg11218872,cg19505546,cg07191657,cg14829814,cg13001142,cg21020378,cg11415498,cg20692569,cg00305797,cg19045573,cg03763300,cg22493216,cg23893806,cg12990614,cg25693132,cg03732728,cg13339563,cg05301470,cg09213502,cg09884851,cg13518852,cg03743982,cg05863502,cg21907521,cg04826368,cg02673352,cg25175009,cg14077898,cg07016145,cg08946781,cg05119200,cg10472919,cg11651896,cg16655765,cg13417066,cg08774009,cg08871495,cg05213896,cg11180750,cg27321460,cg05380920,cg01898573,cg17200669,cg00907668,cg17268658,cg14692377,cg11649376,cg07082267,cg22083892,cg13206721,cg15894389,cg06737494,cg25078444,cg14556683,cg05823563,cg25129960,cg07544187,cg04940570,cg18230220,cg03054277,cg23395688,cg22320795,cg10149600,cg08468401,cg03864443,cg14510445,cg03905236,cg06723863,cg06735626,cg22554456,cg13523510,cg13949829,cg26059468,cg22652747,cg00498942,cg10716444,cg17804348,cg23538901,cg19536810,cg03996822,cg19904058,cg13719901,cg01278873,cg13356175,cg24706980,cg10097598,cg06752955,cg10781276,cg07894352,cg06352730,cg03825335,cg26316885,cg17796960,cg27048095,cg12419863,cg16547529,cg25274915,cg00687714,cg04904561,cg25818583,cg01735357,cg24647428,cg06119452,cg09253736,cg26846030,cg24309739,cg04091563,cg20768358,cg20947470,cg22907979,cg08389814,cg14339214,cg27211295,cg03251378,cg07503069,cg16008966,cg21213853,cg01949324,cg14812628,cg08100129,cg17437939,cg11288218,cg16545019,cg09878971,cg268 82438,cg03994651,cg11503396,cg01828742,cg09988805,cg212888 89,cg09822170,cg16686396,cg22455642,cg05412615,cg19663246, cg07660627,cg19683655,cg18225577,cg11586189,cg14370847,cg00464640,cg06041150,cg12542255,cg19603903,cg21397540,cg189 21954,cg01966334,cg22545206,cg16034541,cg23936410,cg150526 65,cg01531605,cg13552692,cg12642568,cg21620282,cg15366841, cg18071806,cg22736354,cg11693709,cg00277334,cg00143474,cg23078123,cg09472506,cg01119503,cg04453050,cg03763997,cg057 27665,cg14933832,cg23737927,cg10616795,cg00303541,cg273675 26,cg07872519,cg06279276,cg17508941,cg10092685,cg26343883, cg07433723,cg17471939,cg10676327,cg15773296,cg24393673,cg11207143,cg18329187,cg06862636,cg11415496,cg03371609,cg19271162,cg25325636,cg26679004,cg25218220,cg00074818,cg01517680,cg11661000,cg23445321,cg24083051,cg01124132 and cg13714644; Preferably, it includes any one of the following (1)-(11): (1) Includes quantitative values of methylation levels of one or more of the following gene loci: cg16867657,cg13552692,cg17268658,cg22454769,cg08097417,cg07553761,cg12642568,cg23500537,cg18933331,cg03473532,cg21620282,cg15366841,cg18071806,cg22736354,cg116 93709,cg00277334,cg00143474,cg11298844,cg23078123,cg09472506,cg16008966,cg01119503,cg2542788 0,cg04453050,cg13585080,cg03763997,cg05727665,cg14933832,cg23737927,cg10616795,cg17804348,cg0 5213896,cg00303541,cg27367526,cg07872519,cg06279276,cg17508941,cg10092685,cg26343883,cg19878 479,cg06865772,cg07433723,cg17471939,cg10676327,cg13949829,cg15773296,cg24393673,cg11207143,c g18329187,cg06862636,cg11415496,cg03371609,cg19271162,cg25325636,cg26679004,cg25218220,cg0007 4818,cg01517680,cg11661000,cg06213635,cg23445321,cg24083051,cg15052665,cg01124132,cg13714644; (2) Includes quantitative values of methylation levels of one or more of the following gene loci: cg16867657, cg22454769, cg07553761, cg26921969, cg02669012, cg18933331, cg07323488, cg16476710, cg11935615, cg07184080, cg0 1499479,cg08553327,cg25427880,cg16540789,cg14200127,cg08231710,cg09445 550,cg13927769,cg20665157,cg11025681,cg01034094,cg01720616,cg23754382; (3) Includes quantitative values of methylation levels of one or more of the following gene loci: cg03032497,cg22156456,cg21762610,cg07323488,cg03473532,cg21548231,cg25410668,cg13221458,cg12379720,cg13416486,cg21126595,cg00792799,cg10283268,cg22206517,cg02838877,cg237766 28,cg15110296,cg16969368,cg09899215,cg07897508,cg25584771,cg27517999,cg24429836,cg00698382,cg240129 25,cg12650870,cg11793423,cg12754260,cg12183225,cg22720431,cg16440532,cg03621974,cg07895657,cg2399545 9,cg12110659,cg11463113,cg19937979,cg00075507,cg16322747,cg16308533,cg18380497,cg08121845,cg0296844 5,cg18882457,cg21283066,cg18622896,cg09317508,cg12008779,cg04795774,cg20983625,cg05758220,cg25950625 ,cg23754382,cg15140557,cg06512316,cg04231094,cg05306173,cg20060108,cg01718065,cg15074709,cg01409259 ,cg16782161,cg05852568,cg00442507,cg10929690,cg27311590,cg17107388,cg12456927,cg05615996,cg04265523; (4) Includes quantitative values of methylation levels of one or more of the following gene loci: cg26921969,cg22943590,cg05923226,cg11807280,cg16323641,cg09401099,cg18057497,cg27192248,cg18635670,cg00740914,cg13039251,cg25336746,cg064133 98,cg23704995,cg03991512,cg09082066,cg12919873,cg24012925,cg25690897,cg02001279,cg1 2959488,cg10804656,cg03566001,cg18598117,cg25799109,cg24840300,cg21811021,cg0808727 5,cg15627188,cg01052636,cg23060047,cg26964651,cg23530239,cg19479373,cg02191230,cg03 534662,cg25199254,cg08276645,cg03770646,cg02495518,cg02163378,cg11347033,cg11747462 ,cg00449347,cg13943760,cg17906168,cg03486986,cg06213635,cg11106156,cg07518714,cg013 94339,cg04028695,cg24428144,cg16101574,cg20016095,cg15490784,cg24158931,cg07548283; (5) Includes quantitative values of methylation levels of one or more of the following gene loci: cg11298844,cg15028525,cg18675951,cg13287296,cg12211471,cg07497042,cg17373256,cg24012925,cg00048759,cg12650870,cg06865772,cg18045815,cg27162435,cg19439768,cg25021026,cg19545701,cg19560781,cg23448505; (6) Includes quantitative values of methylation levels of one or more of the following gene loci: cg07024568,cg13108341,cg23565569,cg25940196,cg20816447,cg02669012,cg07975200,cg02597337,cg11215901,cg04873577,cg16476710,cg07738234,cg18902238,cg25410668,cg10665321,cg01499479,cg20249566,cg18091225,cg16323641,cg19344626,cg 08993878,cg19965693,cg15393702,cg16540789,cg07582229,cg0510677 0,cg19784428,cg08975875,cg25212397,cg03018753,cg09199338,cg142 00127,cg05657694,cg01062621,cg05202858,cg07485775,cg25150953,c g23563656,cg08928145,cg09445550,cg18815647,cg07320140,cg1358508 0,cg25352836,cg09899215,cg08790320,cg19878479,cg04199077,cg244 29836,cg20665157,cg04189678,cg14395060,cg12754260,cg01901084,c g15726426,cg15846506,cg14643892,cg05157098,cg00537045,cg159511 88,cg14282632,cg16308533,cg07592361,cg08867461,cg21184711,cg18 882457,cg00563824,cg12594502,cg26910511,cg01720616,cg25912474, cg13138147,cg08247321,cg00002033,cg21734651,cg22554913,cg23669 081,cg06306128,cg11126410,cg04135377,cg05574272,cg03551561,cg0 3294458,cg14944696,cg16316394,cg23928763,cg24690094,cg24969147,cg06394621,cg12456927,cg23574427,cg01471572,cg15500907,cg11811828;, (7) Includes quantitative values of methylation levels of one or more of the following gene loci: cg16867657,cg24724428,cg22454769,cg21572722,cg06639320,cg00481951,cg17110586,cg08097417,cg03032497,cg19991948,cg23500537,cg02669012,cg01021271,cg11218872,cg19505546,cg07191657 ,cg14829814,cg13221458,cg15028525,cg13001142,cg21020378,cg25427880,cg08993878,cg16540789,cg09401099,c g11415498,cg20692569,cg00305797,cg19045573,cg03763300,cg22493216,cg23893806,cg00740914,cg12990614,cg2 5693132,cg03732728,cg13339563,cg08231710,cg07320140,cg09899215,cg05301470,cg09213502,cg09884851,cg135 18852,cg03743982,cg17373256,cg24429836,cg05863502,cg21907521,cg04826368,cg02673352,cg15726426,cg25175 009,cg14077898,cg07016145,cg18045815,cg11463113,cg08946781,cg00075507,cg15951188,cg05119200,cg1047291 9,cg18882457,cg11651896,cg16655765,cg13417066,cg05758220,cg11126410,cg08774009,cg00442507,cg08871495; (8) Include quantitative values of methylation levels of one or more of the following gene loci: cg16867657,cg07553761,cg02669012,cg07323488,cg16476710,cg11935615,cg07184080,cg05213896,cg11180750,cg16540789,cg27321460,cg05380920,cg01898573,cg14200127,cg09445550,cg09082066,cg21907521,cg02673352,cg17200669,cg00907668; (9) Includes quantitative values of methylation levels of one or more of the following gene loci: cg16867657,cg24724428,cg17268658,cg22454769,cg07024568,cg03032497,cg07553761,cg14692377,cg11649376,cg07082267,cg26921969,cg02669012,cg18933331,cg07323488,cg03473532,cg11218872,cg16476710,cg22943590,cg22083892,cg13206721,cg 15894389,cg21548231,cg06737494,cg11935615,cg05923226,cg1180728 0,cg13221458,cg15028525,cg25078444,cg14556683,cg05213896,cg058 23563,cg25129960,cg25427880,cg11180750,cg07544187,cg04940570,c g18230220,cg03054277,cg09401099,cg23395688,cg05380920,cg2069256 9,cg22320795,cg10149600,cg08468401,cg03864443,cg14510445,cg039 05236,cg06723863,cg06735626,cg00740914,cg22554456,cg13523510,c g13339563,cg13949829,cg23776628,cg26059468,cg16969368,cg226527 47,cg00498942,cg10716444,cg13518852,cg17804348,cg23538901,cg19 536810,cg03996822,cg19904058,cg13719901,cg01278873,cg13356175, cg24706980,cg10097598,cg06752955,cg10781276,cg07894352,cg06352 730,cg03825335,cg26316885,cg17796960,cg27048095,cg12419863,cg1 6547529,cg25274915,cg00687714,cg14282632,cg04904561,cg25818583,cg17200669,cg01735357,cg24647428,cg06119452,cg09253736,cg26846030,cg24309739,cg04091563,cg23754382,cg20768358,cg20947470,cg22907979,cg08389814,cg17906168,cg23448505,cg14339214,cg27211295,cg03251378,cg24428144;, (10) Includes quantitative values of methylation levels of one or more of the following gene loci: cg03032497,cg07553761,cg25940196,cg23500537,cg26921969,cg04873577,cg07503069,cg16008966,cg05923226,cg21213853,cg01949324,cg07582229,cg14812628,cg g07497042,cg08100129,cg13039251,cg17437939,cg11288218,cg16545019,cg09878971,cg26882438 ,cg20665157,cg03994651,cg11503396,cg25690897,cg00048759,cg01828742,cg09988805,cg212888 89,cg09822170,cg16686396,cg18598117,cg22455642,cg05412615,cg01720616,cg24647428,cg2366 9081,cg19663246,cg07660627,cg23754382,cg06512316,cg11347033,cg19683655,cg18225577,cg11 586189,cg14370847,cg00464640,cg06041150,cg12542255,cg19603903,cg21397540,cg18921954,cg 01966334,cg22545206,cg16034541,cg23574427,cg20016095,cg23936410,cg15052665,cg01531605; (11) Includes quantitative values of methylation levels of one or more of the following gene loci: cg16867657, cg22454769, cg07553761, cg08097417, cg23500537, cg22736354, cg03473532; Preferably, the quantitative value of the DNA methylation level is selected from the beta value and the M value, preferably the beta value.

2. A method for establishing a DNA methylation level dataset, comprising: (1) collecting biological samples from multiple subjects, performing DNA methylation sequencing on the biological samples, and screening for quantitative values of methylation levels of one or more gene loci as recited in claim 1; (2) Using chronological age or biological age as training labels, the dataset is constructed for training an age estimation model.

3. The method of claim 2, wherein the biological sample is selected from the group consisting of blood (eg, whole blood, plasma, or serum), urine, saliva, cell, and tissue extracts.

4. The method of claim 2 or 3, wherein the subjects are Asians (eg, East Asians or Southeast Asians), such as Chinese; preferably, the number of the subjects is ≥100, for example, ≥500, ≥1000, or ≥10,000.

5. The method according to any one of claims 2 to 4, wherein in step (1), the quantitative value of the methylation level of one or more gene loci described in group (11) of claim 1 is screened.

6. The method according to any one of claims 2 to 5, wherein the biological age is an age measured based on biological indicators, wherein the biological indicators are selected from facial features, hormone levels, immunity, lipids, metabolite abundance, clinical phenotype, protein abundance, gene expression and combinations thereof.

7. A method for training an age estimation model, comprising: (1) establishing the DNA methylation level dataset according to the method according to any one of claims 2 to 6; (2) calculating, based on the data set and using the age estimation model, the estimated age (e.g., chronological age or biological age) of the subject corresponding to the data; (3) Training the age estimation model based on the difference between the estimation result and the age training label of the data.

8. The training method of claim 7, wherein the training is performed using the glmnet package.

9. The training method according to claim 7 or 8, wherein the subject's estimated age is A x Represents the methylation beta value of the gene site, a x Represents the corresponding A x The coefficient of , b represents a constant; Through the training, determine A x The coefficient a x and the constant b.

10. The training method according to claim 9, wherein A x When selected from the group (1) to (10) of claim 1, the coefficient a x And the constant b is corresponding to the following 1)-10): 1)： 2)： 3)： 4)： 5)： 6)： 7)： 8)： 9)： 10)： 11. A simplified method for training an age estimation model, comprising: (1) establishing the DNA methylation level dataset according to the method of claim 5; (2) performing stepwise linear regression on the data to construct a simplified age estimation model, wherein the simplified age estimation model includes the quantitative values of the methylation levels of the following gene loci: cg16867657, cg22454769, cg07553761, cg08097417, and cg03473532.

12. The training method of claim 11, wherein the subject's estimated age is A x Represents the methylation beta value of the gene site, a x Represents the corresponding A x The coefficient of , b represents a constant; Through the training, determine A x The coefficient a x and the constant b.

13. The training method according to claim 12, wherein the coefficient a x And the constant b is: 。 14. An age estimation model, trained by the method according to any one of claims 7 to 13.

15. An age estimation device, comprising: Input unit, used to input A x , where A x As defined in claim 10 or 13; An estimation unit, configured to use the age estimation model according to claim 14, according to the input A x , estimate the estimated age of the subjects; and an optional output unit for outputting the estimated age of the subject.

16. A method for estimating age, comprising the step of estimating the age of a subject using the age estimation model according to claim 14 or the age estimation device according to claim 15.

17. An electronic device comprising: Memory; and A processor coupled to the memory, the processor being configured to execute the age estimation model training method according to any one of claims 7 to 13, or the age estimation method according to claim 16, based on instructions stored in the memory.

18. A non-volatile computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the program implements the age estimation model training method according to any one of claims 7 to 13, or the age estimation method according to claim 17.

19. Use of the DNA methylation level dataset of claim 1, the method for establishing a dataset of any one of claims 2-6, the method for training an age estimation model of any one of claims 7-13, the age estimation model of claim 14, the age estimation model or device of claim 15, the age estimation method of claim 16, the electronic device of claim 17, or the non-volatile computer-readable storage medium of claim 18 in preparing a kit, wherein the kit is used for one or more of the following: 1) Estimation of the subject's chronological age and / or biological age; 2) Evaluate or assist in evaluating the aging rate or degree of a subject; 3) Evaluate or assist in evaluating the effectiveness of aging interventions; 4) Evaluate or assist in the evaluation of the risk of aging-related diseases (such as cardiovascular diseases, neurodegenerative diseases, diabetes, or chronic obstructive pulmonary disease); 5) Evaluate or assist in evaluating the therapeutic effects of aging-related diseases; 6) Evaluate or assist in evaluating the risk of age-related death; 7) Assisted diagnosis of aging-related diseases (e.g., hypertension, cytomegalovirus infection); 8) Evaluate or assist in evaluating the rate or extent of complex aging; 9) Evaluate or assist in evaluating the rate or degree of facial aging; 10) Evaluating or assisting in evaluating the rate or extent of transcriptional senescence; 11) Evaluate or assist in evaluating the rate or extent of hormonal aging; 12) Evaluate or assist in evaluating the rate or extent of protein aging; 13) Evaluate or assist in evaluating the rate or extent of phenotypic aging; 14) Evaluate or assist in evaluating the rate or extent of metabolic aging; 15) Evaluate or assist in evaluating the rate or extent of immune senescence; 16) Evaluate or assist in evaluating the rate or extent of lipid aging.

20. The use according to claim 19, wherein the subject is a mammal, preferably a human, more preferably an Asian, such as an East Asian or Southeast Asian, and further such as a Chinese.

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