Method and device for determining age

Abstract

The present invention relates to age determination. Specifically, the present invention relates to a method for determining an age index and a method for determining the age of an individual. The method is based on data comprising DNA methylation levels of a set of genomic DNA sequences. Preferably, the age index is determined by applying a regression method including a Least Absolute Shrinkage and Selection Operator (LASSO) to the data, preferably in combination with subsequent stepwise regression. Furthermore, the present invention relates to a collection of genomic DNA sequences and gene sets, and their use for diagnosing the health and / or fitness status of an individual and identifying molecules that affect aging.

Description

[Technical field]

[0001] The present invention relates to age determination. In particular, the present invention relates to a method for determining an age index and a method for determining the age of an individual, said method being based on data comprising the DNA methylation level of a set of genomic DNA sequences. Preferably, said age index is determined by applying a regression method comprising the Least Absolute Shrinkage and Selection Operator (LASSO) to the data, preferably in combination with a subsequent stepwise regression. Furthermore, the present invention relates to a set of genomic DNA sequences and a set of genes, and their use for diagnosing the health and / or fitness state of an individual and identifying molecules that affect aging. In a further aspect, the present invention relates to a chip or kit, in particular a chip or kit that can be used to detect the DNA methylation level of said set of genomic DNA sequences. [Background technology]

[0002] As humans age, their bodies undergo various changes, for example with regard to teeth, wear of joints, muscle loss, mental decline, etc. However, although health generally declines as people age, there are still large inter-individual differences in health, even among people who share the same birthday. Thus, some people age faster than others.

[0003] Furthermore, studies looking at twins over their lifetimes have found that only about 25% of average lifespan is determined by genetics, with lifestyle and environmental factors accounting for the remaining 75% of variation in lifespan.

[0004] It has been found that some diseases occur more frequently in humans as chronological age advances. However, chronological age is not an ideal indicator of the health status of an individual as they age, which is often referred to as "biological age". Determining an age that is more similar to biological age can help evaluate whether an individual has a high risk for aging-related diseases, such as Alzheimer's disease. If the determined age is higher than the chronological age, preventive measures, such as lifestyle changes, can be prescribed to prevent or slow down the course of aging-related diseases. Determining a surrogate age can also be useful to improve diagnosis, for example to evaluate whether to focus on aging-related diseases.

[0005] Furthermore, if the chronological age of an individual is unknown, the surrogate age can be used as an indication of chronological age, even though they are not the same. If the determination of the surrogate age is based on a biological sample, the surrogate age can also be used in forensic medicine, for example, when bloodstains of a criminal are found at a crime scene.

[0006] It has further been proposed that certain groups of individuals, for example those living in certain countries with certain local habits regarding nutritional intake, etc., age slower than others. Determining the ages of different groups of individuals can help identify factors that affect biological age. See Alegria-Torres et al., Epigenomics, 2011 June; 3(3): 267-277.

[0007] Note that when both the chronological age and the non-chronological age are known, it is the difference from the chronological age that may be presented, rather than the absolute value.

[0008] It is proposed to determine the age of a person based on the methylation level of genomic DNA sequence found in the individual.In particular, refer to WO2012 / 162139.In WO2012 / 162139, it is proposed to observe the cytosine methylation of one or more CG loci in genomic DNA, which are selected from a large group of designated CG loci.

[0009] See also WO2015 / 048665, which lists additional CpG loci.

[0010] It is also proposed in the document WO2012 / 162139 that a reference (training) data set of, for example, 100 individuals of various chronological ages can be collected using a specific technological platform and organization, and then a specific multivariate linear model can be designed to fit this reference data set, including the methylation levels of CpG loci obtained for each individual. For the estimation of the coefficients, for example, least squares regression is proposed. The coefficients assigned to each CpG locus are then used to determine the unknown substitute ages of the individuals not included in the training data set. It is proposed to use "leave-one-out analysis" to determine these coefficients. In such a "leave-one-out analysis", a multivariate regression model is fitted to all subjects except one of the reference data set, and then the prediction is compared with the chronological age of the remaining subjects. WO2012 / 162139 also proposes a test to screen for top predictors to improve the accuracy of the model.

[0011] Nevertheless, despite the use of a very large number of CpG loci and substantial experimental and computational efforts to derive age indices from a large number of corresponding methylation level measurements, the average accuracy achieved by WO2012 / 162139 is still stated to be in the range of only 3-5 years, demonstrating that the accuracy and / or efficiency of current age determination methods is suboptimal.

[0012] Furthermore, measuring and assessing a large number of methylation levels is costly. In this respect, it should be noted that there are about 28 million CpG loci that can be found in the human genome. Even if it is considered that the methylation level of some of these CpG loci may not be affected by aging, there remain a great many CpG loci whose methylation level is affected by age. Although it is considered that the detection methods used to determine methylation levels will improve over time, which may make it possible to determine the methylation levels of more and more CpG loci, it is already possible to determine the methylation levels of at least approximately 800.000 (800,000) CpG loci using commercially available equipment and methods. Nevertheless, such measurements are costly, and therefore determining age based on the measurement of a very large number of CpG loci would also be very costly. Thus, current age determination methods are based on hundreds of CpG loci. However, the cost, equipment and expertise required to determine age based on hundreds of CpG loci still hinder the widespread use of current age determination methods. [Prior art documents] [Non-patent literature]

[0013] [Non-Patent Document 1] Alegria-Torres et al., Epigenomics, 2011 June; 3(3): 267-277 Summary of the Invention [Problem to be solved by the invention]

[0014] Thus, there is a need for improved methods of determining age, and in particular, improved methods of determining age that require less data input but are at least approximately as accurate.

[0015] There is a further need for improved means for screening drugs for treating or preventing aging-related diseases or cancers, or phenotypes associated with aging-related diseases or cancers. In particular, such means are also desirable for diagnosing the health or fitness status of an individual.

[0016] It is also desirable to determine age in a cost-effective manner.

[0017] It would also be desirable to allow for a determination of age that, even if not very cost-effective and / or very rigorous, at least allows other methods of age determination to be independently evaluated. In other words, there is a need for alternative age indices that can be used to validate the determined age with other age indices. Such cross-validation is of great importance in diagnosis. [Means for solving the problem]

[0018] Solutions that address the above technical problems are presented in the claims and outlined herein below.

[0019] In its broadest aspects, the present invention relates to a method for determining an age indicator, a method for determining the age of an individual, and / or a collection of genomic DNA sequences.

[0020] In particular, the present invention and the method for determining an age indicator presented herein comprise the steps of: (a) a training data set for a plurality of individuals, for each individual: (i) DNA methylation levels of a set of genomic DNA sequences, and (ii) Chronological age Preparing a training data set of a plurality of individuals, the training data set comprising: (b) applying a regression method including a Least Absolute Shrinkage and Selection Operator (LASSO) to the training data set, thereby determining an age index and a reduced training data set; The independent variable is the methylation level of a genomic DNA sequence, and preferably the dependent variable is age; The age index is (i) a subset of the set of genomic DNA sequences as a collection; and (ii) at least one coefficient for each genomic DNA sequence contained in the set; Including, The reduced training dataset includes all data in the training dataset except for DNA methylation levels of genomic DNA sequences that are excluded by LASSO. Steps and Includes.

[0021] In particular, the method for determining the age of an individual comprises: (a) a training data set for a plurality of individuals, for each individual: (i) DNA methylation levels of a set of genomic DNA sequences, and (ii) Chronological age Preparing a training data set of a plurality of individuals, the training data set comprising: (b) applying a regression method including a Least Absolute Shrinkage and Selection Operator (LASSO) to the training data set, thereby determining an age index and a reduced training data set; The independent variable is the methylation level of a genomic DNA sequence, and preferably the dependent variable is age; The age index is (i) a subset of the set of genomic DNA sequences as a collection; and (ii) at least one coefficient for each genomic DNA sequence contained in the set; Including, The reduced training dataset includes all data in the training dataset except for DNA methylation levels of genomic DNA sequences that are excluded by LASSO. Steps and (c) obtaining a DNA methylation level for at least 80%, preferably 100%, of the genomic DNA sequences included in the age indicator for the individual whose age is to be determined; (d) determining the age of the individual based on its DNA methylation level and the age index; Including, Preferably, the determined age may differ from the individual's chronological age.

[0022] In particular, the set of genomic DNA sequences includes cg11330075, cg25845463, cg22519947, cg21807065, cg09001642, cg18815943, cg06335143, cg01636910, cg10501210, cg03324695, cg19432688, cg22540792, cg11176990, cg00097800, cg27320127, cg09805798, cg03526652, cg09460489, cg18737844, cg07802350, cg105227 ...09001642, cg09001642, cg09001642, cg09001642, cg09001642, cg09001642, cg09 g12548216, cg00876345, cg15761531, cg05990274, cg05972734, cg036808 98, cg16593468, cg19301963, cg12732998, cg02536625, cg24088134, cg24 319133, cg03388189, cg05106770, cg08686931, cg25606723, cg07782620, cg16781885, cg14231565, cg18339380, cg25642673, cg10240079, cg198514 81, cg17665505, cg13333913, cg07291317, cg12238343, cg08478427, cg07 625177, cg03230469, cg13154327, cg16456442, cg26430984, cg16867657, cg24724428, cg08194377, cg10543136, cg12650870, cg00087368, cg17760 405, cg21628619, cg01820962, cg16999154, cg22444338, cg00831672, cg08 044253, cg08960065, cg07529089, cg11607603, cg08097417, cg07955995, cg03473532, cg06186727, cg04733826, cg20425444, cg07513002, cg14305 139, cg13759931, cg14756158, cg08662753, cg13206721, cg04287203, cg1 8768299, cg05812299, cg04028695, cg07120630, cg17343879, cg07766948,cg08856941, cg16950671, cg01520297, cg27540719, cg24954665, cg05211227, cg06831571, cg19112204, cg12804 730, CG08224787, CG13973351, CG21165089, CG05087008, CG05396610, CG23677767, CG21962791, CG04320377, CG1 6245716, cg21460868, cg09275691, cg19215678, cg08118942, cg16322747, cg12333719, cg23128025, cg27173374 , cg02032962, cg18506897, cg05292016, cg16673857, cg04875128, cg22101188, cg07381960, cg06279276, cg2207 7936, cg08457029, cg20576243, cg09965557, cg03741619, cg04525002, cg15008041, cg16465695, cg16677512, cg 12658720, cg27394136, cg14681176, cg07494888, cg14911690, cg06161948, cg15609017, cg10321869, cg1574353 3, cg19702785, cg16267121, cg13460409, cg19810954, cg06945504, cg06153788, and cg20088545, or at least one, preferably at least 10, preferably at least 50, preferably at least 70, preferably all, or fragments thereof comprising at least 70%, preferably at least 90% of the contiguous nucleotide sequence.

[0023] Preferably, said set of genomic DNA sequences is comprised in a reduced training data set and / or an age index obtained by said method for determining an age index.

[0024] In a further preferred embodiment, the present invention relates to a method for the production and / or storage of a cellular endothelial cell line comprising the steps of: SIM bHLH transcription factor 1 (SIM1), microtubule-associated protein 4 (MAP4), protein kinase C zeta (PRKCZ), glutamate ionotropic receptor AMPA subunit 4 (GRIA4), BCL10, adaptor for immune signaling (BCL10), 5'-nucleotidase domain containing 1 (NT5DC1), suppressor of tumorigenicity 7 (ST7), protein kinase C eta (PRKCH), glial cell line-derived neurotrophic factor (GDNF), muskelin 1 (MKLN1), exocytosis-associated protein 1 (ECP1), endothelial cell line (ECP2), endothelial cell line (ECP3), endothelial cell line (ECP4), endothelial cell line (ECP5), endothelial cell line (ECP6), endothelial cell line (ECP7), endothelial cell line (ECP8), endothelial cell line (ECP9), endothelial cell line (ECP11), endothelial cell line (ECP12), endothelial cell line (ECP13), endothelial cell line (ECP14), endothelial cell line (ECP15), endothelial cell line (ECP16), endothelial cell line (ECP17), endothelial cell line (ECP18), endothelial cell line (ECP19), endothelial cell line (ECP20), endothelial cell line (ECP21), endothelial cell line (ECP22), endothelial cell line (ECP23), endothelial cell line (ECP24), endothelial cell line (ECP25), endothelial cell line (ECP26), endothelial cell line (EC Socyst complex component 6B (EXOC6B), protein S (PROS1), voltage-gated calcium channel subunit alpha 1D (CACNA1D), Kelch-like family member 42 (KLHL42), OTU deubiquitinase 7A (OTUD7A), cell death-associated protein (DAP), coiled-coil domain-containing 179 (CCDC179), iodothyronine deiodinase 2 (DIO2), transient receptor potential cation channel subfamily V member 3 (TRPV3), M T-RNR2-like 5 (MTRNR2L5), filamin B (FLNB), furin, paired basic amino acid cleaving enzyme (FURIN), solute carrier family 25 member 17 (SLC25A17), G-patch domain containing 1 (GPATCH1), UDP-GlcNAc:beta Gal beta-1,3-N-acetylglucosaminyltransferase 9 (B3GNT9), zyg-11 family member A, cell cycle regulator (ZYG11A), seizure-related 6 homolog-like (SEZ6L), Myosin X (MYO10), Acetyl-CoA carboxylase alpha (ACACA), G protein subunit alpha i1 (GNAI1), CUE domain containing 2 (CUEDC2), Homeobox D13 (HOXD13), Krüppel-like factor 14 (KLF14), Solute carrier family 1 member 2 (SLC1A2), Acetoacetyl-CoA synthetase (AACS), Ankyrin repeat and sterile alpha motif domain containing 1A (ANKS1A), MicroRNA 7641-2 (MIR7641-2), Type V collagen alpha 1 chain (COL5A1), Arsenite methyltransferase (AS3MT), Solute carrier family 26 member 5 (SLC26A5), Nucleoporin 107 (NUP107),long intergenic protein noncoding RNA 1797 (LINC01797), myosin IC (MYO1C), ankyrin repeat domain 37 (ANKRD37), phosphodiesterase 4C (PDE4C), EF-hand domain containing 1 (EFHC1), uncharacterized LOC375196 (LOC375196), ELOVL fatty acid elongase 2 (ELOVL2), WAS protein family member 3 (WASF3), chromosome 17 open reading frame 82 (C17orf82), G protein-coupled receptor 158 (GPR158 ), F-box and leucine-rich repeat protein 7 (FBXL7), Ripley transcriptional repressor 3 (RIPPLY3), VPS37C subunit of ESCRT-I (VPS37C), polypeptide N-acetylgalactosaminyltransferase-like 6 (GALNTL6), DENN domain containing 3 (DENND3), nuclear receptor corepressor 2 (NCOR2), endothelial PAS domain protein 1 (EPAS1), PBX homeobox 4 (PBX4), long intergenic protein noncoding RNA 1531 (LINC01531), and have sequence similarity to These include family 110 member A (FAM110A), glycosyltransferase 8 domain containing 1 (GLT8D1), G protein subunit gamma 2 (GNG2), MT-RNR2-like 3 (MTRNR2L3), zinc finger protein 140 (ZNF140), kinase inhibitor of ras1 (KSR1), protein disulfide isomerase family A member 5 (PDIA5), spermatogenesis-associated 7 (SPATA7), pantothenate kinase 1 (PANK1), ubiquitin-specific peptidase 4 (USP4), and G protein subunit Alpha q (GNAQ), Potassium channel modulator subfamily S member 1 (KCNS1), DNA polymerase gamma 2, accessory subunit (POLG2), stalk head box 2 (STOX2), neurexin 3 (NRXN3), BMS1, ribosome biogenesis factor (BMS1), forkhead box E3 (FOXE3), NADH:ubiquinone oxidoreductase subunit A10 (NDUFA10), relaxin family peptide receptor 3 (RXFP3), GATA binding protein 2 (GATA2),Isoprenoid synthase domain-containing (ISPD), adenosine deaminase, RNA-specific B1 (ADARB1), Wnt family member 7B (WNT7B), pleckstrin and Sec7 domain-containing 3 (PSD3), membrane-anchored junction protein (MAJIN), pyridine nucleotide-disulfide oxidoreductase domain 1 (PYROXD1), cingulin-like 1 (CGNL1), chromosome 7 open reading frame 50 (C7orf50), MORN repeat-containing 1 (MORN1), atlastin GTPase 2 (ATL2), WD repeat and FYVE domain-containing 2 (WDFY2), transmembrane protein 136 (TMEM136), inositol polyphosphate-5-phosphorylated The present invention relates to a gene set including at least one, preferably at least 10, preferably at least 30, preferably at least 50, preferably at least 70, preferably all of phatase A (INPP5A), TBC1 domain family member 9 (TBC1D9), interferon regulatory factor 2 (IRF2), sirtuin 7 (SIRT7), collagen type XXIII alpha 1 chain (COL23A1), guanine monophosphate synthase (GMPS), potassium 2-pore domain channel subfamily K member 12 (KCNK12), SIN3-HDAC complex associated factor (SINHCAF), hemoglobin subunit epsilon 1 (HBE1), and Tudor domain containing 1 (TDRD1).

[0025] Preferably, said set of genes is obtained by selecting from said collection of genomic DNA sequences those which code for proteins, or microRNAs or long non-coding RNAs.

[0026] In a further preferred embodiment, the present invention relates to the use of the collection of genomic DNA sequences or the set of genes according to the present invention for diagnosing the health and / or fitness status of an individual.

[0027] In a further preferred embodiment, the present invention relates to an in silico and / or in vitro screening method for identifying molecules that influence aging, comprising the step of providing a collection of genomic DNA sequences or a set of genes according to the present invention, which molecules, when administered to an individual, ameliorate, prevent and / or reverse at least one aging-associated disease, at least one phenotype associated with at least one aging-associated disease, and / or cancer.

[0028] In a further preferred embodiment, the present invention relates to a chip or kit, in particular a chip or kit that can be used to detect the DNA methylation level of a collection of genomic DNA sequences or a set of genes according to the present invention.

[0029] In particular, the chip comprises a set of genomic DNA sequences or genes according to the invention, with each sequence contained in a separate spot.

[0030] In particular, the kit (a) at least one unique primer pair, One primer of each primer pair is a forward primer that binds to the reverse strand of one of the genomic DNA sequences included in the collection of genomic DNA sequences according to the present invention or one of the genes included in the gene set, and the other primer is a reverse primer that binds to the forward strand; The two nucleotides complementary to the 3' ends of the forward and reverse primers are separated by more than 30 nucleotides and less than 3000 nucleotides, preferably less than 1000 nucleotides; Primer pairs, or (b) at least one probe complementary to one of the genomic DNA sequences included in the collection of genomic DNA sequences or one of the genes included in the gene set according to the present invention; Includes.

[0031] The present invention is based, at least in part, on the surprising discovery that by applying a regression method involving a Least Absolute Shrinkage and Selection Operator (LASSO), it is possible to determine an age indicator that includes a further reduced set of genomic DNA sequences, but still has acceptable quality, where the independent variable is the methylation level of the genomic DNA sequences, and the dependent variable is age. This was particularly surprising, since the Ridge regression (L2 parameter) was omitted, which was necessary in previous methods. Even more surprising, the overlap between the set of genomic DNA sequences determined in the present invention and the set of genomic DNA sequences previously determined was very small. Therefore, it is even more surprising that it is possible to find an age indicator that includes genomic DNA sequences that are quite different from known age indicators, but still performs equally well.

[0032] There are many advantages to reducing the number of genomic DNA sequences while ensuring accurate age determination. One advantage is that the cost, effort and / or required expertise for determining DNA methylation levels of genomic DNA sequences is reduced, especially since it allows the use of simpler experimental methods. Another advantage is that the drug target candidates encoded by the reduced set of genomic DNA sequences are narrowed down. A further advantage is that alternative or improved tools for the diagnosis of individual health conditions are provided. Thus, methods for determining alternative or improved age indicators are also useful for validating results obtained by other methods, i.e. diagnostics or drug candidates. General terminology

[0033] Unless otherwise defined, all scientific and technical terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of this invention, preferred methods and materials are described. For the purposes of the present invention, the following terms are defined below.

[0034] As used herein, the articles "a" and "an" refer to one or to more than one (i.e., to at least one) of the grammatical object of the article. By way of example, "an element" means one element or more than one element.

[0035] As used herein, "and / or" refers to and includes any and all possible combinations of one or more of the associated listed items, as well as the absence of combinations when interpreted as alternatives (or).

[0036] One of the aims of the present invention is to provide a novelty for industrial applications.

[0037] This object is achieved by what is claimed in the independent claims.

[0038] Some preferred embodiments are set forth in the dependent claims. It will be apparent to those skilled in the art that preferred embodiments not currently claimed may be found in the detailed description. Furthermore, it should be noted that certain aspects of the present invention, although not currently claimed in an independent claim, may be found in the detailed description and subsequently referenced. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0039] Below are described embodiments and definitions relating to the method for determining an age indicator according to the present invention, the age indicator obtained by said method, the set of genomic DNA sequences contained in said age indicator, and the method for determining the age of an individual according to the present invention.

[0040] As used herein, age index refers to a statistical model that can be used to determine the age of an individual based on the DNA methylation level of a particular genomic DNA sequence of said individual.

[0041] The determined age of an individual, as used herein, is not necessarily the same age as the chronological age of said individual. Usually, the determined age and the chronological age of an individual are different, and if they are the same, it is a coincidence. The determined age is also referred to herein as "surrogate age". Any age can be counted in "age (years)" and / or, preferably, "days". The determined age of an individual, as used herein, is a better indicator of the biological age of said individual than the chronological age. The chronological age of an individual refers to the time that has passed since the individual was born. The biological age, as used herein, relates to the health status of an individual. Preferably, the health status is associated with at least one aging-related disease, at least one phenotype associated with at least one aging-related disease, and / or a cancer status, where the status indicates the absence, presence, or stage of a disease or a phenotype associated with a disease. Thus, the age indicator of the present invention can be used for diagnosing the health status of an individual.

[0042] In particular, age index, as used herein, refers to a linear model that includes an independent variable.In this specification, the independent variable that is included in the age index or linear model that is used to generate age index refers to the DNA methylation level of a certain genomic DNA sequence.

[0043] Preferably, the dependent variable of the age index of the present invention and / or of the linear model used to generate the age index of the present invention is age.

[0044] In linear model, the age of multiple individuals is predicted by a set of independent variables (the methylation level of a certain genomic DNA sequence), and each independent variable has at least one coefficient.Predicted age and chronological age are preferably highly correlated, in other words, on average, are preferably very similar.However, the predicted age of an individual, also referred to herein as "determined age", may differ from the chronological age of the individual by, for example, several years.

[0045] Specifically, methylation level, as used herein, refers to beta value. Beta value, as used herein, describes the ratio of methylated cytosines to the sum of methylated and unmethylated cytosines among all relevant cytosines in a particular portion of genomic DNA of all alleles of all cells contained in a sample. The methylation state of one particular cytosine molecule is binary, either unmethylated (0; 0%) or methylated (1; 100%). Methylated cytosine is also referred to as "5'mC". As a result, the beta value for a cytosine at a particular position in the genomic DNA of a single cell with two alleles is usually 0, 0.5 or 1. Thus, the beta value at a particular CpG position in the genomic DNA of a population of cells can take a value between 0 and 1 (regardless of the number of alleles). Furthermore, the beta value when considering all CpGs in a particular genomic DNA sequence of a single allele can take a value between 0 and 1. Herein, preferably, only one CpG in a certain genomic DNA sequence is considered.Herein, a sample preferably comprises more than one cell, which may comprise more than one allele.Therefore, it is clear that the beta value of a genomic DNA sequence, as used herein, can take virtually any value between 0 and 1.Herein, the methylation level of a CpG is determined by the cytosine contained in said CpG, and not by the guanine.

[0046] Herein, preferably, CG / CpG corresponds to an Illumina™ probe designated by a so-called Cluster CG number (Illumina™ methylation probe ID number). The methylation level of a set of preselected CpGs can be measured using an Illumina™ DNA methylation array. To quantify the methylation level of a CpG, software can be used to calculate a methylation beta value. An Illumina™ methylation probe ID is characterized by the term "cg" followed by a number, e.g., cg11330075 or cg25845463. The terms "CG", "cg", "CpG", "CpG locus", "CpG site" and "cg site" are used interchangeably herein. Although the determination of DNA methylation levels using an Illumina™ DNA methylation array is well known and established and can be used in the present invention, other methods may be preferred for the reasons described and shown. Therefore, alternatively or in addition, the methylation level of CpGs can be quantified using other methods known in the art. Nevertheless, unless otherwise specified, the CGs / CpGs identified in the present invention correspond to Illumina™ methylation probe IDs.

[0047] Moreover, although it is possible to determine the methylation of cytosines at single base resolution, it is not necessary to do so in order to determine the methylation level of a genomic DNA sequence, the average methylation signal of the relevant cytosines in said sequence is sufficient. Herein, preferably only cytosines followed by guanines (CpG dinucleotides) are considered as relevant. The common names of bases and nucleotides, e.g., cytosine and cytidine, respectively, are used interchangeably herein to refer to the specific nucleotides that contain the respective bases. Herein, the terms "methylation level" and "DNA methylation level" are used interchangeably. Herein, when referring to methylation levels, the ranges 0% to 100% and 0 to 1 are used interchangeably.

[0048] As used herein, genomic DNA sequence refers to the interfering portion of genomic DNA of an individual.As used herein, a particular genomic DNA sequence does not necessarily have to be identical to the reference sequence of the genomic DNA sequence that it is related to, but can be its variant.Preferably, the genomic DNA sequence is a unique sequence.Those skilled in the art can easily determine whether a sequence is a variant of a particular reference genomic DNA sequence by consulting databases such as "GenBank" or "EMBL-NAR" and using general knowledge.

[0049] As used herein, the methylation level of a genomic DNA sequence refers to the methylation level of at least one cytosine within at least one CpG dinucleotide contained in said genomic DNA sequence.

[0050] In the present specification, the methylation level of genomic DNA sequence preferably refers to the methylation level of exactly one cytosine in exactly one CpG dinucleotide contained in said genomic DNA sequence.Preferably, said genomic DNA sequence does not consider methylation level, but contains additional nucleotides that allow identification of said CpG dinucleotide.Therefore, in the present specification, genomic DNA sequence can be defined by CpG locus.

[0051] As used herein, genomic DNA sequences are most preferably defined by Illumina™ methylation probe IDs. The terms "Illumina™ methylation probe ID", "Illumina™ CpG cluster ID", "Illumina™ Cluster CG number", "Illumina™ probe", Illumina™ methylation probe ID number, and equivalents thereof, with or without the terms "Illumina™" or "(trademark)", are used interchangeably herein.

[0052] A plurality of individuals, as used herein, refers to more than one individual. An individual, as used herein, refers to an organism having 5'-methylated cytosine (5'-mc) in its genomic DNA. Preferably, the organism is a vertebrate, more preferably a mammal, and most preferably a human. Preferably, the methylation level of at least one genomic DNA sequence of the individual is associated with the aging and / or health status of the individual. As used herein, an individual can be of any gender, e.g., male, female, hermaphrodite, or other. Thus, the terms "he", "she", "it", or "his", "her", and "it" are used interchangeably herein with respect to an individual.

[0053] Typically, the identity of the individual is known, but this is not necessary. In particular, the age of an individual can be determined by the method of the present invention even if the identity and / or chronological age of the individual is unknown. Thus, the method for determining the age of an individual according to the present invention allows prediction of the chronological age of an individual for which only a biological sample is available. Such biological samples include, for example, hair cells, buccal cells, saliva, blood and / or sperm. Thus, the method for determining the age of an individual is useful for estimating the chronological age of an individual at a crime scene who left some of his / her biological material there. Furthermore, the method for determining the age of an individual is useful for estimating the chronological age of an individual when data regarding the chronological age of said individual is not recorded or is not available.

[0054] Regression, as used herein, refers to a statistical process for estimating relationships between variables, particularly between a dependent variable and one or more independent variables. Regression analysis is also used to understand which of the independent variables are associated with the dependent variable and to explore the form of these relationships. Preferably, the regression method includes linear regression. Preferably, the regression method includes linear regression using shrinkage. Shrinkage is the shrinking of data values ​​to a central point such as the mean. In this specification, regression methods include Least Absolute Shrinkage and Selection Operator (LASSO).

[0055] LASSO encourages simple sparse models (i.e., models with few parameters). This particular type of regression is well suited for models that exhibit high levels of multicollinearity or when automation of certain parts of the model selection, such as variable selection and / or parameter exclusion, is desired. LASSO regression implements L1 regularization, which adds a penalty equal to the absolute value of the coefficient magnitude. This type of regularization can result in a sparse model with few coefficients; some coefficients can be zero and are excluded from the model. A larger penalty will result in coefficient values ​​closer to zero, which is ideal for creating simpler models. In other words, LASSO can be used to reduce the number of independent variables in a linear model. The terms "LASSO", "lasso" and "Lasso regression" are used interchangeably herein.

[0056] In a preferred embodiment, LASSO is performed using the biglasso R package, preferably by applying the command "cv.biglasso". Preferably "nfold" is 20.

[0057] In a preferred embodiment, the LASSO L1 regularization parameter / alpha parameter is 1.

[0058] Preferably, the regression method of the present invention does not include ridge regression (L2 regularization) or the L2 regularization parameter / lambda parameter is zero.

[0059] In contrast, in the Elastic Net method, the L1 regularization parameter or alpha parameter is not 1, but is approximately 0.1 to 0.9. Furthermore, the Elastic Net method includes ridge regression. Therefore, preferably, the regression method of the present invention does not include the Elastic Net method. Furthermore, it is preferable that the age index of the present invention is not determined by applying the Elastic Net method.

[0060] Preferably, the regression method of the present invention further comprises applying stepwise regression after LASSO. Preferably, the stepwise regression is applied to a reduced training data set.

[0061] Thus, in a particularly preferred embodiment, the method for determining an age indicator comprises: (a) a training data set for a plurality of individuals, for each individual: (i) DNA methylation levels of a set of genomic DNA sequences, and (ii) Chronological age Preparing a training data set of a plurality of individuals, the training data set comprising: (b) The training data set is (i) applying a Least Absolute Shrinkage and Selection Operator (LASSO) to determine a reduced training data set; (ii) applying a subsequent stepwise regression to determine the age index; applying a regression method, comprising: Preferably, applying said stepwise regression to said reduced training data set; The independent variable is the methylation level of a genomic DNA sequence, and preferably the dependent variable is age; The age index is (i) a subset of the set of genomic DNA sequences as a collection; and (ii) at least one coefficient for each genomic DNA sequence contained in the set; Including, The reduced training dataset includes all data in the training dataset except for DNA methylation levels of genomic DNA sequences that are excluded by LASSO; Includes.

[0062] Thus, in a particularly preferred embodiment, the method for determining the age of an individual comprises: (a) a training data set for a plurality of individuals, for each individual: (i) DNA methylation levels of a set of genomic DNA sequences, and (ii) Chronological age Preparing a training data set of a plurality of individuals, the training data set comprising: (b) The training data set is (i) a Least Absolute Shrinkage and Selection Operator (LASSO), which determines a reduced training dataset; and (ii) subsequent stepwise regression to determine age indicators and applying a regression method, comprising: Preferably, applying said stepwise regression to said reduced training data set; Preferably, the independent variable is the methylation level of a genomic DNA sequence, and preferably the dependent variable is age; The age index is (i) a subset of the set of genomic DNA sequences as a collection; and (ii) at least one coefficient for each genomic DNA sequence contained in the collection; Including, The reduced training dataset includes all data in the training dataset except for DNA methylation levels of genomic DNA sequences that are excluded by LASSO; (c) obtaining a DNA methylation level for at least 80%, preferably 100%, of the genomic DNA sequences included in the age indicator for the individual whose age is to be determined; (d) determining the age of the individual based on its DNA methylation level and the age index; and preferably the determined age may differ from the individual's chronological age.

[0063] Stepwise regression, as used herein, is a method of fitting a regression model in which the selection of predictor variables is performed by an automated procedure. At each step, variables are considered for addition or subtraction to the set of explanatory variables based on some pre-specified criterion. This can take the form of an array of F-tests or t-tests, but other techniques such as adjusted R2, Akaike information criterion (AIC), Bayesian information criterion, Mallows's Cp, PRESS, or false discovery rate are also possible. The main techniques are forward stepping, backward stepping, and two-way elimination. Forward stepping involves testing the addition of each variable using a selected model fitting criterion, adding the variable (if any) whose inclusion results in the most statistically significant improvement in fit, and repeating this process until the model is no longer improved to a statistically significant extent.

[0064] The elimination method involves testing the removal of each variable using a selected model fit criterion, removing the variable (if any) whose removal results in the least statistically significant deterioration of the model fit, and repeating this process until no further variables can be removed without losing a statistically significant fit. Two-way elimination is a combination of the forward and backward elimination methods, where at each step, a variable is tested for inclusion or exclusion. Herein, the variables considered by stepwise regression are preferably the variables selected by LASSO regression.

[0065] In a preferred embodiment, the stepwise regression is two-way elimination. Preferably, independent variables that are not statistically significant are eliminated when applying said stepwise regression. Preferably, the significance level for deciding whether to add / include or eliminate / exclude a variable is 0.05.

[0066] In order to determine age index according to the present invention, the set of genomic DNA sequences is reduced by the regression method according to the present invention in at least one step, preferably in two steps.Preferably, the set of starting genomic DNA sequences is preselected from the genomic DNA sequences whose methylation level can be associated with chronological age.Such a preselected set is, for example, Illumina™ DNA methylation array.Then, LASSO is applied, thereby determining age index and reduced training data set, both of which include a set of genomic DNA sequences.

[0067] In certain embodiments, the set of genomic DNA sequences included in the training data set is preselected from genomic DNA sequences whose methylation levels can be associated with chronological age.Preferably, the preselected set comprises at least 400000, preferably at least 800000 genomic DNA sequences.Sequences assayed by the Infinium MethylationEPIC BeadChip Kit are particularly suitable.

[0068] In certain embodiments, the genomic DNA sequences included in the training data set do not overlap with each other and / or occur only once per allele. This is particularly preferred when only a small set of genomic DNA sequences is preselected, i.e., less than 10,000.

[0069] In a preferred embodiment, stepwise regression is applied to a reduced training data set to determine an age index that comprises a set of genomic DNA sequences.

[0070] More surprisingly, it has been found that the sets of genomic DNA sequences determined by applying LASSO and subsequent stepwise regression are smaller and the respective age indices perform better than sets of genomic DNA sequences or age indices determined by applying LASSO alone without stepwise regression.

[0071] Even more surprisingly, it was found that age indices determined by applying LASSO and subsequent stepwise regression, although with fewer variables, have at least about the same or even improved accuracy compared to prior art methods such as those in Horvath, Genome Biology 2013, 14: R115.

[0072] In this specification, the subset included in the age index is also referred to as a "set" or a "set of genomic DNA sequences." As used herein, a subset (set) of genomic DNA sequences is at most as large as the set of genomic DNA sequences.

[0073] Preferably, the population included in the age index of the present invention is smaller than the set of genomic DNA sequences used to determine said age index.

[0074] Preferably, the population included in the age index of the present invention is smaller than the set of genomic DNA sequences included in the reduced training data set used to determine said age index.

[0075] In certain embodiments, the reduced training dataset comprises at least 90, preferably at least 100, preferably at least 140 genomic DNA sequences.

[0076] In certain embodiments, the reduced training dataset comprises fewer than 5000, preferably fewer than 2000, preferably fewer than 500, preferably fewer than 350, preferably fewer than 300 genomic DNA sequences.

[0077] The set of genomic DNA sequences included in the reduced training data set is preferably much reduced compared to the set of preselected genomic DNA sequences, preferably more than 90%, preferably more than 99%, preferably more than 99.9%. However, the set of said genomic DNA sequences must be large enough so that the optimization potential of the subsequent stepwise regression is not limited too quickly and / or a poorly performing age index is not obtained. It is intended herein that the performance of an age index that comprises less than 30 genomic DNA sequences is rather poor compared to an age index that comprises at least 30, preferably at least 50, preferably at least 60, preferably at least 80 genomic DNA sequences. However, an age index that comprises as few genomic DNA sequences as possible is preferred.

[0078] Thus, in certain embodiments, the age indicator comprises at least 30, preferably at least 50, preferably at least 60, preferably at least 80 genomic DNA sequences.

[0079] In a preferred embodiment, the age indicator comprises less than 300, preferably less than 150, preferably less than 110, preferably less than 100, preferably less than 90 genomic DNA sequences.

[0080] In a highly preferred embodiment, the age index comprises 80 to 100, preferably 80 to 90, preferably 88, genomic DNA sequences.

[0081] Furthermore, the age index of the present invention comprises at least one coefficient for each genomic DNA sequence contained in the collection. Since one coefficient is sufficient, it is preferred that the age index comprises exactly one coefficient for each genomic DNA sequence contained in the collection.

[0082] Coefficient, as used herein, refers to the weight of independent variable, and independent variable herein is the methylation level of a certain genomic DNA sequence.To predict or determine the age of an individual, the coefficient is multiplied by the methylation level of genomic DNA sequence, or in other words, weight each genomic DNA sequence and its methylation level, and then sum all weighted methylation levels.Preferably, the methylation level is between 0 and 1 (respectively unmethylated and fully methylated).

[0083] In this specification, the data set used to generate age index is also referred to as "training data set".As used herein, reduced training data set refers to a training data set that excludes or does not consider the data of a certain genomic DNA sequence.In this specification, reduced training data set is determined by applying a regression method, including LASSO, to the training data set.

[0084] In a preferred embodiment, the training data set comprises a matrix comprising as columns the methylation levels of the genomic DNA sequences comprised in the set of genomic DNA sequences and as rows a plurality of individuals, preferably with the chronological ages of said individuals comprised in a further column of the matrix.

[0085] In certain embodiments, the age index of the invention is iteratively updated, with data of at least one additional individual being added to the training data in each iteration, thereby iteratively expanding the training data set.

[0086] Said iterative updating of the age index is expected to iteratively improve the performance of the age index, in particular its accuracy.

[0087] As used herein, iterative update refers to successive rounds of updating age index. As used herein, one round of update or update round is specified in a certain or preferred embodiment of the present invention. As used herein, different rounds of update can refer to the same embodiment or different embodiments. Preferably, each update round of iterative is specified by the same embodiment of the present invention. With respect to updating age index, further individual refers to an individual who has not contributed data to training data set, but whose data is added in the update round. As used herein, augmentation of training data set refers to adding data of at least one further individual to training data set.

[0088] In one particular embodiment, in one update round, the added data for each additional individual is: (i) at least 5%, preferably 50%, and more preferably 100% of the set of genomic DNA sequences included in either the initial training data set or the expanded training data set; and / or

[0089] (ii) Genomic DNA sequences contained in the reduced training data set , including the DNA methylation level of the individual.

[0090] Option (i) above particularly refers to a set of starting genomic DNA sequences, particularly a set of preselected genomic DNA sequences.Generally, this set of starting genomic DNA sequences is large, for example, comprises at least 800000 genomic DNA sequences.Therefore, adding at least 5% methylation levels of starting set to training data set results in a sufficiently large training data set that can be used to determine age indicators.Preferably, training data set is limited to genomic DNA sequences in which all DNA methylation levels of individuals included in training data set are present.

[0091] Thus, in a preferred embodiment, all genomic DNA sequences (independent variables) that are not present for all individuals contributing data to the expanded training data set are removed from the expanded training data set. Preferably, updating the age index according to option (i) above comprises adding at least 50%, preferably 100%, of the set of genomic DNA sequences contained in either the initial training data set or the expanded training data set, especially when performing several or multiple update rounds.

[0092] In a preferred embodiment, in one update round, the set of genomic DNA sequences to which methylation levels are added is identical for each additional individual(s). This is particularly useful to avoid excessive removal of genomic DNA sequences within one round of update.

[0093] In the present specification, by updating the age index, the set of genomic DNA sequences contained therein (independent variables) and / or the coefficient(s) of each of said genomic DNA sequences can be changed. Notably, said option (i) allows the set of genomic DNA sequences to be expanded, restricted and / or modified, while option (ii) only allows the set of genomic DNA sequences to be restricted. Both options (i) and (ii) allow the coefficients to be changed. However, the advantage of option (ii) is that only the methylation levels of a reduced set of genomic DNA sequences of at least one further individual need to be prepared. Moreover, said option (ii) is particularly useful for further reducing the size of the set of said genomic DNA sequences. In other words, option (i) is particularly useful for generating different age indices for different purposes, for example age indices for a certain group of individuals, or for determining different age indices as a basis for further refinement; option (ii) is particularly useful for fine-tuning and optimizing an age index that is already useful in general, i.e., for further reducing the number of independent variables, for example for non-individualized off-the-shelf use. Both options (i) and (ii) can be combined to combine the flexibility of option (i) with the rationalization of option (ii).

[0094] In one particular embodiment, one update round involves applying LASSO to the enlarged training dataset, thereby determining an updated age index and / or an updated reduced training dataset.

[0095] In certain embodiments, the training dataset to which data for at least one further individual is added is a reduced training dataset, which may be either the original reduced training dataset or an updated reduced training dataset, preferably the reduced training dataset is a previous reduced training dataset in an iteration.

[0096] Thus, the updated reduced training dataset may result from applying LASSO to the enlarged training dataset and / or by adding data of at least one further individual to the reduced training dataset.

[0097] In a preferred embodiment, one update round involves applying a stepwise regression to the reduced training data set, thereby determining an updated age index.

[0098] In certain embodiments, data for at least one individual is removed from the training data set and / or the reduced training data set in one update round.

[0099] In certain embodiments, the training dataset, the reduced training dataset and / or the added data further comprises at least one factor relating to lifestyle or risk patterns and / or characteristics of the individual(s) that can be associated with the individual(s). Preferably, the factor is selected from drug consumption, environmental pollutants, shift work and stress.

[0100] In certain embodiments, the preselection of genomic DNA sequence and / or the addition and / or removal of individual data depends on at least one characteristic of individual.Herein, individual characteristic is, for example, ethnicity, sex, chronological age, place of residence, place of birth, at least one disease and / or at least one lifestyle factor.As used herein, lifestyle factor is selected from drug consumption, exposure to environmental pollutants, shift work or stress.

[0101] In certain embodiments, the training dataset and / or reduced training dataset are limited to genomic DNA sequences whose DNA methylation levels and / or encoded protein activity / levels are associated with at least one of said traits and / or lifestyle factors.

[0102] At any step, i.e. at the beginning of the pre-selection phase of genomic DNA sequences and / or the update phase of said data set and / or age index, the data in the training data set and / or reduced training data set are selected based on the lifestyle factors and / or characteristics of individuals, making it possible to determine the age index that is particularly well suited for determining the age of an individual or a certain group of individuals with a certain combination of said characteristics and / or lifestyle factors.Furthermore, applying different age indexes for age determination can be useful for determining the main influence of a certain predisposition of an individual or a group of individuals, such as stress or drug consumption.For example, if the determined individual age is much larger than the predicted age when using the age index optimized for smoking-related aging than when using the age index optimized for shift work-related aging, this can indicate that smoking is a more important factor than shift work for the health status associated with the aging of an individual.

[0103] In certain embodiments, the quality of the age indicator is determined, and said determining the quality comprises: (a) preparing a test dataset for a plurality of individuals who have not contributed data to the training dataset, the test dataset comprising, for each of the individuals: (i) DNA methylation levels of a set of genomic DNA sequences included in the age index; and (ii) Chronological age and (b) determining the quality of the age indicator by statistical evaluation and / or evaluation of domain boundaries, Statistical evaluation: (i) determining the ages of individuals included in a test dataset; (ii) correlating the determined age and chronological age of the individual(s) and determining at least one statistical parameter describing this correlation; (iii) determining whether the statistical parameter(s) are indicative of an acceptable quality of the age index, said statistical parameter(s) being determined by a coefficient of determination (R 2) and the mean absolute error (MAE), and R 2 a β value greater than 0.50, preferably greater than 0.70, preferably greater than 0.90, preferably greater than 0.98 and / or a MAE less than 6 years, preferably less than 4 years, preferably maximum 1 year indicates acceptable quality; Including, The domain boundary evaluation (iv) determining a domain boundary of the age indicator, the domain boundaries being the minimum and maximum DNA methylation levels of each genomic DNA sequence included in the age index; The minimum and maximum DNA methylation levels are found in a training data set used to determine an age index. Steps and (v) determining whether the test data set exceeds a domain boundary, where failure to exceed the domain boundary indicates acceptable quality; and Includes.

[0104] As used herein, test data set is a data set that can be used to evaluate the age indicator determined based on training data set.Normally, said training data set and test data set have the same structure.In particular, test data set and training data set contain the same set of genomic DNA sequences.However, the essential difference is that only test data set contains the data of individuals who do not contribute data to each training data set.

[0105] Assessment of an age index, as used herein, includes statistical assessment and / or assessment of domain boundaries.

[0106] For statistical evaluation, the age of the individuals in the test data set is determined and compared with the chronological age of said individuals. Any statistical measure or parameter commonly used to describe the correlation between two variables can be applied. Preferably, the statistical parameter is the coefficient of determination (R 2 ) and Mean Absolute Error (MAE). Preferably, R 2 A ≥ 0.50, preferably ≥ 0.70, preferably ≥ 0.90, preferably ≥ 0.98 and / or a MAE < 6 years, preferably < 4 years, preferably max 1 year indicates acceptable quality. If not specified herein, the skilled person can evaluate the measurement results or parameters based on general knowledge. In case of doubt, the quality should be considered unacceptable.

[0107] If the test data set is not completely contained within the boundary of the domain of age index, the age index is judged to be not of acceptable quality.The domain boundary of age index as used herein refers to the minimum and maximum DNA methylation levels of each genomic DNA sequence contained in age index.More specifically, said minimum and maximum DNA methylation levels are found in the training data set used to determine age index.

[0108] The test data set should have a reasonable size. In particular for statistical evaluation, the test data set should not be too small, but should include at least 10 individuals, preferably at least 30 individuals, preferably at least 200 individuals. In terms of determining the domain boundaries, the test data set should additionally not be too large, thus including at most 1000 individuals, preferably at most 200 individuals. If larger, some variation of the domain boundaries may be tolerated, for example for 5%, preferably 1% of the individuals of the test data set.

[0109] In certain embodiments, the training dataset and / or the testing dataset comprises at least 10 individuals, preferably at least 30 individuals, preferably at least 200 individuals. Preferably, the training dataset comprises at least 200 individuals and the testing dataset comprises at least 30 individuals.

[0110] Of note, an age index that is determined not to have acceptable quality may still be useful for determining the age of an individual. The term "acceptable quality" as used herein refers in particular to the determination of an optimal age index through updating. Thus, the acceptable or unacceptable quality of an age index as used herein does not refer to the absolute quality of the age index, but rather to its relative quality compared to other age indexes, in particular age indexes determined in different rounds of updating according to the method of the present invention.

[0111] In a preferred embodiment, the age index is updated if its quality is unacceptable, the quality being determined to be acceptable or unacceptable as explained above with respect to the evaluation of the age index.

[0112] In certain embodiments, no further updates to the age index are made if the number of individuals included in the data reaches a predetermined value and / or a predetermined time has passed since the previous update. The predetermined time may also refer to the number of quality assessments for a potential update round.

[0113] For example, if an age index contains historical data for thousands or even millions of individuals, or if the last 10 or even 100 evaluations with a new test dataset have shown acceptable quality, further optimization of the age index is not expected and updates can be stopped.

[0114] In certain embodiments, the DNA methylation level of a genomic DNA sequence of an individual is measured in a sample of biological material of said individual comprising said genomic DNA sequence, preferably the sample comprises buccal cells.

[0115] Suitable methods for determining DNA methylation levels are e.g. methylation sequencing, bisulfite sequencing, PCR methods, high-resolution melting analysis (HRM), methylation-sensitive single-base primer extension (MS-SnuPE), methylation-sensitive single-strand conformation analysis, methyl-sensitive cleavage counting (MSCC), base-specific cleavage / MALDI-TOF, combined bisulfite restriction analysis (COBRA), methylated DNA immunoprecipitation (MeDIP), microarray-based methods, bead array-based methods, pyrosequencing and / or direct sequencing without bisulfite treatment (nanopore technology).

[0116] In a preferred embodiment, the DNA methylation level of an individual is measured using a DNA methylation array, such as an Illumina™ DNA methylation array, preferably an Infinium MethylationEPIC BeadChip Kit. DNA methylation arrays are particularly suitable for measuring the DNA methylation levels of a large number of genomic DNA sequences, particularly starting and / or preselected genomic DNA sequences.

[0117] In a preferred embodiment, the DNA methylation level of the individual's genomic DNA sequences is measured by base-specific cleavage / MALDI-TOF and / or PCR techniques, preferably the base-specific cleavage / MALDI-TOF is Agena technology and the PCR technique is methylation-specific PCR. The base-specific cleavage / MALDI-TOF and / or PCR techniques are particularly suitable when measuring the DNA methylation level of a reduced set of genomic DNA sequences, in particular for adding data of individuals whose age is determined using the age index of the present invention to a reduced training data set and / or for providing methylation levels.

[0118] Further details regarding the determination of DNA methylation levels are further described below in further aspects of the invention and in the Examples.

[0119] In certain embodiments, the method for determining an age indicator and / or the method for determining the age of an individual according to the present invention further comprises the step of obtaining a sample of biological material of the individual. The biological material may be from any part of the individual, but preferably the sample is obtained non-invasively. Preferably the individual is not a fetus.

[0120] In a preferred embodiment, the sample is obtained from a buccal swab.

[0121] Herein, the age indicator of the present invention can be used as a tool for determining the age of an individual.Accordingly, the method for determining the age of an individual according to the present invention includes all steps of the method for determining an age indicator of the present invention or includes a step of producing an age indicator of the present invention.Furthermore, said method for determining the age of an individual includes a step of producing the DNA methylation level of at least 80%, preferably 100%, of the genomic DNA sequences contained in said age indicator of the individual whose age is to be determined, and a step of determining the age of the individual based on its DNA methylation level and said age indicator.

[0122] In other words, the methylation levels of at least 80%, preferably 100%, of the genomic DNA sequences included in the derived age indicator must be derived for the individual whose age is to be determined. The missing methylation levels of the genomic DNA sequences of said individual can be entered, for example, by using the median or mean of the derived methylation levels.

[0123] In certain embodiments, the age of an individual is determined based on the DNA methylation level of the individual and the updated age index, particularly if the quality of the initially provided age index is not acceptable.

[0124] In a preferred embodiment, if the age of an individual is not provided in the training dataset used or has been used to generate the age index, the age of the individual is determined using the age index alone.

[0125] In certain embodiments, the method for determining the age of an individual further comprises determining at least one lifestyle factor associated with the difference between the determined age and the chronological age of the individual.

[0126] In a preferred embodiment, the collection of genomic DNA sequences according to the present invention does not include cg27320127.

[0127] In certain embodiments, the collection of genomic DNA sequences according to the present invention is selected from the group consisting of: cg11330075, cg00831672, cg27320127, cg27173374, cg14681176, cg06161948, cg08224787, cg05396610, cg15609017, cg09805798, cg19215678, cg12333719, cg03741619, cg16677512, cg03230469, cg19851481, cg10543136, cg07291317, cg26430984, cg16950 671, cg16867657, cg22077936, cg08044253, cg12548216, cg05211227, cg 13759931, cg08686931, cg07955995, cg07529089, cg01520297, cg0008736 8, CG05087008, CG24724428, CG19112204, CG04525002, CG08856941, CG164 65695, cg08097417, cg21628619, cg09460489, cg13460409, cg25642673, c g19702785, cg18506897, cg21165089, cg27540719, cg21807065, cg18815 943, cg23677767, cg07802350, cg11176990, cg10321869, cg17343879, cg0 8662753, cg14911690, cg12804730, cg16322747, cg14231565, cg10501210 , cg09275691, cg15008041, cg05812299, cg24319133, cg12658720, cg2057 6243, cg03473532, cg07381960, cg05106770, cg04320377, cg19432688, c g22519947, cg06831571, cg08194377, cg01636910, cg14305139, cg040286 95, cg15743533, cg03680898, cg20088545, cg13333913, cg19301963, cg13 973351, cg16781885, cg04287203, cg27394136, cg10240079, cg02536625,and cg23128025, or a fragment thereof comprising at least 70%, preferably at least 90%, of the contiguous nucleotide sequence.

[0128] Preferably, said set of genomic DNA sequences is included in an age index obtained by a method for determining an age index, said method comprising applying LASSO followed by stepwise regression.

[0129] Herein, gene refers to a genomic DNA sequence (coding sequence; CDS) that codes for a protein, or a microRNA or long non-coding RNA. Herein, a genomic DNA sequence that codes for a protein also codes for an mRNA for translating said protein. MicroRNA (miRNA) is a small non-coding RNA molecule (containing about 22 nucleotides) that functions in RNA silencing and post-transcriptional regulation of gene expression. Long non-coding RNA (long ncRNA, lncRNA) is a type of transcript that is not translated into protein (but is occasionally translated into peptide), generally having more than 200 nucleotides. Nevertheless, the majority of long non-coding RNAs are likely to be functional, i.e., functional in transcription regulation.

[0130] In a preferred embodiment, the gene set of the present invention does not include KCNK12.

[0131] In certain embodiments, the gene set of the present invention is ISPD, KCNK12, GNG2, SIRT7, GPATCH1, GRIA4, LINC01531, LOC101927577, NCOR2, WASF3, TRPV3, ACACA, GDNF, EFHC1, MYO10, COL23A1, TDRD1, ELOVL2, GNAI1, MAP4, CCDC179, KLF14, ST7, INPP5A, SIM1, SLC1A2, AS3MT, KSR1, DSCR6, IRF2, KCNS1, NRXN3, C11orf85, HBE1, FOXE3, TMEM136, HOXD13, LO At least one, preferably at least five, preferably at least ten, preferably at least 30, preferably all of C375196, PANK1, MIR107, COL5A1, PBX4, ZNF140, GALNTL6, NUP107, LOC100507250, MTRNR2L5, C17orf82, MKLN1, FURIN, KLHL42, MORN1, ANKS1A, BCL10, DENND3, FAM110A, PROS1, WNT7B, FBXL7, GATA2, VPS37C, NRP1, POLG2, ANKRD37, GMPS, and WDFY2 are included.

[0132] Preferably, said set of genes is obtained by selecting from a set of genomic DNA sequences encoding proteins or microRNAs or long non-coding RNAs, wherein said set of genomic DNA sequences is included in an age index obtained by a method for determining an age index, said method comprising applying a LASSO followed by stepwise regression.

[0133] In a preferred embodiment, the set of genomic DNA sequences according to the present invention is: cg11330075, cg00831672, cg27320127, cg27173374, cg14681176, cg06161948, cg08224787, cg05396610, cg15609017, cg09805798, cg19215678, cg12333719, cg03741619, cg03230469, cg198 The present invention includes at least one, preferably at least four, preferably at least ten, and preferably all of: 51481, cg10543136, cg07291317, cg26430984, cg16950671, cg16867657, cg13973351, cg16781885, cg04287203, cg27394136, cg10240079, cg02536625, and cg23128025.

[0134] It is preferred that the set of genomic DNA sequences is included in an age index obtained by a method for determining an age index, wherein the method includes applying stepwise regression followed by LASSO, and the absolute value of each coefficient of the genomic DNA sequences included in the age index is greater than 20.

[0135] In a highly preferred embodiment, the collection of genomic DNA sequences according to the invention comprises at least one, preferably at least four, preferably all of cg11330075, cg00831672, cg27320127, cg10240079, cg02536625, and cg23128025.

[0136] Preferably, the set of genomic DNA sequences is included in an age index obtained by a method for determining an age index, wherein the method includes applying stepwise regression followed by LASSO, and the absolute value of each coefficient of the genomic DNA sequences included in the age index is greater than 40.

[0137] In a preferred embodiment, the genomic DNA sequences included in the collection of genomic DNA sequences according to the invention are complete sequences and not fragments thereof.

[0138] In a preferred embodiment, the collection of genomic DNA sequences according to the present invention includes, in addition to and / or instead of the collection of genomic DNA sequences, its complementary sequence.Herein, genomic DNA sequence refers to the described sequence and / or its reverse complementary sequence.Those skilled in the art can easily determine whether to use the described sequence or its reverse complementary sequence.In default settings and for the majority of applications, the described sequence is used, but for some applications, for example, to determine the methylation level of the sequence using a probe, its complementary sequence is used as a probe.

[0139] In a preferred embodiment, the gene set of the present invention includes microtubule-associated protein 4 (MAP4), protein kinase C zeta (PRKCZ), glutamate ionotropic receptor AMPA type subunit 4 (GRIA4), suppressor of tumorigenicity 7 (ST7), protein kinase C eta (PRKCH), voltage-dependent calcium channel subunit alpha 1D (CACNA1D), cell death-associated protein (DAP), transient receptor potential cation channel subfamily V member 3 (TRPV3), furin, paired basic amino acid cleaving enzyme (FURIN), acetyl-CoA carboxylase alpha (ACACA), G protein subunit alpha i1 (GNAI1), solute carrier family 1 member 2 (SLC1A2), phosphodiesterase 4C (PDE4C), ELOVL fatty acid elongase 2 (ELOVL2), nuclear receptor corepressor 2 (NCOR2), endothelial PAS domain protein 1 (EPAS1), G protein At least one, preferably at least five, preferably at least ten, preferably at least twenty, preferably all of the following: protein subunit gamma 2 (GNG2), pantothenate kinase 1 (PANK1), ubiquitin-specific peptidase 4 (USP4), G protein subunit alpha q (GNAQ), voltage-dependent potassium channel modulator subfamily S member 1 (KCNS1), DNA polymerase gamma 2, accessory subunit (POLG2), NADH:ubiquinone oxidoreductase subunit A10 (NDUFA10), relaxin family peptide receptor 3 (RXFP3), isoprenoid synthase domain-containing (ISPD), inositol polyphosphate-5-phosphatase A (INPP5A), sirtuin 7 (SIRT7), guanine monophosphate synthase (GMPS), SIN3-HDAC complex-associated factor (SINHCAF), and Tudor domain-containing 1 (TDRD1).

[0140] Preferably, said gene set is obtained by further filtering the gene set of the present invention for genes encoding proteins whose levels and / or activities can be determined using available assays, in other words, said gene set is further enriched for candidate drug targets.

[0141] Generally speaking, the method for determining an age index according to the present invention and the set of genomic DNA sequences according to the present invention are closely linked and based on a common inventive concept.Therefore, the description and definition of the set of genomic DNA sequences according to the present invention in this specification can be used to further specify the age index and / or the reduced training data set of the present invention, both of which comprise a set of genomic DNA sequences.Furthermore, said age index and / or reduced training data set can be used to further specify the method for determining an age index and / or the method for determining the age of an individual.Similarly, the set of genomic DNA sequences according to the present invention that can be included in the age index of the present invention is preferably obtained by the method for determining an age index of the present invention.Furthermore, this also applies to the gene set of the present invention, which is preferably selected from the set of genomic DNA sequences according to the present invention.

[0142] In a further preferred embodiment, the present invention relates to an age indicator obtained by the method for determining an age indicator according to the invention and / or to a set of genomic DNA sequences comprised in said age indicator obtained by said method.

[0143] In a further preferred embodiment, the present invention relates to an age indicator as described in the Examples herein.

[0144] With regard to the use of the age indicator described in the Examples, the same applies to the age indicator obtained by the method for determining the age indicator and / or the set of genomic DNA sequences contained therein as is described herein with regard to the use of the set of genomic DNA sequences and / or gene sets according to the invention, in particular in methods for diagnosing the health and / or fitness state of an individual and / or in in silico and / or in vitro screening methods for identifying molecules that influence aging.

[0145] In a further preferred embodiment, the present invention relates to a method for diagnosing the health and / or fitness status of an individual comprising the step of providing a collection of genomic DNA sequences according to the present invention, or a gene set according to the present invention.

[0146] As used herein, a health condition preferably includes at least one aging-associated disease, at least one phenotype associated with at least one aging-associated disease, and / or a cancer condition, the condition indicating the absence, presence, or stage of a disease or a phenotype associated with a disease. Thus, a health condition as used herein is preferably associated with aging.

[0147] In this specification, the phenotype associated with aging-related disease preferably refers to at least one symptom of aging-related disease.Moreover, the phenotype associated with aging-related disease or cancer usually progresses or is at a certain stage.Therefore, in this specification, the phenotype associated with aging-related disease or cancer or is at a certain stage may not exist or exist or be at a certain stage.

[0148] In a preferred embodiment, the ageing-related disease is Alzheimer's disease, Parkinson's disease, atherosclerosis, cardiovascular disease, cancer, arthritis, cataracts, osteoporosis, type 2 diabetes, hypertension, age-related macular degeneration and / or benign prostatic hyperplasia.

[0149] As used herein, preferably fitness status includes blood pressure, body weight, levels of immune cells, levels of inflammation and / or cognitive function of an individual.

[0150] As used herein, preferably the health and / or fitness state of an individual is related to the biological age of the individual. Furthermore, the age of an individual determined according to the present invention better describes said biological age and / or said health and / or fitness state than is described by the chronological age of the individual.

[0151] In particular, diagnosing the health and / or fitness status of an individual is complementary to diagnosing one specific disease and / or health / fitness parameter. Primarily, diagnosing the health and / or fitness status may provide a holistic or integrated perspective on the individual. For example, if the diagnosis is somewhat negative, it may indicate that the individual changes their lifestyle and / or environment. Furthermore, diagnosing the health and / or fitness status is particularly useful for assessing whether a certain medical treatment or change to the individual's lifestyle or environment has improved the individual's overall health and / or fitness status. It is self-evident that the overall health and / or fitness status of an individual is a crucial factor with regard to the well-being of said individual, especially when it is related to aging. In other words, the method for diagnosing the health and / or fitness status according to the present invention may allow not only the disease status of an individual to be assayed, but also how young or old an individual is biologically to be diagnosed.

[0152] In certain embodiments, the method for diagnosing the health and / or fitness status of an individual further comprises determining the methylation level of a genomic DNA sequence in a biological sample of said individual comprising said genomic DNA sequence.

[0153] With regard to determining genomic DNA sequences and methylation levels of biological samples, the same applies as described above with regard to the methods for determining age indicators of the present invention and / or the age of an individual.

[0154] The methods for diagnosing the health and / or fitness status of an individual according to the present invention include medical and / or non-medical applications of said methods.

[0155] In a further preferred embodiment, the present invention relates to an in silico and / or in vitro screening method for identifying molecules that influence aging, comprising the step of providing a set of genomic DNA sequences according to the invention, or a set of genes according to the invention. Preferably, the molecules, when administered to an individual, ameliorate, prevent and / or reverse at least one aging-related disease, at least one phenotype associated with at least one aging-related disease, and / or cancer. Preferably, said screening method is an in vitro method.

[0156] The same applies with respect to the method for diagnosing the health and / or fitness status of an individual with respect to aging-related diseases and associated phenotypes as described above.Furthermore, prevention of an aging-related disease and / or associated phenotypes relates to maintaining its absence, amelioration relates to slowing the progression through stages, maintaining a stage, and / or reversing to an earlier stage, and reversal relates to reversing to an earlier stage, preferably to the absence of the disease and / or associated phenotypes.

[0157] Herein, cancer is a preferred aging-related disease.

[0158] In certain embodiments, the screening method of the present invention further comprises a step of determining the DNA methylation level of at least one of the genomic DNA sequences included in the collection of genomic DNA sequences and / or at least one of the genes included in the gene set.

[0159] In a preferred embodiment, the identified molecule, when administered to an individual, increases the DNA methylation level of at least one of said genomic DNA sequences or genes in said individual and / or decreases the DNA methylation level of at least one of said genomic DNA sequences or genes, preferably such that the DNA methylation level is associated with a younger chronological age than before the alteration.

[0160] Therefore, the collection of genomic DNA sequences or gene sets according to the present invention can be used to screen for molecules, i.e., drug candidates, that change the methylation status of said sequences or genes so that it is associated with a younger chronological age than before the change.For example, if the methylation level of a genomic DNA sequence increases with chronological age, the drug should decrease the methylation level of said genomic DNA sequence.Similarly, if the methylation level of a genomic DNA sequence decreases with chronological age, the drug should increase the methylation level of said genomic DNA sequence.

[0161] In certain embodiments, the screening method of the present invention further comprises, when the gene set of the present invention is prepared, determining the activity of at least one protein encoded by the gene set. Preferably, the gene set includes only genes that code for proteins.

[0162] In a preferred embodiment, the identified molecule inhibits the activity of at least one protein encoded by the gene set and / or enhances the activity of at least one protein encoded by the gene set. Preferably, the protein activity is modified so that it is associated with a younger chronological age than before modification. For example, if the protein activity of the protein encoded by the genomic DNA sequence increases with chronological age, the drug should decrease / inhibit the activity of said protein. Similarly, if the protein activity of the protein encoded by the genomic DNA sequence decreases with chronological age, the drug should increase / enhance the activity of said protein.

[0163] As used herein, the activity of a protein also encompasses the level of said protein, particularly the level of its active form.

[0164] In a further preferred embodiment, the present invention relates to a chip comprising the collection of genomic DNA sequences according to the present invention or the gene set of the present invention as spots, each sequence being contained in a separate spot. Preferably, the chip is a microarray chip.

[0165] In a further preferred embodiment, the present invention provides a method for producing a method for the treatment of a pulmonary arthritis, comprising: (a) at least one unique primer pair, wherein one primer of each primer pair is a forward primer that binds to the reverse strand of one of the genomic DNA sequences included in the collection of genomic DNA sequences according to the present invention or one of the genes included in the gene set of the present invention, and the other primer is a reverse primer that binds to the forward strand, and the two nucleotides complementary to the 3' ends of the forward primer and the reverse primer are separated by more than 30 nucleotides and less than 3000 nucleotides, preferably less than 1000 nucleotides; (b) at least one probe complementary to one of the genomic DNA sequences included in the collection of genomic DNA sequences according to the invention or one of the genes included in the gene set of the invention; and / or (c) Chip according to the present invention The present invention relates to a kit comprising:

[0166] Preferably, said primer pairs are used in polymerase chain reaction (PCR). Said primers may or may not be DNA methylation specific. Preferably, said primers are used in methylation specific PCR methods. DNA methylation levels can be determined by assaying the amplified PCR products, for example by sequencing or by comparing the quantity of products obtained by different PCRs with primers that bind to either methylated or unmethylated sequences. Preferably, said probes are used in hybridization methods, for example in-situ hybridization methods or microarray methods.

[0167] In certain embodiments, the primers or probes specifically bind to either methylated or unmethylated DNA, where unmethylated cytosines are converted to uracil.

[0168] Preferably, the conversion of unmethylated cytosines to uracil is achieved herein by bisulfite treatment.

[0169] In certain embodiments, the kit further comprises a container for biological material and / or material for a buccal swab.

[0170] In certain embodiments, the kit further comprises materials for extracting, purifying, and / or amplifying genomic DNA from a biological sample, where the materials are a spin column and / or an enzyme.

[0171] In certain embodiments, the kit further comprises a bisulfite salt.

[0172] In a further preferred embodiment, the present invention relates to the use of the chip of the present invention and / or the kit of the present invention for determining the DNA methylation level of at least one of the genomic DNA sequences comprised in the collection of genomic DNA sequences according to the present invention and / or one of the genes comprised in the gene set of the present invention.

[0173] In a further preferred embodiment, the present invention relates to the use of the chip of the invention and / or the kit of the invention for diagnosing the health and / or fitness status of an individual.

[0174] In a further preferred embodiment, the present invention relates to the use of the chip according to the invention and / or the kit according to the invention in in silico and / or in vitro screening methods for identifying molecules that influence aging.

[0175] With regard to diagnosing the health and / or fitness state of an individual, as well as in silico and / or in vitro screening methods for identifying molecules that affect aging, the same applies as described above with regard to methods for diagnosing the health and / or fitness state of an individual and in silico and / or in vitro screening methods for identifying molecules that affect aging.

[0176] In a further preferred embodiment, the present invention relates to a data carrier comprising the age indicator of the present invention, the collection of genomic DNA sequences according to the present invention and / or the gene set of the present invention.

[0177] In certain embodiments, the kit and / or data carrier of the invention further comprises a questionnaire regarding the individual whose age is to be determined, which questionnaire may be blank or may contain information regarding said individual.

[0178] The invention further relates to the following further aspects and embodiments.

[0179] In a further aspect, the invention provides a method for determining the age of an individual based on the level of methylation of genomic DNA sequences found in the individual, comprising the steps of preselecting a set of genomic DNA sequences from genomic DNA sequences having a level of methylation that can be correlated with the age of the individual, determining for a plurality of individuals the level of methylation of the preselected genomic DNA sequences, and selecting a collection of genomic DNA sequences from the preselected set, such that the number of genomic DNA sequences in the collection is less than the number of genomic DNA sequences in the preselected set, and the age of the individual can be calculated based on the methylation levels of the sequences of the collection, and a statistical evaluation of the calculated age can be performed based on the calculated age. indicating an acceptable quality; determining a level of methylation of the sequences of the collection in a sample of biological material from the individual; calculating an age of the individual based on the level of methylation of the sequences of the collection; determining a quality criterion for the calculated age; determining whether the determined quality is acceptable; and if the quality is determined to be acceptable, outputting the calculated age of the individual; reselecting genomic DNA sequences for the collection taking into account the determination; amending the group of individuals to include the individual in response to the determination; and reselecting the set of genomic DNA sequences from the preselected subset based on the determination of the level of methylation of the individuals of the amended group.

[0180] In some embodiments, a set of genomic DNA sequences selected from several genomic DNA sequences having methylation levels that can be associated with the age of an individual is first used, and generally the number of genomic DNA sequences in the set is less than the number from which they are selected. Then, methylation levels are obtained from the set of genomic DNA sequences, and age is determined therefrom. In the course of a series of age determinations, the manner of determining age based on the composition of the set and / or the methylation levels obtained for the set of genomic DNA sequences is iteratively changed based on additional information generated or obtained during the series of determinations, in particular based on the further determined methylation levels. In some embodiments of the present invention, the age determination is based on the evaluation of the methylation levels of a particular genomic DNA sequence (or CpG locus) from a plurality of individuals, where the plurality of individuals includes the individual whose age is to be determined, but it is not necessary that this is the case.

[0181] It has surprisingly been found that in this way significant improvements over the prior art can be realized.

[0182] Generally speaking, such adaptation of the (best) way of determining age based on the respective methylation levels obtained for the genomic DNA sequences of the currently considered collection and / or collection can be modified for further individuals whose methylation levels and preferably chronological ages are known. Sometimes this can be done not for each individual, but only for a portion of these individuals.

[0183] Matching can be performed only after the level of methylation of genomic DNA sequences has been determined for more than one additional plurality of individuals, such as 5, 8, 10, 20, 50 or 100 individuals. This is particularly advantageous when the effort of statistical evaluation to select certain genomic DNA sequences into the collection and / or to determine the best way to determine age based on the methylation level of certain genomic DNA sequences is substantial.

[0184] Therefore, when outliers are measured, it is not necessary to simply iterate over and over again on best practices for determining age based on population composition and / or methylation levels.

[0185] Instead, the quality according to the (statistical) evaluation criterion may simply be judged to be unacceptable simply because the (statistical) evaluation criterion indicates that the size of the reference plurality of individuals is smaller than a certain number, e.g. smaller than the total number of all individuals for which the methylation level has been determined and / or all individuals for which the methylation level of selected genomic DNA sequences has been determined and whose chronological age is also known.

[0186] It is possible to first perform multiple iterations of best practices for determining age based on population composition and / or methylation levels obtained for genomic DNA sequences and then perform age determinations for particular individuals, and / or to first calculate the ages of additional individuals and then perform multiple iterations of the population and / or best practices at a later time.

[0187] As used herein, the terms "individuum" and "individual" are used interchangeably.

[0188] If a best practice for deriving age based on the composition of the population and / or respective methylation levels is performed after outputting the age of the individual, the methylation levels can be stored together with additional information about the individual, such as the individual's chronological age (if known), so that the stored information can be used for statistical (re)evaluation at a later time. Thus, the population and / or best practice can be repeated multiple times after such methylation level information has been collected for multiple additional individuals.

[0189] As is evident from the above, the invention basically proposes, in one embodiment, to improve the determination of an unknown age based on a statistical evaluation of measurements that result in an unknown outcome to be determined by itself. Surprisingly, this is not in itself contradictory, since an overall improvement in the reliability of the method can be achieved by including such information in the reference group. It has therefore been found that a self-learning approach can be easily implemented.

[0190] On average, the age determined by the method should, for a large population, correspond to the average of their chronological ages. The determined age is only useful because it is the biological age or at least is closer to the biological age, and may differ from the chronological age, and often varies relative to the chronological age, from which it is then possible to determine whether a particular individual ages faster than average.

[0191] Therefore, any deviation of the age determined according to the best available information relative to the chronological age is significant. The method can be concluded or restated as relating to a method for establishing the age difference between biological age and a known chronological age, or as assessing the difference between biological ages obtained by measuring the difference and / or the method.

[0192] Using the best available information for such comparisons, it is generally found to include the maximum number of individuals available, rather than a fixed number. Overall, the ages determined for one and the same specimen from an individual will change if the best way to determine age based on methylation levels obtained for populations and / or genomic sequences changes.

[0193] Such variations may result in overall accuracy and / or variability, but the present invention provides for improving the overall accuracy and / or variability resulting therefrom.

[0194] It should be noted that if the samples are stored in a manner that prevents changes in methylation levels, it is easy to detect changes in the determined age if the measurements are sufficiently noise-free and if the changes due to repetition are large enough. Thus, it has been found that frequent implementations of the self-learning technique of the present invention can be easily detected.

[0195] A general approach does not require an active preselection of a set of fewer than about 28 million known genomic DNA sequences from the total of 28 million genomic DNA sequences that have levels of methylation known to be associated with an individual's age or adverse health condition. Instead, such an active preselection should be considered to have already been done if, for example, due to the methodology chosen, only a limited number of known sites are evaluated.

[0196] Pre-selection can be done by choosing a specific method for determining methylation levels, such as those provided by Illumina™, and / or by choosing a DNA chip with a limited collection of spots, each of which can be used to determine only one, or some, but not all, of the genomic DNA sequences found in an individual, and with a level of methylation associated with age. Thus, the decision to use a specific detection method is an implicit pre-selection.

[0197] Preselection can also be considered to have taken place when only data derived in such a manner is evaluated, i.e., when data from less than all of the approximately 28 million sites that form the basis of the collection and / or preselected set is evaluated.

[0198] Typically, the preselected set is significantly smaller than 28 million different genomic DNA sequences. In particular, it will be understood that, although commercially available methods allow for the determination of the methylation level of 800,000 (800000) or more different genomic DNA sequences, a method using chips that allows for the determination of the methylation level of only a very limited number of different genomic DNA sequences using a collection of specific sites or "spots" is significantly less expensive for use in determining the age of an individual.

[0199] For example, in certain methods, chips can be used which allow for determining the level of methylation of only one or several thousand different genomic DNA sequences, preferably even fewer, in particular 1000 or less CpG loci, preferably 500 different genomic DNA sequences or CpG loci, preferably less than 200 different genomic DNA sequences or CpG loci, preferably 150 or less different genomic DNA sequences or CpG loci.

[0200] It is possible to only determine the methylation level for genomic DNA sequences that constitute a part of the collection. In this case, during the re-iteration, the composition of the collection can only be changed if a certain genomic DNA sequence that was previously considered is no longer considered after the re-iteration, and / or if the best way to determine age based on the methylation level obtained for the genomic sequences of the collection itself is changed, for example, if the regression coefficient obtained from the multivariate (linear) correlation between the methylation level obtained for the genomic DNA sequences of the collection and the chronological age of the individual changes. It is also possible to determine the methylation level of further sequences before the re-iteration.

[0201] It is possible that each determination of an individual's level of methylation includes (somewhat) more genomic DNA sequences (or CpG loci) than currently form part of the collection, for example about or at least 10 or about or at least 20 or about or at least 50 more sequences or CpG loci.

[0202] Nevertheless, the number of genomic DNA sequences that have a level of methylation that can be associated with the age of the individual and are determined for each individual or for some individuals that are not currently part of the collection is usually rather small.For example, it is possible to determine the level of methylation for genomic DNA sequences that are not currently part of the collection, not more than 5 times the number of genomic DNA sequences that are currently part of the collection.Therefore, if a collection contains, for example, 100 different genomic DNA sequences, the total number of different genomic sequences is usually less than 500.Generally, there are much fewer additional genomic DNA sequences or CpG loci.

[0203] In some embodiments, the additional genomic DNA sequences that do not form part of the set of genomic DNA sequences from which the respective genomic DNA sequences are determined, but that determine the level of methylation that can be associated with age, are less than 400, preferably less than 300, in particular less than 100, in particular less than 60, less than 50 or less than 40 CpG loci. Additionally and / or alternatively, the ratio of genomic DNA sequences that are not part of the set to genomic DNA sequences in the set is preferably less than 5, preferably less than 4, preferably less than 3, preferably less than 2. It is noted that the additional sequences that are not currently part of the set, but are used to provide additional levels of methylation only if they may be useful for re-iteration, are also generally carefully selected. This can be done by pre-selection.

[0204] For example, CpG loci whose methylation levels correlate well with those of CpG loci that are also selected and placed in the set, but have very low overall methylation or high variance, can be selected into the set. Also, CpG loci that are known to indicate certain deleterious lifestyles can be included, even though such loci are not prevalent in statistical multivariate analysis. Furthermore, CpG loci associated with a subset of the initial reference group can be further selected.

[0205] As is clear from the above, the exact number of sets and / or entire collections depends on the availability of affordable measurement methods, such as sufficiently inexpensive chips.In addition, data processing costs can be an obstacle.It may be preferable to use chips that are adapted to determine the methylation level of genomic DNA sequences with 1000 or less, 500 or less, 200 or less spots, each of which is adapted to be used in determining the methylation level of different CpG loci.

[0206] The chip detects the following genomic DNA sequences or CpG loci: cg11330075、cg25845463、cg22519947、cg21807065、cg09001642、cg18815943、cg06335143、cg01636910、cg10501210、cg03324695、cg19432688、cg22540792、cg11176990、cg00097800、cg09805798、cg03526652、cg09460489、cg18737844、cg07802350、cg10522765、cg12548216、cg00876345、cg15761531、cg05990274、cg05972734、cg03680898、cg16593468、cg19301963、cg12732998、cg02536625、cg24088134、cg24319133、cg03388189、cg05106770、cg08686931、cg25606723、cg07782620、cg16781885、cg14231565、cg18339380、cg25642673、cg10240079、cg19851481、cg17665505、cg13333913、cg07291317、cg12238343、cg08478427、cg07625177、cg03230469、cg13154327、cg16456442、cg26430984、cg16867657、cg24724428、cg08194377、cg10543136、cg12650870、cg00087368、cg17760405、cg21628619、cg01820962、cg16999154、cg22444338、cg00831672、cg08044253、cg08960065、cg07529089、cg11607603、cg08097417、cg07955995、cg03473532、cg06186727、cg04733826、cg20425444、cg07513002、cg14305139、cg13759931、cg14756158、cg08662753、cg13206721、cg04287203、cg18768299、cg05812299、cg04028695、cg07120630、cg17343879、cg07766948、cg08856941、cg16950671、cg01520297、cg27540719, cg24954665, cg05211227, cg06831571, cg19112204, cg12804730, cg08224787, cg13973351, cg21165089, cg05 087008, cg05396610, cg23677767, cg21962791, cg04320377, cg16245716, cg21460868, cg09275691, cg19215678, cg0811894 2, CG16322747, CG12333719, CG23128025, CG27173374, CG02032962, CG18506897, CG05292016, CG16673857, CG04875128, CG 22101188, cg07381960, cg06279276, cg22077936, cg08457029, cg20576243, cg09965557, cg03741619, cg04525002, cg15008 041, cg16465695, cg16677512, cg12658720, cg27394136, cg14681176, cg07494888, cg14911690, cg06161948, cg15609017, cg10321869, cg15743533, cg19702785, cg16267121, cg13460409, cg19810954, cg06945504, cg06153788, and cg20088545 It is particularly preferred to include at least one spot, preferably at least 10 spots, in particular at least 20 spots, 30 spots, 40 spots, 50 spots, 60 spots, 70 spots, 80 spots, 90 spots or 100 spots, in particular all spots, which allows the determination of the level of methylation of one or more of the above, in particular at least 20, 30, 40 spots, 50 spots, 60 spots, 70 spots, 80 spots, 90 spots or 100 spots, in particular all spots.

[0207] In particular, each of said genomic DNA sequences or CpG loci is included in a separate spot of said chip. In other words, one spot of said chip is defined by one of said genomic DNA sequences or CpG loci. It will be clear that it is useful to refer to at least a plurality of CpG loci when using a chip to measure methylation levels. In particular, at least 10, preferably at least 20, preferably at least 50, and particularly preferably all of said CG loci constitute part of a set of preselected genomic DNA sequences having a level of methylation that can be associated with the age of an individuum, and thus a set of genomic DNA sequences comprising all of said CG loci, or in a preferred embodiment, either at least some or only a small portion of said CG loci, can be easily obtained. In a preferred embodiment of the present invention, said chip can be used to determine the DNA methylation level of a set of genomic DNA sequences, in particular of genomic DNA sequences included in a reduced training dataset according to the present invention.

[0208] In some cases, the CpG locus further comprises cg27320127, which is a known WO2012 / 162139 updated CpG locus among others. The CpGs identified above are identified using Illumina™ Methylation Probe IDs.

[0209] In certain embodiments, the chip has a low overall number of spots that allow for determining the level of methylation of the following genomic DNA sequences, in particular less than 1600 spots, in particular less than 800 spots, in particular less than 400 spots, preferably less than 200 spots.

[0210] It should be noted that when defining a set of genomic DNA sequences having a level of methylation that can be associated with the age of an individuum, the set can include some or all of the CpG loci assumed to be known in the art to have a level of methylation that can be associated with the age of an individuum, such as those listed in WO2012 / 162139A1, which are different from the entire human genomic DNA sequence having a level of methylation that can be associated with the age of an individuum. However, at least 10, preferably 20, particularly preferably 50, 100, in particular all of the above CpG loci considered to be novel with respect to those known in the art may constitute part of a set of genomic DNA sequences having a level of methylation that can be associated with the age of a preselected individuum, in particular comprising no more than 5000, in particular no more than 2000, in particular no more than 1000, in particular no more than 250 genomic DNA sequences or CpG loci and / or constituting at least 10%, preferably at least 10% thereof, and particularly preferably at least 15%, 20%, 25%, 33%, 50%, 66%, 75%, 80%, 100% fraction of the total number of genomic DNA sequences in the preselected set. Thus, in preferred cases, the CpG loci listed herein as relevant and the newly disclosed CpG loci constitute a significant part of the collection.

[0211] It will be noted that the total number of CG loci considered for inclusion in the set from which the population is selected will depend on the number of different loci that can be measured in an easy and cost-effective manner according to the respective state of the art. For example, the price of a DNA chip having oligonucleotides that bind to DNA fragments containing each CpG locus in the measurement process varies greatly depending on the number of different sites, with the cost dropping significantly from chips with 1000 or more sites to chips with 500, 384, 192 or 96 different sites.

[0212] It should be noted that the numbers 96 or 384 refer to the numbers frequently used in current testing methods, but are in no way limiting. It has already been mentioned that a preselection step can be considered to have been performed once it has been decided not to use all known CpG loci in humans, but only those that are readily available. Thus, such a preselection step can be made by referring to a data set that contains only a correspondingly small number of methylation levels.

[0213] It is also noted that the determination of the level of methylation of a set of sequences in a sample of biological material from an individual can be performed with reference to measurements already made on said sample. Thus, the determination of the level of methylation of a particular sequence can be performed by opening the corresponding data file. The same applies to the specific selection of a set of genomic DNA sequences from a preselected set. This selection should be considered to have been made when a database containing a set determined by a previous analysis of reference data from an individual is referenced.

[0214] With regard to the calculation of age, it is noted that most often the number of genomic DNA sequences in the set and / or in the plurality is somewhat large, for example, more than 5, particularly more than 10, particularly including at least 50 different genomic DNA sequences. The number of individuals in the group is also somewhat large, preferably including at least 10 individuals, preferably at least 50 individuals, particularly at least 100 individuals, particularly at least 200 individuals, and in a preferred embodiment at least 1000 individuals. Therefore, mathematical analysis, particularly statistical analysis, is usually required to determine the best way to calculate the age of an individual based on the determined methylation level. It should be noted that the "best" way of such calculation does not have to be the absolute best way, but can refer to a very good way. In other words, a way of determination can be said to be the "best" way even if the calculation is particularly simple and / or because a local extreme value of a statistical function is used rather than an absolute extreme value.

[0215] As is clear from the above, in general, the calculation of an individual's age based on the methylation levels of the sequences of a set is performed in a manner that uses values ​​related to the methylation levels, such as percentages, to calculate the age in a manner that also uses regression coefficients from a multivariate regression, in particular from a multivariate linear regression. The calculation of evaluation criteria, such as statistical evaluation criteria, is performed in different ways. For example, it can be determined whether the methylation level of the sequences itself can be considered reliable or not. If the levels are exceptionally low, it may be desirable to use the respective sequences and / or methylation levels, for example, since a determination error may have occurred (for example due to noise in the methylation level measurement) and therefore the measurement should be ignored or weighted lower compared to other levels. Also, if the methylation levels are particularly high or low, it may be better not to apply assumptions made during the initial calculation, such as in a multivariate linear regression that assumes a linear correlation between the methylation levels and age.

[0216] In general, it is useful to assume that the level of methylation is linearly correlated with the age of an individual, but it should be noted that this need not be the case when very high or very low levels of methylation are observed, or when the chronological age of an individual is significantly lower or higher than the average of the individuals in the reference group. It may be useful to determine a more linear correlation by determining how the age of an individual is best calculated based on the level of methylation of the sequences found in the reference set after grouping a particular individual. For example, it may be desirable to distinguish between male and female individuals, children, teenagers, young adults, middle-aged and elderly people. It may also be useful to distinguish between, for example, smokers and non-smokers, between people with certain different nutritional habits, for example, between people who frequently eat fatty foods and those who do not, between people who frequently eat fish and those who frequently eat red meat, between people who frequently and / or regularly drink alcohol or certain alcohol, such as alcoholic beverages such as beer or wine, between people who regularly exercise and those who do not, and people who work in hazardous environments exposed to pollutants or hazardous substances such as radioactive materials and / or certain chemicals.

[0217] Thus, the calculation of a statistical criterion for the quality of the calculated age can take into account whether the significant deviation of the known chronological age against the calculated biological age is greater than the total deviation obtained for the reference set and / or greater than a number of other individuals in which the methylation levels of the sequences of the set have been measured. A difference can be considered significantly greater if the significance is at least 2σ, 3σ, 4σ, 5σ or 6σ.

[0218] The statistical evaluation criterion of quality can also be estimated by determining whether the reference set of individuals is large enough. This is not the case, for example, when a regression is made with a Spearman correlation of less than 0.85, preferably less than 0.90, preferably less than 0.91 and preferably 0.92, and a mean average error (MAE) of greater than 6 years, preferably greater than 5 years, in particular greater than 4 years. It will be understood that it is also possible to estimate a confidence interval for each calculated separate age, and the calculation of the statistical evaluation criterion can include the determination of the confidence interval of the calculated age. However, the calculation of the statistical evaluation criterion of the quality of the calculated age can be more easily performed, for example, by determining whether the underlying reference group is large enough. If the group is considered to be too small, it can be determined that the quality is not high enough. This is the case when the number of individuals in the entire reference group is too small and / or when the number of individuals in the reference group is too small considering the number of genomic DNA sequences or CpG loci in the preselected set of genomic DNA sequences or the selected collection, respectively.

[0219] In determining the best way to estimate and calculate age, it can also be determined that the quality of the calculated age is not high enough if not all individuals for whom the methylation level has been determined or all individuals with a certain characteristic, such as being a smoker or being female, have been referenced. In this case, the (statistical) evaluation criterion becomes the number of individuals for whom the level of methylation and, if applicable, additional information is available, relative to the number of members in the reference group.

[0220] Such a calculation of difference can be done by determining that no new data has been put into the reference group even before the calculation of the individual's age. Then, at least the individual's age is output if the quality is judged to be acceptable, but it should be noted that it is possible to output the age even if the quality of the calculation is questionable or insufficient. For example, it may still be useful to output the individual's age, since it allows the operator to check whether it can detect any specific problem that easily explains why the quality of the calculated age is considered to be questionable. For example, it may happen that an individual is wrongly grouped, and thus the age is determined using a set of genomic DNA sequences and regression coefficients obtained for a group of young men who are heavy smokers, but the individual is an older non-smoking woman. In some embodiments, multiple sets of CpG loci are defined based on the set, and one of these sets is selected based on specific information derived from the methylation level of one or more specific CpG loci analyzed and / or additional information independently provided therein. Some of the CpG loci in the preselected set can be selected, and thus a specific set can be selected. Such preparation and / or selection of a particular set from several sets should be considered an invention in itself. It also makes it possible to output the calculated age together with an explanation to the individual and return that a standard or guaranteed quality was not achieved.

[0221] Also, once a re-iteration of the best way to obtain ages based on the methylation levels of the population and / or sequences is obtained, the ages can be recalculated and only then can the calculated ages of individuals be output in a revised format.

[0222] The modification of the group of individuals is usually performed by including one or more individuals whose methylation level has been further determined. However, it is also possible to replace an individual or modify the group of individuals by splitting the group, etc. For example, it may occur that the initial group of individuals is somewhat small, and therefore it is not desirable, feasible or reasonable to distinguish between smokers and non-smokers, men and women, young and old, alcohol drinkers and non-drinkers. Then, after some time, a number of measurements are performed, and in some cases additional characteristics are determined, such as individuals who are smokers or non-smokers, and therefore the group can then be modified by adding one or several individuals based on their characteristics and splitting the group according to such characteristics.

[0223] It should be noted that the level of methylation changes in many living organisms in a manner related to the age of the organism. However, the methods for determining age are usually used to determine the age of mammals, particularly primates, and especially humans. Nevertheless, at least a rough estimate of age may be useful for other organisms, for example when trading particularly valuable animals.

[0224] In a preferred embodiment, the individual is a human. Naturally, this also applies to the individuals of the reference group. It has already been mentioned above that many of the steps listed above require extensive calculations. Therefore, it is extremely important to implement these steps in an automated manner carried out by a computer. It should be noted that when at least 20 different genomic DNA sequences are considered in a set or collection, and at least 20 individuals, preferably 100 individuals from the reference group, calculations without computer implementation are expected to be particularly error-prone, and therefore their overall results must be considered completely useless and unreliable. Such calculations are also not affordable in terms of the cost of calculations carried out by humans, nor are waiting for the results acceptable to every individual. Therefore, it is considered extremely important to carry out at least one and preferably all of the calculation and evaluation steps by a computer.

[0225] For the manner in which the level of methylation of genomic DNA sequences found in an individual is determined accordingly, reference is made to the following methods known per se in the art: methylation sequencing / bisulfite sequencing, PCR methods, in particular at least one of methylation-specific PCR (MSP), real-time methylation-specific PCR, quantitative methylation-specific PCR (QMSP), COLD-PCR, PCR using methylated DNA-specific binding proteins, targeted multiplex PCR, real-time PCR and microarray-based PCR, high-resolution melting analysis (HRM), methylation-sensitive single-base primer extension (MS-SnuPE), methylation-sensitive single-strand conformation analysis, methyl-sensitive cleavage counting (MSCC), base-specific cleavage / MALDI-TOF, e.g. Agena, combined bisulfite restriction analysis (COBRA), methylated DNA immunoprecipitation (MeDIP), microarray-based methods, bead array-based methods, pyrosequencing, direct sequencing without bisulfite treatment (nanopore technology).

[0226] The inventors anticipate that alternative ways of determining methylation levels will become available using upcoming technologies or technologies that are known but have so far seen little use or market acceptance. Thus, the list of methods presented is not exhaustive. Also, it may be possible to use different methods of determining methylation levels of different CpGs. Also, it may be possible to use different methods of determining methylation levels for pre-selection and selection.

[0227] Among the methods for detecting the level of methylation in a manner usable for the present invention, the following methods are currently particularly preferred: methylation sequencing / bisulfite sequencing, methylation specific PCR (MSP), real-time methylation specific PCR, quantitative methylation specific PCR (QMSP), COLD-PCR, base specific cleavage / MALDI-TOF, e.g. Agena, microarray-based methods, bead array-based methods, pyrosequencing.

[0228] In some embodiments, the group of individuals for which the methylation level is initially determined is large enough to obtain a calculated age that remains sufficiently stable even if self-learning still guides the process. In other words, the initial training of the process by repeated iterations of the best practices for selecting the set and / or obtaining the results should concern at least 50 individuals to have sufficiently stable values ​​for the initial reference, although it is usually preferred that the reference group has a larger number, such as 100 or 200 individuals, before the actual measurements begin. As mentioned above, the repeated iterations of the composition of the set and all the best practices for calculating the age therefrom can be postponed until a sufficiently large number of additional individuals can be further considered or added to the reference group.

[0229] In some embodiments, the number of genomic DNA sequences in the preselected set can be somewhat reduced, yet still allow the collection to be modified in useful ways.

[0230] In some embodiments, the preselected set, on the one hand, comprises at least 90 CpG loci, preferably at least 100 CpG loci, particularly preferably at least 140 CpG loci, in particular at least 150 CpG loci.

[0231] It should be noted that, when investigating a wide range of individuals, a larger number of CpG loci in the preselected set is desirable, but the measurement of methylation levels in clearly defined and well-defined groups may rely on a smaller number of reference CpG loci in the preselected set, and sometimes even 90 CpG loci or less are necessary. On the other hand, the preselected set is not excessive for various reasons. First of all, determining the methylation levels of CpG loci is more expensive and complicated than investigating a larger number of CpG loci for methylation levels.

[0232] Therefore, methods that rely on a large number of CpG loci are expensive, and by reducing the number of CpG loci in a collection or preselected set, the cost is significantly reduced. Also, data processing is significantly simplified when fewer CpG loci need to be considered. This applies both to the repetition of CpG loci in a collection and the best way to process the methylation obtained for such loci. It should be noted that in general, the calculation should be expanded to repeat a particular collection many times, or that the best way should be considered to grow in a highly non-linear manner by considering several genomic DNA sequences. Therefore, from the point of view of data analysis, it is preferable to reduce the number of CpG loci considered as well. However, even if only 350, 170, 150 or even 100 CpG loci are considered in the preselected set, the overall computational effort of multivariate analysis, such as multiple linear regression, principal component analysis, partial least squares analysis, to determine the methylation level of the most important CpG loci without overdetermining the system, becomes in any case unmanageable without computer implementation.

[0233] It may be necessary to provide the determined methylation levels in an electronic, automated manner, for example by establishing an electronic record or file for the methylation levels that can be used when processing the data even if such data processing does not occur immediately after the determination of the methylation level; not using a computer interface for data transmission between the final step used to obtain the methylation levels from the sample and the step used for data analysis introduces a source of error that should be considered unacceptable.

[0234] It should therefore be noted that the methods are generally computer-implemented methods having computer-implemented steps, and that at least some of the steps must necessarily be performed using a computer.

[0235] In some embodiments, the selected set has a somewhat small number of CpG loci, in particular comprising less than 150 CpG loci, in particular less than 110 CpG loci, in particular less than 100 CpG loci, in particular less than 90 CpG loci, in particular less than 80 CpG loci, in particular less than 70 CpG loci. It has been found that taking into account such a relatively small number of CpG loci still allows for factoring in a large number of different influences resulting from, for example, lifestyle factors, such as food, vitamin intake, such as folic acid and vitamin B12 intake, polyphenols, selenium intake, obesity and / or physical activity, tobacco smoke, alcohol consumption, environmental pollutants such as arsenic and air pollution, aromatic hydrocarbons and other organic pollutants, psychological stress, shift work, etc. In this regard, see the article "Alegria-Torres et al., Epigenomics, 2011 June; 3(3): 267-277". These authors show that lifestyle has a profound effect on the epigenetics of multiple factors and that DNA methylation is influenced by lifestyle.

[0236] On the other hand, it is sufficient to take into account a somewhat small number of CpG loci in the collection, but the collection should not be too small.Otherwise, there is a risk that the age or age deviation determined relative to chronological age is affected by measurement error, insufficient database of reference group, etc.Therefore, in some embodiments, it is desirable to include at least 30 CpG loci in the collection, preferably at least 50 CpG loci, in particular at least 60 CpG loci.

[0237] It should be noted that the numbers shown above as suitable for the collection are confirmed after one or more repetitions of the best practices determining data from the methylation levels of the reference CpG loci of the collection.

[0238] In some embodiments, when a set is repeated multiple times, the number of members in the set after the re-repetition may be different from the number of members in the set before the re-repetition.

[0239] However, in some embodiments, such repetitions may vary the number of CpG loci in the collection as desired, i.e., the mere replacement of one or more CpG loci with one or more other CpG loci within the collection of the collection is not prohibited.

[0240] As mentioned above, usually the best way to determine age from the methylation levels of the CpG loci of a collection may rely on coefficients obtained by multiple regression (multiple linear regression is preferred) of the methylation levels against the known chronological ages of the individuals in the group. In some embodiments, the methylation levels are used by considering values ​​ranging from 0% for the minimum methylation of a given CpG locus to 100% for a given CpG locus, the latter value being used when the methylation level corresponds to the maximum possible methylation for the given CpG locus. In other words, the values ​​of the methylation levels are centered and normalized. Of course, instead of using percentages ranging between 0% and 100%, values ​​between 0 and 1 can also be used. Although other ranges of values ​​can be used, it is particularly intuitive to use values ​​between 0 and 1 or 0% and 100%, such as when evaluating the results.

[0241] As noted above, in some embodiments, after outputting the calculated age of the individual, the set reselection is performed independently of judging the quality of the measurements.

[0242] Additionally, if the calculated age of an individual is determined to be unacceptable, there are embodiments in which the age is output only after a reselection of the set of genomic DNA sequences has been performed and after the age has been recalculated for the reselected set.

[0243] Regarding the statistical analysis of the methylation level or values ​​related to the methylation level, different methods can in principle be used. However, it has been found appropriate to carry out a statistical analysis using at least one regression method, such as principal component analysis to search for the main components responsible for the deviation of the calculated age, least squares regression, partial least squares regression, LAS-SO / elastic net regression and / or XPG Boost method to identify relevant CpGs. It is worth noting that, as further explained above, LASSO and elastic net are different regression methods, at least because LASSO does not include ridge regression and / or because in elastic net the L1 regularization parameter is not 1.

[0244] It is noted that protection is sought not only for the method itself, but also for kits for use in carrying out the methods according to the invention, i.e. kits for use in such methods.

[0245] In particular, such a kit comprises at least a container for an individual's biological material obtained and / or prepared in a manner that makes it possible to determine the age according to the method disclosed herein, the kit also comprises an information medium carrying information regarding the identity of the patient or individual, the kit further comprises instructions for carrying out the method of the invention and / or instructions on how to carry out such a method of the invention, for example by sending a probe together with a certificate to a particular laboratory, and / or instructions for preparing data for creating a data carrier comprising age-related data determined by the method according to the invention and / or instructions for preparing a data carrier comprising age-related data determined by the method according to the invention.

[0246] As noted above, it is often necessary to determine the absolute age of an individual because, for example, biological material with DNA from a perpetrator has been sampled at a crime scene to provide an estimate of the perpetrator's chronological age, but it is also frequently preferable to compare the determined age with a known chronological age.

[0247] It may also be useful to evaluate the difference between chronological age and biological age by considering the methylation level of the specific CpG loci whose methylation level is determined.It should be noted that these specific CpG loci do not necessarily constitute a part of the set.For example, a certain CpG locus may have a methylation level that is highly dependent on whether a person smokes or not, and whether the smoker is a particularly heavy smoker or not.

[0248] When calculating a standard biological age for an individual, it may not be desirable to include such methylation levels in the set, however it may be useful to indicate to an individual that certain methylation levels are indicative of the individual's environmental or other stresses.

[0249] For example, the age of a biological individual can be determined using a set of CpG loci that are particularly useful for non-smokers. This can be useful when an individual is indicated as a non-smoker. However, cases may arise where a non-smoker has been forced to be exposed to passive smoking for a long period of time, for example, due to growing up with a smoking parent. In such cases, the methylation level of certain CpG loci may be substantially altered relative to a true non-smoker, and therefore, even if an accurate biological age is otherwise determined, it may be useful to show an individual that the methylation level of certain CpGs that indicate smoking behavior indicates that the individual is strongly exposed to (passive) smoking.

[0250] This indicates that in certain cases, the preselected set may not be representative of biological age in a larger reference group, but may still contain additional CpG loci that may be relevant to a particular individual.

[0251] Considering the association between aging behavior and methylation level, it should be noted that it may be useful to change the behavior of methylation level. This can be done using reasonable means. In particular, it is reasonable to assume that drugs may form part of such means. Thus, if it is found that the methylation level is changed relative to the control group for a certain CpG locus and this change is related to a harmful effect, drugs may be useful to prevent or not cause the adverse biochemical effects that cause the change in methylation level.

[0252] Recognizing this, a method of drug screening is also proposed for screening a number of molecules for influencing aging, comprising the step of determining, for a particular CpG locus, whether a molecule of the number of molecules screened has a positive effect on the methylation level of the CpG locus, which can in particular be done by a determination made at least partly in-silico.

[0253] Thus, in the method of determining age according to the present invention, after selecting a first set of genomic DNA sequences, the age can be determined for a set of individuals, and for at least some of the individuals in the set, the methylation levels of additional genomic DNA sequences relative to the genomic DNA sequences in the set can be determined, the group of individuals can be amended to include at least some of the individuals from the set, and a decision can be made as to whether to modify the set of genomic DNA sequences taking into account the methylation levels obtained for the additional genomic DNA sequences determined for at least some of the individuals in the set.

[0254] Thus, even if each single determination of a series of individuals gives an acceptable result, a result with a rather small and easily acceptable confidence interval, during the course of measurements using the ever-increasing data obtained during the series of individuals, the determination of biological age is iteratively modified. The iteratively performed re-iterations may only involve the modification of the regression parameters obtained in the statistical analysis and used to calculate the age of the individual using the methylation levels obtained for the individual, as shown before, or it may determine that the entire set should be modified, i.e., additional DNA sequences should be added and / or the DNA sequences currently considered should not be taken into account.

[0255] It will be understood that even if the result itself is acceptable, the overall quality will be improved. However, when the set itself is changed by adding additional DNA sequences, and when the number of available genomic DNA sequences that can be selected into such a set is small, care should be taken to define the DNA sequences that form the pool or set that can be selected in a manner that makes it practical to add additional sequences. Therefore, it is considered useful at least in some cases to start with a very large number of genomic DNA sequences that have methylation levels that can be associated with age, and then to reduce this large number of genomic DNA sequences to be considered, so that selection is performed after iteratively and frequently, especially during standard measurement, for example, every 8th, 10th or 100th individual, or x% more individuals can be added to the reference group, for example x=10%, 20%, 25%, 33%, 50%, 66%, 75%, 100%, etc. Therefore, the set should be carefully selected, and multiple selection steps to determine useful pre-selections may be desirable in many cases.

[0256] For example, first, the methylation level of genomic DNA sequences of hundreds of individuals can be measured for 800,000 (800000) different genomic DNA sequences.From the obtained data set, for example, taking into consideration the principal component analysis that determines the main components of the data set of methylation level obtained against the actual age of the patient, several thousand genomic DNA sequences can be selected.Then, for several thousand selected genomic DNA sequences, additional measurements can be carried out for several hundred or more, for example, several thousand individuals, and thus, from the generated data set, for example, among 384 DNA sequences that have methylation levels that can be detected by DNA chips with 384 different or oligonucleotide spots, several hundred genomic DNA sequences can be selected.

[0257] Again, reducing the number of genomic DNA sequences from several thousand to 384 genomic DNA sequences can be done by considering further principal component analysis, taking into account the values ​​of each methylation level, taking into account the methylation levels of some of the different genomic DNA sequences that are highly correlated, etc.

[0258] After the final selection has been made and the set of genomic DNA sequences is small enough to allow inexpensive determination of all methylation levels that may apply to 384 different genomic DNA sequences or 96 genomic DNA sequences, the set can be determined from those remaining genomic DNA sequences, but the determination of the methylation level can be determined without excessive cost for all of the remaining methylated DNA sequences.

[0259] In some embodiments of the invention, when deciding whether to change the population, or the best way of determining age taking into account the obtained methylation levels, the decision is made based on as large a set of individuals as possible. Thus, additional data other than the population methylation levels can be provided for at least some individuals in addition to the individuals in the reference group currently being used. The decision as to whether to change the population, or the best way of determining age is then made (again) taking into account the obtained methylation levels for the additional individuals.

[0260] It should be noted that information about additional individuals is usually used to determine how best to calculate age, or to select genomic DNA sequences to put into or take out of a set by simply expanding a given group of individuals, etc. However, there may be certain cases where it is useful to simultaneously remove individuals from the reference group, or to divide the reference group into several groups, each group having individuals with specific characteristics. One reason for removing individuals from the previously used reference group is that a large number of additional individuals are added to the reference group, and by doing so, when analyzing the entire group of both the previous and added groups, it may be found that the previously existing individuals have methylation levels that now constitute statistical outliers.

[0261] Furthermore, although a pre-selection is performed using a first detection method for detecting methylation levels, such as a detection method for measuring the methylation levels of 850,000 or so CpG loci, the actual measurement is performed using a method that allows the CpG loci to determine their methylation levels in only a few ways, and cases may arise where the methylation levels of these CpG loci behave differently from the behavior of the methylation levels of the same CpGs in cross-comparisons. Here, it may be useful to initially rely on the initial measurements obtained by the first means, but once a large enough database is available for the exact second method that is actually used to produce the methylation levels of the population, data obtained using the more complex first method can be deleted. Another reason why deletion may be useful is that when ultimately sampling from a sufficiently large number of individuals that share a common characteristic, the individuals deleted from the reference group do not share this characteristic. For example, if it is determined after some time that the best way to determine the population and age should be determined to give the best results for thoroughly trained athletes who are not obese, it is possible to delete obese individuals from the initial reference group.

[0262] While it is possible to revise the population and / or best practice for age determination based on methylation levels only once data from a large number of individuals are available, it can additionally and / or alternatively be decided that a re-evaluation of the population and / or best practice should be performed taking into account the methylation levels for a particular individual if at least one of the following conditions is met: some or all of the methylation levels detected in the genomic DNA sequence are deemed to be too low, the predicted age of a single individual is too far from the known chronological age of the individual, the predicted ages of some individuals show systematic deviations from the known chronological age of some individuals, the predicted ages of some individuals are scattered around the known chronological ages of said some individuals and the variance is deemed to be too large, the predicted ages of some individuals show systematic deviations from the known chronological age of the individual, the number of individuals whose ages have been determined based on a given population has reached a predetermined number, a specified time has passed since the previous reselection.

[0263] It may be decided that a re-iteration or re-evaluation of the set and / or best practices is necessary immediately and / or it may be decided to postpone such re-iteration until data for a sufficiently large number of individuals is available if the above conditions are met. Another reason for postponing re-iteration is that such re-iteration is only performed at certain intervals; essentially, for all of these cases, information about the individuals, in particular the methylation levels detected in the genomic DNA sequence, and preferably the chronological age of the individuals, if known, is stored before the re-iteration, and then the re-iteration using all the stored information is performed.

[0264] In some embodiments, the quality of the determined age is judged in terms of making a comparison with known chronological ages. In most cases, a confidence interval is known that can be used as a quality criterion. A very wide confidence interval may be an indication that the determined age is not reliable. Also, once a large population has been surveyed, the determined age is unlikely to be too far from the previously determined ages of other individuals. In other words, once a large reference group has been surveyed, if the determined biological age of a new individual shows a much faster or slower aging behavior than other individuals whose data have been previously analyzed, with faster or slower aging, it is not unlikely that an error has occurred, especially if additional factors influencing aging are not known. In such cases, the age may be correct, but its quality has not been evaluated in a satisfactory manner. Nevertheless, in such cases, even if it cannot be guaranteed that the determined age is reliable, it may be in the individual's interest to act as if the determined age is reliable, and therefore to inform the individual of the determined age. For example, if particularly rapid aging behavior is observed that has not been previously observed in larger populations, it may be necessary for an individual to consult a physician, although the quality of being older compared to actual chronological age has not been evaluated. Thus, the present invention relates to the following:

[0265] 1. A method for determining an age index, comprising: (a) For each individual, (i) DNA methylation levels of a set of genomic DNA sequences, and (ii) Chronological age Preparing a training data set of a plurality of individuals, the training data set comprising: (b) applying a regression method including a Least Absolute Shrinkage and Selection Operator (LASSO) to the training data set, thereby determining an age index and a reduced training data set; The independent variable is the methylation level of a genomic DNA sequence, and preferably the dependent variable is age; The age index is (i) a subset of the set of genomic DNA sequences as a collection; and (ii) at least one coefficient for each genomic DNA sequence contained in the set; Including, The reduced training dataset includes all data in the training dataset except for DNA methylation levels of genomic DNA sequences that are excluded by LASSO. Steps and The method includes:

[0266] 2. A method for determining the age of an individual, comprising: (a) For each individual, (i) DNA methylation levels of a set of genomic DNA sequences, and (ii) Chronological age Preparing a training data set of a plurality of individuals, the training data set comprising: (b) applying a regression method including a Least Absolute Shrinkage and Selection Operator (LASSO) to the training data set, thereby determining an age index and a reduced training data set; The independent variable is the methylation level of a genomic DNA sequence, and preferably the dependent variable is age; The age index is (i) a subset of the set of genomic DNA sequences as a collection; and (ii) at least one coefficient for each genomic DNA sequence contained in the set; Including, The reduced training dataset includes all data in the training dataset except for DNA methylation levels of genomic DNA sequences that are excluded by LASSO. Steps and (c) providing DNA methylation levels for at least 80%, preferably 100%, of the genomic DNA sequences included in the age indicator for the individual whose age is to be determined; (d) determining the age of the individual based on its DNA methylation level and the age index; Including, Preferably, the determined age may differ from the individual's chronological age.

[0267] 3. The method according to item 1 or 2, wherein the regression method further comprises applying stepwise regression after LASSO.

[0268] 4. The method of item 3, applying stepwise regression to a reduced training dataset.

[0269] 5. The method according to any one of items 1 to 4, wherein the set included in the age index is smaller than the set of genomic DNA sequences.

[0270] 6. The method according to any one of items 1 to 5, wherein the set included in the age index is smaller than the set of genomic DNA sequences included in the reduced training dataset.

[0271] 7. The method according to any of items 3 to 6, wherein the stepwise regression is two-way elimination, and independent variables that are not statistically significant are removed, preferably at a significance level of 0.05.

[0272] 8. The method according to any of items 1 to 7, wherein LASSO is performed using the biglasso R package, preferably by applying the command "cv.biglasso", and preferably "nfold" is 20.

[0273] 9. The method according to any of items 1 to 8, wherein the regression method does not include ridge regression (L2 regularization) or the L2 regularization parameter / lambda parameter is 0.

[0274] 10. The method according to any of items 1 to 9, wherein the LASSO L1 regularization parameter / alpha parameter is 1.

[0275] 11. The method according to any of items 1 to 10, wherein the age indicator is iteratively updated, including adding data of at least one further individual to the training data in each iteration, thereby iteratively expanding the training data set.

[0276] 12. In one update round, the data added for each further individual is (i) at least 5%, preferably 50%, and more preferably 100% of the set of genomic DNA sequences included in either the initial training data set or the expanded training data set; and / or (ii) Genomic DNA sequences contained in the reduced training data set The method of claim 11, comprising determining an individual's DNA methylation level.

[0277] 13. The method according to item 11 or 12, wherein all genomic DNA sequences (independent variables) that are not present for all individuals contributing data to the extended training dataset are removed from the extended training dataset.

[0278] 14. The method of any of items 11 to 13, wherein in one update round, the set of genomic DNA sequences to which methylation levels are added is identical for each further individual.

[0279] 15. The method according to any of items 11 to 14, wherein one update round comprises applying LASSO to the enlarged training dataset, thereby determining an updated age index and / or an updated reduced training dataset.

[0280] 16. The method of any of items 11 to 15, wherein the training dataset to which data relating to at least one further individual is added is a reduced training dataset, which may be either the initial reduced training dataset or an updated reduced training dataset.

[0281] 17. The method of claim 16, wherein the reduced training data set is a previous reduced training data set in an iteration.

[0282] 18. The method of any of items 11 to 17, wherein one update round comprises applying a stepwise regression to the reduced training data set, thereby determining an updated age index.

[0283] 19. The method of any of items 1 to 18, wherein in one update round, data relating to at least one individual is removed from the training dataset and / or the reduced training dataset.

[0284] 20. The method according to any of items 11 to 19, wherein the addition and / or removal of an individual's data depends on at least one characteristic of the individual, the characteristic being ethnicity, sex, chronological age, place of residence, place of birth, at least one disease and / or at least one lifestyle factor, the lifestyle factor being selected from drug consumption, exposure to environmental pollutants, shift work or stress.

[0285] 21. Determining the quality of an age index, said determining the quality comprising: (a) preparing a test dataset for a plurality of individuals who have not contributed data to the training dataset, the test dataset comprising, for each of the individuals: (i) DNA methylation levels of a set of genomic DNA sequences included in the age index; and (ii) Chronological age and (b) determining the quality of the age indicator by statistical evaluation and / or evaluation of domain boundaries, Statistical evaluation: (i) determining the ages of individuals included in a test dataset; (ii) correlating the determined age and chronological age of the individual(s) and determining at least one statistical parameter describing this correlation; (iii) determining whether the statistical parameter(s) are indicative of an acceptable quality of the age index, preferably the statistical parameter(s) being a coefficient of determination (R 2 ) and the mean absolute error (MAE), and R 2 a β value greater than 0.50, preferably greater than 0.70, preferably greater than 0.90, preferably greater than 0.98 and / or a MAE less than 6 years, preferably less than 4 years, preferably maximum 1 year indicates acceptable quality; Including, The domain boundary evaluation (iv) determining a domain boundary of the age indicator, the domain boundaries being the minimum and maximum DNA methylation levels of each genomic DNA sequence included in the age index; The minimum and maximum DNA methylation levels are found in a training data set used to determine an age indicator. Steps and (v) determining whether the test data set exceeds a domain boundary, where failure to exceed the domain boundary indicates acceptable quality; Steps 21. The method according to any one of items 1 to 20, comprising:

[0286] 22. The method according to any of items 1 to 21, wherein the training dataset and / or the testing dataset comprises at least 10 individuals, preferably at least 30 individuals, preferably at least 200 individuals, preferably the training dataset comprises at least 200 individuals and the testing dataset comprises at least 30 individuals.

[0287] 23. The method according to item 21 or 22, wherein the age index is updated if its quality is unacceptable.

[0288] 24. The method according to any of items 11 to 23, wherein the age of the individual is determined based on the DNA methylation level of the individual and the updated age index.

[0289] 25. The method according to any of items 2 to 24, wherein the age of an individual is determined using only the age index if that individual's data was not provided in the training dataset used to generate the age index.

[0290] 26. The method according to any of items 1 to 25, wherein the age indicator is not further updated if the number of individuals contained in the data has reached a predetermined value and / or a predetermined time has elapsed since the previous update.

[0291] 27. The method according to any of items 1 to 26, wherein the set of genomic DNA sequences included in the training dataset is preselected from genomic DNA sequences whose methylation levels can be associated with chronological age.

[0292] 28. The method according to item 27, wherein the preselected set comprises at least 400,000, preferably at least 800,000 genomic DNA sequences.

[0293] 29. The method according to any of items 1 to 28, wherein the genomic DNA sequences included in the training data set do not overlap with each other and / or occur only once per allele.

[0294] 30. The method according to any of items 1 to 29, wherein the reduced training dataset comprises at least 90, preferably at least 100, preferably at least 140 genomic DNA sequences.

[0295] 31. The method according to any of items 1 to 30, wherein the reduced training dataset comprises less than 5000, preferably less than 2000, preferably less than 500, preferably less than 350, preferably less than 300 genomic DNA sequences.

[0296] 32. The method according to any of items 1 to 31, wherein the age indicator comprises at least 30, preferably at least 50, preferably at least 60, preferably at least 80 genomic DNA sequences.

[0297] 33. The method according to any of items 1 to 32, wherein the age index comprises less than 300, preferably less than 150, preferably less than 110, preferably less than 100, preferably less than 90 genomic DNA sequences.

[0298] 34. The method according to any of items 1 to 33, wherein the DNA methylation level of a genomic DNA sequence of an individual is measured in a sample of biological material of said individual comprising the genomic DNA sequence.

[0299] 35. The method of claim 34, wherein the sample comprises buccal cells.

[0300] 36. The method of claim 34 or 35, further comprising the step of non-invasively obtaining the sample.

[0301] 37. The method according to any of items 34 to 36, wherein the DNA methylation level is measured by methylation sequencing, bisulfite sequencing, PCR, high-resolution melting analysis (HRM), methylation-sensitive single-base primer extension (MS-SnuPE), methylation-sensitive single-strand conformation analysis, methyl-sensitive cleavage counting (MSCC), base-specific cleavage / MALDI-TOF, combined bisulfite restriction analysis (COBRA), methylated DNA immunoprecipitation (MeDIP), microarray-based methods, bead array-based methods, pyrosequencing and / or direct sequencing without bisulfite treatment (nanopore technology).

[0302] 38. The method according to any of items 34 to 37, wherein the DNA methylation level of the individual's genomic DNA sequence is measured by base-specific cleavage / MALDI-TOF and / or PCR methods, wherein the base-specific cleavage / MALDI-TOF is the Agena technology and preferably the PCR method is methylation-specific PCR.

[0303] 39. The method according to any of items 34 to 38, wherein the DNA methylation level of the genomic DNA sequence included in the age indicator is determined in a sample of biological material containing said genomic DNA sequence of the individual whose age is to be determined.

[0304] 40.cg11330075、cg25845463、cg22519947、cg21807065、cg09001642、cg18815943、cg06335143、cg01636910、cg10501210、cg03324695、cg19432688、cg22540792、cg11176990、cg00097800、cg27320127、cg09805798、cg03526652、cg09460489、cg18737844、cg07802350、cg10522765、cg12548216、cg00876345、cg15761531、cg05990274、cg05972734、cg03680898、cg16593468、cg19301963、cg12732998、cg02536625、cg24088134、cg24319133、cg03388189、cg05106770、cg08686931、cg25606723、cg07782620、cg16781885、cg14231565、cg18339380、cg25642673、cg10240079、cg19851481、cg17665505、cg13333913、cg07291317、cg12238343、cg08478427、cg07625177、cg03230469、cg13154327、cg16456442、cg26430984、cg16867657、cg24724428、cg08194377、cg10543136、cg12650870、cg00087368、cg17760405、cg21628619、cg01820962、cg16999154、cg22444338、cg00831672、cg08044253、cg08960065、cg07529089、cg11607603、cg08097417、cg07955995、cg03473532、cg06186727、cg04733826、cg20425444、cg07513002、cg14305139、cg13759931、cg14756158、cg08662753、cg13206721、cg04287203、cg18768299、cg05812299、cg04028695、cg07120630、cg17343879、cg07766948、cg08856941、cg16950671, cg01520297, cg27540719, cg24954665, cg05211227, cg06831571, cg19112204, cg12804730, cg082 24787, cg13973351, cg21165089, cg05087008, cg05396610, cg23677767, cg21962791, cg04320377, cg16245716 , cg21460868, cg09275691, cg19215678, cg08118942, cg16322747, cg12333719, cg23128025, cg27173374, cg02 032962, cg18506897, cg05292016, cg16673857, cg04875128, cg22101188, cg07381960, cg06279276, cg22077936 , cg08457029, cg20576243, cg09965557, cg03741619, cg04525002, cg15008041, cg16465695, cg16677512, cg12 658720, cg27394136, cg14681176, cg07494888, cg14911690, cg06161948, cg15609017, cg10321869, cg15743533 A set of genomic DNA sequences comprising at least 10, preferably at least 50, preferably at least 70, preferably all of the following: cg19702785, cg16267121, cg13460409, cg19810954, cg06945504, cg06153788, and cg20088545, or fragments thereof comprising at least 70%, preferably at least 90% of the contiguous nucleotide sequence.

[0305] 41.cg11330075, cg00831672, cg27320127, cg27173374, cg14681176, cg06 161948, cg08224787, cg05396610, cg15609017, cg09805798, cg19215678, cg12333719, cg03741619, cg16677512, cg03230469, cg19851481, cg10543 136, cg07291317, cg26430984, cg16950671, cg16867657, cg22077936, cg08 044253, cg12548216, cg05211227, cg13759931, cg08686931, cg07955995, cg07529089, cg01520297, cg00087368, cg05087008, cg24724428, cg19112 204, cg04525002, cg08856941, cg16465695, cg08097417, cg21628619, cg0 9460489, cg13460409, cg25642673, cg19702785, cg18506897, cg21165089, cg27540719, cg21807065, cg18815943, cg23677767, cg07802350, cg11176 990, cg10321869, cg17343879, cg08662753, cg14911690, cg12804730, cg1 6322747, cg14231565, cg10501210, cg09275691, cg15008041, cg05812299 , cg24319133, cg12658720, cg20576243, cg03473532, cg07381960, cg05106 770, cg04320377, cg19432688, cg22519947, cg06831571, cg08194377, cg01636910, cg14305139, cg04028695, cg15743533, cg03680898, cg20088545, cg13333913, cg19301963, cg13973351, cg16781885, cg04287203, cg27394136, cg10240079, cg02536625, and cg23128025,39. A set of genomic DNA sequences according to item 39, comprising at least 30, preferably at least 70, preferably all or a fragment thereof comprising at least 70%, preferably at least 90% of the contiguous nucleotide sequence.

[0306] 42.cg11330075, cg00831672, cg27320127, cg27173374, cg14681176, cg06161948, cg08224787, cg0539661 0, cg15609017, cg09805798, cg19215678, cg12333719, cg03741619, cg03230469, cg19851481, cg10543136, 42. The set of genomic DNA sequences according to item 41, comprising at least four, preferably at least ten, preferably all of: cg07291317, cg26430984, cg16950671, cg16867657, cg13973351, cg16781885, cg04287203, cg27394136, cg10240079, cg02536625, and cg23128025.

[0307] 43. The set of genomic DNA sequences according to item 41 or 42, comprising at least four, and preferably all, of cg11330075, cg00831672, cg27320127, cg10240079, cg02536625, and cg23128025.

[0308] 44. A collection of genomic DNA sequences according to any of items 40 to 43, comprising in addition and / or instead of said collection of genomic DNA sequences, complementary sequences thereof.

[0309] 45. SIM bHLH transcription factor 1 (SIM1), microtubule-associated protein 4 (MAP4), protein kinase C zeta (PRKCZ), glutamate ionotropic receptor AMPA subunit 4 (GRIA4), BCL10, immune signaling adaptor (BCL10), 5' nucleotidase domain containing 1 (NT5DC1), suppressor of tumorigenicity 7 (ST7), protein kinase C eta (PRKCH), glial cell line-derived neurotrophic factor (GDNF), muskelin 1 (MKLN1), excretory kinase C eta (KLCC), mitochondrial ... Socyst complex component 6B (EXOC6B), protein S (PROS1), voltage-gated calcium channel subunit alpha 1D (CACNA1D), Kelch-like family member 42 (KLHL42), OTU deubiquitinase 7A (OTUD7A), cell death-associated protein (DAP), coiled-coil domain-containing 179 (CCDC179), iodothyronine deiodinase 2 (DIO2), transient receptor potential cation channel subfamily V member 3 (TRPV3), M T-RNR2-like 5 (MTRNR2L5), filamin B (FLNB), furin, paired basic amino acid cleaving enzyme (FURIN), solute carrier family 25 member 17 (SLC25A17), G-patch domain containing 1 (GPATCH1), UDP-GlcNAc:beta Gal beta-1,3-N-acetylglucosaminyltransferase 9 (B3GNT9), zyg-11 family member A, cell cycle regulator (ZYG11A), seizure-related 6 homolog-like (SEZ6L), Myosin X (MYO10), Acetyl-CoA carboxylase alpha (ACACA), G protein subunit alpha i1 (GNAI1), CUE domain containing 2 (CUEDC2), Homeobox D13 (HOXD13), Krüppel-like factor 14 (KLF14), Solute carrier family 1 member 2 (SLC1A2), Acetoacetyl-CoA synthetase (AACS), Ankyrin repeat and sterile alpha motif domain containing 1A (ANKS1A), MicroRNA 7641-2 (MIR7641-2), Type V collagen alpha 1 chain (COL5A1), Arsenite methyltransferase (AS3MT), Solute carrier family 26 member 5 (SLC26A5), Nucleoporin 107 (NUP107),long intergenic protein noncoding RNA 1797 (LINC01797), myosin IC (MYO1C), ankyrin repeat domain 37 (ANKRD37), phosphodiesterase 4C (PDE4C), EF-hand domain containing 1 (EFHC1), uncharacterized LOC375196 (LOC375196), ELOVL fatty acid elongase 2 (ELOVL2), WAS protein family member 3 (WASF3), chromosome 17 open reading frame 82 (C17orf82), G protein-coupled receptor 158 (GPR158 ), F-box and leucine-rich repeat protein 7 (FBXL7), Ripley transcriptional repressor 3 (RIPPLY3), VPS37C subunit of ESCRT-I (VPS37C), polypeptide N-acetylgalactosaminyltransferase-like 6 (GALNTL6), DENN domain containing 3 (DENND3), nuclear receptor corepressor 2 (NCOR2), endothelial PAS domain protein 1 (EPAS1), PBX homeobox 4 (PBX4), long intergenic protein noncoding RNA 1531 (LINC01531), and have sequence similarity to These include family 110 member A (FAM110A), glycosyltransferase 8 domain containing 1 (GLT8D1), G protein subunit gamma 2 (GNG2), MT-RNR2-like 3 (MTRNR2L3), zinc finger protein 140 (ZNF140), kinase inhibitor of ras1 (KSR1), protein disulfide isomerase family A member 5 (PDIA5), spermatogenesis-associated 7 (SPATA7), pantothenate kinase 1 (PANK1), ubiquitin-specific peptidase 4 (USP4), and G protein subunit Alpha q (GNAQ), Potassium channel modulator subfamily S member 1 (KCNS1), DNA polymerase gamma 2, accessory subunit (POLG2), stalk head box 2 (STOX2), neurexin 3 (NRXN3), BMS1, ribosome biogenesis factor (BMS1), forkhead box E3 (FOXE3), NADH:ubiquinone oxidoreductase subunit A10 (NDUFA10), relaxin family peptide receptor 3 (RXFP3), GATA binding protein 2 (GATA2),Isoprenoid synthase domain-containing (ISPD), adenosine deaminase, RNA-specific B1 (ADARB1), Wnt family member 7B (WNT7B), pleckstrin and Sec7 domain-containing 3 (PSD3), membrane-anchored junction protein (MAJIN), pyridine nucleotide-disulfide oxidoreductase domain 1 (PYROXD1), cingulin-like 1 (CGNL1), chromosome 7 open reading frame 50 (C7orf50), MORN repeat-containing 1 (MORN1), atlastin GTPase 2 (ATL2), WD repeat and FYVE domain-containing 2 (WDFY2), transmembrane protein 136 (TMEM136), inositol phosphate A gene set including at least 10, preferably at least 30, preferably at least 50, preferably at least 70, preferably all of the following: phosphate-5-phosphatase A (INPP5A), TBC1 domain family member 9 (TBC1D9), interferon regulatory factor 2 (IRF2), sirtuin 7 (SIRT7), collagen type XXIII alpha 1 chain (COL23A1) guanine monophosphate synthase (GMPS), potassium 2-pore domain channel subfamily K member 12 (KCNK12), SIN3-HDAC complex associated factor (SINHCAF), hemoglobin subunit epsilon 1 (HBE1), and Tudor domain containing 1 (TDRD1).

[0310] 46.ISPD, KCNK12, GNG2, SIRT7, GPATCH1, GRIA4, LINC01531, LOC101927577, NCOR2, WASF3, TRPV3, ACACA, GDNF, EFHC1, MYO10, COL23A1, TDRD1, ELOVL2, GNA I1, MAP4, CCDC179, KLF14, ST7, INPP5A, SIM1, SLC1A2, AS3MT, KSR1, DSCR6, IRF2, KCNS1, NRXN3, C11orf85, HBE1, FOXE3, TMEM136, HOXD13, LOC375196, PANK 46. ​​The gene set according to Item 45, comprising at least five, preferably at least ten, preferably at least 30, and preferably all of the following: 1, MIR107, COL5A1, PBX4, ZNF140, GALNTL6, NUP107, LOC100507250, MTRNR2L5, C17orf82, MKLN1, FURIN, KLHL42, MORN1, ANKS1A, BCL10, DENND3, FAM110A, PROS1, WNT7B, FBXL7, GATA2, VPS37C, NRP1, POLG2, ANKRD37, GMPS, and WDFY2.

[0311] 47. Microtubule-associated protein 4 (MAP4), protein kinase C zeta (PRKCZ), glutamate ionotropic receptor AMPA type subunit 4 (GRIA4), suppressor of tumorigenicity 7 (ST7), protein kinase C eta (PRKCH), voltage-dependent calcium channel subunit alpha 1D (CACNA1D), cell death-associated protein (DAP), transient receptor potential cation channel subfamily V member 3 (TRPV3), furin, paired basic amino acid cleaving enzyme (FURIN), acetyl-CoA carboxylase alpha (ACACA), G protein subunit alpha i1 (GNAI1), solute carrier family 1 member 2 (SLC1A2), phosphodiesterase 4C (PDE4C), ELOVL fatty acid elongase 2 (ELOVL2), nuclear receptor corepressor 2 (NCOR2), endothelial PAS domain protein 1 (EPAS1), G protein subunit gamma 2 46. ​​The gene set according to item 45, comprising at least five, preferably at least ten, preferably at least twenty, preferably all of the following: (GNG2), pantothenate kinase 1 (PANK1), ubiquitin-specific peptidase 4 (USP4), G protein subunit alpha q (GNAQ), voltage-dependent potassium channel modulator subfamily S member 1 (KCNS1), DNA polymerase gamma 2, accessory subunit (POLG2), NADH: ubiquinone oxidoreductase subunit A10 (NDUFA10), relaxin family peptide receptor 3 (RXFP3), isoprenoid synthase domain-containing (ISPD), inositol polyphosphate-5-phosphatase A (INPP5A), sirtuin 7 (SIRT7), guanine monophosphate synthase (GMPS), SIN3-HDAC complex-associated factor (SINHCAF), and Tudor domain-containing 1 (TDRD1).

[0312] 48. A set of genomic DNA sequences is included in a reduced training data set and / or an age index according to the method described in items 2 to 39; The set of genes is obtained by selecting from the set of genomic DNA sequences that code for a protein, or a microRNA or a long non-coding RNA. A collection of genomic DNA sequences according to any one of items 40 to 44 or a set of genes according to any one of items 45 to 47, obtained by the method according to items 2 to 39.

[0313] 49. A collection of genomic DNA sequences according to items 40 to 44 or 48, or a set of genes according to any of items 45 to 48, for use in diagnosing a health condition of an individual.

[0314] 50. The health condition comprises at least one aging-associated disease, at least one phenotype associated with at least one aging-associated disease, and / or a cancer condition; A status indicates the absence, presence, or stage of a disease or a phenotype associated with a disease; A collection of genomic DNA sequences or a set of genes for use according to item 49.

[0315] 51. The collection of genomic DNA sequences or gene set for use according to item 50, wherein the aging-related disease is Alzheimer's disease, Parkinson's disease, atherosclerosis, cardiovascular disease, cancer, arthritis, cataracts, osteoporosis, type 2 diabetes, hypertension, age-related macular degeneration and / or benign prostatic hyperplasia.

[0316] 52. Use of a collection of genomic DNA sequences according to any of items 40 to 44 or 48, or a set of genes according to any of items 45 to 48, for determining the fitness state of an individual.

[0317] 53. The use according to item 52, wherein the fitness status comprises the individual's blood pressure, body weight, level of immune cells, level of inflammation and / or cognitive function.

[0318] 54. A method for diagnosing the health and / or fitness status of an individual, comprising the step of providing a collection of genomic DNA sequences according to any of items 40 to 44 or 48, or a set of genes according to any of items 45 to 48.

[0319] 55. The method according to item 54, further comprising determining the methylation level of a genomic DNA sequence in a biological sample of an individual comprising said genomic DNA sequence.

[0320] 56. The health condition comprises at least one aging-associated disease, at least one phenotype associated with at least one aging-associated disease, and / or a cancer condition; Preferably, the ageing-related disease is Alzheimer's disease, Parkinson's disease, atherosclerosis, cardiovascular disease, cancer, arthritis, cataracts, osteoporosis, type 2 diabetes, hypertension, age-related macular degeneration and / or benign prostatic hyperplasia, and / or Fitness status includes the individual's blood pressure, weight, levels of immune cells, levels of inflammation, and / or cognitive function. 56. The method according to item 54 or 55.

[0321] 57. The method according to item 55 or 56, wherein the biological sample is obtained non-invasively, preferably by buccal swab.

[0322] 58. An in silico and / or in vitro screening method for identifying molecules that affect aging, comprising the steps of preparing a set of genomic DNA sequences according to any one of items 40 to 44 or 48, or a set of genes according to any one of items 45 to 48, The method, wherein the molecule, when administered to an individual, ameliorate, prevent and / or reverse at least one aging-associated disease, at least one phenotype associated with at least one aging-associated disease, and / or cancer.

[0323] 59. The method according to item 58, further comprising determining the DNA methylation level of at least one of the genomic DNA sequences.

[0324] 60. The method according to item 58 or 59, wherein the identified molecule, when administered to the individual, increases the DNA methylation level of at least one of the genomic DNA sequences in the individual and / or decreases the DNA methylation level of at least one of the genomic DNA sequences in the individual.

[0325] 61. The method according to item 60, wherein the DNA methylation level is altered so as to be associated with a younger chronological age than before the alteration.

[0326] 62. The method according to any of items 58 to 61, further comprising providing a gene set according to items 45 to 48 and determining the activity of at least one protein encoded by the gene set.

[0327] 63. The method according to item 62, wherein the identified molecule inhibits the activity of at least one protein encoded by the set of genes and / or enhances the activity of at least one protein encoded by the set of genes.

[0328] 64. The method according to item 63, wherein the protein activity is altered so as to be associated with a younger chronological age than before the alteration.

[0329] 65. A chip comprising as spots the collection of genomic DNA sequences according to any of items 40 to 44 or 48, or the set of genes according to any of items 45 to 48, wherein each sequence is contained in a separate spot.

[0330] 66. A kit comprising at least one unique primer pair, One primer of each primer pair is a forward primer that binds to a reverse strand of one of the genomic DNA sequences included in the set of genomic DNA sequences according to any one of items 40 to 44 or 48 or one of the genes included in the gene set according to any one of items 45 to 48, and the other primer is a reverse primer that binds to the forward strand; The two nucleotides complementary to the 3' ends of the forward and reverse primers are separated by more than 30 nucleotides and less than 3000 nucleotides, preferably less than 1000 nucleotides; kit.

[0331] 67. A kit comprising at least one probe complementary to one of the genomic DNA sequences included in the collection of genomic DNA sequences according to any one of items 40 to 44 or 48 or one of the genes included in the gene set according to any one of items 45 to 48.

[0332] 68. The kit according to item 65 or 66, wherein the primer or probe specifically binds to either methylated or unmethylated DNA, and unmethylated cytosines are converted to uracils.

[0333] 69. A kit comprising the chip according to item 65.

[0334] 70. The kit according to any of items 51 to 57, further comprising a container for biological material and / or material for a buccal swab.

[0335] 71. The kit according to any of items 66 to 70, further comprising materials for extracting, purifying and / or amplifying genomic DNA from a biological sample, the materials being a spin column and / or an enzyme.

[0336] 72. The kit according to any of items 66 to 71, further comprising bisulfite.

[0337] 73. A data carrier comprising an age indicator obtained by the method according to any one of items 2 to 39, the collection of genomic DNA sequences according to any one of items 40 to 44 or 48, and / or the gene set according to any one of items 45 to 48.

[0338] 74. The kit according to any of items 66 to 72 or the data carrier according to item 73, further comprising a questionnaire for the individual whose age is to be determined, which questionnaire may be blank or may contain information regarding said individual.

[0339] 75. The method of any of items 1 to 39, wherein the training dataset, the reduced training dataset and / or the added data further comprises at least one factor relating to lifestyle or risk patterns that can be associated with an individual(s).

[0340] 76. The method according to item 75, wherein the factors are selected from drug consumption, environmental pollutants, shift work and stress.

[0341] 77. The method according to item 75 or 76, wherein the training dataset and / or the reduced training dataset are restricted to sequences whose DNA methylation levels and / or the activity / level of their encoded proteins are associated with at least one of the lifestyle factors.

[0342] 78. The method according to any of items 75 to 77, further comprising a step of determining at least one lifestyle factor associated with the difference between the determined age and the chronological age of the individual.

[0343] In a further aspect, the present invention relates to the following:

[0344] Item number 79 is a method for determining the age of an individual based on the level of methylation of genomic DNA sequences found in the individual, comprising: preselecting a set of genomic DNA sequences from the genomic DNA sequences having a level of methylation that can be associated with the age of the individual; determining the level of methylation of preselected genomic DNA sequences for a plurality of individuals; selecting a set of genomic DNA sequences from the preselected set, the number of genomic DNA sequences in the collection is less than the number of genomic DNA sequences in the preselected set; The age of the individual can be calculated based on the methylation level of the sequences of the set. and, Statistical evaluation of the calculated ages indicates acceptable quality of the calculated ages. and selecting such that determining the level of methylation of the sequences of the set in a sample of biological material from the individual; calculating the age of the individual based on the methylation levels of the sequences of the population; calculating a statistical measure of the quality of the calculated ages; determining whether the quality according to statistical criteria is acceptable; outputting the calculated age of the individual if the quality is determined to be acceptable; determining that the genomic DNA sequence needs to be reselected if the quality is determined to be unacceptable; amending the set of individuals to include the individual; reselecting the set of genomic DNA sequences from the preselected subset based on the determination of the levels of methylation of the individuals of the revised group; The present invention relates to a method comprising the steps of:

[0345] Further, the present invention discloses item 80, which relates to the method of determining age according to numbered item 79 listed above, wherein the individual is a human.

[0346] Further, the present invention relates to a method for producing ... liquid crystal display comprising at least one step: In particular at least one of the following steps: and preferably all of the following steps: The computer implemented steps include: The present invention discloses item number 81, which relates to a method for determining age according to one of the numbered items listed above: selecting a set of genomic DNA sequences from the preselected set, the number of genomic DNA sequences in the collection is less than the number of genomic DNA sequences in the preselected set; The age of the individual can be calculated based on the methylation level of the sequences of the set. and, Statistical evaluation of the calculated ages indicates acceptable quality of the calculated ages. Selecting the step of: determining the level of methylation of the sequences of the set in a sample of biological material from the individual; calculating the age of the individual based on the methylation levels of the sequences of the population; calculating a statistical measure of the quality of the calculated age; determining whether the quality according to statistical criteria is acceptable; outputting the calculated age of the individual if the quality is determined to be acceptable; determining that the genomic DNA sequence needs to be reselected if the quality is deemed unacceptable; amending the set of individuals to include the individual; Reselecting the set of genomic DNA sequences from the preselected subset based on the determination of the levels of methylation of the individuals of the revised group.

[0347] Furthermore, the present invention relates to a method for determining the level of methylation of genomic DNA sequences found in an individual by using methylation sequencing / bisulfite sequencing, PCR methods, in particular at least one of methylation specific PCR (MSP), real-time methylation specific PCR, quantitative methylation specific PCR (QMSP), COLD-PCR, PCR using methylated DNA specific binding proteins, targeted multiplex PCR, real-time PCR and microarray-based PCR, high-resolution melting analysis (HRM), methylation-sensitive single nucleotide primer extension (MS-SnuPE), Item number 82 discloses a method for determining age according to one of the above listed numbered items, measured by at least one of methylation-sensitive single-strand conformation analysis, methyl-sensitive cleavage counting (MSCC), base-specific cleavage / MALDI-TOF, e.g. Agena, combined bisulfite restriction analysis (COBRA), methylated DNA immunoprecipitation (MeDIP), microarray-based methods, bead array-based methods, pyrosequencing, direct sequencing without bisulfite treatment (nanopore technology).

[0348]

[0349] Furthermore, the present invention discloses item number 83, which relates to a method for determining age according to one of the above listed numbered items, wherein the level of methylation of genomic DNA sequences is measured by at least one of the following: methylation sequencing / bisulfite sequencing, methylation specific PCR (MSP), real-time methylation specific PCR, quantitative methylation specific PCR (QMSP), COLD-PCR, base specific cleavage / MALDI-TOF, e.g. Agena, microarray based methods, bead array based methods, pyrosequencing.

[0350] Furthermore, the present invention discloses item number 84, which shows a method for determining age according to one of the numbered items listed above, wherein the plurality of individuals for which the level of methylation is determined for a preselected genomic DNA sequence comprises at least 50 individuals, preferably at least 100 individuals, in particular at least 200 individuals.

[0351] Further, the present invention discloses item number 85, which relates to a method for determining age according to one of the numbered items listed above, wherein a group of individuals is modified by adding an individual to the group.

[0352] Furthermore, the present invention discloses item number 86 relating to the method for determining age according to one of the above listed numbered items, wherein the step of amending the group of individuals to include the individual comprises excluding at least one other individual from the group, taking into account factors that are not related to the individual's age and / or the methylation level of part or all of the individual's genomic DNA sequence.

[0353] Furthermore, the present invention relates to a method for producing a After selecting the first set of genomic DNA sequences, determining ages for a set of individuals; determining, for at least some of the individuals in the set, a methylation level of a genomic DNA sequence additional to the genomic DNA sequences in the set; amending the group of individuals to include at least some individuals from the set of individuals; making a decision as to whether to modify the set of genomic DNA sequences taking into account the methylation levels obtained for the additional genomic DNA sequences determined for at least some of the individuals of the set of individuals; Item number 87 discloses a method for determining age according to one of the numbered items listed above.

[0354] Furthermore, the present invention relates to a method for producing a determining the methylation levels of all genomic DNA sequences in the preselected set for at least some of the individuals; taking into account all of these methylation levels obtained for at least some of the individuals, a decision is made as to whether to alter the set of genomic DNA sequences; It discloses item number 88 relating to the method of determining age as described in the numbered items listed above.

[0355] Furthermore, the present invention provides a method for producing a pharmaceutical composition comprising the steps of: Some or all of the methylation levels detected in the genomic DNA sequence are considered to be too low; the predicted age of a single individual deviates too far from the individual's known chronological age, the predicted ages of some individuals show systematic deviations from the known chronological ages of some individuals; The predicted ages of some individuals are scattered across the known chronological ages of the individuals, and the variance is deemed to be too large; the predicted ages of some individuals show systematic deviations from the known chronological ages of said individuals; The number of individuals whose ages have been determined based on a given set has reached a predetermined number; A specified time has passed since the previous reselection The present invention discloses item number 89 of the method for determining age according to the above numbered items, wherein the decision to modify the set is based on the methylation levels obtained for additional individuals if at least one or preferably several of the following conditions are met:

[0356] Furthermore, the present invention discloses item number 90 relating to the method for determining age according to one of the numbered items listed above, wherein the step of determining whether the quality according to statistical evaluation criteria is acceptable or not comprises a statistical evaluation of the age taking into account the known chronological age of at least some of the individuals, in particular taking into account whether the predicted age of a single individual is far from the known chronological age of the individuals, in particular with respect to known outlier behavior. Furthermore, the present invention discloses item number 91 relating to a method for determining age according to one of the above listed numbered items, wherein the preselected set comprises at least 90 genomic DNA sequences, preferably at least 100 genomic DNA sequences, particularly preferably at least 140 genomic DNA sequences, and / or wherein the preselected set comprises less than 2000 genomic DNA sequences, in particular less than 500 genomic DNA sequences, in particular less than 350 genomic DNA sequences, in particular less than 170 genomic DNA sequences, in particular less than 150 genomic DNA sequences.

[0357] Furthermore, the present invention discloses item number 92 relating to a method for determining age according to one of the above listed numbered items, wherein the selected set comprises at least 30 genomic DNA sequences, preferably at least 50 genomic DNA sequences, particularly preferably at least 60 genomic DNA sequences, and / or the selected set comprises less than 150 genomic DNA sequences, in particular less than 110 genomic DNA sequences, in particular less than 100 genomic DNA sequences, in particular less than 90 genomic DNA sequences, in particular less than 80 genomic DNA sequences, in particular less than 70 genomic DNA sequences.

[0358] Furthermore, the present invention discloses item number 93 relating to a method for determining age according to one of the above listed numbered items, wherein the reselected set comprises at least 30 genomic DNA sequences, preferably at least 50 genomic DNA sequences, particularly preferably at least 60 genomic DNA sequences, and / or the selected set comprises less than 150 genomic DNA sequences, in particular less than 110 genomic DNA sequences, in particular less than 100 genomic DNA sequences, in particular less than 90 genomic DNA sequences, in particular less than 80 genomic DNA sequences, in particular less than 70 genomic DNA sequences.

[0359] Furthermore, the present invention discloses item number 94, which relates to a method for determining age according to one of the numbered items listed above, wherein the number of genomic DNA sequences in the reselected set is different from the number of genomic DNA sequences in the initially selected set.

[0360] Furthermore, the present invention discloses item number 95, which relates to a method for determining age according to one of the numbered items listed above, wherein at least one genomic DNA sequence included in the selected set is not included in the genomic DNA sequences of the reselected set.

[0361] Furthermore, the present invention discloses item number 96 relating to a method for determining age according to one of the above listed numbered items, wherein the age is determined from a statistical analysis of the methylation levels of the genomic DNA sequences of the population, taking into account the known ages of the individuals in the group, in particular using the coefficients obtained in a multiple linear regression of the methylation level values ​​against the known ages of the individuals in the group for each population of genomic DNA sequences.

[0362] Further, the present invention discloses item number 97, which relates to a method for determining age according to one of the above listed numbered items, wherein a methylation level value is determined from the methylation level by centering and / or normalizing the obtained levels, and the methylation level value is subjected to statistical analysis.

[0363] Furthermore, the present invention discloses item number 98, which relates to a method for determining age according to one of the numbered items listed above, wherein after outputting the calculated age of the individual, a reselection of the set is performed independently of the judgment of the quality of the measurement.

[0364] Further, the present invention discloses item number 99 relating to a method for determining age according to one of the above listed numbered items, wherein the calculated age of an individual is determined to be unacceptable and the output of the age is only performed after reselection of the set of genomic DNA sequences has been performed and after the age has been recalculated for the reselected set.

[0365] Furthermore, the present invention discloses item number 100, which shows a method for determining age according to one of the above listed numbered items, wherein the selection of the genomic DNA sequence is based on a statistical analysis of values ​​related to the methylation level of the individual's genomic DNA sequence, in particular using at least one regression method to identify relevant CpG loci, in particular at least one of the following methods: principal component analysis, LASSO / elastic net regression and / or XPG Boost method to identify relevant CpGs.

[0366] Furthermore, the present invention discloses item number 101 relating to a kit comprising at least a container for an individual's biological material obtained and / or prepared in a manner that allows determining the age according to the method according to one of said numbered items listed above, also comprising an information medium carrying information regarding the patient's identity, and further comprising instructions for performing or how the method according to one of said numbered items listed above has been performed and / or for preparing data for making a data carrier comprising age-related data determined by the method according to said method described in the numbered items listed above and / or for preparing a data carrier comprising age-related data determined by the method according to said method described in the numbered items listed above.

[0367] Furthermore, the present invention discloses item number 102, which relates to a method for assessing the difference between chronological age and biological age, comprising the steps of determining age based on methylation levels by a method according to one of the methods described in the numbered items listed above, and comparing the determined biological age with a known chronological age.

[0368] Further, the present invention discloses item number 103 which relates to a method for assessing the difference between chronological age and biological age as described in the above listed numbered items, which comprises determining the difference for a number of individuals, determining values ​​of factors that may or may not affect the difference for a number of individuals, and determining factors that have a significant effect on the difference between chronological age and biological age in a large number of individuals.

[0369] Furthermore, the present invention discloses item number 104 relating to a method for screening several molecules for influencing aging, comprising the steps of determining several genomic DNA sequences that correlate well with biological age, in particular by reference to the genomic DNA sequences selected for collection in the method described in numbered item 79 listed above, and determining whether a molecule among the several molecules has a positive effect on the methylation level of the genomic DNA sequences, in particular by in-silico determination.

[0370] Furthermore, the present invention discloses item number 105, which relates to a method for determining the age of an individual based on evaluation of the methylation level of genomic DNA sequences selected from a plurality of individuals, wherein the plurality of individuals comprises said individuals.

[0371] Furthermore, the present invention relates to a chip comprising several spots, in particular less than 500, preferably less than 385, in particular less than 193, in particular less than 160 spots, adapted for use in determining methylation levels, said spots being selected from the group consisting of: cg11330075, cg25845463, cg22519947, cg21807065, cg09001642, cg18815943, cg06335143, cg01636910, cg10501210, cg03324695, cg19432688, cg22540792, cg11176990, cg0 0097800, cg09805798, cg03526652, cg09460489, cg18737844, cg07802350 , cg10522765, cg12548216, cg00876345, cg15761531, cg05990274, cg0597 2734, cg03680898, cg16593468, cg19301963, cg12732998, cg02536625, cg 24088134, cg24319133, cg03388189, cg05106770, cg08686931, cg2560672 3, CG07782620, CG16781885, CG14231565, CG18339380, CG25642673, CG102 40079, cg19851481, cg17665505, cg13333913, cg07291317, cg12238343, c g08478427, cg07625177, cg03230469, cg13154327, cg16456442, cg264309 84, cg16867657, cg24724428, cg08194377, cg10543136, cg12650870, cg00 087368, cg17760405, cg21628619, cg01820962, cg16999154, cg22444338, cg00831672, cg08044253, cg08960065, cg07529089, cg11607603, cg08097 417, cg07955995, cg03473532, cg06186727, cg04733826, cg20425444, cg0 7513002, cg14305139, cg13759931, cg14756158, cg08662753, cg13206721,cg04287203, cg18768299, cg05812299, cg04028695, cg07120630, cg17343879, cg07766948, cg08856941, cg16950671, cg01 520297, cg27540719, cg24954665, cg05211227, cg06831571, cg19112204, cg12804730, cg08224787, cg13973351, cg211650 89, cg05087008, cg05396610, cg23677767, cg21962791, cg04320377, cg16245716, cg21460868, cg09275691, cg19215678, c g08118942, cg16322747, cg12333719, cg23128025, cg27173374, cg02032962, cg18506897, cg05292016, cg16673857, cg048 75128, cg22101188, cg07381960, cg06279276, cg22077936, cg08457029, cg20576243, cg09965557, cg03741619, cg0452500 2, CG15008041, CG16465695, CG16677512, CG12658720, CG27394136, CG14681176, CG07494888, CG14911690, CG06161948, CG 15609017, cg10321869, cg15743533, cg19702785, cg16267121, cg13460409, cg19810954, cg06945504, cg06153788, and cg20088545.

[0372] The present invention further provides a method for determining an age index for an individual in a series of individuals, the determination being based on the level of methylation of genomic DNA sequences found in the individual, and the age index for the individual is derived in a manner that relies on a statistical evaluation of the levels of methylation of genomic DNA sequences for a plurality of individuals, based on the methylation levels of a set of genomic DNA sequences selected from a set of genomic DNA sequences having a level of methylation that can be associated with the age of the individual, in which the age index for the individual is determined based on the methylation levels of genomic DNA sequences for a plurality of individuals that are different from the plurality of individuals referenced for the preceding statistical evaluation used to determine the same age index for the preceding individual in the series of individuals. Item number 107 relates to a method characterized in that the plurality of individuals is derived in a manner that relies on a statistical evaluation of the level of methylation of the A sequence, the plurality of individuals used for the first statistical evaluation is corrected at least by the inclusion of at least one additional preceding individual from the series, resulting in a difference between the plurality of individuals, and preferably the age index for the individuals is derived in a manner that results from at least two different statistical evaluations for two different plurality of individuals, resulting in a change in at least one coefficient used when calculating the age index from the methylation level of an aggregate, and / or taking into account the levels of methylation of different genomic DNA sequences or CgP loci found.

[0373] In some embodiments, a method is disclosed for determining an age index for an individual in a set of individuals based on the level of methylation of genomic DNA sequences, where a set of genomic DNA sequences is selected and an age index for the individual is provided in a manner that successively improves the statistical evaluation of previous measurements to obtain a better model. [Brief description of the drawings]

[0374] [Figure 1]Figure 1 shows a graph of the LASSO performance. A set of 148 cg sites was determined to be optimal. Four plots are shown illustrating the Lasso regression and its performance. In all four plots, the dotted vertical lines represent the automatic threshold chosen for the number of variables selected. All plots report the average values ​​generated from 20 cross-validation runs plus the range intervals. The different axes show different model metrics according to the biglaso package. The top two plots report the cross-validated sum of error and the coefficient of determination (R2), while the bottom two plots report two specific parameters from the R implementation of the LASSO regression: signal-to-noise ratio and <bs>More details are available at https: / / cran.rstudio.com / web / packages / biglasso / biglasso.pdf.

[0375] [Diagram 2] Figure 2 is a graph showing the performance of the age index obtained by LASSO and subsequent stepwise regression. Chronological age (actual age) and determined age (predicted age) are shown for a training data set of 259 individuals and a test data set of 30 individuals. No relevant or significant differences were found between the training and test data sets. The coefficient of variation R2 shown is based on the merged training and test data.

[0376] [Diagram 3] Figure 3 shows the correlation between representative CpG sites and chronological age. Individuals from the merged training and testing data were grouped based on their chronological age (>48 years, 25-48 years, and <25 years; “old”, “middle-aged”, and “young”, respectively). The distribution of DNA methylation levels ("values") for eight representative CpG sites for each age group is shown. Genes contained within the CpG sites are annotated.

[0377] [Figure 4] Figure 4 shows the overlap of CG sites with the set of CG sites described by Horvath in Genome Biology 2013, 14: R115. The Venn diagram reports the amount of overlap between the set of 148 genomic DNA sequences (CpGs) determined by applying LASSO in the present invention (IME-Cerascreen) and the list of 353 CpGs reported by Horvath in Genome Biology 2013, 14: R115. See also Figure 5.

[0378] [Diagram 5] Figure 5 shows the overlap of CG sites determined in the present invention by applying LASSO (IME-Cerascreen) and subsequent stepwise regression (IME_Cerascreen_8). The overlap with the set of CG sites described by Horvath in Genome Biology 2013, 14: R115 is also shown. See also Figure 4. EXAMPLES

[0379] Example 1 Measurement of CpG methylation levels in DNA from biological samples For a very large number of CpGs, approximately 850,000 (850,000), the methylation level of each was measured in the following manner:

[0380] Buccal cells were collected from several study subjects by buccal swabs, and genomic DNA was purified from the buccal cells using the QIAamp 96 DNA Swab BioRobot Kit (Qiagen, Hilden, Germany). The purified genomic DNA was treated with sodium bisulfite using the Zymo EZ DNA Methylation Kit (Zymo, Irvine, CA, USA). This treatment converts unmethylated cytosines to uracils, while methylated cytosines remain unchanged.

[0381] All further steps were carried out using components of the Infinium MethylationEPIC Kit (Illumina™, San Diego, CA, USA) according to the manufacturer's instructions. Briefly, the bisulfite-treated samples were denatured, neutralized, and prepared for amplification. The amplified DNA was then isothermally amplified and enzymatically fragmented in an overnight step. The fragmented DNA was precipitated with isopropanol, collected by centrifugation at 4°C, and resuspended in hybridization buffer. The fragmented and resuspended DNA samples were then distributed to Infinium MethylationEPIC BeadChips (Illumina™), and the BeadChips were incubated overnight in an Illumina™ Hybridization Oven to hybridize the samples to the BeadChips by annealing the fragments to the locus-specific 50-mers covalently linked to the beads.

[0382] Unhybridized and non-specifically hybridized DNA was washed away, and the BeadChip was prepared for staining and extension in a capillary flow-through chamber. Single-base extension of oligos on the BeadChip using the captured DNA as a template incorporates fluorescent labels on the BeadChip, thereby determining the methylation level of the interrogated CpG site. The BeadChip was scanned using an iScan System, using a laser to excite the fluorophore of the single-base extension product on the bead, and recording a high-resolution image of the light emitted from the fluorophore. Data was analyzed using GenomeStudio Methylation Module (Illumina™), which allows calculation of beta values ​​for each analyzed CpG.

[0383] Using this procedure, the methylation levels of more than 850'000 (850000) different Illumina™ defined CpGs per sample and person were measured, resulting in a methylation level and numerical value for each of the more than 850'000 (850000) different CpGs. This was done for a large number of samples, each sample coming from a different individual. The numerical values ​​were normalized such that 0 corresponds to the minimum possible methylation for a CpG and 1 corresponds to the maximum methylation for a CpG. Of note, 1 also corresponds to 100% or complete methylation. Example 2 Measurement of CpG methylation levels by base-specific cleavage / MALDI-TOF (Agena)

[0384] To determine the methylation levels of a preselected set of hundreds of different CpGs, we used the EpiTYPER DNA Methylation Analysis Kit from Agena Bioscience (San Diego, CA, USA). In this example, the methylation levels of 384 of the 384 different CpGs were determined.

[0385] Again, buccal cells were collected from a number of individuals by buccal swabs, and genomic DNA was purified from the buccal cells using the QIAamp 96 DNA Swab BioRobot Kit (Qiagen, Hilden, Germany). The purified genomic DNA was treated with sodium bisulfite using the Zymo EZ DNA Methylation Kit (Zymo, Irvine, CA, USA). This treatment converts unmethylated cytosines to uracils, but leaves methylated cytosines unchanged.

[0386] Target regions containing the CpGs of interest were then amplified by PCR using specific primer pairs for each target region, each containing a reverse primer tagged with a T7 promoter.

[0387] The PCR products were then treated with shrimp alkaline phosphatase to remove unreacted nucleotides from the samples, and in vitro transcription was performed using T7 RNA polymerase. The resulting RNA transcripts were specifically cleaved at uracil residues and distributed onto a SpectroCHIP array. The chip was placed into a MALDI-TOF mass spectrometer for data acquisition, and the resulting data was analyzed with EpiTYPER software.

[0388] The results yielded a numerical value for the methylation level of each of the 384 different CpGs. The numerical values ​​were again normalized so that 0 corresponds to the minimum possible methylation for a CpG and 1 (100%) corresponds to the maximum methylation for a CpG.

[0389] The methylation levels of 384 different genomic DNA sequences were determined by the method of Example 2, and it was observed that the cost of analysis according to Example 2 is significantly lower, less than one-fifth of the cost when applied, compared to approximately 850,000 (850,000) different genomic DNA sequences. Example 3 Measurement of CpG methylation levels by methylation-specific PCR (msPCR)

[0390] To determine the methylation levels of a preselected set of 192 different CpGs, real-time quantitative methylation-specific PCR (msPCR) was performed in the following manner:

[0391] For each of the 192 CpG-containing target regions analyzed, three specific sets of oligonucleotides were designed containing one forward primer and two reverse primers, one of which had a G at its 3' end complementary to the methylated unchanged C, and the second forward primer had an A at its 3' end complementary to the converted uracil.

[0392] Buccal cells were then collected from a number of individuals using buccal swabs, and genomic DNA was purified from the buccal cells using the QIAamp 96 DNA Swab BioRobot Kit (Qiagen, Hilden, Germany). The purified genomic DNA was treated with sodium bisulfite using the Zymo EZ DNA Methylation Kit (Zymo, Irvine, CA, USA). This treatment converts unmethylated cytosines to uracils, while methylated cytosines remain unchanged.

[0393] To determine the methylation level of the CpGs contained in the samples, two PCR reactions were initiated for each of the three sets of oligonucleotides, the first using the forward primer and the first of the two reverse primers, and the second using the forward primer and the second of the two reverse primers. The methylation level of each CpG was determined using real-time quantitative msPCR with TaqMan probes specific for each of the amplified target regions.

[0394] The results yielded a numerical value for the methylation level of each of the 192 different CpGs. The values ​​were again normalized so that 0 corresponds to the minimum possible methylation for the CpG and 1 (100%) corresponds to the maximum methylation for the CpG.

[0395] Although the number of different genomic DNA sequences is less than in the method of Example 2, this method is very competitive in terms of cost. Example 4 Generating Age Predictors Using LASSO

[0396] The DNA methylation levels of 289 individuals (259 for the training data set and 30 for the test data set) were determined as described in Example 1 unless otherwise stated. Briefly, the DNA methylation levels of 850000 different genomic DNA sequences were determined from buccal swab samples using the Infinium MethylationEPIC BeadChip (Illumina™). The methylation levels were normalized as beta values ​​using the program R v3.4.2, which can therefore have values ​​between 0 and 1. The data set, i.e. the training data set, was a data matrix with the structure of Table 1. [Table 1]

[0397] We used the statistical software R v3.4.1 and the biglasso package, and the command cvfit<-cv.biglasso(Vars800bm, Age, seed=2401, nfolds=20) LASSO regression was performed using: where Vars800bm is the training data set on the exemplary matrix shown in Table 1, cg site is the independent variable, and age is the dependent variable to be modeled; seed is the number used by the random generator; and nfolds is the number of cross-validation iterations that should be used to build the model. A value of 20 was used for cross-validation. The biglaso package was: "The biglasso Package: A Memory-and Computation-Effic Solver for LASSO Model Fitting with Big Data in R" , Yaohui Zeng and Patrick Breheny in arXiv:1701.05936v2 [statCO] 11 March 2018.

[0398] The equation for the model (age index) obtained by LASSO regression was as follows: Age = +53.9126*cg27320127 +43.1588*cg16267121 +31.5464*cg00831672 +30.4384*cg27173374 +26.5197*cg16867657 +20.9302*cg14681176 +19.0975*cg25606723 +16.8674*cg11607603 +16.6092*cg08097417 +15.0595*cg11330075 +14.5786*cg12333719 + 14.1955*cg10543136+13.6743*cg21807065+12.4988*cg19851481+12.19 54*cg08224787+11.7822*cg19702785+11.7706*cg13759931+11.6845*cg 19112204+11.4521*cg07955995+10.869*cg18815943+10.829*cg2472442 8+10.7537*cg22101188+10.4571*cg19215678+9.551*cg22519947+9.5225 *cg06161948+9.3932*cg16677512+9.2647*cg05396610+8.9059*cg21628 619+8.7864*cg15609017+8.6846*cg24954665+8.5015*cg25642673+8.28 4*cg07802350+7.9408*cg05087008+7.8335*cg12548216+7.7144*cg0996 5557+7.6203*cg16999154+7.6057*cg12238343+7.5126*cg08044253+7.06 73*cg16465695+6.939*cg13206721+6.6733*cg09001642+6.1215*cg1117 6990+6.0675*cg07625177+6.0657*cg05292016+5.9961*cg16593468+5.9 511*cg07291317+5.5409*cg18506897+5.4739*cg07120630+5.2279*cg08 662753+5.1938*cg24088134+5.1655*cg00097800+4.8623*cg16950671+4.6431*cg16245716+4.6364*cg06279276+4.6224*cg08686931+4.1089*cg27540719+4.0082*cg07529089+3.9294*cg06945504+3.8147*cg23677767+3.7304*cg07766948+3.7296*cg00876345+3.541*cg05972734+3.5305*cg22540792+3.4169*cg08118942+3.1845*cg02032962+3.1329*cg09460489+3.0723*cg22444338+3.0498*cg08856941+2.8317*cg03741619+2.7707*cg03230469+2.6979*cg06153788+2.6678*cg10522765+2.6533*cg14911690+2.5934*cg06186727+2.5488*cg03526652+2.5152*cg01520297+2.4409*cg09805798+2.3836*cg07513002+2.3539*cg08960065+2.3285*cg06335143+2.3044*cg16673857+2.2379*cg05990274+2.0254*cg04525002+1.9303*cg13154327+1.8016*cg07494888+1.7889*cg03388189+1.7543*cg08478427+1.7476*cg18768299+1.6312*cg21165089+1.6196*cg17665505+1.613*cg13460409+1.5347*cg14305139+1.4346*cg12804730+1.2032*cg04875128+1.2025*cg05211227+1.1767*cg18737844+1.1712*cg21460868+1.15*cg26430984+1.135*cg10321869+1.0067*cg14756158+1.0021*cg16322747+0.9948*cg17343879+0.9605*cg22077936+0.7994*cg18339380+0.5436*cg00087368+0.3003*cg05812299+0.281*cg12732998+0.0507*cg16456442+0.0277*cg17760405+0.0165*cg12658720-0.2038*cg08457029-0.4098*cg21962791-0.423 2*cg15761531-0.4506*cg19810954-0.4626*cg20425444-0.5866*cg23128025-0.6731*cg25845463-0.6945*cg 03324695-1.0445*cg01636910-1.4555*cg12650870-1.8012*cg01820962-2.2813*cg07782620-2.4468*cg043 20377-2.6024*cg09275691-2.6286*cg15008041-2.7124*cg20576243-3.4046*cg13973351-3.5199*cg0819437 7-3.5713*cg07381960-4.0608*cg10240079-4.2758*cg14231565-4.8117*cg24319133-4.8449*cg03680898-5 .694*cg19301963-6.83*cg03473532-7.515*cg13333913-8.0702*cg05106770-8.3397*cg04287203-9.4713*cg 27394136-9.4931*cg10501210-10.8424*cg19432688-12.9786*cg02536625-13.2229*cg04028695-14.2271*c g16781885-14.728*cg15743533-14.9252*cg04733826-15.7917*cg20088545-16.5954*cg06831571-367.4866. .

[0399] This age index contains 148 items, such as +16.6092*cg08097417, where the plus sign indicates that the methylation level is positively correlated with age and the minus sign indicates that the methylation level is negatively correlated with age. The numbered cg refers to the genomic DNA sequence according to the Infinium MethylationEPIC BeadChip, and the absolute value of the coefficient multiplied by the cg indicates the importance of this cg.

[0400] Various model performance checks confirmed that the selection of 148 CG sites was optimal (Figure 1).

[0401] This age index had the following performance: R 2 = 0.72, selected variables = 148 (non-zero coefficients), where R 2 is the coefficient of determination. The statistic was determined using an independent test data set consisting of data from 30 individuals (approximately 10%) different from the 259 (289-30) individuals used in the training data set but drawn from the same population as the 289 individuals.

[0402] In addition, LASSO was applied to data from 64 or 150 individuals from the 289 individuals (Table 2). [Table 2]

[0403] This suggested that the performance of LASSO improved when data from additional individuals were repeatedly added to the dataset and the age index was repeatedly updated. Example 5 Generating age predictors using LASSO and subsequent stepwise regression

[0404] Stepwise regression was applied to the reduced training dataset obtained after performing LASSO (Example 4) to extract the best significant set of cg sites / CpGs, thereby optimizing the model. The reduced training dataset (IME_blasso[,-1]) was the same as the training dataset used in Example 4, except that only the 148 columns for the 148 cg sites selected by LASSO were retained.

[0405] Stepwise regression was performed using the statistical software R v3.4.1 and the following command: model_blasso<-step(lm(Age~.,data=IME_blasso[,-1]),direction="both"), where the direction for removing insignificant variables was “both”, i.e., both adding and removing variables were allowed.

[0406] The equation for the model (age index) obtained by LASSO regression and subsequent stepwise regression was as follows: Age = +66.2822*cg11330075 +65.203*cg00831672 +55.7265*cg27320127 +44.4116*cg27173374 +38.3902*cg14681176 +37.8069*cg06161948 +36.6564*cg08224787 +31.9397*cg05396610 +30.1919*cg15609017 +28.089*cg09805798 +27.939 2*cg19215678+27.8502*cg12333719+27.226*cg03741619+27.0323*cg16 677512+25.9599*cg03230469+25.3932*cg19851481+24.5374*cg1054313 6+22.5525*cg07291317+21.8666*cg26430984+20.3621*cg16950671+20. 3269*cg16867657+19.7973*cg22077936+18.7137*cg08044253+18.2047*c g12548216+18.1936*cg05211227+18.0812*cg13759931+17.6857*cg0868 6931+17.5303*cg07955995+16.1143*cg07529089+14.8703*cg01520297+ 14.6684*cg00087368+14.4397*cg05087008+14.4361*cg24724428+14.30 55*cg19112204+14.2968*cg04525002+14.2302*cg08856941+13.3831*cg1 6465695+11.8127*cg08097417+11.7798*cg21628619+11.3523*cg094604 89+11.2461*cg13460409+10.6268*cg25642673+10.4347*cg19702785+9. 7844*cg18506897+9.5931*cg21165089+9.093*cg27540719+8.9361*cg21 807065+8.8577*cg18815943+8.6138*cg23677767+7.1699*cg07802350+7.0528*cg11176990+6.5416*cg10321869+6.5049*cg17343879+5.8296*cg08662753+5.696*cg14911690+3 .2983*cg12804730+3.1388*cg16322747-4.8653*cg14231565-5.5608*cg10501210-6.047*cg09275691-6 .35*cg15008041-9.1942*cg05812299-9.3144*cg24319133-9.4566*cg12658720-9.8704*cg20576243-10 .4082*cg03473532-10.6429*cg07381960-11.1592*cg05106770-12.0021*cg04320377-12.3296*cg19432 688-12.9858*cg22519947-13.7116*cg06831571-13.8029*cg08194377-13.8668*cg01636910-14.6975* cg14305139-15.0408*cg04028695-16.3295*cg15743533-16.3314*cg03680898-18.6196*cg20088545-19 .0952*cg13333913-19.3068*cg19301963-21.5752*cg13973351-23.0892*cg16781885-26.0415*cg04287 203-32.3606*cg27394136-48.0918*cg10240079-50.0227*cg02536625-63.4434*cg23128025-519.3495. .

[0407] The meaning of the terms and statistics is as explained in Example 4. Further details regarding the cg sequences and coefficients can be found in Table 6.

[0408] Therefore, applying stepwise regression further reduced the number of selected variables: indeed, the age indicator was contained in only 88 genomic DNA sequences (cg sites / CpGs). Furthermore, the performance of the age index obtained by LASSO and subsequent stepwise regression was as follows:

[0409] R when using training data 2 = 0.9884 (when using a test data set containing data for 30 test individuals as described in Example 4). 2 = 0.9929. Thus, the performance was enhanced compared to the age index obtained by LASSO without stepwise regression.

[0410] The performance on the test data is as good as the training data set, suggesting that the age index has superior performance (Figure 2). Moreover, such high coefficient of determination values ​​indicate a significant improvement over prior art age indexes.

[0411] By grouping individuals (merged training and testing datasets) based on their chronological age, we were able to confirm that the methylation levels of representative CG sites selected by regression analysis correlated well with age groups (Figure 3).

[0412] The age indices and their determinations were then compared to the age indices of Horvath, Genome Biology 2013, 14: R115 in Table 3: [Table 3]

[0413] This confirms that the performance of the age index obtained by LASSO+stepwise regression is at least as good as, or even better than, relevant prior art age indexes, despite only having about 25% of the number of genomic DNA sequences (independent variables).

[0414] The small set of genomic DNA sequences included in the age index makes it possible to determine the DNA methylation levels of the individuals whose ages are being determined using alternative, i.e. simpler, methods (see Examples 2 and 3).

[0415] Furthermore, the sets of cg sites determined using LASSO alone or LASSO plus subsequent stepwise regression had very little overlap with the cg sites determined in Horvath, Genome Biology 2013, 14: R115 (Figures 4 and 5). Example 6 Determining gene sets from sets of cg sites / CpGs

[0416] The lists of cg sites determined by applying LASSO (Example 4) or LASSO + stepwise regression (Example 5) were filtered for cg sites that are completely contained within genes. In the first list (Table 4), based on 148 CpGs determined by LASSO, 106 (partially overlapping) coding sequences and non-coding sequences such as miRNAs or long non-coding RNAs were selected: [Table 4-1] [Table 4-2] [Table 4-3] [Table 4-4] [Table 4-5] [Table 4-6] [Table 4-7]

[0417] For the reduced gene set (Table 5), druggable gene targets were selected from Table 4. Specifically, genes were selected for which in vitro assays are known in the art to determine the activity or function of the encoded proteins. [Table 5-1] [Table 5-2]

[0418] Finally, we selected a list of 68 (partially overlapping) coding sequences and non-coding sequences such as miRNA or long non-coding RNA from 88 CpGs determined by LASSO + stepwise regression (Table 6). This table also shows the coefficients of each age indicator and their standard errors (see Example 5).

[0419] [Table 6-1] [Table 6-2] [Table 6-3] [Table 6-4] [Table 6-5] [Table 6-6] Example 7 Repeated updates of age index

[0420] The age index was automatically updated for each case based on determining whether the domain boundaries of the test data were outside the domain boundaries of the training set of the age index (proband; individual). The domain boundaries were the minimum and maximum DNA methylation levels of each genomic DNA sequence included in the age index. The minimum and maximum DNA methylation levels were found in the original training data set used to determine the age index. These values ​​change whenever another individual's values ​​come in and replace the original minimum and maximum values ​​for the CpGs. Thus, the minimum value for each CpG becomes smaller (if the minimum is not already 0) and the maximum value becomes larger (if it is not already 1). In doing so, the domain boundaries of the age index expand to the optimal values, making further updates of the age index less and less likely.

[0421] The update was done using the following R code:

number

number

number

[0422] DNA was sampled from approximately 200 individuals. All of these samples were obtained in Northern Germany, but in order to have a broad database, care was taken to not exclude any individuals, taking into account factors such as chronological age, general health, obesity, level of physical fitness, drug consumption, including drugs such as nicotine and alcohol. Therefore, the group is considered to be representative of the general population.

[0423] Methylation levels of CpGs in DNA from biological samples of approximately 100 individuals were determined using the method of Example 1, resulting in a number of approximately eight hundred and fifty thousand (850,000) CpGs for each individual.

[0424] Considering the amount of data and computational costs, the data was divided into smaller arbitrary groups, and then the data of these smaller groups was analyzed.

[0425] Using the data of the first group of 16 individuals, a principal component analysis was performed and it was found that about 10 principal components accounted for almost all of the variance observed in the methylation levels of CpGs in the group samples, with the first two components already accounting for 98% of the variation, thereby clearly indicating that, despite taking into account the methylation levels of a very large number of different CpGs, a reduction in the number is recommended. Based on the principal component analysis, regression techniques were used to establish predictor models for each group, but the constructed models essentially showed that some of the coefficients were still not significant.

[0426] Even so, it was also determined that some of the coefficients determined did not have statistical significance.

[0427] With this in mind, data from a first larger group of 98 individuals was analyzed with the intention of establishing a model that maintains high statistical significance of all parameters while the number of CpGs considered is obviously reduced. For this purpose, a first LASSO regression was performed; it is noted that LASSO regression is a well-known technique in the art, and software packages for implementing LASSO regression are readily available. It is noted that it is possible to distinguish whether the methylation level of a given CpG has a certain statistical association or not, which makes it possible to consider only CpGs that have some association. In particular, in this respect, reference is made to "The biglasso Package: A Memory-and Computation-Effic Solver for LASSO Model Fitting with Big Data in R" by Yaohui Zeng and Patrick Breheny in arXiv: 1701.05936v2 [statCO] 11 March 2018. An attempt was made to further optimize the derived model using a selection of only 50 different CpGs that were determined by LASSO regression to constitute the optimal set. This was done using the XgBoost algorithm. Note that XgBoost is a well-known open source software library that provides a gradient boosting framework for several languages. Note that XgBoost serves to modify the coefficients used in the statistical model. For further details regarding the XgBoost algorithm and its implementation, see “XGBoost: A Scalable Tree Boosting System”, by T. Chen and C. Guestrin, arXiv: 1603.02754v3, 10. Juni 2016. The contents of the cited documents are incorporated herein in their entirety for disclosure purposes.

[0428] It was found that high performance models yielding good regression coefficients could be obtained.

[0429] However, rather than being satisfied with the high regression coefficients achieved for the group considered and the high performance model remaining intact, data from another 98 individuals were analyzed in the same manner as before. For the second group, it was found that approximately 78 CpGs should be considered in the model, and of the 78 CpGs, 8 CpGs overlap with the 50 CpGs selected for the first arbitrary group of 98 individuals.

[0430] Another run was then performed and it was determined that in the merged set, 70 CpGs constituted a useful selection of CpGs from the approximately 850,000 different CpGs initially considered. From these 70 CpGs, 10 CpGs overlapped only with those in the first set, 12 CpGs overlapped only with those in the second set, and 8 CpGs overlapped with both sets.

[0431] Regression performed with XgBoost allowed us to maintain the same high performance after 20 rounds of cross-validation.

[0432] This allows the number of CpGs to be significantly reduced from a very large overall set to a somewhat smaller set by statistical means, in particular LASSO regression, PCA or other means of distinguishing whether a particular CpG among many CpGs has statistical relevance or not, thereby allowing inexpensive detection using the methods mentioned in Examples 2 and 3 above.

[0433] Then, for only a small set of CpGs, a useful model can be established that allows to determine the age with high accuracy and small confidence intervals, despite the small number of CpGs considered, in particular by repeating the parameters of the established statistical model many times.

[0434] In this way, despite the overall small number of CpGs considered, age determinations can be extremely accurate initially, with increasing confidence over time. The present invention provides, for example, the following items. (Item 1) 1. A method for determining an age indicator, comprising: (a) For each individual, (i) DNA methylation levels of a set of genomic DNA sequences, and (ii) Chronological age Preparing a training data set of a plurality of individuals, the training data set comprising: (b) The training data set is subjected to Least Absolute Shrinkage applying a regression method including a Lasso-Based Algorithm and Selection Operator (LASSO) to thereby determine the age index and reduced training data set; the independent variable is the methylation level of said genomic DNA sequence, and preferably the dependent variable is said age; The age indicator is (i) a subset of the set of genomic DNA sequences as a collection, and (ii) at least one coefficient for each genomic DNA sequence contained in the collection; Including, The reduced training dataset includes all data in the training dataset except for the DNA methylation levels of the genomic DNA sequences that are excluded by the LASSO; The method includes: (Item 2) 1. A method for determining the age of an individual, comprising: (a) For each individual, (i) DNA methylation levels of a set of genomic DNA sequences, and (ii) Chronological age Preparing a training data set of a plurality of individuals, the training data set comprising: (b) The training data set is subjected to Least Absolute Shrinkage applying a regression method including a Lasso-Based Algorithm and Selection Operator (LASSO) to thereby determine the age index and reduced training data set; the independent variable is the methylation level of said genomic DNA sequence, and preferably the dependent variable is said age; The age indicator is (i) a subset of the set of genomic DNA sequences as a collection, and (ii) at least one coefficient for each genomic DNA sequence contained in the collection; Including, The reduced training dataset includes all data in the training dataset except for DNA methylation levels of genomic DNA sequences excluded by the LASSO; (c) obtaining a DNA methylation level for at least 80%, preferably 100%, of the genomic DNA sequences included in said age indicator for the individual whose age is to be determined; (d) determining the age of the individual based on its DNA methylation level and the age indicator; Including, Preferably, said determined age may differ from the individual's chronological age. (Item 3) 3. The method of claim 1 or 2, wherein the regression method further comprises applying a stepwise regression after the LASSO. (Item 4) 4. The method of claim 3, wherein the stepwise regression is applied to the reduced training data set. (Item 5) 5. The method according to any one of items 1 to 4, wherein the set included in the age index is smaller than the set of genomic DNA sequences. (Item 6) 6. The method of any of items 1 to 5, wherein the set included in the age index is smaller than the set of genomic DNA sequences included in the reduced training dataset. (Item 7) 7. The method according to any of items 3 to 6, wherein the stepwise regression is two-way elimination, whereby independent variables that are not statistically significant are removed, preferably at a significance level of 0.05. (Item 8) 8. The method according to any of items 1 to 7, wherein the LASSO is performed using the biglasso R package, preferably by applying the command "cv.biglasso", and preferably "nfold" is 20. (Item 9) 9. The method according to any of items 1 to 8, wherein the regression method does not include ridge regression (L2 regularization) or the L2 regularization parameter / lambda parameter is 0. (Item 10) 10. The method of any of items 1 to 9, wherein the LASSO L1 regularization parameter / alpha parameter is 1. (Item 11) 11. The method of any of items 1 to 10, wherein the age index is iteratively updated, including adding data of at least one further individual to the training data in each iteration, thereby iteratively expanding the training data set. (Item 12) In one update round, the data added for each further individual is (i) at least 5%, preferably 50%, and more preferably 100% of the set of genomic DNA sequences included in either the initial training data set or the expanded training data set; and / or (ii) the genomic DNA sequences contained in the reduced training data set. 12. The method of claim 11, comprising determining the individual's DNA methylation level. (Item 13) 13. The method of claim 11 or 12, wherein all genomic DNA sequences (independent variables) that are not present for all individuals contributing data to the expanded training dataset are removed from the expanded training dataset. (Item 14) 14. The method according to any of items 11 to 13, wherein in one update round, the set of genomic DNA sequences to which methylation levels are added is identical for each of the further individual(s). (Item 15) 15. The method according to any of items 11 to 14, wherein one update round comprises applying the LASSO to the enlarged training data set, thereby determining an updated age index and / or an updated reduced training data set. (Item 16) 16. The method of any of items 11 to 15, wherein the training dataset to which data for the at least one further individual is added is a reduced training dataset, which may be either the initial reduced training dataset or the updated reduced training dataset. (Item 17) 17. The method of claim 16, wherein the reduced training data set is a previous reduced training data set in the iteration. (Item 18) 18. The method of any of items 11 to 17, wherein one update round comprises applying the stepwise regression to the reduced training data set, thereby determining an updated age index. (Item 19) 19. The method of any of items 1 to 18, wherein in one update round, data relating to at least one individual is removed from the training data set and / or the reduced training data set. (Item 20) 20. The method according to any of items 11 to 19, wherein the addition and / or removal of an individual's data depends on at least one characteristic of said individual, said characteristic being ethnicity, sex, chronological age, place of residence, place of birth, at least one disease and / or at least one lifestyle factor, said lifestyle factor being selected from drug consumption, exposure to environmental pollutants, shift work or stress. (Item 21) determining a quality of the age indicator, the determining of the quality comprising: (a) preparing a testing dataset for a plurality of individuals who have not contributed data to the training dataset, the testing dataset comprising, for each of the individuals: (i) the DNA methylation level of a set of genomic DNA sequences included in the age indicator; and (ii) said chronological age; and (b) determining the quality of said age indicator by statistical evaluation and / or evaluation of domain boundaries, The statistical evaluation comprises: (i) determining the ages of individuals included in the test dataset; (ii) correlating the determined age and chronological age of the individual(s) and determining at least one statistical parameter describing this correlation; (iii) determining whether said statistical parameter(s) are indicative of an acceptable quality of said age index, preferably said statistical parameter being a coefficient of determination (R 2 ) and mean absolute error (MAE), R 2 a β value greater than 0.50, preferably greater than 0.70, preferably greater than 0.90, preferably greater than 0.98 and / or a MAE less than 6 years, preferably less than 4 years, preferably maximum 1 year indicates acceptable quality; Including, The domain boundary evaluation (iv) determining a domain boundary of the age indicator, the domain boundaries being the minimum and maximum DNA methylation levels of each genomic DNA sequence included in the age index; The minimum and maximum DNA methylation levels are found in a training data set used to determine the age indicator. Steps and (v) determining whether the test data set exceeds the domain boundary, where failure to exceed the domain boundary indicates acceptable quality; Steps 21. The method according to any one of items 1 to 20, comprising: (Item 22) 22. The method according to any of items 1 to 21, wherein the training dataset and / or the testing dataset comprises at least 10 individuals, preferably at least 30 individuals, preferably at least 200 individuals, preferably wherein the training dataset comprises at least 200 individuals and the testing dataset comprises at least 30 individuals. (Item 23) 23. The method according to item 21 or 22, wherein the age index is updated if its quality is unacceptable. (Item 24) 24. The method according to any of items 11 to 23, wherein the age of the individual is determined based on the DNA methylation level of the individual and an updated age index. (Item 25) 25. The method according to any of items 2 to 24, wherein the age of an individual is determined using only the age index if the individual's data did not contribute to the training data set used to generate the age index. (Item 26) 26. The method according to any of the preceding claims, wherein the age indicator is not further updated if the number of individuals contained in the data has reached a predetermined value and / or a predetermined time has elapsed since the previous update. (Item 27) 27. The method of any of items 1 to 26, wherein the set of genomic DNA sequences included in the training dataset is preselected from genomic DNA sequences whose methylation levels can be associated with chronological age. (Item 28) 28. The method of claim 27, wherein the preselected set comprises at least 400,000, preferably at least 800,000 genomic DNA sequences. (Item 29) 29. The method of any of items 1 to 28, wherein the genomic DNA sequences included in the training dataset do not overlap with each other and / or occur only once per allele. (Item 30) 30. The method of any of items 1 to 29, wherein the reduced training dataset comprises at least 90, preferably at least 100, preferably at least 140 genomic DNA sequences. (Item 31) 31. The method of any of items 1 to 30, wherein the reduced training dataset comprises less than 5000, preferably less than 2000, preferably less than 500, preferably less than 350, preferably less than 300 genomic DNA sequences. (Item 32) 32. The method according to any of items 1 to 31, wherein the age indicator comprises at least 30, preferably at least 50, preferably at least 60, preferably at least 80 genomic DNA sequences. (Item 33) 33. The method according to any of items 1 to 32, wherein the age indicator comprises less than 300, preferably less than 150, preferably less than 110, preferably less than 100, preferably less than 90 genomic DNA sequences. (Item 34) 34. The method according to any of items 1 to 33, wherein the DNA methylation level of a genomic DNA sequence of an individual is measured in a sample of biological material of the individual comprising the genomic DNA sequence. (Item 35) 35. The method of claim 34, wherein the sample comprises buccal cells. (Item 36) 36. The method of any of items 34 or 35, further comprising the step of non-invasively obtaining the sample. (Item 37) 37. The method according to any of items 34 to 36, wherein the DNA methylation level is measured by methylation sequencing, bisulfite sequencing, PCR, high-resolution melting analysis (HRM), methylation-sensitive single-base primer extension (MS-SnuPE), methylation-sensitive single-strand conformation analysis, methyl-sensitive cleavage counting (MSCC), base-specific cleavage / MALDI-TOF, combined bisulfite restriction analysis (COBRA), methylated DNA immunoprecipitation (MeDIP), microarray-based methods, bead array-based methods, pyrosequencing and / or direct sequencing without bisulfite treatment (nanopore technology). (Item 38) 38. The method according to any of items 34 to 37, wherein the DNA methylation level of an individual's genomic DNA sequence has been measured by base-specific cleavage / MALDI-TOF and / or PCR method, wherein base-specific cleavage / MALDI-TOF is Agena technology and preferably the PCR method is methylation-specific PCR. (Item 39) 39. The method according to any of items 34 to 38, wherein the DNA methylation level of the genomic DNA sequence included in the age indicator is determined in a sample of biological material containing the genomic DNA sequence of the individual whose age is to be determined. (Item 40) cg11330075、cg25845463、cg22519947、cg21807065、cg09001642、cg18815943、cg06335143、cg01636910、cg10501210、cg03324695、cg19432688、cg22540792、cg11176990、cg00097800、cg27320127、cg09805798、cg03526652、cg09460489、cg18737844、cg07802350、cg10522765、cg12548216、cg00876345、cg15761531、cg05990274、cg05972734、cg03680898、cg16593468、cg19301963、cg12732998、cg02536625、cg24088134、cg24319133、cg03388189、cg05106770、cg08686931、cg25606723、cg07782620、cg16781885、cg14231565、cg18339380、cg25642673、cg10240079、cg19851481、cg17665505、cg13333913、cg07291317、cg12238343、cg08478427、cg07625177、cg03230469、cg13154327、cg16456442、cg26430984、cg16867657、cg24724428、cg08194377、cg10543136、cg12650870、cg00087368、cg17760405、cg21628619、cg01820962、cg16999154、cg22444338、cg00831672、cg08044253、cg08960065、cg07529089、cg11607603、cg08097417、cg07955995、cg03473532、cg06186727、cg04733826、cg20425444、cg07513002、cg14305139、cg13759931、cg14756158、cg08662753、cg13206721、cg04287203、cg18768299、cg05812299、cg04028695、cg07120630、cg17343879、cg07766948、cg08856941、cg16950671、cg01520297, cg27540719, cg24954665, cg05211227, cg06831571, cg19112204, cg12804730, cg08224787, cg13 973351, cg21165089, cg05087008, cg05396610, cg23677767, cg21962791, cg04320377, cg16245716, cg214608 68, cg09275691, cg19215678, cg08118942, cg16322747, cg12333719, cg23128025, cg27173374, cg02032962, c g18506897, cg05292016, cg16673857, cg04875128, cg22101188, cg07381960, cg06279276, cg22077936, cg084 57029, cg20576243, cg09965557, cg03741619, cg04525002, cg15008041, cg16465695, cg16677512, cg1265872 0, cg27394136, cg14681176, cg07494888, cg14911690, cg06161948, cg15609017, cg10321869, cg15743533, cg A set of genomic DNA sequences comprising at least 10, preferably at least 50, preferably at least 70, preferably all of 19702785, cg16267121, cg13460409, cg19810954, cg06945504, cg06153788, and cg20088545, or fragments thereof comprising at least 70%, preferably at least 90% of the contiguous nucleotide sequence. (Item 41) cg11330075, cg00831672, cg27320127, cg27173374, cg14681176, cg06161 948, cg08224787, cg05396610, cg15609017, cg09805798, cg19215678, cg1 2333719, cg03741619, cg16677512, cg03230469, cg19851481, cg10543136 , cg07291317, cg26430984, cg16950671, cg16867657, cg22077936, cg0804 4253, cg12548216, cg05211227, cg13759931, cg08686931, cg07955995, cg 07529089, cg01520297, cg00087368, cg05087008, cg24724428, cg1911220 4, CG04525002, CG08856941, CG16465695, CG08097417, CG21628619, CG094 60489, cg13460409, cg25642673, cg19702785, cg18506897, cg21165089, c g27540719, cg21807065, cg18815943, cg23677767, cg07802350, cg111769 90, cg10321869, cg17343879, cg08662753, cg14911690, cg12804730, cg16 322747, cg14231565, cg10501210, cg09275691, cg15008041, cg05812299, cg24319133, cg12658720, cg20576243, cg03473532, cg07381960, cg05106 770, cg04320377, cg19432688, cg22519947, cg06831571, cg08194377, cg01636910, cg14305139, cg04028695, cg15743533, cg03680898, cg20088545, cg13333913, cg19301963, cg13973351, cg16781885, cg04287203, cg27394136, cg10240079, cg02536625, and cg23128025,39. A set of genomic DNA sequences according to item 39, comprising at least 30, preferably at least 70, preferably all or a fragment thereof comprising at least 70%, preferably at least 90% of the contiguous nucleotide sequences. (Item 42) cg11330075, cg00831672, cg27320127, cg27173374, cg14681176, cg06161948, cg08224787, cg05396610, c g15609017, cg09805798, cg19215678, cg12333719, cg03741619, cg03230469, cg19851481, cg10543136, cg 42. The set of genomic DNA sequences according to item 41, comprising at least four, preferably at least ten, preferably all of: 07291317, cg26430984, cg16950671, cg16867657, cg13973351, cg16781885, cg04287203, cg27394136, cg10240079, cg02536625, and cg23128025. (Item 43) 43. The set of genomic DNA sequences according to item 41 or 42, comprising at least four, and preferably all, of cg11330075, cg00831672, cg27320127, cg10240079, cg02536625, and cg23128025. (Item 44) 44. The collection of genomic DNA sequences according to any of items 40 to 43, comprising in addition and / or instead of said collection of genomic DNA sequences, complementary sequences thereof. (Item 45) SIM bHLH transcription factor 1 (SIM1), microtubule-associated protein 4 (MAP4), protein kinase C zeta (PRKCZ), glutamate ionotropic receptor AMPA subunit 4 (GRIA4), BCL10, immune signaling adaptor (BCL10), 5'-nucleotidase domain-containing 1 (NT5DC1), suppressor of tumorigenicity 7 (ST7), protein kinase C eta (PRKCH), glial cell line-derived neurotrophic factor (GDNF), muskelin 1 (MKLN1), excretory kinase C eta (KLCC), and mitochondrial kinase C (KLCC). Socyst complex component 6B (EXOC6B), protein S (PROS1), voltage-gated calcium channel subunit alpha 1D (CACNA1D), Kelch-like family member 42 (KLHL42), OTU deubiquitinase 7A (OTUD7A), cell death-associated protein (DAP), coiled-coil domain-containing 179 (CCDC179), iodothyronine deiodinase 2 (DIO2), transient receptor potential cation channel subfamily V member 3 (TRPV3), M T-RNR2-like 5 (MTRNR2L5), filamin B (FLNB), furin, paired basic amino acid cleaving enzyme (FURIN), solute carrier family 25 member 17 (SLC25A17), G-patch domain containing 1 (GPATCH1), UDP-GlcNAc:beta Gal beta-1,3-N-acetylglucosaminyltransferase 9 (B3GNT9), zyg-11 family member A, cell cycle regulator (ZYG11A), seizure-related 6 homolog-like (SEZ6L), Myosin X (MYO10), Acetyl-CoA carboxylase alpha (ACACA), G protein subunit alpha i1 (GNAI1), CUE domain containing 2 (CUEDC2), Homeobox D13 (HOXD13), Krüppel-like factor 14 (KLF14), Solute carrier family 1 member 2 (SLC1A2), Acetoacetyl-CoA synthetase (AACS), Ankyrin repeat and sterile alpha motif domain containing 1A (ANKS1A), MicroRNA 7641-2 (MIR7641-2), Type V collagen alpha 1 chain (COL5A1), Arsenite methyltransferase (AS3MT), Solute carrier family 26 member 5 (SLC26A5), Nucleoporin 107 (NUP107),long intergenic protein noncoding RNA 1797 (LINC01797), myosin IC (MYO1C), ankyrin repeat domain 37 (ANKRD37), phosphodiesterase 4C (PDE4C), EF-hand domain containing 1 (EFHC1), uncharacterized LOC375196 (LOC375196), ELOVL fatty acid elongase 2 (ELOVL2), WAS protein family member 3 (WASF3), chromosome 17 open reading frame 82 (C17orf82), G protein-coupled receptor 158 (GPR158 ), F-box and leucine-rich repeat protein 7 (FBXL7), Ripley transcriptional repressor 3 (RIPPLY3), VPS37C subunit of ESCRT-I (VPS37C), polypeptide N-acetylgalactosaminyltransferase-like 6 (GALNTL6), DENN domain containing 3 (DENND3), nuclear receptor corepressor 2 (NCOR2), endothelial PAS domain protein 1 (EPAS1), PBX homeobox 4 (PBX4), long intergenic protein noncoding RNA 1531 (LINC01531), and have sequence similarity to These include family 110 member A (FAM110A), glycosyltransferase 8 domain containing 1 (GLT8D1), G protein subunit gamma 2 (GNG2), MT-RNR2-like 3 (MTRNR2L3), zinc finger protein 140 (ZNF140), kinase inhibitor of ras1 (KSR1), protein disulfide isomerase family A member 5 (PDIA5), spermatogenesis-associated 7 (SPATA7), pantothenate kinase 1 (PANK1), ubiquitin-specific peptidase 4 (USP4), and G protein subunit Alpha q (GNAQ), Potassium channel modulator subfamily S member 1 (KCNS1), DNA polymerase gamma 2, accessory subunit (POLG2), stalk head box 2 (STOX2), neurexin 3 (NRXN3), BMS1, ribosome biogenesis factor (BMS1), forkhead box E3 (FOXE3), NADH:ubiquinone oxidoreductase subunit A10 (NDUFA10), relaxin family peptide receptor 3 (RXFP3), GATA binding protein 2 (GATA2),Isoprenoid synthase domain containing (ISPD), adenosine deaminase, RNA specific B1 (ADARB1), Wnt family member 7B (WNT7B), pleckstrin and Sec7 domain containing 3 (PSD3), membrane anchored junction protein (MAJIN), pyridine nucleotide-disulfide oxidoreductase domain 1 (PYROXD1), cingulin-like 1 (CGNL1), chromosome 7 open reading frame 50 (C7orf50), MORN repeat containing 1 (MORN1), atlastin GTPase 2 (ATL2), WD repeat and FYVE domain containing 2 (WDFY2), transmembrane protein 136 (TMEM136), inositol poly(Asp1) A gene set including at least 10, preferably at least 30, preferably at least 50, preferably at least 70, preferably all of phosphate-5-phosphatase A (INPP5A), TBC1 domain family member 9 (TBC1D9), interferon regulatory factor 2 (IRF2), sirtuin 7 (SIRT7), collagen type XXIII alpha 1 chain (COL23A1), guanine monophosphate synthase (GMPS), potassium 2-pore domain channel subfamily K member 12 (KCNK12), SIN3-HDAC complex associated factor (SINHCAF), hemoglobin subunit epsilon 1 (HBE1), and Tudor domain containing 1 (TDRD1). (Item 46) ISPD, KCNK12, GNG2, SIRT7, GPATCH1, GRIA4, LINC01531, LOC101927577, NCOR2, WASF3, TRPV3, ACACA, GDNF, EFHC1, MYO10, COL23A1, TDRD1, ELOVL2, GNAI1 , MAP4, CCDC179, KLF14, ST7, INPP5A, SIM1, SLC1A2, AS3MT, KSR1, DSCR6, IRF2, KCNS1, NRXN3, C11orf85, HBE1, FOXE3, TMEM136, HOXD13, LOC375196, PANK1 46. ​​The gene set according to Item 45, comprising at least five, preferably at least ten, preferably at least 30, and preferably all, of the following: MIR107, COL5A1, PBX4, ZNF140, GALNTL6, NUP107, LOC100507250, MTRNR2L5, C17orf82, MKLN1, FURIN, KLHL42, MORN1, ANKS1A, BCL10, DENND3, FAM110A, PROS1, WNT7B, FBXL7, GATA2, VPS37C, NRP1, POLG2, ANKRD37, GMPS, and WDFY2. (Item 47) Microtubule-associated protein 4 (MAP4), protein kinase C zeta (PRKCZ), glutamate ionotropic receptor AMPA type subunit 4 (GRIA4), suppressor of tumorigenicity 7 (ST7), protein kinase C eta (PRKCH), voltage-dependent calcium channel subunit alpha 1D (CACNA1D), cell death-associated protein (DAP), transient receptor potential cation channel subfamily V member 3 (TRPV3), furin, paired basic amino acid cleaving enzyme (FURIN), acetyl-CoA carboxylase alpha (ACACA), G protein subunit alpha i1 (GNAI1), solute carrier family 1 member 2 (SLC1A2), phosphodiesterase 4C (PDE4C), ELOVL fatty acid elongase 2 (ELOVL2), nuclear receptor corepressor 2 (NCOR2), endothelial PAS domain protein 1 (EPAS1), G protein subunit gamma 2 ( GNG2), pantothenate kinase 1 (PANK1), ubiquitin-specific peptidase 4 (USP4), G protein subunit alpha q (GNAQ), voltage-dependent potassium channel modulator subfamily S member 1 (KCNS1), DNA polymerase gamma 2, accessory subunit (POLG2), NADH: ubiquinone oxidoreductase subunit A10 (NDUFA10), relaxin family peptide receptor 3 (RXFP3), isoprenoid synthase domain-containing (ISPD), inositol polyphosphate-5-phosphatase A (INPP5A), sirtuin 7 (SIRT7), guanine monophosphate synthase (GMPS), SIN3-HDAC complex-associated factor (SINHCAF), Tudor domain-containing 1 (TDRD1). The gene set according to item 45, comprising at least five, preferably at least ten, preferably at least twenty, preferably all of the following genes. (Item 48) The set of genomic DNA sequences is included in a reduced training dataset and / or an age index according to the method of any one of items 2 to 39, 48. The set of genomic DNA sequences according to any one of items 40 to 44 or the set of genes according to any one of items 45 to 47, obtained by the method according to items 2 to 39, wherein the set of genes is obtained by selecting from the set of genomic DNA sequences that code for a protein, or a microRNA or a long non-coding RNA. (Item 49) 49. A collection of genomic DNA sequences according to items 40 to 44 or 48, or a set of genes according to any of items 45 to 48, for use in diagnosing a health condition of an individual. (Item 50) the health condition comprises at least one aging-associated disease, at least one phenotype associated with at least one aging-associated disease, and / or a cancer condition; The condition indicates the absence, presence, or stage of the disease or a phenotype associated with the disease; A collection of genomic DNA sequences or a set of genes for use according to item 49. (Item 51) 51. The collection of genomic DNA sequences or gene set for use according to item 50, wherein the aging-related disease is Alzheimer's disease, Parkinson's disease, atherosclerosis, cardiovascular disease, cancer, arthritis, cataracts, osteoporosis, type 2 diabetes, hypertension, age-related macular degeneration and / or benign prostatic hyperplasia. (Item 52) 49. Use of the collection of genomic DNA sequences according to any of items 40 to 44 or 48, or the set of genes according to any of items 45 to 48, for determining the fitness state of an individual. (Item 53) 53. The use according to item 52, wherein the fitness status comprises the blood pressure, body weight, level of immune cells, level of inflammation and / or cognitive function of the individual. (Item 54) 50. A method for diagnosing the health and / or fitness status of an individual, comprising the step of providing a collection of genomic DNA sequences according to any of items 40 to 44 or 48, or a set of genes according to any of items 45 to 48. (Item 55) 55. The method of claim 54, further comprising determining a methylation level of the genomic DNA sequence in a biological sample of the individual comprising the genomic DNA sequence. (Item 56) the health condition comprises at least one aging-associated disease, at least one phenotype associated with at least one aging-associated disease, and / or a cancer condition; Preferably, said age-related disease is Alzheimer's disease, Parkinson's disease, atherosclerosis, cardiovascular disease, cancer, arthritis, cataracts, osteoporosis, type 2 diabetes, hypertension, age-related macular degeneration and / or benign prostatic hyperplasia, and / or The fitness status comprises the individual's blood pressure, weight, immune cell levels, inflammation levels and / or cognitive function. 56. The method according to item 54 or 55. (Item 57) 57. The method according to item 55 or 56, wherein the biological sample is obtained non-invasively, preferably by buccal swab. (Item 58) An in silico and / or in vitro screening method for identifying molecules that affect aging, comprising the steps of providing a set of genomic DNA sequences according to any one of items 40 to 44 or 48, or a set of genes according to any one of items 45 to 48, The method, wherein the molecule, when administered to an individual, ameliorate, prevent and / or reverse at least one aging-related disease, at least one phenotype associated with at least one aging-related disease, and / or cancer. (Item 59) 59. The method of claim 58, further comprising determining a DNA methylation level of at least one of the genomic DNA sequences. (Item 60) 60. The method of claim 58 or 59, wherein the identified molecule, when administered to an individual, increases the DNA methylation level of at least one of the genomic DNA sequences in the individual and / or decreases the DNA methylation level of at least one of the genomic DNA sequences in the individual. (Item 61) 61. The method of claim 60, wherein the DNA methylation level is altered to be associated with a younger chronological age than before the alteration. (Item 62) 62. The method according to any of items 58 to 61, further comprising providing a gene set according to items 45 to 48 and determining the activity of at least one protein encoded by the gene set. (Item 63) 63. The method of claim 62, wherein the identified molecule inhibits the activity of at least one protein encoded by the set of genes and / or enhances the activity of at least one protein encoded by the set of genes. (Item 64) 64. The method of claim 63, wherein the protein activity is altered to be associated with a younger chronological age than before the alteration. (Item 65) A chip comprising as spots the collection of genomic DNA sequences according to any one of items 40 to 44 or 48, or the gene set according to any one of items 45 to 48, wherein each sequence is contained in a separate spot. (Item 66) A kit comprising at least one unique primer pair, One primer of each primer pair is a forward primer that binds to a reverse strand of one of the genomic DNA sequences included in the set of genomic DNA sequences according to any one of items 40 to 44 or 48 or one of the genes included in the gene set according to any one of items 45 to 48, and the other primer is a reverse primer that binds to the forward strand; The two nucleotides complementary to the 3' ends of the forward and reverse primers are separated by more than 30 nucleotides and less than 3000 nucleotides, preferably less than 1000 nucleotides; kit. (Item 67) A kit comprising at least one probe complementary to one of the genomic DNA sequences included in the collection of genomic DNA sequences according to any one of items 40 to 44 or 48, or one of the genes included in the gene set according to any one of items 45 to 48. (Item 68) 67. The kit according to item 65 or 66, wherein the primer or probe specifically binds to either methylated or unmethylated DNA, and unmethylated cytosines are converted to uracil. (Item 69) A kit comprising the chip according to item 65. (Item 70) 58. The kit according to any of items 51 to 57, further comprising a container for a biological material and / or material for a buccal swab. (Item 71) 71. The kit according to any of items 66 to 70, further comprising materials for extracting, purifying and / or amplifying genomic DNA from a biological sample, said materials being a spin column and / or an enzyme. (Item 72) 72. The kit according to any of items 66 to 71, further comprising bisulfite. (Item 73) A data carrier comprising an age indicator obtained by the method according to any one of items 2 to 39, the collection of genomic DNA sequences according to any one of items 40 to 44 or 48, and / or the gene set according to any one of items 45 to 48. (Item 74) 73. The kit according to any of items 66 to 72 or the data carrier according to item 73, further comprising a questionnaire for the individual whose age is to be determined, said questionnaire being blank or comprising information regarding said individual. (Item 75) 40. The method of any of the preceding claims, wherein the training dataset, reduced training dataset and / or added data further comprises at least one factor related to lifestyle or risk patterns that can be associated with the individual(s). (Item 76) 76. The method of claim 75, wherein the factors are selected from drug consumption, environmental pollutants, shift work and stress. (Item 77) 77. The method of item 75 or 76, wherein the training dataset and / or the reduced training dataset are restricted to sequences whose DNA methylation levels and / or encoded protein activity / levels are associated with at least one of the lifestyle factors. (Item 78) 78. The method according to any of items 75 to 77, further comprising determining at least one lifestyle factor associated with the difference between the determined age and the chronological age of the individual. (Item 79) 1. A method of determining an age index for an individual in a set of individuals, said determination being based on the level of methylation of genomic DNA sequences found in said individuals, based on the methylation levels of a set of genomic DNA sequences selected from a set of genomic DNA sequences having a level of methylation that can be correlated with the age of the individual; A method in which an age index for said individual is provided in a manner that relies on a statistical evaluation of the level of methylation of genomic DNA sequences for a plurality of individuals, said age index for said individual is derived in such a manner that it relies on a statistical evaluation of the levels of methylation of genomic DNA sequences for a plurality of individuals different from the plurality of individuals referenced for the preceding statistical evaluation used to determine the same age index for the preceding individual in the series of individuals, A difference is made between the plurality of individuals used for the first statistical evaluation in that the plurality of individuals is modified by including at least one additional preceding individual from the set of individuals; Preferably, said age index for said individual is derived in such a manner that at least two different statistical evaluations for two different individuals result in a change in at least one coefficient used in calculating said age index from the methylation levels of the population and / or take into account the levels of methylation of different genomic DNA sequences or CgP loci found. A method comprising: (Item 80) providing a set of genomic DNA sequences from the genomic DNA sequences having a level of methylation that can be correlated with the age of the individual; determining the level of methylation of said set of genomic DNA sequences for a plurality of individuals; selecting a set of genomic DNA sequences from said set, the number of genomic DNA sequences in the collection is less than or equal to the number of genomic DNA sequences in the set; and selecting a sequence of said set such that the age of said individual can be calculated based on the methylation level of said sequence; determining the level of methylation of at least said set of sequences in a sample of biological material from said individual; calculating the age of the individual based on the methylation level of sequences of the set; In particular, taking into account the statistical evaluation, determining whether the genomic DNA sequences of said set need to be reselected and / or whether the way in which the age of said individual is calculated based on the level of methylation should be changed; modifying a group of individuals to include the individual in response to said determining; reselecting a set of genomic DNA sequences from said set based on the determination of the methylation levels of the individuals of the revised group. and / or Varying at least one coefficient used in calculating said age index from the methylation levels of the population. At least one of 80. The method of determining the age of an individual according to Item 79, based on the level of methylation of genomic DNA sequences found in the individual, comprising: (Item 81) preselecting a set of genomic DNA sequences from the genomic DNA sequences having a level of methylation that can be associated with the age of the individual; determining the level of methylation of said preselected genomic DNA sequences for a plurality of individuals; selecting a set of genomic DNA sequences from the preselected set, the number of genomic DNA sequences in the collection is less than the number of genomic DNA sequences in the preselected set; the age of the individual can be calculated based on the methylation levels of the sequences of the set; and selecting a statistical evaluation of the calculated age such that it indicates an acceptable quality of said calculated age; determining the level of methylation of sequences of said collection in a sample of biological material from said individual; calculating the age of the individual based on the methylation level of sequences of the set; calculating a statistical measure of the quality of the calculated ages; determining whether the quality according to said statistical criteria is acceptable; if the quality is determined to be acceptable, outputting the calculated age of the individual; determining that the genomic DNA sequence needs to be reselected if the quality is determined to be unacceptable; amending the set of individuals to include said individual; reselecting a set of genomic DNA sequences from said preselected subset based on the determination of the levels of methylation of the individuals of the revised group. 81. The method for determining the age of an individual according to Item 80, comprising: (Item 82) The method for determining age according to any one of the preceding items, wherein the selection of the genomic DNA sequence is based on a statistical analysis of values ​​relating to the methylation level of the genomic DNA sequence of the individual, in particular a statistical analysis using at least one regression method to identify relevant CpG loci, in particular at least one of the following methods: principal component analysis, LASSO / elastic net regression and / or XPG Boost to identify relevant CpGs. (Item 83) The preselected set comprises: At least 90 genomic DNA sequences, Preferably at least 100 genomic DNA sequences, Particularly preferably, at least 140 genomic DNA sequences Including, and / or The preselected set comprises: <2000 genomic DNA sequences, In particular, genomic DNA sequences less than 500 In particular, genomic DNA sequences less than 350 In particular, less than 170 genomic DNA sequences, Especially genomic DNA sequences less than 150 Including, and / or The selected set is At least 30 genomic DNA sequences, Preferably at least 50 genomic DNA sequences, Particularly preferably, at least 60 genomic DNA sequences Including, and / or The selected set is Fewer than 150 genomic DNA sequences, In particular, genomic DNA sequences less than 110 In particular, less than 100 genomic DNA sequences, In particular, genomic DNA sequences less than 90 In particular, less than 80 genomic DNA sequences, In particular, genomic DNA sequences less than 70 Including, A method for determining age according to any one of the preceding items. (Item 84) 1. A method for assessing the difference between chronological age and biological age, comprising: determining an age based on the methylation level according to one of the method items above; Comparing the determined biological age with a known chronological age, especially, determining a difference for a plurality of individuals, determining factors that may or may not affect the difference for a plurality of individuals, and determining factors that have a large effect on the difference between chronological age and the biological age for a large number of individuals; The method includes: (Item 85) determining the methylation levels of more than one fixed set of CpG loci; providing data enabling the determination of said age indicator for more than one population, such that an age can be calculated based on the respective data; the selection of one set for calculating the age is based on either a particular methylation level of the individual measured or on factors related to lifestyle or risk patterns that can be associated with the individual. The method according to any one of the preceding claims. (Item 86) determining the level of methylation of genomic DNA sequences found in said individual; Methylation sequencing / bisulfite sequencing, PCR methods, in particular at least one of methylation specific PCR (MSP), real-time methylation specific PCR, quantitative methylation specific PCR (QMSP), COLD-PCR, PCR using methylated DNA specific binding proteins, targeted multiplex PCR, real-time PCR and microarray-based PCR; High resolution melting analysis (HRM), Methylation-sensitive single-nucleotide primer extension (MS-SnuPE), Methylation-sensitive single-strand conformation analysis, Methyl-sensitive cleavage count (MSCC), Base-specific cleavage / MALDI-TOF, e.g. Agena; Combined Bisulfite Restriction Analysis (COBRA), Methylated DNA immunoprecipitation (MeDIP), Microarray-based methods; Bead array based methods; Pyrosequencing, direct sequencing without bisulfite treatment (nanopore technology) The method for determining age according to any one of the preceding claims, wherein the age is determined by at least one of the following: (Item 87) A method for determining age according to any one of the preceding items, wherein the group of individuals is modified by adding the individual to the group. (Item 88) The method for determining age described in any one of the preceding items, wherein the step of amending the group of individuals to include the individual comprises excluding at least one other individual from the group, taking into particular consideration factors unrelated to the individual's age and / or the methylation level of some or all of the individual's genomic DNA sequence. (Item 89) The following conditions: Some or all of the methylation levels detected in the genomic DNA sequence are considered to be too low; the predicted age of a single individual deviates too far from the known chronological age of said individual; the predicted ages of some individuals show systematic deviations from the known chronological ages of some individuals; The predicted ages of some individuals are scattered across the known chronological ages of the individuals, and the variance is deemed to be too large; the predicted ages of some individuals show systematic deviations from the known chronological ages of said individuals; The number of individuals whose ages have been determined based on a given set has reached a predetermined number; A specified time has passed since the previous reselection The method for determining age according to any one of the preceding items, further comprising the step of deciding to modify said set based on methylation levels obtained for additional individuals if at least one or preferably several of the following conditions are met: (Item 90) A kit comprising at least a container for individual biological material obtained and / or prepared in a manner that allows the age to be determined according to one of the items of the method, also comprising an information medium carrying information regarding the patient's identity, - preparing data for carrying out or how the method according to one of the methods has been carried out and / or for producing a data carrier containing age-related data determined by the method according to the method and / or a kit further comprising instructions for preparing a data carrier comprising age-related data determined by the method described in the method section above. (Item 91) A method for screening several molecules for influencing aging, comprising the steps of determining the number of several genomic DNA sequences that correlate well with biological age, in particular by reference to the genomic DNA sequences selected for collection in the method according to item 79, and determining whether a molecule among said several molecules has a positive effect on the methylation level of said genomic DNA sequences, in particular by in silico determination. (Item 92) A chip comprising a number of spots adapted for use in determining methylation levels, in particular less than 500, preferably less than 385, in particular less than 193, in particular less than 160 spots, said spots being selected from the group consisting of: cg11330075, cg25845463, cg22519947, cg21807065, cg09001642, cg18815943, cg06335143, cg01636910, cg10501210, cg03324695, cg19432688, cg22540792, cg11176990, cg00097800, c g09805798, cg03526652, cg09460489, cg18737844, cg07802350, cg105227 65, cg12548216, cg00876345, cg15761531, cg05990274, cg05972734, cg03 680898, cg16593468, cg19301963, cg12732998, cg02536625, cg24088134, cg24319133, cg03388189, cg05106770, cg08686931, cg25606723, cg077826 20, cg16781885, cg14231565, cg18339380, cg25642673, cg10240079, cg19 851481, cg17665505, cg13333913, cg07291317, cg12238343, cg08478427, cg07625177, cg03230469, cg13154327, cg16456442, cg26430984, cg16867 657, cg24724428, cg08194377, cg10543136, cg12650870, cg00087368, cg17 760405, cg21628619, cg01820962, cg16999154, cg22444338, cg00831672, cg08044253, cg08960065, cg07529089, cg11607603, cg08097417, cg07955 995, cg03473532, cg06186727, cg04733826, cg20425444, cg07513002, cg1 4305139, cg13759931, cg14756158, cg08662753, cg13206721, cg04287203,cg18768299, cg05812299, cg04028695, cg07120630, cg17343879, cg07766948, cg08856941, cg16950671, cg01520297, cg27540719, cg24954665, cg05211227, cg06831571, cg19112204, cg12804730, cg08224787, cg13973351, cg21165089, c g05087008, cg05396610, cg23677767, cg21962791, cg04320377, cg16245716, cg21460868, cg09275691, cg19215678, c g08118942, cg16322747, cg12333719, cg23128025, cg27173374, cg02032962, cg18506897, cg05292016, cg16673857, cg 04875128, cg22101188, cg07381960, cg06279276, cg22077936, cg08457029, cg20576243, cg09965557, cg03741619, cg 04525002, cg15008041, cg16465695, cg16677512, cg12658720, cg27394136, cg14681176, cg07494888, cg14911690, cg0 6161948, cg15609017, cg10321869, cg15743533, cg19702785, cg16267121, cg13460409, cg19810954, cg06945504, cg06153788, and cg20088545. (Item 93) Item 93. The chip according to item 92, wherein the spots comprise at least 10 spots for the CpG loci listed in the above item, preferably 20 spots for the CpG loci listed in the above item, particularly at least 50 spots for the CpG loci listed in the above item, particularly spots for all of the CpG loci listed in the above item. < / bs>

Claims

1. 1. A method for determining an age indicator, comprising: (a) for each individual, (i) the DNA methylation level of a set of genomic DNA sequences, and (ii) Chronological age Preparing a training data set of a plurality of individuals, the training data set comprising: (b) applying a regression method to the training data set, the regression method comprising: (i) determining a reduced training dataset by a Least Absolute Shrinkage and Selection Operator (LASSO); (ii) determining the age index by stepwise regression after the LASSO; Including, the independent variable is the methylation level of said genomic DNA sequence; The age indicator is (i) a subset of the set of genomic DNA sequences as a collection; and (ii) at least one coefficient for each genomic DNA sequence contained in the collection; Including, the reduced training dataset includes all data in the training dataset except for DNA methylation levels of the genomic DNA sequences that are excluded by the LASSO; The method includes:

2. 1. A method for determining the age of an individual, comprising: (a) for each individual, (i) the DNA methylation level of a set of genomic DNA sequences, and (ii) Chronological age Preparing a training data set of a plurality of individuals, the training data set comprising: (b) applying a regression method to the training data set, the regression method comprising: (i) determining a reduced training dataset by a Least Absolute Shrinkage and Selection Operator (LASSO); (ii) determining an age index by stepwise regression after the LASSO; Including, the independent variable is the methylation level of said genomic DNA sequence; The age indicator is (i) a subset of the set of genomic DNA sequences as a collection; and (ii) at least one coefficient for each genomic DNA sequence contained in the collection; Including, the reduced training dataset includes all data in the training dataset except DNA methylation levels of genomic DNA sequences excluded by the LASSO; (c) providing a DNA methylation level of at least 80% of the genomic DNA sequences included in said age indicator for the individual whose age is to be determined; (d) determining the age of the individual based on its DNA methylation level and the age indicator; A method comprising:

3. 3. The method of claim 1 or 2, wherein the stepwise regression is two-way elimination, removing independent variables that are not statistically significant.

4. the regression method does not include ridge regression (L2 regularization); and / or The method of claim 1 , wherein the L1 regularization parameter of the LASSO is 1.

5. 5. The method of claim 1 , wherein the age index is iteratively updated, including adding data of at least one further individual to the training data in each iteration, thereby iteratively expanding the training data set, to generate an expanded training data set for each iteration.

6. one update round applying the LASSO to the enlarged training data set to thereby determine an updated reduced training data set; 6. The method of claim 5, comprising applying the stepwise regression to the updated reduced training data set, thereby determining an updated age index.

7. determining a quality of the age indicator, the determining of the quality comprising: (a) preparing a testing dataset for a plurality of individuals who did not contribute data to the training dataset, the testing dataset comprising, for each of the individuals: (i) the DNA methylation level of a set of genomic DNA sequences included in the age indicator; and (ii) said chronological age; and (b) determining the quality of said age indicator by statistical evaluation and / or evaluation of domain boundaries, The statistical evaluation comprises: (i) determining the ages of individuals included in the test dataset; (ii) correlating the determined age and chronological age of the individual(s) and determining statistical parameters describing this correlation; (iii) determining whether the statistical parameter is indicative of an acceptable quality of the age index, the statistical parameter being determined by a coefficient of determination (R ２ ) and R ２ A value greater than 0.90 indicates acceptable quality. Including, said evaluating a domain boundary comprising: (iv) determining a domain boundary of the age indicator, the domain boundaries being the minimum and maximum DNA methylation levels of each genomic DNA sequence included in the age index; The minimum and maximum DNA methylation levels are found in the training data set used to determine the age indicator. Steps and (v) determining whether the test data set exceeds the domain boundary, where failure to exceed the domain boundary indicates acceptable quality; Steps Includes The method according to claim 5 or 6, wherein the age indicator is updated if its quality is unacceptable.

8. The method according to any one of claims 1 to 7, wherein the age indicator comprises at least 80 genomic DNA sequences and / or comprises less than 300 genomic DNA sequences.

9. 9. The method of claim 1, wherein the DNA methylation level of a genomic DNA sequence of an individual is measured in a sample of biological material of the individual comprising the genomic DNA sequence.

10. The method of claim 9 , wherein the sample comprises buccal cells.

11. 11. The method of any of claims 2 to 10, wherein the training dataset further comprises at least one lifestyle factor that can be associated with the individual(s), and the method further comprises determining at least one lifestyle factor that is associated with a difference between the determined age and chronological age of the individual.

12. A method described in any one of claims 2 to 10, wherein the determined age is an indication of the individual's biological age related to the individual's health and / or fitness status.

13. The health condition is (i) at least one aging-related disease selected from Alzheimer's disease, Parkinson's disease, atherosclerosis, cardiovascular disease, arthritis, cataracts, osteoporosis, type 2 diabetes, hypertension, age-related macular degeneration, and benign prostatic hyperplasia; (ii) at least one phenotype associated with said aging-related disease; and / or (iii) Cancer It relates to the state of The fitness status comprises the individual's blood pressure, weight, immune cell levels, inflammation levels and / or cognitive function. The method of claim 12.

14. 14. A method according to any preceding claim, which is a computer-implemented method.

Images