Wrinkle determination of a human individual

Abstract

The present invention relates to a method for determining the predicted wrinkle grade of human skin comprising providing human skin cells, determining a methylation level of at least one CpG-dinucleotides, or an expression level of at least one gene and determining the predicted wrinkle grade of said skin cells by comparing said determined methylation level with empirically determined data representing a correlation between the methylation level of the CpG-nucleotide and the visual wrinkle grade of at least one human individual, or determining the predicted wrinkle grade of said skin cells by comparing said determined gene expression level with empirically determined data representing a correlation between the gene expression level and the visual wrinkle grade of at least one human individual.

Description

WRINKLE DETERMINATION OF A HUMAN INDIVIDUALFIELD OF THE INVENTION

[0001] The present invention relates to the determination of the predicted wrinkle grade of human skin by analysing the epigenetic pattern of particular sites of the DNA or by analysing the expression level of particular genes. The methylation levels at CpG sites or gene expression levels of a skin sample are evaluated to determine the predicted wrinkle grade of human skin accurately. The predicted wrinkle grade is then correlated to the visual wrinkle grade with a mean absolute error of less than 20 wrinkle grade units.BACKGROUND OF THE INVENTION

[0002] Aging is a time-dependent general loss of functionality and fitness across the lifespan of an individual. The accumulation of molecular damage is considered one of the key hallmarks of aging. For example, epigenetic changes of DNA are known to occur over time and have been recognised as indicators of the extent of the aging process in human individuals. In particular, the extent of methylation of CpG dinucleotides has been identified as being useful in methods of determining biological age and predicting chronological age. Such known methods are based on the analysis of various CpG-dinucleotides in both skin cells and blood cells. A good correlation between the chronological age and the biological age of an individual was achieved with methods that evaluate the extent of methylation of CpG dinucleotides. In general terms, biological age relates to the overall health status of an individual. Methods of predicting biological age can be useful in addressing a host of questions in development biology, cancer and aging research as discussed in, for example, Horvath, Genome Biology 2013, 14:R115.

[0003] Although biological age provides an indication of the overall health and extent of aging of an individual, a direct correlation between the biological age and a particular phenotype, such as a particular skin aging phenotype, has not yet been established. Measuring and monitoring a particular characteristic related to the phenotype of the skin on a molecular level could be useful in many applications, such as identifying active agents to minimise the visual signs of aging.

[0004] It is therefore desirable to provide a method to determine the phenotype characteristic of skin of an individual.SUMMARY OF THE INVENTION

[0005] The present invention is defined in the appended claims.

[0006] In accordance with a first aspect, there is provided a method for determining the predicted wrinkle grade of human skin comprising: a) providing human skin cells; b) determining a methylation level of at least one CpG-dinucleotide of a specific region of at least one chromosome of said skin cells or determining an expression level of at least one gene of said skin cells; and c) determining the predicted wrinkle grade of said skin cells by comparing said determined methylation level with empirically determined data representing a correlation between the methylation level of said CpG-nucleotide and the visual wrinkle grade of at least one human individual or determining the predicted wrinkle grade of said skin cells by comparing said determined gene expression level with empirically determined data representing a correlation between the gene expression level and the visual wrinkle grade of at least one human individual.

[0007] In accordance with a second aspect, there is provided a method of testing an active agent comprising the method according to the first aspect, further comprising the steps of: d) contacting the skin cells of step a) with an active agent; e) determining the predicted wrinkle grade of the skin cells of step d) according to the method of the first aspect; and f) comparing the predicted wrinkle grade determined in steps a) to c) with the predicted wrinkle grade determined in step e).

[0008] In accordance with a third aspect, there is provided a composition comprising one or more active agents identified by the method of the second aspect.

[0009] In accordance with a fourth aspect, there is provided the use of DNA methylation level or gene expression level for determining the predicted wrinkle grade of human skin according to the method of the first aspect.

[0010] In accordance with a fifth aspect, there is provided a computer-readable medium having stored computer-executable instructions for causing a computer toperform a method for determining the predicted wrinkle grade of human skin comprising: a) inputting at least one value of a determined methylation level of at least one CpG-dinucleotide of a specific region of at least one chromosome of said skin cells or inputting at least one value of a determined expression level of at least one gene of said skin cells; b) comparing said value of the determined methylation level with stored data representing a correlation between the methylation level of said CpG- dinucleotide and the visual wrinkle grade of at least one human individual or comparing said value of gene expression level with stored data representing a correlation between the gene expression level and the visual wrinkle grade of at least one human individual; and c) displaying the predicted wrinkle grade.

[0011] In accordance with a sixth aspect, there is provided a kit for determining the predicted wrinkle grade of human skin according to the method of the first aspect, comprising at least one oligonucleotide primer for amplifying and / or sequencing at least one CpG-nucleotides of at least one nucleotide sequence or at least one oligonucleotide primer for amplifying and / or sequencing fractions of at least one exon of at least one nucleotide sequence.

[0012] Certain embodiments of the present invention may provide one or more of the following advantages:• desired ease of sample collection from the individual;• desired ease of obtaining bioinformatic data;• desired cost-effective data collection;• desired accuracy of wrinkle grade prediction;• desired ease of identifying active agents to control wrinkle grade; and• desired ease of predicting the effect of active agents on the wrinkle grade over time.

[0013] The details, examples and preferences provided in relation to any particular one or more of the stated aspects of the present invention apply equally to all aspects of the present invention. Any combination of the embodiments, examples and preferences described herein in all possible variations thereof is encompassed by the present invention unless otherwise indicated herein, or otherwise clearly contradicted by context.BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The invention will further be illustrated by reference to the following figures:Fig. 1 : graph represents a correlation of predicted wrinkle grade with visual wrinkle grade based on the test set using the DNA methylation method; the regression line (or fit line) on the graph provides the best estimate of the linear relationship between the predicted wrinkle grade and visual wrinkle grade; the Pearson correlation (R), mean absolute error (MAE), and significance of correlation (p) shown to provide additional statistical context;Fig. 2: graph represents a correlation of predicted wrinkle grade with visual wrinkle grade based on the independent data set; using the DNA methylation method; the regression line (or fit line) on the graph provides the best estimate of the linear relationship between the predicted wrinkle grade and visual wrinkle grade; the Pearson correlation (R), mean absolute error (MAE), and significance of correlation (p) shown to provide additional statistical context;Fig. 3: graph represents a correlation of predicted wrinkle grade with visual wrinkle grade based on the test set using the DNA methylation method based on low methylated regions (LMRs); the regression line (or fit line) on the graph provides the best estimate of the linear relationship between the predicted wrinkle grade and visual wrinkle grade; the Pearson correlation (R), mean absolute error (MAE), and significance of correlation (p) shown to provide additional statistical context;Fig. 4: graph represents a correlation of predicted wrinkle grade with visual wrinkle grade based on the independent data set; using the DNA methylation method based on low methylated regions (LMRs); the regression line (or fit line) on the graph provides the best estimate of the linear relationship between the predicted wrinkle grade and visual wrinkle grade; the Pearson correlation (R), mean absolute error (MAE), and significance of correlation (p) shown to provide additional statistical context.Fig. 5: graph represents a correlation of predicted wrinkle grade with visual wrinkle grade based on the test set using the gene expression method;the regression line (or fit line) on the graph provides the best estimate of the linear relationship between the predicted wrinkle grade and visual wrinkle grade; the Pearson correlation (R), mean absolute error (MAE), and significance of correlation (p) shown to provide additional statistical context;Fig. 6: graph represents a correlation of predicted wrinkle grade with visual wrinkle grade based on the independent data set; using the gene expression method; the regression line (or fit line) on the graph provides the best estimate of the linear relationship between the predicted wrinkle grade and visual wrinkle grade; the Pearson correlation (R), mean absolute error (MAE), and significance of correlation (p) shown to provide additional statistical context.

[0015] It is understood that the following description and references to the figures concern exemplary embodiments of the present invention and shall not be limiting the scope of the claims.DETAILED DESCRIPTION

[0016] The present invention is based on the finding that the methylation level of selected CpG nucleotides or gene expression level can be used to predict the wrinkle grade of human skin with good accuracy.

[0017] Wrinkles are lines, creases or folds that form in the skin, such as the cutaneous layer, and are a natural phenomenon associated with the aging process. Wrinkles are most prominent on skin that is exposed to the sun. Such areas include, but are not limited to, regions of the face, neck (cervical region), hands and forearms (antebrachium), where the skin may exhibit heightened susceptibility to photodamage, thereby resulting in the aforementioned wrinkling. The extent of appearance of wrinkles is also referred to as the wrinkle grade. The evaluation of the wrinkle grade is typically carried out by visual analysis, which is referred to herein as “visual wrinkle grade”. There are various methods that can be implemented to determine the visual wrinkle grade. In the present disclosure the visual wrinkle grade is determined using a method which involves the visual analysis of the face of an individual by trained individuals, also known herein as “judges”. In this method, judges are asked to evaluate the visual appearance of wrinkles in an individual and award a wrinkle grade unit to the individual.The wrinkle grade unit awarded to an individual is based on the overall appearance of the skin on the face, specifically the extent of crow’s feet. The wrinkle grade unit associated with an individual can be between 0 and 100 is in accordance to predefined and / or known grading scales, as seen, for example in “Atlas du Vieillissement Cutane - Population Europeenne" (“Skin Aging Atlas - European Population”); Pr. Roland BAZIN et Al.; 2007; ISBN: 978-2-914738-94-1. In some examples, in order to assess the wrinkle grade, the judges are provided with ten photos of individuals with varying degrees of wrinkles, ranging from no wrinkles (wrinkle grade unit = 0) to a high degree of wrinkles (wrinkle grade unit = 100). Despite the guidance provided to the judges by means of the method use, the assessment of the wrinkle grade unit using this method has a subjective element. Therefore, multiple judges are asked to provide their assessment of a single individual and the visual wrinkle grade is an average of the assessments provided by the judges. In this method, the assessment of at least 10 judges are applied, for example, at least 12 judges, at least 15 judges, at least 18 judges, at least 20 judges, at least 25 judges, at least 30 judges.

[0018] As used herein, the term “predicted wrinkle grade” refers to the wrinkle grade of an individual and is based on the methylation level (i.e. epigenetic level) of DNA and / or gene expression level. The methylation level (i.e. epigenetic level) of DNA and / or gene expression level is determined in skin samples. The predicted wrinkle grade as disclosed herein is determined using the method of the first aspect of this invention. In particular, the methylation level of specific CpG-dinucleotides or expression level of specific genes are assessed as part of this invention.

[0019] The DNA methylation level can be determined using a DNA methylation level detection method. Examples of DNA methylation level detection methods include, but are not limited to: (a) base-resolution and sequencing approaches like Whole Genome Bisulfite Sequencing (WGBS aka MethylC-Seq), Enzymatic Methyl-Seq (EM-Seq), Anchor-Based Bisulfite Sequencing (ABBS), Post-Bisulfite Adaptor Tagging (PBAT), Reduced Representation Bisulfite Sequencing (RRBS) or Shotgun Bisulfite Sequencing (BS-Seq), (b) base-resolution and microarray approaches either based on oligonucleotides (e.g., Reinders et al. “Genome-wide, high-resolution DNA methylation profiling using bisulfite-mediated cytosine conversion”; Genome Res. 2008 Mar;18(3):469-76) or bead-chips (e.g., Illumina BeadChip), (c) non-base resolution and sequencing approaches like Methylated DNA Immunoprecipitation-Sequencing (MeDIP-Seq), Methylated-CpG Island Recovery Assay (MIRA), Methylation-SensitiveRestriction Enzyme Sequencing (MRE-Seq) or MethylCap-Seq (aka MBDCap-Seq), (d) non-base resolution and microarray approaches like MeDIP-Chip or Comprehensive High-throughput Arrays for Relative Methylation (CHARM) and (e) targeted approaches like targeted bisulfite sequencing, targeted enzymatic methyl-seq, ligation-mediated PCR, Methylation-Specific PCR (MS-PCR), Methylation-Sensitive High Resolution Melting (MS-HRM) or Combined Bisulfite Restriction Analysis (COBRA).

[0020] The DNA methylation level can be represented either as beta- or m-values. Beta-values are defined as number of detected methylated (M) molecules (i.e. nucleotides) divided by the sum of methylated (M) and unmethylated (II) molecules. In other words, M equals number of detected methylated molecules or signal intensities of methylated probes; II equals number of detected unmethylated molecules or signal intensities of unmethylated probes: (see equation 1).(Equation 1)M-values are defined as the Iog2 ratio of the number of methylated versus unmethylated molecules (see equation 2). Complementary, number of detected methylated and unmethylated molecules can also represented by the signal intensities of methylated and unmethylated probes, respectively.(Equation 2)

[0021] In addition to the DNA methylation levels determined in the detection methods disclosed above, in some examples, specific regions known as low methylated regions (LMRs) are used to carry out the methods according to the present invention. LMRs are characterised by relatively low levels of DNA methylation in the range of 10-50% and frequently coincide with gene regulatory regions such as enhancers and promoters. LMRs can be determined in a number of known ways, including the application of, for example, the MethylSeekR.

[0022] In some embodiments, the method according to the first aspect may involve determining the methylation level of at least two CpG-dinucleotides, or determining the methylation level of at least three CpG-dinucleotides, or determining the methylation level of at least four CpG-dinucleotides, or determining the methylation level of at leastfive CpG-dinucleotides, or determining the methylation level of at least seven CpG- dinucleotides, or determining the methylation level of at least ten CpG-dinucleotides, or determining the methylation level of at least twelve CpG-dinucleotides, or determining the methylation level of at least fourteen CpG-dinucleotides, or determining the methylation level of at least sixteen CpG-dinucleotides, or determining the methylation level of at least nineteen CpG-dinucleotides, or determining the methylation level of at least thirty CpG-dinucleotides, or determining the methylation level of at least fifty CpG- dinucleotides, or determining the methylation level of at least one hundred CpG- dinucleotides. In some embodiments, the method according to the first aspect may involve determining the methylation level of two CpG-dinucleotides, or determining the methylation level of three CpG-dinucleotides, or determining the methylation level of four CpG-dinucleotides, or determining the methylation level of five CpG-dinucleotides, or determining the methylation level of seven CpG-dinucleotides, or determining the methylation level of ten CpG-dinucleotides, or determining the methylation level of twelve CpG-dinucleotides, or determining the methylation level of fourteen CpG- dinucleotides, or determining the methylation level of sixteen CpG-dinucleotides, or determining the methylation level of nineteen CpG-dinucleotides, or determining the methylation level of thirty CpG-dinucleotides, or determining the methylation level of fifty CpG-dinucleotides, or determining the methylation level of one hundred CpG- dinucleotides. In some embodiments, the method according to the first aspect may involve determining the methylation level of between two CpG-dinucleotides and nineteen CpG-dinucleotides, or between five CpG-dinucleotides and fifty CpG- dinucleotides, between ten CpG-dinucleotides and one hundred CpG-dinucleotides.

[0023] The gene expression level can be determined using a gene expression level detection method. Examples of gene expression level detection methods include, but are not limited to: (a) sequencing-based high-throughput assays such as Ribonucleic Acid-Sequencing (RNA-Seq), single cell RNA-Sequencing (scRNA-Seq), spatial transcriptomics (e.g., Visium Spatial Gene Expression and FISSEQ), Serial Analysis of Gene Expression (SAGE), or variations of Serial Analysis of Gene Expression (SAGE) (e.g., LongSAGE, RL-SAGE and SuperSAGE), (b) hybridization-based high- to medium-throughput assays such as microarrays (e.g., in situ synthesized oligonucleotide-based, cDNA-based, bead-based and tiling-based microarray platforms), Luminex xMAP Technology, NanoString nCounter technology, Multiplexed Error Robust Fluorescence In Situ Hybridization (MERFISH), digital spatialtranscriptomics (e.g., GeoMx DSP) or RNAscope ISH, (c) polymerase chain reaction (PCR) based medium- to low-throughput assays such as digital PCR or quantitative and reverse transcription PCR (qRT-PCR and RT-PCR) e.g., TaqMan-assay and Low Density Arrays (LDA) and (d) hybridization-based medium- to low-throughput assays such as QuantiGene RNA Assays, Fluorescent in situ hybridization (FISH), Northern- Blot, Dot-Blot or Slot-Blot.

[0024] The gene expression level may be represented based on counts (e.g., raw counts, raw counts normalized to housekeeping genes and or internal standards as well as normalized counts like TPMs, RPKMs and FPKMs), fluorescence signal intensities (e.g., of hybridization-based assays) or a change (e.g., log2FC or FC).

[0025] In addition to the gene expression levels determined in the detection methods disclosed above, further training of models with reduced number of genes to e.g., 17,653, 11,769, 5,885, 1 ,177, 100, 10, 2 or 1 gene is also possible. Such gene selection may be performed by taking a model trained on all genes and performing an in-silico knock-out for each gene to calculate the overall effect size of the gene on the wrinkle grade prediction.

[0026] In some embodiments, the method according to the first aspect may involve determining the expression level of at least two genes, or determining the expression level of at least three genes, or determining the expression level of at least five genes, or determining the expression level of at least ten genes, or determining the expression level of at least fifty genes, or determining the expression level of at least one hundred genes, or determining the expression level of at least one thousand genes, or determining the expression level of at least two thousand genes, or determining the expression level of at least five thousand genes, or determining the expression level of at least ten thousand genes, or determining the expression level of at least fifteen thousand genes, or determining the expression level of at least twenty thousand genes, or determining the expression level of at least twenty five thousand genes, or determining the expression level of at least thirty thousand genes, or determining the expression level of at least thirty five thousand genes. In some embodiments, the method according to the first aspect may involve determining the expression level of two genes, or determining the expression level of three genes, or determining the expression level of five genes, or determining the expression level of ten genes, or determining the expression level of fifty genes, or determining the expression level of one hundred genes, or determining the expression level of one thousand genes, ordetermining the expression level of two thousand genes, or determining the expression level of five thousand genes, or determining the expression level of ten thousand genes, or determining the expression level of fifteen thousand genes, or determining the expression level of twenty thousand genes, or determining the expression level of twenty five thousand genes, or determining the expression level of thirty thousand genes, or determining the expression level of thirty five thousand genes, or determining the expression level of thirty eight thousand eight hundred and ninety two genes. In some embodiments, the method according to the first aspect may involve determining the expression level of between two genes and thirty eight thousand eight hundred and ninety two genes, or between five genes and thirty five thousand genes, or between ten genes and thirty thousand genes, or between fifty genes and twenty five thousand genes, or between one hundred genes and twenty thousand genes, or between five hundred genes and fifteen thousand genes, or between one thousand genes and ten thousand genes, or between two thousand genes and eight thousand genes.

[0027] In some embodiments, the genes may be selected from one or more of the following:

[0028] The empirically determined data representing a correlation between the methylation level of the said CpG-nucleotide and the visual wrinkle grade according to the method of the present invention is obtained as follows: reference data for a subject is obtained by 1) measuring the DNA methylation level of at least one CpG-dinucleotide of a specific region of at least one chromosome of one subject, and 2) determining the visual wrinkle grade of said subject. The methylation level of at least one CpG- dinucleotide is determined of skin cells collected from part of the human body of the said subject, such as the arm, the face, the neck, the decollete and / or the hand. Reference data of at least 100 subjects are collected to establish the empirically determined data. For example, reference data is collected for at least 110 subjects, or at least 130 subjects, or at least 150 subjects, or at least 200 subjects, or at least 250 subjects, or at least 300 subjects, or at least 350 subjects, or at least 400 subjects, or at least 450 subjects, or at least 500 subjects. The subjects: a) have an age of between 1 year to 120 years; b) have any gender, such as male, female and mixed gender; b) have any ethnicity, such as Caucasian, African, Asian or mixed ethnicities; and d) have any phototype, such as phototype 1, phototype 2, phototype 3, phototype 4, phototype 5, phototype 6 or mixed phototypes. The empirically determined data is obtained by correlating the measured DNA methylation level with the corresponding visual wrinklegrade from at least 100 subjects. The correlation can be represented as mathematical equation e.g., via linear regression and / or machine learning approaches like support vector machines, generalized linear models or artificial neural networks. This correlation represents the empirically determined data.

[0029] The empirically determined data representing a correlation between the gene expression level and the visual wrinkle grade according to the method of the present invention is obtained as follows: reference data for a subject is obtained by 1) measuring gene expression level of at least one gene of one subject, and 2) determining the visual wrinkle grade of said subject. The gene expression level is determined of skin cells collected from part of the human body of the said subject, such as the arm, the face, the neck, the decollete and / or the hand. Reference data of at least 100 subjects is collected to establish the empirically determined data. For example, reference data is collected for at least 110 subjects, or at least 130 subjects, or at least 150 subjects, or at least 200 subjects, or at least 250 subjects, or at least 300 subjects, or at least 350 subjects, or at least 400 subjects, or at least 450 subjects, or at least 500 subjects. The subjects: a) have an age of between 1 year to 120 years; b) have any gender, such as male, female and mixed gender; b) have any ethnicity, such as Caucasian, African, Asian or mixed ethnicities; and d) have any phototype, such as phototype 1 , phototype 2, phototype 3, phototype 4, phototype 5, phototype 6 or mixed phototypes. The empirically determined data is obtained by correlating the gene expression level with the corresponding visual wrinkle grade from at least 100 subjects. The correlation can be represented as mathematical equation e.g., via linear regression and / or machine learning approaches like support vector machines, generalized linear models or artificial neural networks. This correlation represents the empirically determined data.

[0030] The visual wrinkle grade and / or the predicted wrinkle grade may be influenced by many parameters such as genetic background, age, exposure to ultraviolet light, smoking, exposure to pollution and / or repeated facial expressions.

[0031] The visual wrinkle grade and / or the predicted wrinkle grade may be influenced by a number of factors such as, for example, protecting the skin from sun, moisturising, refraining from smoking and eating a healthy diet. The wrinkle grade may also be affected by the use of active agents.

[0032] In certain embodiments the skin cells used according to the method of the invention are harvested from a human individual. In certain embodiments, the humanskin cells used according to the method of the invention are obtained by harvesting the entire skin sample (i.e. all skin layers) required from the individual. In certain embodiments, the human skin cells used according to the method of the invention are obtained by harvesting a skin sample from the arm, face, neck, decollete and / or hand. In certain embodiments, the human skin cells used according to the method of the invention are obtained by culturing the skin cells using an in vitro method.

[0033] Harvesting a sample from the individual may be carried out using suction blistering, punch biopsy, shave biopsy, microbiopsy, tape stripping or during any surgical procedure such as plastic surgery, lifting, grafting, or the like.

[0034] The skin samples may be taken from the epidermis and / or dermis. The skin sample may be taken from the whole skin, i.e., from both the epidermis and dermis.

[0035] Human skin cells may be cultured from a small sample of skin cells harvested from an individual. The harvested human skin cells are grown in vitro in a vessel such as a petri dish in a medium or substrate that supplies essential nutrient.

[0036] The human skin cells used may be a mixture of harvested cells and cultured cells.

[0037] In certain embodiments, the specific region of the chromosome comprising CpG-dinucleotides according the invention may be coding regions or non-coding regions. In certain embodiments, the CpG dinucleotides may be present in a coding region and / or non-coding region. The CpG dinucleotides may be found in a single specific region or in different specific regions.

[0038] In certain embodiments, the method of the first aspect further comprises the step of estimating the predicted wrinkle grade of human individuals. The correlation between the predicted wrinkle grade and visual wrinkle grade is high using the method according to the invention, wherein the difference between the predicted wrinkle grade and the visual wrinkle grade can be expressed using the the mean absolute error (MAE). The mean absolute error (MAE) of the wrinkle grade unit determined by the method described herein, is no more than about 20, no more than about 18, no more than about 16, no more than about 14, no more than about 12, no more than about 11, no more than about 10, no more than about 9, no more than about 8.8, no more than about 8, no more than about 6, no more than about 4, no more than 2.

[0039] The difference in predicted wrinkle grade and visual wrinkle grade may be determined for a single individual. The deviation of a single data point from the best fit line superimposed onto the data points from all individuals can vary from about 0 toabout 20 wrinkle grade units. For example, the deviation of a single data point from the best fit line would be a wrinkle grade unit of about 0, about 1, about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, about 10, about 11, about 12, about 13, about 14, about 15, about 16, about 17, about 18, about 19, about 20.

[0040] In certain embodiments, the predicted wrinkle grade of an unknown individual is determined as described above. From this predicted wrinkle grade, the visual wrinkle grade of the individual could be estimated by applying the expected MAE for the CpG data points used or by applying the expected MAE for the gene expression levels used.

[0041] The visual wrinkle grade and / or the predicted wrinkle grade may also be reduced by applying active agents onto the skin cells in the form of pharmaceutical and / or cosmetic agents. By means of the method of the present invention, the mid-term and / or long-term reduction of the wrinkle grade upon application of an active agent may also be predicted in a reliable manner. This prediction may be made at an early stage and without the need of monitoring the phenotype progression of the skin over a long period of time, which can be costly and time consuming. Without wishing to be bound by theory, it is considered that the predictive method of wrinkle grade progression with time using the method of the invention is possible due to the connection that the present inventors have discovered between the specific biological changes determined in the method and the associated wrinkle grade. Such a method is not known or contemplated in the art. The active agent may contact the skin cells in vitro or in vivo. Using the in vitro method, the active agent is added to the culture medium of skin cells. Using the in vivo method, the skin cells of a human individual may be contacted with the active agent using topical, subcutaneous or intradermal administration.

[0042] An “active agent” as used herein is any agent that has a therapeutic effect and / or cosmetic effect on the individual. A therapeutic effect is the treatment and / or prevention of a disease. A cosmetic effect is the improvement of appearance, such as treating and / or preventing the signs of molecular aging. The cosmetic effect may be the reduction in the wrinkle grade, which includes the visual wrinkle grade and / or predicted wrinkle grade. In certain embodiments the active agent is a cosmetic agent. A cosmetic agent when applied to an individual may promote attractiveness, alter appearance, beautify and / or cleanse. The cosmetic agent may also prevent and / or treat the signs of phenotypical aging of human skin which are for example wrinkle formation, pale complexion, reduced wound healing capacity, loss of elasticity and tightness.

[0043] In certain embodiments, the predicted wrinkle grade is determined before and after contacting the skin cells with an active agent. For example, the predicted wrinkle grade is determined, the skin cells are contacted with an active agent, followed by determining the predicted wrinkle grade. The predicted wrinkle grade before and after contacting the skin cells with an active agent are compared. A reduction in the predicted wrinkle grade after treatment indicates a therapeutic effect and / or cosmetic effect of the active agent. The time between contacting the skin cells with an active agent and determining the predicted wrinkle grade may be varied. The steps of contacting the skin cells with an active agent and determining the predicted wrinkle grade may be repeated to provide information about the effect of the active agent on the skin cells over time. This method may have the advantage of providing fast and cost-effective way to identify active cosmetic and / or pharmaceutical compounds and / or extracts, especially to treat the phenotype properties of the skin such as wrinkle grade. In certain embodiments active agents identified by the method according to the invention are formulated to produce dermatologic compositions. The dermatological composition may be a cosmetic composition and / or a pharmaceutical composition. The dermatological compositions may be used to reduce the predicted wrinkle grade and / or visual wrinkle grade.

[0044] The present disclosure may be described by one or more of the following paragraphs:A. A method for determining the predicted wrinkle grade of human skin comprising: a) providing human skin cells; b) determining a methylation level of at least one CpG-dinucleotide of a specific region of at least one chromosome of said skin cells or determining the expression level of at least one gene of said skin cells; and c) determining the predicted wrinkle grade of said skin cells by comparing said determined methylation level with empirically determined data representing a correlation between the methylation level of said CpG-nucleotide and the visual wrinkle grade of at least one human individual or determining the predicted wrinkle grade of said skin cells by comparing said determined gene expression level with empirically determined data representing a correlation between the gene expression level and the visual wrinkle grade of at least one human individual.B. The method of paragraph A, wherein the skin cells are harvested from a human individual.C. The method of paragraph A, wherein the skin cells are cultured in vitro.D. The method according to any one of the preceding paragraphs, wherein the methylation level of step b) is determined using a DNA methylation level detection method.E. The method according to paragraph D, wherein the methylation level determined from the DNA methylation level detection method is represented as beta- or m- values.F. The method according to paragraph D or paragraph E wherein the DNA methylation level detection method is selected from a) a base-resolution and sequencing approach, b) a base-resolution and microarray approach, c) a nonbase resolution and sequencing approach, d) non-base resolution and microarray approach or e) a targeted approach.G. The method according to paragraph F, where the base-resolution and sequencing approach is selected from Whole Genome Bisulfite Sequencing (WGBS), Enzymatic Methyl-Seq (EM-Seq), Anchor-Based Bisulfite Sequencing (ABBS), Post-Bisulfite Adaptor Tagging (PBAT), Reduced Representation Bisulfite Sequencing (RRBS) or Shotgun Bisulfite Sequencing (BS-Seq).H. The method according to paragraph F, wherein the base-resolution and microarray approach is based on oligonucleotides or bead-chips.I. The method according to paragraph F, wherein the non-base resolution and sequencing approach is selected from Methylated DNA Immunoprecipitation- Sequencing (MeDIP-Seq), Methylated-CpG Island Recovery Assay (MIRA), Methylation-Sensitive Restriction Enzyme Sequencing (MRE-Seq) or MethylCap- Seq (MBDCap-Seq).J. The method according to paragraph F, wherein the non-base resolution and microarray approach is selected from MeDIP-Chip or Comprehensive High- throughput Arrays for Relative Methylation (CHARM)K. The method according to paragraph F, wherein the targeted approach is selected from targeted bisulfite sequencing, targeted enzymatic methyl-seq, ligation- mediated PCR, Methylation-Specific PCR (MS-PCR), Methylation-Sensitive High Resolution Melting (MS-HRM) and Combined Bisulfite Restriction Analysis (COBRA).L. The method according to any one of paragraphs A to C, wherein the expression level of step b) is determined using a gene expression level detection method.M. The method according to any one of paragraph L, wherein the gene expression level detection method is selected from a) sequencing-based high-throughput assays, b) hybridization-based high- to medium-throughput assays, c) polymerase chain reaction (PCR) based medium- to low-throughput assays or d) hybridization-based medium- to low-throughput assaysN. The method according to paragraph M, wherein the sequencing-based high- throughput assay is selected from Ribonucleic Acid-Sequencing (RNA-Seq), single cell RNA-Sequencing (scRNA-Seq), spatial transcriptomics, Serial Analysis of Gene Expression (SAGE) and variations of Serial Analysis of Gene Expression (SAGE).O. The method according to paragraph N, wherein the spatial transcriptomics are selected from Visium Spatial Gene Expression and FISSEQ.P. The method of paragraph N wherein the variations of Serial Analysis of Gene Expression (SAGE) are selected from LongSAGE, RL-SAGE and SuperSAGE.Q. The method according to paragraph M, wherein the hybridization-based high- to medium-throughput assay is selected from microarrays, Luminex xMAP Technology, NanoString nCounter technology, Multiplexed Error Robust Fluorescence In Situ Hybridization (MERFISH), digital spatial transcriptomics and RNAscope ISH.R. The method according to paragraph Q, wherein the microarrays are selected from in situ synthesized oligonucleotide-based, cDNA-based, bead-based and tiling-based microarray platforms.S. The method according to paragraph Q, wherein the digital spatial transcriptomics is GeoMx DSP.T. The method according to paragraph M, wherein the polymerase chain reaction (PCR) based medium- to low-throughput assay is selected from digital PCR, quantitative PCR, reverse transcription PCR and Low-Density Arrays (LDA).II. The method according to paragraph T, wherein the reverse transcription PCR includes qRT-PCR and RT-PCR.V. The method according to paragraph T or paragraph II, wherein the reverse transcription PCR is TaqMan-assay.W. The method according to paragraph M, wherein the hybridization-based medium- to low-throughput assay is selected from QuantiGene RNA Assays, Fluorescent in situ hybridization (FISH), Northern blotting, Dot-Blot and Slot-Blot.X. The method according to paragraphs A, B, or D to W, further comprising the step of estimating the visual wrinkle grade of the human individual.Y. The method of paragraph X, wherein the predicted wrinkle grade is estimated to be within 20 wrinkle grade units of the visual wrinkle grade.Z. The method according to any one of the preceding paragraphs further comprising the step of contacting the skin cells with an active agent.AA. The method according to paragraph Z, wherein the predicted wrinkle grade is determined before and after contacting the skin cells with an active agent.BB. A method of testing an active agent comprising the method of any one of paragraphs A to W, further comprising the steps of: d) contacting the skin cells of step a) with an active agent; e) determining the predicted wrinkle grade of the skin cells of step d) according to the method of any one of paragraphs A to W; and f) comparing the predicted wrinkle grade determined in steps a) to c) with the predicted wrinkle grade determined in step e).CC. The method of paragraph BB, wherein step d) is carried out either in vivo or in vitro.DD. The method of paragraph BB or paragraph CC, wherein the active agent is a cosmetic agent and / or a therapeutic agent.EE. The method of any one of paragraphs BB to DD to identify an active agent that prevents and / or treats the signs of phenotypical aging of human skin.FF. A dermatological composition comprising one or more active agents identified by the method of any one of paragraphs BB to EE.GG. Use of the composition of paragraph FF for reducing the predicted wrinkle grade.HH. Use of DNA methylation levels or gene expression level for determining the predicted wrinkle grade of human skin according to the method of any one of paragraphs A to W.II. Computer-readable medium having stored computer-executable instructions for causing a computer to perform a method for determining the predicted wrinkle grade of human skin comprising:a) inputting at least one value of a determined methylation level of at least one CpG-dinucleotide of a specific region of at least one chromosome of said skin cells or inputting at least one value of a determined expression level of at least one gene of said skin cells; b) comparing said value of the determined methylation level with stored data representing a correlation between the methylation level of said CpG- dinucleotide and the visual wrinkle grade of at least one human individual or comparing said value of gene expression level with stored data representing a correlation between the gene expression level and the visual wrinkle grade of at least one human individual; and c) displaying the predicted wrinkle grade.JJ. Computer-readable medium according to paragraph II, wherein said stored data comprise at least one linear regression equation.KK. A kit for determining the predicted wrinkle grade of human skin according to the method of any one of the paragraphs A to W comprising at least one oligonucleotide primer for amplifying and / or sequencing at least one CpG- nucleotides of at least one nucleotide sequence or at least one oligonucleotide primer for amplifying and / or sequencing fractions of at least one exon of at least one nucleotide sequence.

[0045] In certain embodiments, the method according to the invention may have one or more of the following effects:• efficient method of predicting the wrinkle grade;• reliable correlation between visual wrinkle grade and predicted wrinkle grade;• reduced time required to determine the wrinkle grade;• efficient method to determining the effect of pharmaceutical or cosmetic agents on wrinkle grade;• efficient method to predict the effect of active agent on wrinkle grade;• effective method to predict the reduction in wrinkle grade over time as a result of applying active agents, e.g. applying active agent over long period of time such as one year or more;• efficient method to predict the effect of active agents over extended periods of time for an individual, providing a personalised prediction.

[0046] It should be noted that the present invention may comprise any combination of the features and / or limitations referred to herein, except for combinations of suchfeatures which are mutually exclusive. The foregoing description is directed to particular embodiments of the present invention for the purpose of illustrating it. It will be apparent, however, to one skilled in the art, that many modifications and variations to the embodiments described herein are possible. All such modifications and variations are intended to be within the scope of the present invention, as defined in the appended claims.EXAMPLESExample 1:

[0047] Suction blisters of 7 mm diameter were collected from the volar forearms of 461 female participants of the population-based Study of Health in Pomerania (SHIP- TREND-1) (Volzke et al., “Cohort Profile Update: The Study of Health in Pomerania (SHIP)”; Int J Epidemiol, 51, e372-e383, 2022) aged between 29 and 84 years. Suction blisters were obtained by applying a negative pressure of 180 mbar for 30 min followed by 320 mbar until blister formation. The roof of the suction blisters was prepared with surgery scissors and tweezers, and after snap freezing in liquid nitrogen stored at -80 °C until further use.

[0048] The DNA from the suction blister roofs of each of the 461 participants was isolated using the QIAmp DNA Investigator Kit (Qiagen) following the manufacturer’s instructions. DNA methylation patterns of the 461 female epidermal samples were determined via Infinium® MethylationEPIC BeadChip arrays (Illumina).

[0049] Methylation data analysis was carried out using the R Bioconductor package minfi (Aryee et al., "Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays"; Bioinformatics, 30, 1363-9, 2014) and limma (Ritchie et al., "limma powers differential expression analyses for RNA- sequencing and microarray studies"; Nucleic Acids Res, 43, e47, 2015). Specifically, raw .idat files were read and preprocessed. Methylation loci (probes) were filtered based on several criteria: a high detection p-value (with a threshold of P>0.01, a parameter as provided by minfi), location on the sex chromosomes, the ability to selfhybridize, and potential SNP contamination. Array normalization was performed using the preprocessQuantile function, available in minfi. Quality control was performed after every preprocessing step.

[0050] In addition to the DNA methylation level, portrait pictures were taken from each of the 461 study participants. Each volunteer had to close their eyes, cover their hair,remove the make-up and jewellery before photographing. The images were taken under following standardized setup: flash diffuser to ensure evenly lit, non-polarized and colour controlled. Based on those pictures, an expert panel consisting of more than 30 experts judged the grade of wrinkling within a range of 0 to 100 for each study participant. Each expert was provided with picture cards of exemplified crow’s feet wrinkles (area around the eye) in ten steps wrinkle grade units as orientation and trained on a panel set of 100 pictures. To avoid biases in judgment of the volunteers, the pictures were randomly presented to each expert in such a way that none of the 30 experts had judged the volunteers in the same order. Finally, the average of those expert judgments resulted in a volunteer’s individual visual wrinkle grade.

[0051] The sample group was then split in two, with 378 epidermal samples of female participants use for training and 83 epidermal samples of female participants used for testing. It was considered that the division of the sample group into two subsets of about 80% allocated for training purposes and about 20% for the validation, ensured that the target variable was equally distributed between the training and validation sets. The training of the wrinkle grade predictor was conducted with the beta-values of the training dataset applying the cv.glmnet function (alpha = 0, lambda = 1708.46) of the glmnet R package (Friedman et al., "Regularization Paths for Generalized Linear Models via Coordinate Descent"; Journal of Statistical Software, 33, 1-22; 2010) in a 10-fold cross-validation mode using the visual wrinkle grade as outcome variable, i.e. target variable. This method established the empirically determined data as a representation of the correlation between the methylation level of said CpG-nucleotide and the visual wrinkle grade of at least one human individual and is also referred to herein as the “trained wrinkle grade predictor”. The trained wrinkle grade predictor was validated with the test set and an independent publicly available dataset (Holzscheck et al., "Multi-omics network analysis reveals distinct stages in the human aging progression in epidermal tissue"; AGING, 12; 2020). Prediction of the wrinkle grade for each of the volunteers from the test set of the empirically determined data compared to the corresponding visual wrinkle grade resulted in a very high correlation (R = 0.86 and P = 1.03x1 O'21, Pearson correlation) and a mean absolute prediction error of 8.81 wrinkle grade units (see Figure 1). Similar holds true for the validation of the trained wrinkle predictor with an independent dataset with a mean absolute prediction error of 9.66 wrinkle grade units (see Figure 2).Example 2:

[0052] Example 1 was then repeated by reducing the determined DNA methylation patterns of the 378 female epidermal samples via Infinium® MethylationEPIC BeadChip arrays (Illumina) down to the DNA patterns of specific low methylated regions (LMRs) which were used as input for the model training.

[0053] The LMRs used were determined by combining whole-genome bisulfite sequencing (WGBS) methylation data from epidermis of young and old subjects (Raddatz, G. et al., “Aging is associated with highly defined epigenetic changes in the human epidermis”; EPIGENETICS CHROMATIN’ 6(1), 36; 2013) leading to an average strand-specific CpG coverage of 14x. MethylSeekR (Burger, L. et al., ..Identification of active regulatory regions from DNA methylation data”, NUCLEIC ACIDS RES; 41(16), e155; 2013) was then applied using default parameters to this dataset to identify skinspecific LMRs and those, which were overlapping with at least one probe of the EPIC Methylation array, were finally selected for further analysis. This led to a set of 40,140 LMRs. For training of the wrinkle grade predictor, the average beta value of each LMR was calculated by considering the CpGs located within that specific region and was used as data input following a methodology similar to the one described in Example 1 with parameters alpha set to 0 and lambda set to 125.62.

[0054] Prediction of the wrinkle grade for each of the volunteers from the test set of the empirically determined data compared to the corresponding visual wrinkle grade resulted in a very high correlation (R = 0.86 and P < 2.2x1 O'16, Pearson correlation) and a mean absolute prediction error of 8.73 wrinkle grade units (see Figure 3). Similar holds true for the validation of the trained wrinkle grade predictor with an independent dataset (R = 0.89 and P < 2.2x1 O'16) with a mean absolute prediction error of 13.95 wrinkle grade units (see Figure 4).Example 3:

[0055] Suction blister roofs of 437 female participants of the population-based Study of Health in Pomerania (SHIP-TREND-1) (Volzke et al., “Cohort Profile Update: The Study of Health in Pomerania (SHIP)”; Inf J Epidemiol, 51, e372-e383, 2022) aged between 29 and 84 years were suspended in lysis buffer for RNA extraction and homogenized using an MM 301 bead mill (Retsch). Using the RNeasy Fibrous Tissue Mini Kit (Qiagen) RNA was extracted following the manufacturer’s instructions.

[0056] Using the TruSeq Library Prep Kit (Illumina) the transcriptome libraries were prepared following the manufacturer’s instruction. Sequencing was performed on Illumina’s HiSeq system in single end mode with a read length of 50 bp to a final sequencing throughput of 100 million reads per sample. Generated raw reads were processed as follows: (i) quality control using FastQC 0.11.7 (Andrews, S. s- andrews / FastQC. GitHub https: / / github.com / s-andrews / FastQC), (ii) trimming of read sequences via Trimmomatic 0.36 (Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114-2120 (2014).) with parameters for adapter trimming using the TruSeq3-SE adapter sequences, seed mismatches set to 2, palindrome clip threshold set to 30 and simple clip threshold set to 10, parameters for sliding window with a window size set to 4 and the required quality to 20, parameters for leading and trailing as minimum quality required to keep a base was set to 20 and a minimum required length of a read to be kept after trimming was set to 40 bp, (iii) mapping of reads against the GRCh38.106 build of the human transcriptome by Salmon 0.8.1 (Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417-419 (2017).) with parameters “bootstrap” set to 0, “flsd” set to 10 and “flmean” set to 180 and (iv) quantification of reads as transcripts per million (TPM) reads.

[0057] The gene expression data were log 10 transformed by applying the following formula transformed gene expression data - log10(TPM + 1). Genes were then filtered by average TPM = 0, coefficient of variation (CV) < 0.075 (calculated as follows „,rstandard d.emation(gene<') . , , ...CV = - - - and missing official human gene symbol resulting in a final set of 23,539 features. Finally, the sample group was split into 75% training and 25% test set controlling for age and wrinkle grade to ensure a similar distribution of wrinkle grade among the two generated data sets.

[0058] The wrinkle grade predictor based on gene expression data was obtained by training a fully connected neural network which was implemented in R (R Core Team. R: A Language and Environment for Statistical Computing. The R Foundation (2018).) using Keras (Chollet, F. keras-team / keras. GitHub https: / / github.com / keras- team / keras.) with a TensorFlow backend (Abadi, M. et al. TensorFlow: A system for large-scale machine learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16) 265-283 (The Advanced Computing Systems Association, 2016).). The neural network had the following architecture: (i) input layerwith nodes equal to the number of features, (ii) hidden layer 1 with nodes equal to the sqrt of the first layer, (iii) hidden layer 2 with nodes equal to the sqrt of the second layer, and (iv) output layer with one node for the wrinkle grade / target. The weights in the two hidden layers were initialized using the He-function (He, K., Zhang, X., Ren, S. & Sun, J. Delving Deep into Rectifiers: Surpassing HumanLevel Performance on ImageNet Classification. Preprint at arXiv: 1502.01852 [cs] (2015).) and the “elu” (exponential linear units) activation function (Clevert, D. -A., Unterthiner, T. & Hochreiter, S. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). Preprint at arXiv: 1511.07289 [cs] (2015).) was used in all hidden layers. The loss function for the training was defined as mean squared error. To improve the generalization ability of the model the weight decay was implemented, and the regularization factor (L2) was set to 0.01. Furthermore, the model was trained using the stochastic gradient descent with Adam (Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at arXiv:1412.6980 [cs] (2017).) and a learning rate of 0.001 with a batch size of 15 samples for training until 100 epochs. All parameters of the neural network are summarized in Table 1.Table 1 : Parameters of the trained neural network to predict the wrinkle grade based on gene expression data as input.

[0059] The trained wrinkle grade predictor was validated with the test set and an independent publicly available dataset (Holzscheck et al., "Multi-omics network analysis reveals distinct stages in the human aging progression in epidermal tissue"; AGING, 12; 2020).

[0060] Prediction of the wrinkle grade for each of the volunteers from the test set of the empirically determined data compared to the corresponding visual wrinkle grade resulted in a very high correlation (R = 0.752 and P < 2.2x1 O'16, Pearson correlation) and a mean absolute prediction error of 9.49 wrinkle grade units (see Figure 5).Similarly, validation with the independent data set showed a very high correlation between predicted and visual wrinkle grade (R = 0.858 and P = P < 2.2x1 O'16, Pearson correlation) with a mean absolute error of 12.83 wrinkle grade units (see Figure 6).Example 4:

[0061] Example 3 was then repeated by reducing the determined gene expression levels of the 437 female epidermal samples via RNA-Seq down to the gene expression levels of various gene subsets which were used as input for the model training.

[0062] The gene(s) of interest selection was performed as follows: based on the initially trained model from Example 3, an in-silico knock-out for each gene was performed to calculate the overall “effect size” of the gene on the predicted wrinkle grade. More specifically, the predicted wrinkle grade values of the test set were compared to the predicted wrinkle grade values based on the altered expression levels of the test set, wherein the expression of the gene of interest is set to 0, as used herein “modified test set”. The “effect size” of said gene, as used herein, is the difference between the predicted values of the “normal” test set, i.e. , the test set from Example 3, and the predicted values of the “modified” test set, i.e., the test set from Example 3 where the expression level of the gene of interest is set to 0. The effect size of the said gene was then averaged among the subjects within the test set. The genes are then sorted by their absolute averaged effect size in a decreasing order and the top 75% genes (i.e., 17,653 genes), 50% genes (i.e., 11 ,769 genes), 25% genes (i.e., 5,885 genes), 5% genes (i.e., 1 ,177 genes) are used to train a model with a reduced gene number. In some cases, the top 100 genes, 10 genes, and / or 2 genes are selected to train a model.

[0063] For training of the wrinkle grade predictor, the gene expression level represented as log 10 transformed TPM value was used as data input following a methodology described in Example 3 with altered parameters as listed in Table 2.Table 2: Parameters of the trained neural network to predict the wrinkle grade based on gene expression data as input varying the number of input genes. Only parameters which are altered with respects to Table 1 of Example 3 are shown.

[0064] Prediction of the wrinkle grade for each of the volunteers from the test set of the empirically determined data compared to the corresponding visual wrinkle grade resulted in a high and significant correlation (R > 0.34 and P < 2.7x1 O'4, Pearson correlation) and a mean absolute prediction error of 13.78 wrinkle grade units (see Table 3).Table 3: Performance of the trained models from Table 2 on the test set are shown by the mean absolute error (MAE), pearson correlation (R) and significance of correlation (p).

Claims

CLAIMS1. A method for determining the predicted wrinkle grade of human skin comprising: a) providing human skin cells; b) determining a methylation level of at least one CpG-dinucleotide of a specific region of at least one chromosome of said skin cells or determining an expression level of at least one gene of said skin cells; and c) determining the predicted wrinkle grade of said skin cells by comparing said determined methylation level with empirically determined data representing a correlation between the methylation level of said CpG-nucleotide and the visual wrinkle grade of at least one human individual or determining the predicted wrinkle grade of said skin cells by comparing said determined gene expression level with empirically determined data representing a correlation between the gene expression level and the visual wrinkle grade of at least one human individual.

2. The method of claim 1, wherein the skin cells are harvested from a human individual.

3. The method of claim 1, wherein the skin cells are cultured in vitro.

4. The method according to any one of the preceding claims, wherein the methylation level of step b) is determined using a DNA methylation level detection method.

5. The method according to claim 4, wherein the DNA methylation level detection method is selected from a) a base-resolution and sequencing approach, b) a base-resolution and microarray approach, c) a non-base resolution and sequencing approach, d) non-base resolution and microarray approach or e) a targeted approach.

6. The method according to any one of claims 1 to 3, wherein the expression level of step b) is determined using a gene expression level detection method.

7. The method according to claim 6, wherein the gene expression level detection method is selected from a) sequencing-based high-throughput assays, b) hybridization-based high- to medium-throughput assays, c) polymerase chain reaction (PCR) based medium- to low-throughput assays or d) hybridizationbased medium- to low-throughput assays.

8. The method according to claims 1 , 2 or 4 to 7, further comprising the step of estimating the visual wrinkle grade of the human individual, optionally wherein the predicted wrinkle grade is estimated to be within 20 wrinkle grade units of the visual wrinkle grade.

9. The method according to any one of the preceding claims further comprising the step of contacting the skin cells with an active agent, optionally wherein the predicted wrinkle grade is determined before and after contacting the skin cells with an active agent.

10. A method of testing an active agent comprising the method of any one of claim 1 to 7, further comprising the steps of: d) contacting the skin cells of step a) with an active agent; e) determining the predicted wrinkle grade of the skin cells of step d) according to the method of any one of claims 1 to 7; and f) comparing the predicted wrinkle grade determined in steps a) to c) with the predicted wrinkle grade determined in step e).

11. The method of claim 10, wherein step d) is carried out either in vivo or in vitro and / or, wherein the active agent is a cosmetic agent and / or a therapeutic agent.

12. The method of claims 10 or claim 11 to identify an active agent that prevents and / or treats the signs of phenotypical aging of human skin.

13. A composition comprising one or more active agents identified by the method of any one of claims 10 to 12.

14. Use of DNA methylation levels or gene expression level for determining the predicted wrinkle grade of human skin according to the method of any one of claims 1 to 7.

15. Computer-readable medium having stored computer-executable instructions for causing a computer to perform a method for determining the predicted wrinkle grade of human skin comprising: a) inputting at least one value of a determined methylation level of at least one CpG-dinucleotide of a specific region of at least one chromosome of said skin cells or inputting at least one value of a determined expression level of at least one gene of said skin cells; b) comparing said value of the determined methylation level with stored data representing a correlation between the methylation level of said CpG- dinucleotide and the visual wrinkle grade of at least one human individual or comparing said value of gene expression level with stored data representing a correlation between the gene expression level and the visual wrinkle grade of at least one human individual; and c) displaying the predicted wrinkle grade.

16. A kit for determining the predicted wrinkle grade of human skin according to the method of any one of the claims 1 to 7, comprising at least one oligonucleotide primer for amplifying and / or sequencing at least one CpG-nucleotides of at least one nucleotide sequence or at least one oligonucleotide primer for amplifying and / or sequencing fractions of at least one exon of at least one nucleotide sequence.

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