Determining the protein age of a person's skin

Abstract

The present invention relates to biological clocks for measuring skin aging based on the protein expression level measured in a sample of a person's stratum corneum.

Description

Determining the protein age of an individual's skin technical field

[0001] This disclosure falls within the field of protein clocks which allow aging to be measured from biological markers (genetic, epigenetic, protein, telomeric etc.) from various biological samples. Previous technique

[0002] In constant contact with the environment, the skin is the largest human organ. Its aging results from both intrinsic and extrinsic factors linked to its exposure to various agents (toxins, solar radiation, pollutants, stress, etc.), collectively known as the "exposome." Researchers are therefore aiming to develop methods to assess skin condition, identify early signs of disease, and propose appropriate therapeutic solutions.

[0003] Assessing the biological age of the skin is a major issue, allowing us to measure the impact of cosmetic and medical treatments.

[0004] Measuring the biological age of the skin allows us to track how quickly this organ ages, but also to what extent a cosmetic, therapeutic, medical, aesthetic medicine or dermatological intervention is able to change the biological age and therefore rejuvenate the skin.

[0005] Another objective is to develop non-invasive techniques for analyzing the skin, in order to make diagnoses more accessible and comfortable for patients.

[0006] It is common knowledge that current methods for assessing biological skin age rely primarily on morphological approaches and invasive analyses. These methods can be uncomfortable for patients and do not always provide an accurate assessment of skin biological age. These approaches therefore have limitations, particularly in terms of accuracy and patient comfort. Invasive methods can cause discomfort and are not always suitable for frequent or large-scale use.

[0007] There is therefore a need for a non-invasive method to accurately determine the biological age of the skin, in order to improve the evaluation of cosmetic and medical treatments, and to better understand the skin aging process.

[0008] The use of algorithms to process biological data has emerged in recent years (3, 4). Argentieri et al. (5) described the development of a chronological clock using a machine learning approach, based on the study of the blood plasma proteome of thousands of individuals. However, although a set of protein factors had been identified in the stratum corneum (1, 2) and some proteins were known for their involvement in cellular aging (6, 7, 8, 9), no one had yet developed a biological clock based on stratum corneum proteins.

[0009] López-Ötín et al. (10) defined the following 12 characteristics of aging: genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, deactivated macroautophagy, deregulation of nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell depletion, impaired intercellular communication, chronic inflammation and dysbiosis.

[0010] The inventors selected a limited number of proteins found in both fibroblast secretions and the stratum corneum to calculate the biological age of the skin. These proteins, based on their biological activity, are associated with different markers of aging.

[0011] Based on a comprehensive quantitative proteomic analysis of skin fibroblast secretions from 18 individuals aged 19 to 72, they selected 210 proteins present in the stratum corneum. Protein expression levels were correlated with the individuals' age using a machine learning approach. The researchers then selected 27 proteins whose expression levels correlated with the individuals' chronological age.

[0012] To determine biological age, they then selected a group of 34 proteins whose expression was correlated with the biological age of individuals. This biological clock allows, from a sample of an individual's stratum corneum, the measurement of skin biological age and thus the assessment of the deviation from their chronological age, that is to say, the acceleration of their skin aging. Brief description of the invention

[0013] This application relates to a method for determining the protein age of an individual's skin from a sample of stratum corneum, comprising - the measurement of the expression level of at least three specific proteins from the stratum corneum sample, chosen from the following list of 37 proteins: COL1A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, SOD1, QPCT, NUDT5, COL1A1, POSTN, ARPC2, GRN, SERPINC1, ATP6AP2, PRSS3, ECM1, DSG1, GNB1, UBE2V1, APOD, KRT5, LMNA, GSN, HEXA, ARHGDIA, GDI2, CFD, PDIA4, YWHAE, KRT9, HIST1 H2AH, PRDX1, PLTP, ATRN; - the determination of the protein age of the skin using a multiparametric model defined by a multiparametric linear equation of the form Protein Age = p0 + p1 p1 + p2 p2 +...+ pn pn, where p0 is the intercept, p1, p2, ...pn are coefficients, p1, p2, ...pn represent the level of expression of each protein, the number of proteins chosen from the list being equal to n.

[0014] In one embodiment, said protein age is of the biological age type, and wherein said at least three proteins are chosen from the list of the following 34 proteins: COL1A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, SOD1, QPCT, NUDT5, COL1A1, POSTN, ARPC2, GRN, SERPINC1, ATP6AP2, PRSS3, ECM1, DSG1, GNB1, UBE2V1, APOD, KRT5, LMNA, GSN, HEXA, ARHGDIA, GDI2, CFD, PDIA4, YWHAE, KRT9, HIST1 H2AH.

[0015] In another embodiment, said protein age is of the chronological age type, and in which said at least three proteins are chosen from the list of the following 27 proteins: PRDX1, COL1A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, SOD1, NUDT5, COL1A1, POSTN, PLTP, ARPC2, SERPINC1, ATP6AP2, PRSS3, DSG1, ATRN, KRT5, GSN, HEXA, ARHGDIA, GDI2, YWHAE and HIST1 H2AH.

[0016] In another respect, this application concerns a method for assessing the aging of an individual's skin, comprising: - the determination at a time T0 of the protein age, - the determination of the protein age at a given time Tx, - if the value (Vx) of the protein age at Tx is greater than the value (Vo) of the protein age at To, skin aging of the individual is proven.

[0017] In another respect, this application concerns a method for assessing the aging of an individual's skin, comprising: - the determination of the protein age of the biological age type; - the determination of protein age of the chronological age type; - if the biological age is greater than the chronological age, premature aging of the individual's skin is proven.

[0018] In another respect, the present application relates to a method for evaluating the effect of a product on an individual's skin, comprising the following steps: - A To, determination of the biological age of the individual (Age_bioo) from a sample of stratum corneum, and determination of the chronological age of the individual (Age_chronoo); - Determination of the value of the difference between biological age and chronological age at To (ATo) by subtracting chronological age from biological age (ATo = Age_bioo - Age_chronoo); - At Tx, determination of the individual's biological age (Age_bio) x ) from a sample of stratum corneum, and determination of the chronological age of the individual (Chronological_Age x ); - Determination of the difference between biological age and chronological age at Tx (ATx) by subtracting chronological age from biological age (ATx = Age_bio x - Age_chrono x ); - Comparison of ATo and ATx; - Evaluation of the effect of the product: positive effect when ATx is less than ATo, negative effect or no effect when ATx is greater than ATo. Brief description of the drawings Fig. 1

[0019] [Fig. 1] Sequence diagram of the algorithm used for the design of biological clock models

[0020] [Fig. 2] Sequence diagram of the algorithm used for the design of chronological clock models Fig. 3

[0021] [Fig. 3] Graph A: Pearson correlation coefficient between protein predictive age and biological age as a function of the number of predictors (proteins) used for the biological clock model. Graph B: Upset diagram of comparisons of the predictors that make up the biological clock. At the bottom of the diagram are the different datasets to be compared. The horizontal columns represent the datasets. Connected black dots indicate an intersection. At the top, the vertical bars show the size of each intersection. The proteins (P) for each dataset are as follows: 1P = HNRNPA2B1; 2P = HNRNPA2B1 and GANAB; 3P = COL1A2, HNRNPA2B1 and GANAB; 4P = COL1A2, HNRNPA2B1, GANAB and NUDT5; 5P = COL1A2, HNRNPA2B1, GANAB, NUDT5, and KRT5; 6P = COL1 A2, HNRNPA2B1, GANAB, NUDT5, KRT5 and YWHAE; 7P = COL1A2, HNRNPA2B1, GANAB, NUDT5, KRT5, YWHAE and HEXA; 9P = COL1A2, HNRNPA2B1, GANAB, NUDT5, KRT5, YWHAE, HEXA, SERPINC1 and PSMA7;10P = COL1A2, HNRNPA2B1, GANAB, NUDT5, KRT5, YWHAE, HEXA, SERPINC1, PSMA7 and POSTN; 12P = COL1A2, HNRNPA2B1, GANAB, NUDT5, KRT5, YWHAE, HEXA, SERPINC1, PSMA7, POSTN, CD44 and DSG1; 15P = COL1 A2, HNRNPA2B1, GANAB, PSMA7, GSTO1, NUDT5, POSTN, SERPINC1, ATP6AP2, DSG1, GNB1, KRT5, GSN, HEXA, YWHAE; 20P = COL1A2, PRDX2, HNRNPA2B1, GANAB, PSMA7, GSTO1, DAG1, NUDT5, POSTN, ARPC2, SERPINC1, ATP6AP2, ECM1, DSG1, GNB1, APOD, KRT5, GSN, HEXA, and YWHAE; 26P = COL1A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, QPCT, NUDT5, COL1 A1, POSTN, ARPC2, SERPINC1, ATP6AP2, ECM1, DSG1, GNB1, UBE2V1, APOD, KRT5, GSN, HEXA, ARHGDIA, CFD and YWHAE; 31 P = COL1 A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, SOD1, NUDT5, COL1 A1, POSTN, ARPC2, SERPINC1, ATP6AP2, PRSS3, ECM1, DSG1, GNB1, UBE2V1, APOD, KRT5, GSN, HEXA, ARHGDIA, GDI2, GRN, PDIA4, YWHAE, KRT9 et HIST1 H2AH;34P = COL1A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, SOD1, QPCT, NUDT5, COL1A1, POSTN, ARPC2, GRN, SERPINC1, ATP6AP2, PRSS3, ECM1, DSG1, GNB1, UBE2V1, APOD, KRT5, LMNA, GSN, HEXA, ARHGDIA, GDI2, CFD, PDIA4, YWHAE, KRT9, HIST1 H2AH. For example, the vertical bar representing an intersection of 5 proteins corresponds to proteins present only in the 20, 26, 31, and 34-protein clocks, as indicated by the connected black dots located opposite the vertical bar and the sets involved in this intersection. Fig. 4

[0022] [Fig. 4] Graph A: Pearson correlation coefficient between protein predictive age and chronological age, as a function of the number of predictors (proteins) used for the chronological clock model. Graph B: Upset diagram comparing the predictors that make up the chronological clock. At the bottom of the diagram are the different datasets to be compared. The horizontal columns represent the datasets. Connected black dots indicate an intersection. At the top, the vertical bars show the size of each intersection. The proteins (P) for each set are as follows: 1P = COL1A2; 2P = COL1A2 and HNRNPA2B1; 3P = COL1A2, HNRNPA2B1 and GANAB; 4P = COL1A2, HNRNPA2B1, GANAB and NUDT5; 5P = COL1A2, HNRNPA2B1, GANAB, GSTO1 and NUDT5; 6P = COL1A2, HNRNPA2B1, GANAB, GSTO1, NUDT5 and DSG1; 7P = COL1A2, HNRNPA2B1, GANAB, GSTO1, NUDT5, SERPINC1 and DSG1; 11 P= COL1A2, HNRNPA2B1, GANAB, PSMA7, GSTO1, NUDT5, POSTN, SERPINC1, DSG1, KRT5 and HEXA; 12P = COL1 A2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, NUDT5, POSTN, SERPINC1, DSG1, KRT5 and HEXA; 15P = COL1A2, HNRNPA2B1, GANAB, PSMA7, GSTO1, NUDT5, POSTN, ARPC2, SERPINC1, ATRN, KRT5, GSN, HEXA, ARHGDIA and YWHAE; 16P = COL1A2, HNRNPA2B1, GANAB, PSMA7, GSTO1, NUDT5, POSTN, ARPC2, SERPINC1, ATRN, KRT5, GSN, HEXA, ARHGDIA, YWHAE and HIST1 H2AH; 18P = COL1A2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, NUDT5, COL1A1, POSTN, ARPC2, SERPINC1, DSG1, ATRN, KRT5, GSN, HEXA, ARHGDIA and YWHAE;21 P = COL1A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, NUDT5, COL1A1, POSTN, PLTP, ARPC2, SERPINC1, DSG1, ATRN, KRT5, GSN, HEXA, ARHGDIA, YWHAE and HIST1 H2AH; 27P = PRDX1, COL1 A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, SOD1, NUDT5, COL1 A1, POSTN, PLTP, ARPC2, SERPINC1, ATP6AP2, PRSS3, DSG1, ATRN, KRT5, GSN, HEXA, ARHGDIA, GDI2, YWHAE and HIST1 H2AH.; Description of the invention

[0023] The present invention proposes a method for determining the protein age of an individual's skin from a sample of stratum corneum, through the measurement of the level of expression of chosen proteins and the use of a multiparametric model.

[0024] According to one embodiment, the method for determining protein age makes it possible to determine the biological age of an individual's skin from a sample of stratum corneum, through the measurement of the level of expression of chosen proteins and the use of a multiparametric model.

[0025] According to one embodiment, the method for determining protein age allows the chronological age of an individual's skin to be determined from a sample of stratum corneum, through the measurement of the level of expression of chosen proteins and the use of a multiparametric model.

[0026] Chronological age is the measure of time elapsed since an individual's birth, expressed in years. It represents a person's actual age without taking into account biological or physiological variations that can influence aging. In the context of this invention, it is used as a reference point for comparison with biological age, which can differ depending on various health and aging factors.

[0027] Biological age is a measure that assesses the aging state of an organism based on biological markers rather than chronological age. It reflects the organism's actual physiological condition, taking into account factors such as the expression of proteins, epigenetic modifications, and other indicators of cellular health. In the context of the skin, biological age can be used to determine the rate of skin aging and the effectiveness of cosmetic or dermatological interventions.

[0028] Thus, individuals of the same calendar age, i.e. chronological age, can have different biological ages depending on intrinsic factors (genetics, epigenetics...) and extrinsic factors (UV exposure, pollution, tobacco...).

[0029] The stratum corneum (SC), or horny layer, is the outermost cell layer of the epidermis, the most superficial tissue of the skin. It is composed primarily of dead cells called corneocytes. This layer plays a crucial role in protecting the skin against external aggressors, forming a physical and chemical barrier that prevents water loss and the entry of harmful substances. It is also involved in the desquamation process, where dead cells are shed to be replaced by new cells from the lower layers of the epidermis. The stratum corneum contains biological markers that can be used as biomarkers of age.

[0030] This could include, for example, the stratum corneum of the face, hands, arms, legs, or any other part of the surface of the human body.

[0031] Protein expression level refers to the amount of that protein produced in a cell or tissue at a given time. It is determined by various factors, including the activity of the genes that code for the protein, post-transcriptional and post-translational regulatory mechanisms, environmental conditions, physiology, health status, and age. In the context of this invention, the expression level of specific proteins in the stratum corneum can be used to assess skin aging.

[0032] According to a first aspect, the present invention relates to a method for determining the protein age of an individual's skin from a sample of stratum corneum, comprising: - the measurement of the expression level of at least three specific proteins from the stratum corneum sample, chosen from the following list of 37 proteins: COL1A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, SOD1, QPCT, NUDT5, COL1A1, POSTN, ARPC2, GRN, SERPINC1, ATP6AP2, PRSS3, ECM1, DSG1, GNB1, UBE2V1, APOD, KRT5, LMNA, GSN, HEXA, ARHGDIA, GDI2, CFD, PDIA4, YWHAE, KRT9, HIST1 H2AH, PRDX1, PLTP, ATRN; - the determination of the protein age of the skin using a multiparametric model defined by a multiparametric linear equation of the form Protein Age = p0 + p1 p1 + p2 p2 +...+ pn pn, where p0 is intercept, p1, p2, ...pn are coefficients, p1, p2, ...pn represent the level of expression of each protein, the number of proteins chosen from the list being equal to n.

[0033] p0 corresponds to intercept, that is, it represents the value of the dependent variable Age when all the protein predictors (p1, p2, ... pn) are equal to zero (intercept). p0 is therefore a basic constant.

[0034] The coefficients p1 and p2 correspond to the coefficients associated with the predictors (quantity of proteins 1 and 2) defined as the foundation of the clock, and the coefficients pn correspond to the coefficients associated with the n predictors added to the equation. p1, p2, ... pn can also be called weighting coefficients.

[0035] In one embodiment, the method includes a preliminary step of training said multiparametric linear equation, in which intercept o and the are weighting coefficients determined by learning.

[0036] Typically, learning is done using the elastic net method.

[0037] The Elastic Net method is a regularization technique used primarily in linear and logistic regression models. It combines two popular regularization methods: Lasso (Least Absolute Shrinkage and Selection Operator) and Ridge (or Tikhonov regularization). These two techniques are commonly used to avoid overfitting in models with numerous or highly correlated explanatory variables.

[0038] The Lasso method favors variable selection by penalizing the sum of the absolute values ​​of the coefficients, which can lead to some coefficients being reduced to zero, thus eliminating irrelevant variables.

[0039] The Ridge method, on the other hand, penalizes the sum of squares of the coefficients, which makes it possible to reduce the amplitude of the coefficients while keeping all the variables in the model, even the least influential ones.

[0040] Elastic Net combines these two approaches by adding two penalty terms: one for the L1 norm (Lasso) and another for the L2 norm (Ridge). The model can therefore both perform variable selection and reduce the effects of multicollinearity, that is, the problems caused by strong correlations between explanatory variables in a regression model. Multicollinearity occurs when two or more explanatory variables (in this case, protein expression levels) are highly correlated, meaning they provide redundant information.

[0041] Protein expression levels can be quantified by mass spectrometry.

[0042] Other protein quantification techniques can be considered, ranging from the simplest immunological techniques to more complex and comprehensive methods. Examples include: antigen testing, Western blot, dot blot, ELISA, reverse phase protein arrays, and protein microarrays (e.g., ProtoArray® Human Protein Microarray, SOMAscan® Assay, SomaLogic HuProt™ Human Proteome Microarray, NanoString protein assays, and Olink-type PEA (Proximity Extension Assay)). These techniques are well-known to any professional seeking to measure the expression level of a protein in a sample.

[0043] The proteins cited in this application, also known as biomarkers, are referenced in the table below with their accession identifier in the UniProt database and the name of the gene that codes for the protein in humans.

[0044] [Table 1]

[0045] For the purposes of this application, the stratum corneum sample may be collected from the individual using non-invasive techniques, that is, techniques that do not require penetration through the skin or access to a body cavity by means of an instrument. For example, this includes tape stripping and extraction using double-sided adhesive tape (D-squame stripping discs).

[0046] Slightly invasive techniques can be used such as cyanoacrylate skin surface stripping, microdermabrasion or curettage.

[0047] This could include, for example, the stratum corneum of the face, hands, arms, legs, or any other part of the surface of the human body.

[0048] According to one embodiment, the protein age is of the biological age type, and said at least three proteins are chosen from the list of the following 34 proteins: COL1A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, SOD1, QPCT, NUDT5, COL1A1, POSTN, ARPC2, GRN, SERPINC1, ATP6AP2, PRSS3, ECM1, DSG1, GNB1, UBE2V1, APOD, KRT5, LMNA, GSN, HEXA, ARHGDIA, GDI2, CFD, PDIA4, YWHAE, KRT9, HIST1 H2AH.

[0049] Thus, in particular, the said method for determining protein age makes it possible to determine the biological age of an individual's skin from a sample of stratum corneum, and includes the measurement of the level of expression of at least three proteins chosen from the list of the following 34 proteins: COL1 A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, SOD1, QPCT, NUDT5, COL1A1, POSTN, ARPC2, GRN, SERPINC1, ATP6AP2, PRSS3, ECM1, DSG1, GNB1, UBE2V1, APOD, KRT5, LMNA, GSN, HEXA, ARHGDIA, GDI2, CFD, PDIA4, YWHAE, KRT9, HIST1 H2AH.

[0050] "At least 3" means that 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33 or all 34 proteins can be randomly selected from the list to implement the biological age determination process.

[0051] In particular, 3 proteins are selected from the list. These include, for example, COL1A2, HNRNPA2B1 and GANAB.

[0052] In particular, 4 proteins are selected from the list. These include, for example, COL1 A2, HNRNPA2B1, GANAB and NUDT5.

[0053] In particular, 5 proteins are selected from the list. These include, for example, COL1 A2, HNRNPA2B1, GANAB, NUDT5, and KRT5.

[0054] In particular, 6 proteins are selected from the list. These include, for example, COL1 A2, HNRNPA2B1, GANAB, NUDT5, KRT5 and YWHAE.

[0055] Specifically, 7 proteins are selected from the list. These include, for example, COL1A2, HNRNPA2B1, GANAB, NUDT5, KRT5, YWHAE, and HEXA. Thus, according to a particular embodiment, the proteins are: COL1A2, HNRNPA2B1, GANAB, NUDT5, KRT5, YWHAE, and HEXA.

[0056] In particular, 9 proteins are selected from the list. These include, for example, COL1 A2, HNRNPA2B1, GANAB, NUDT5, KRT5, YWHAE, HEXA, SERPINC1 and PSMA7.

[0057] In particular, 10 proteins are selected from the list. These include, for example, COL1 A2, HNRNPA2B1, GANAB, NUDT5, KRT5, YWHAE, HEXA, SERPINC1, PSMA7 and POSTN.

[0058] In particular, 12 proteins are selected from the list. These include, for example, COL1A2, HNRNPA2B1, GANAB, NUDT5, KRT5, YWHAE, HEXA, SERPINC1, PSMA7, POSTN, CD44 and DSG1.

[0059] In particular, 15 proteins are selected from the list. These include, for example, COL1 A2, HNRNPA2B1, GANAB, PSMA7, GSTO1, NUDT5, POSTN, SERPINC1, ATP6AP2, DSG1, GNB1, KRT5, GSN, HEXA, YWHAE.

[0060] In particular, 20 proteins are selected from the list. These include, for example, COL1 A2, PRDX2, HNRNPA2B1, GANAB, PSMA7, GSTO1, DAG1, NUDT5, POSTN, ARPC2, SERPINC1, ATP6AP2, ECM1, DSG1, GNB1, APOD, KRT5, GSN, HEXA, and YWHAE.

[0061] In particular, 26 proteins are selected from the list. These include, for example, COL1 A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, QPCT, NUDT5, COL1A1, POSTN, ARPC2, SERPINC1, ATP6AP2, ECM1, DSG1, GNB1, UBE2V1, APOD, KRT5, GSN, HEXA, ARHGDIA, CFD and YWHAE.

[0062] In particular, 31 proteins are selected from the list. These include, for example, COL1 A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, SOD1, NUDT5, COL1A1, POSTN, ARPC2, SERPINC1, ATP6AP2, PRSS3, ECM1, DSG1, GNB1, UBE2V1, APOD, KRT5, GSN, HEXA, ARHGDIA, GDI2, GRN, PDIA4, YWHAE, KRT9 and HIST1 H2AH.

[0063] In particular, the 34 proteins are selected from the list.

[0064] In a particular embodiment, the present invention therefore relates to a method for determining the protein age of an individual's skin from a sample of stratum corneum, said protein age being of the biological age type, comprising: - the measurement of the expression level of at least three specific proteins from the stratum corneum sample, chosen from the following list of 34 proteins: COL1A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, SOD1, QPCT, NUDT5, COL1A1, POSTN, ARPC2, GRN, SERPINC1, ATP6AP2, PRSS3, ECM1, DSG1, GNB1, UBE2V1, APOD, KRT5, LMNA, GSN, HEXA, ARHGDIA, GDI2, CFD, PDIA4, YWHAE, KRT9, HIST1 H2AH; - the determination of the biological age (Age_bio) of the skin using a multiparametric model defined by a multiparametric linear equation of the form Biological Age = p0 + p1 p1 + p2 p2 +...+ pn pn, where p0 is the intercept, p1, p2,...pn are coefficients, p1, p2,....pn represent the level of expression of each protein, the number of proteins chosen from the list being equal to n.

[0065] The intercept and weighting coefficients associated with the expression level of each protein are estimated during model training, for example using the elastic net method implemented in the glmnet package, such as glmnet version 4.1-8. This estimation is based on the input database, i.e. the number, type and expression level of proteins present in the stratum corneum of each individual in the model training cohort.

[0066] Table 2 below presents the intercept and the weighting coefficient p to be applied to each expression level of each protein, depending on the number and type of proteins included in the multiparameter linear equation for determining biological age. Proteins with a negative coefficient are shown in gray and intercepts in bold italics.

[0067] [Table 2]

[0068] In a particular embodiment, the present invention therefore relates to a method for determining the protein age of an individual's skin from a sample of stratum corneum, said protein age being of the biological age type, comprising: - measurement of the expression level of six specific proteins from the stratum corneum sample: COL1 A2, HNRNPA2B1, GANAB, NUDT5, KRT5, and YWHAE; - the determination of the biological age (Age_bio) of the skin using a multiparametric model defined by a multiparametric linear equation of the form Biological Age = p0 + p1 p1 + p2 p2 + p3 p3 + p4 p4 + p5 p5 + p6 p6 with p1 corresponding to the expression level of COL1A2, p2 corresponding to the expression level of HNRNPA2B1, p3 corresponding to the expression level of GANAB, p4 corresponding to the expression level of NUDT5, p5 corresponding to the expression level of KRT5, p6 corresponding to the expression level of YWHAE, where the intercept p0 has a value of 91.9837094249468 where p1 has a value of -0.00424446485636704, p2 has a value of -0.0279782523651577, p3 has a value of -0.111675335254189, p4 has a value of -0.150178622628235, p5 has a value of 0.0142523819182876, p6 has a value of 0.0618549163120401.

[0069] In a particular embodiment, the present invention therefore relates to a method for determining the protein age of an individual's skin from a sample of stratum corneum, said protein age being of the biological age type, comprising: - measurement of the expression level of nine specific proteins from the stratum corneum sample: COL1A2, HNRNPA2B1, GANAB, NUDT5, KRT5, YWHAE, PSMA7, SERPINC1 and HEXA; - the determination of the biological age (Age_bio) of the skin using a multiparametric model defined by a multiparametric linear equation of the form Biological Age = p0 + p1 p1 + p2 p2 + p3 p3 + p4 p4 + p5 p5 + p6 p6 + p7 p7 + p8 p8 + p9 p9, with p1 corresponding to the expression level of COL1A2, p2 corresponding to the expression level of HNRNPA2B1, p3 corresponding to the expression level of GANAB, p4 corresponding to the expression level of NUDT5, p5 corresponding to the expression level of KRT5, p6 corresponding to the expression level of YWHAE, p7 corresponding to the expression level of PSMA7, p8 corresponding to the expression level of SERPINC1, p9 corresponding to the expression level of HEXA, where the intercept p0 has a value of 91 ,1370464900088 where p1 has a value of -0.00401317298712054, p2 has a value of -0.0417935038376829, p3 has a value of -0.0458803799667517, p4 has a value of -0.147933198469151, p5 has a value of 0.014857643266839, p6 has a value of 0,0296399638147097, p7 has a value of -0.0711575967842609, p8 has a value of -0.0951110140646376, p9 has a value of 0.0693549118788835.

[0070] In a particular embodiment, the present invention therefore relates to a method for determining the protein age of an individual's skin from a sample of stratum corneum, said protein age being of the biological age type, comprising: - measurement of the expression level of 34 specific proteins from the stratum corneum sample: PSMA7, ARPC2, ATP6AP2, SOD1, SERPINC1, COL1 A1, COL1 A2, CD44, HNRNPA2B1, GRN, PRDX2, GSTO1, DAG1, GANAB, POSTN, ECM1, QPCT, HIST1 H2AH, NUDT5, CFD, LMNA, APOD, GSN, HEXA, KRT5, PDIA4, PRSS3, KRT9, GDI2, ARHGDIA, YWHAE, GNB1, DSG1, and UBE2V1; - the determination of the biological age (Age_bio) of the skin using a multiparametric model defined by a multiparametric linear equation of the form Biological Age = p0 + p1 p1 + p2 p2 + p3 p3 + p4 p4 + p5 p5 + p6 p6 + p7 p7 + p8 p8 + p9 p9 + p10 p10 + p11 p11 + p12 p12 + p13 p13 + p14 p14 + p15 p15 + p16 p16 + p17 p17 + p18 p18 + p19 p19 + p20 p20 + p21 p21 + p22 p22 + p23 p23 + p24 p24 + p25 p25 + p26 p26 + p27 p27 + p28 p28 + p29 p29 + p30 p30 + p31 p31+ p32 p32+ p33 p33+ p34 p34, with p1 corresponding to the expression level of PSMA7, p2 corresponding to the expression level of ARPC2, p3 corresponding to the expression level of ATP6AP2, p4 corresponding to the expression level of SOD1, p5 corresponding to the expression level of SERPINC1, p6 corresponding to the expression level of COL1A1, p7 corresponding to the expression level of COL1A2, p8 corresponding to the expression level of CD44, p9 corresponding to the expression level of HNRNPA2B1, p10 corresponding to the expression level of GRN,p11 corresponding to the PRDX2 expression level, p12 corresponding to the GSTO1 expression level, p13 corresponding to the DAG1 expression level, p14 corresponding to the GANAB expression level, p15 corresponding to the POSTN expression level, p16 corresponding to the ECM1 expression level, p17 corresponding to the QPCT expression level, p18 corresponding to the HIST1 H2AH expression level, p19 corresponding to the NUDT5 expression level, p20 corresponding to the CFD expression level, p21 corresponding to the LMNA expression level, p22 corresponding to the APOD expression level, p23 corresponding to the GSN expression level, p24 corresponding to the HEXA expression level, p25 corresponding to the KRT5 expression level, p26 corresponding to the PDIA4 expression level, p27 corresponding to the PRSS3 expression level, p28 corresponding to the expression level of KRT9, p29 corresponding to the expression level of GDI2,p30 corresponds to the expression level of ARHGDIA, p31 to the expression level of YWHAE, p32 to the expression level of GNB1, p33 to the expression level of DSG1, p34 to the expression level of UBE2V1, where the intercept pO has a value of 79.4680521908027, where p1 has a value of -0.0447572537143951, p2 has a value of -0.0310893990991177, p3 has a value of -0.00526910616360357, p4 has a value of -0.0154536611514652, p5 has a value of -0.0251078166283137, p6 has a value of -0.00123780799939871, p7 has a value of -0.00129147564954815, p8 has a value of -0.0112571354761862, p9 has a value of -0.0235927873242526, p10 has a value of -0.00428361067532905, p11 has a value of -0.014156859383358, p12 has a value of -0.0411456223759342, p13 has a value of -0.00785694846132329, p14 has a value of -0.0551672865234715, p15 has a value of -0.00161450096578245, p16 has a value of -0.00195564946365248, p17 has a value of -, 0.00261730638760361, p18 has a value of -0.00265332762617516, p19 has a value of -0.0882663376927517, p20 has a value of 0.00224875794993041, p21 has a value of 0.000130851101415848, p22 has a value of 0.0127107649500224, p23 has a value of 0.0165063824751699, p24 has a value of 0.0392367219664753, p25 has a value of 0.0103881509964209, p26 has a value of 0.00372364646634297, p27 has a value of 0.0165315774827813, p28 has a value of 0.00201489153002412, p29 has a value of 0.00431305397957548, p30 has a value of 0.0185028036575522, p31 has a value of 0.0220669277076333, p32 has a value of 0.0485986821523148, p33 has a value of 0.0405096395016236, p34 has a value of 0.0109736173871865.

[0071] According to one embodiment, the protein age is of the chronological age type and said at least three proteins are chosen from the list of the following 27 proteins: PRDX1, COL1A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, SOD1, NUDT5, COL1 A1, POSTN, PLTP, ARPC2, SERPINC1, ATP6AP2, PRSS3, DSG1, ATRN, KRT5, GSN, HEXA, ARHGDIA, GDI2, YWHAE and HIST1 H2AH.

[0072] Thus, in particular, the said method for determining protein age makes it possible to determine the chronological age of an individual's skin from a sample of stratum corneum, and includes the measurement of the level of expression of at least three proteins chosen from the list of the following 27 proteins: PRDX1, COL1A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, SOD1, NUDT5, COL1A1, POSTN, PLTP, ARPC2, SERPINC1, ATP6AP2, PRSS3, DSG1, ATRN, KRT5, GSN, HEXA, ARHGDIA, GDI2, YWHAE and HIST1 H2AH.

[0073] "At least 3" means that 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, or all 27 proteins, can be randomly selected from the list to implement the chronological age determination process.

[0074] In particular, 3 proteins are selected from the list. These include, for example, COL1 A2, HNRNPA2B1 and GANAB.

[0075] In particular, 4 proteins are selected from the list. These include, for example, COL1 A2, HNRNPA2B1, GANAB and NUDT5.

[0076] In particular, 5 proteins are selected from the list. These include, for example, COL1 A2, HNRNPA2B1, GANAB, GSTO1 and KRT5.

[0077] In particular, 6 proteins are selected from the list. These include, for example, COL1 A2, HNRNPA2B1, GANAB, GSTO1, NUDT5 and DSG1.

[0078] In particular, 7 proteins are selected from the list. These include, for example, COL1 A2, HNRNPA2B1, GANAB, GSTO1, KRT5, NUDT5, SERPINC1 and DSG1.

[0079] Specifically, 11 proteins are selected from the list. These include, for example, COL1 A2, HNRNPA2B1, GANAB, PSMA7, GSTO1, NUDT5, POSTN, SERPINC1, DSG1, KRT5, and HEXA. Thus, In one particular embodiment, the proteins are: COL1A2, HNRNPA2B1, GANAB, PSMA7, GSTO1, NUDT5, POSTN, SERPINC1, DSG1, KRT5 and HEXA.

[0080] In particular, 12 proteins are selected from the list. These include, for example, COL1A2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, NUDT5, POSTN, SERPINC1, DSG1, KRT5 and HEXA.

[0081] In particular, 15 proteins are selected from the list. These include, for example, COL1 A2, HNRNPA2B1, GANAB, PSMA7, GSTO1, NUDT5, POSTN, ARPC2, SERPINC1, ATRN, KRT5, GSN, HEXA, ARHGDIA and YWHAE.

[0082] In particular, 16 proteins are selected from the list. These include, for example, COL1 A2, HNRNPA2B1, GANAB, PSMA7, GSTO1, NUDT5, POSTN, ARPC2, SERPINC1, ATRN, KRT5, GSN, HEXA, ARHGDIA, YWHAE and HIST1 H2AH.

[0083] In particular, 18 proteins are selected from the list. These include, for example, COL1A2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, NUDT5, COL1A1, POSTN, ARPC2, SERPINC1, DSG1, ATRN, KRT5, GSN, HEXA, ARHGDIA and YWHAE.

[0084] In particular, 21 proteins are selected from the list. These include, for example, COL1 A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, NUDT5, COL1 A1, POSTN, PLTP, ARPC2, SERPINC1, DSG1, ATRN, KRT5, GSN, HEXA, ARHGDIA, YWHAE and HIST1 H2AH.

[0085] In particular, the 27 proteins are selected from the list.

[0086] In a particular embodiment, the present invention therefore relates to a method for determining the protein age of an individual's skin from a sample of stratum corneum, said protein age being of the chronological age type, comprising: - the measurement of the expression level of at least three specific proteins from the stratum corneum sample, chosen from the following list of 27 proteins: PRDX1, COL1A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, SOD1, NUDT5, COL1A1, POSTN, PLTP, ARPC2, SERPINC1, ATP6AP2, PRSS3, DSG1, ATRN, KRT5, GSN, HEXA, ARHGDIA, GDI2, YWHAE and HIST1 H2AH; - the determination of the chronological age (Age_chrono) of the skin using a multiparametric model defined by a multiparametric linear equation of the form Chronological Age = p0 + p1 p1 + p2 p2 +...+ pn pn, where p0 is the intercept, p1, p2, p3... pn are coefficients, p1, p2,....pn represent the level of expression of each protein, the number of proteins chosen from the list being equal to n.

[0087] The intercept and weighting coefficients associated with the expression level of each protein are estimated during model training, for example using the elastic net method implemented in the glmnet package, such as glmnet version 4.1-8. This estimation is based on the input database, i.e. the number, type and expression level of proteins present in the stratum corneum of each individual in the model training cohort.

[0088] Table 3 below presents the intercept and the weighting coefficient p to be applied to each expression level of each protein, depending on the number and type of proteins included in the multiparameter linear equation for determining chronological age. Proteins with a negative coefficient are shown in gray and intercepts in bold italics.

[0089] [Table 3]

[0090] In a particular embodiment, the present invention relates to a method for determining the protein age of an individual's skin from a sample of stratum corneum, said protein age being of the chronological age type, comprising: - measurement of the expression level of seven proteins specific to the stratum corneum sample: COL1 A2, HNRNPA2B1, GANAB, NUDT5, GSTO1, SERPINC1, and DSG1, - the determination of the chronological age (Chronological_Age) of the skin using a multiparametric model defined by a multiparametric linear equation of the form Chronological Age = p0 + p1 p1 + p2 p2 + p3 p3 + p4 p4 + p5 p5 + p6 p6 + p7 p7, with p1 corresponding to the expression level of COL1A2, p2 corresponding to the expression level of HNRNPA2B1, p3 corresponding to the expression level of GANAB, p4 corresponding to the expression level of NUDT5, p5 corresponding to the expression level of GSTO1, p6 corresponding to the expression level of SERPINC1, p7 corresponding to the expression level of DSG1 where the intercept p0 has a value of 77.8072752355772 where p1 has a value of -0.00338336126413298, p2 has a value of -0.0190120901871719, p3 has a value of -0.0459620434776537, p4 has a value of -0.114398643955703, p5 has a value of -0.0268160371770119, p6 has a value of -0.0147769989797443 and p7 has a value of 0.0531949722591303.

[0091] In a particular embodiment, the present invention relates to a method for determining the protein age of an individual's skin from a sample of stratum corneum, said protein age being of the chronological age type, comprising: - measurement of the expression level of 11 proteins specific to the stratum corneum sample: COL1A2, HNRNPA2B1, GANAB, NUDT5, GSTO1, SERPINC1, DSG1, PSMA7, POSTN, HEXA and KRT5, - the determination of the chronological age (Age_chrono) of the skin using a multiparametric model defined by a multiparametric linear equation of the form Chronological Age = p0 + p1 p1 + p2 p2+ p3 p3+ p4 p4+ p5 p5+ p6 p6 + p7 p7 + p8 p8+ p9 p9+ p10 p10+ p11 p11 , with p1 corresponding to the expression level of COL1A2, p2 corresponding to the expression level of HNRNPA2B1, p3 corresponding to the expression level of GANAB, p4 corresponding to the expression level of NUDT5, p5 corresponding to the expression level of GSTO1, p6 corresponding to the expression level of SERPINC1, p7 corresponding to the expression level of DSG1, p8 corresponding to the expression level of PSMA7, p9 corresponding to the expression level of POSTN, p10 corresponds to the expression level of HEXA, and p11 corresponds to the expression level of KRT5, where the intercept p0 has a value of 87.7607313714849 where p1 has a value of -0.00539528956188052, p2 has a value of -0.0476808808971591 ,p3 has a value of -0.00589752000715537, p4 has a value of -0.216167597972179, p5 has a value of -0.0152812312485426, p6 has a value of -0.144722928657, p7 has a value of 0.0173966503166221, p8 has a value of -0.0544061648836112, p9 has a value of -, 0.00244095729665806, p10 has a value of 0.103563250165618, p11 has a value of 0.00767579945596927.

[0092] In a particular embodiment, the present invention relates to a method for determining the protein age of an individual's skin from a sample of stratum corneum, said protein age being of the chronological age type, comprising: - measuring the expression level of 27 proteins specific to the stratum corneum sample: COL1A2, HNRNPA2B1, GANAB, NUDT5, GSTO1, SERPINC1, PSMA7, POSTN, CD44, ARPC2, ATRN, HIST1 H2AH, COL1A1, PRDX2, PLTP, SOD1, DAG1, ATP6AP2, PRDX1, DSG1, HEXA, KRT5, YWHAE, ARHGDIA, GSN, PRSS3, GDI2, - the determination of the chronological age (Age_chrono) of the skin using a multiparametric model defined by a multiparametric linear equation of the form Chronological Age = p0 + p1 p1 + p2 p2+ p3 p3+ p4 p4+ p5 p5+ p6 p6 + p7 p7 + p8 p8+ p9 p9+ p10 p10+ p11 p11+ p12 p12+ p13 p13+ p14 p14+ p15 p15+ p16 p16+ p17 p17+ p18 p18+ p19 p19+ p20 p20+ p21 p21 + p22 p22 + p23 p23+ p24 p24+ p25 p25+ p26 p26+ p27 p27, with p1 corresponding to the expression level of COL1A2, p2 corresponding to the level of HNRNPA2B1 expression, p3 corresponding to the expression level of GANAB, p4 corresponding to the expression level of NUDT5, p5 corresponding to the expression level of GSTO1, p6 corresponding to the expression level of SERPINC1, p7 corresponding to the expression level of PSMA7, p8 corresponding to the expression level of POSTN, p9 corresponding to the expression level of CD44, p10 corresponding to the expression level of ARPC2, p11 corresponding to the expression level of ATRN,p12 corresponds to the expression level of HIST1 H2AH, p13 to the expression level of COL1A1, p14 to the expression level of PRDX2, p15 to the expression level of PLTP, p16 to the expression level of SOD1, p17 to the expression level of DAG1, p18 to the expression level of ATP6AP2, p19 to the expression level of PRDX1, p20 to the expression level of DSG1, p21 to the expression level of HEXA, p22 to the expression level of KRT5, p23 to the expression level of YWHAE, p24 to the expression level of ARHGDIA, p25 to the expression level of GSN, p26 to the expression level of PRSS3, p27 to the expression level of GDI2, where the intercept p0 has a value of 75.4220292850186 where p1 has a value of -0.00175401900642419, p2 has a value of -0.0174433918906582, p3 has a value of -0.0443047568242454,p4 has a value of -0.108485046631952, p5 has a value of -0.0359817567656793, p6 has a value of -0.0867027371711276, p7 has a value of -0.0239783505329508, p8 has a value of -0.000810781057423142, p9 has a value of -0.0297267816485288, p10 has a value of -0.00342485511521615, p11 has a value of -0.00475425193652521, p12 has a value of -0.0018476522553004, p13 has a value of -0.00139661433973241, pi4 has a value of -0.0015248972712091, p15 has a value of -0.00424124523189471, p16 has a value of -0.0107333361137629, p17 has a value of -0.00650763321268368, p18 has a value of -0.000537799397525631, p19 has a value of -, 0.000308343896009134, p20 has a value of 0.0555410765787683, p21 has a value of 0.030124517455003, p22 has a value of 0.00263692195722626, p23 has a value of 0.00818219468202281, p24 has a value of 0.00654925605234984, p25 has a value of 0.00567909644544739, p26 has a value of 0.0819112709621279, p27 has a value of 0.00281327797423664.

[0093] Another aspect of this application relates to a method for assessing the aging of an individual's skin, comprising comparing the protein age obtained by the method described above, measured at time T0, with the protein age obtained by the method described above, measured at time Tx. If the protein age value at Tx is greater than the protein age value at T0, there is evidence of skin aging in the individual. If the protein age value at Tx is less than the protein age value at T0, there is evidence of skin rejuvenation in the individual.

[0094] For the purposes of this application, x can be in weeks, months, or years, and Tx is greater than To. This applies to all methods described in this application.

[0095] Therefore, this application also relates to a method for assessing the aging of an individual's skin, comprising: - the determination at a time T0 of the protein age according to the previously described method, - the determination at a time Tx of the protein age according to the previously described method, - if the value (Vx) of the protein age at Tx is greater than the value (Vo) of the protein age at To, skin aging of the individual is proven.

[0096] If the Vx value of the protein age at Tx is less than the Vo value of the protein age at To, skin rejuvenation of the individual is proven.

[0097] This application also relates to a method for assessing the aging of an individual's skin, comprising comparing the biological age (Age_bio) obtained by the method described above with the chronological age (Age_chrono). The chronological age is subtracted from the biological age (Age_bio - Age_chrono). If the result is positive, this indicates premature aging of the individual's skin. This aging is more pronounced the higher the result. If the result is zero or negative, the individual does not show premature skin aging. In other words, premature skin aging is confirmed when the biological age is greater than the chronological age. The greater the difference, the more significant the premature aging.

[0098] Thus, the present invention relates to a method for evaluating the aging of an individual's skin, comprising: - the determination of the biological age type protein age (Age_bio) according to the previously described process; - the determination of the protein age of the chronological age type (Age_chrono), for example according to the procedure described above; - if the biological age is greater than the chronological age (Age_bio > Age_chrono), premature aging of the individual's skin is confirmed.

[0099] If biological age is less than chronological age (Age_bio < Age_chrono), no premature aging of the individual's skin is proven.

[0100] Chronological age is the actual age of an individual. It is obtained from the time elapsed since the individual's date of birth. It can also be obtained by the method for determining the chronological age of an individual from a sample of stratum corneum as described above.

[0101] In particular, it involves the assessment of facial skin aging.

[0102] Another aspect of the invention relates to a method for evaluating the effect of a product on an individual's skin, comprising the following steps: - A To, determination of the protein age of the individual from a sample of stratum corneum using a previously described method; - A Tx, determination of the individual's protein age from a sample of stratum corneum using a previously described method; - Comparison of protein age at To and protein age at Tx; - Evaluation of the product's effect.

[0103] A positive effect of the product is demonstrated when the protein age at Tx is lower than the protein age at To. A negative effect or no effect of the product is demonstrated when the protein age at Tx is greater than the protein age at To.

[0104] Another aspect of the invention relates to a method for evaluating the effect of a product on an individual's skin, comprising the following steps: - A To, determination of the biological age of the individual (Age_bioo) from a sample of stratum corneum from a previously described process, and determination of the chronological age of the individual (Age_chronoo); - Determination of the value of the difference between biological age and chronological age at To (ATo) by subtracting chronological age from biological age (ATo = Age_bioo - Age_chronoo); - At Tx, determination of the individual's biological age (Age_bio) x ) from a sample of stratum corneum using a previously described method, and determination of the chronological age of the individual (Chronological_Age) x ); - Determination of the difference between biological age and chronological age at Tx (ATx) by subtracting chronological age from biological age (ATx = Age_bio x - Age_chrono x ); - Comparison of ATo and ATx; - Evaluation of the product's effect.

[0105] A positive effect of the product is confirmed when ATx is less than ATo. A negative effect or lack of effect of the product is confirmed when ATx is greater than ATo.

[0106] An alternative method for evaluating the effect of a product on an individual's skin, comprising the following steps: - A To, determination of the biological age of the individual (Age_bioo) from a sample of stratum corneum from a previously described process, and determination of the chronological age of the individual (Age_chronoo); - Determination of the value of the difference between biological age and chronological age at To (ATo) by subtracting biological age from chronological age (ATo = Age_chronoo - Age_bioo) - At Tx, determination of the individual's biological age (Age_bio) x ) from a sample of stratum corneum using a previously described method, and determination of the chronological age of the individual (Chronological_Age) x ); - Determination of the value of the difference between biological age and chronological age at Tx (ATx) by subtracting biological age from chronological age (ATx = Age_chrono x - Age_bio x ) - Comparison of ATo and ATx; - Evaluation of the product's effect.

[0107] A positive effect of the product is confirmed when ATx is greater than ATo. A negative effect or lack of effect of the product is confirmed when ATx is less than ATo.

[0108] In these methods, chronological age can be obtained from the time elapsed since the individual's date of birth, or from the procedure for determining an individual's chronological age from a sample of stratum corneum as described previously.

[0109] In particular, it can be a method of evaluating the effect of a cosmetic or therapeutic product.

[0110] As an example of a cosmetic product, we can cite a product to combat skin aging such as an anti-wrinkle product, an antioxidant product, a UV protection product, a moisturizing product, an injectable product used in aesthetic medicine such as a product containing hyaluronic acid or botulinum toxin.

[0111] Thus, in particular, the positive effect is a skin rejuvenation effect.

[0112] A therapeutic product can be a product for treating a skin condition such as atopic dermatitis, eczema, or acne.

[0113] A cosmetic or therapeutic product can be a product for topical application to the skin. Topical application is application to the targeted area.

[0114] The cosmetic or therapeutic product can thus be in the form of: cream, emulsion, oil, serum.

[0115] A cosmetic or therapeutic product can be a product with a remote action. For example, it could be a product ingested to act on the skin at a distance. It can have a direct or indirect effect on the skin.

[0116] The cosmetic or therapeutic product can thus be in solid form (for example: powders, tablets, coated tablets, dragees, pills, capsules, caplets and dispersible granules) or liquid form (for example: solutions, emulsions, syrups, elixirs or suspensions).

[0117] In particular, it can be a method of assessing the negative effect of a product on the skin, for example, its harmfulness to the skin.

[0118] The negative or harmful effect therefore corresponds to skin aging.

[0119] A harmful product can, for example, be a cigarette, a food product, pollutants from skin exposure or not, pesticides, drugs, UV exposure, radioactivity. Examples

[0120] 1. Algorithmic machine learning solution

[0121] Using the R software (R version 4.4.1 (2024-06-14 ucrt)), the user runs the following commands sequentially: 1. Installation of the glmnet (version: 4.1-8) and data.table (version: 1.16.0) packages and libraries 2. Import the data and prepare the training objects for the "machine learning" algorithm: x_train and y_train in matrix and vector form respectively. 3. Launch the model training command (cv.glmnet) in cross-validation with Relax mode activated. 4. Choose the hyperparameters corresponding to the model with the minimum mean squared error 5. Run the (glmnet) command on the selected model 6. Display the model parameters (intercept and predictors) as well as the coefficients.

[0122] The glmnet library (v. 4.1-8) is an R package dedicated to estimating linear models and regularized generalized regressions, optimized for efficient computation on high-dimensional data. The data.table (v. 1.16.0) is a high-performance extension of R's data frames, enabling the manipulation, aggregation, and rapid processing of datasets through a concise syntax and optimized memory management.

[0123] The model developed thus makes it possible to determine the chronological predictive ages of individuals (or biological) from protein data (imported and prepared in matrix form as explained in 2) using the prediction command (predict) of the glmnet package.

[0124] This process is illustrated in Figures 1 and 2. The sequence diagrams describe the process of training an Elastic Net model in R to predict biological (Figure 1) or chronological (Figure 2) age from protein data. The user loads the libraries and data, prepares the training matrices, and configures cross-validation to optimize the parameters A_min and y_min. Cross-validation is an iterative machine learning process. which selects the optimal parameters (A_min and y_min) minimizing the validation error. The final model is fitted with these parameters, and the results (coefficients and graphs) are displayed to the user for analysis.

[0125] 2. Measurement of protein expression levels

[0126] As an example and proof of concept, the inventors quantified, using mass spectrometry, the protein extracts secreted by fibroblasts from skin biopsies of 18 individuals aged between 19 and 72 years. From the proteins identified, they selected a set of 210 proteins found in the human stratum corneum and used them as input data to develop chronological and biological clocks.

[0127] The same algorithmic sequence can be applied to other protein quantification methodologies.

[0128] Materials and methods of mass spectrometry analysis

[0129] After secretome collection and protein extraction, each sample was separated by SDS-PAGE (Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis) and then analyzed using HPLC (Bruker NanoElute) coupled to a mass spectrometer ("Impact II" by Bruker Daltonics). Peptides were separated on a C18 column (75 µm x 500 mm; Acclaim Pepmap RSLC, C18, 2 µm, 100 Angstroms) following a 5–26% B gradient in 45 min (A = 0.1% formic acid, 2% acetonitrile; B = 0.1% formic acid in 90% acetonitrile) at a flow rate of 400 nl / min. The spectra were reprocessed using DataAnalysis (Bruker) version 4.4 to generate an mgf file. Spectral data (mgf file) were analyzed via ProteinScape (Bruker) version 4.0.3 to query the databases with Mascot software (Matrix Science) version 2.6.0 (parameters: Database: SwissProt_2017_07_28; Enzyme: Trypsin; Var.Modifications: Oxidation (M); Deamidated (NQ); Fixed Modifications: Carbamidomethyl (C); Missed Cleavages: 2; Taxonomy: Homo sapiens (human); Instrument Type CID: ESI-QUAD-TOF; Peptide Tolerance: 10.0 ppm; MS / MS Tolerance: 0.05 Da; Peptide Charge: 1+, 2+, and 3+; Mass: Monoisotopic; C13: 1; Min. Peptide Length: 5; Peptide Decoy: ON; Adjust FDR [%]: 1; Percolator: ON; Ions Score Cut-off: 12; Ions Score Threshold for Significant Peptide IDs: 12). The quantity of proteins present in the samples is represented by a score. This score is based on the amount of peptide found, as well as the percentage of coverage. The score reflects the certainty of the protein's presence and its quantity.

[0130] 3. Determination of the weighting coefficient p

[0131] From the tables of quantification of stratum corneum proteins, the machine learning algorithmic solutions detailed in the sequence diagrams allow us to establish the coefficients of the multiparametric equations (Age = p0 + p1 p1 + p2p2 + ...+ pnpn) which allow us to determine the age (chronological or biological) of individuals. p0 corresponds to the intercept, That is, it represents the value of the dependent variable Age when all the protein predictors (p1, p2,... pn) are equal to zero (intercept). The coefficients p1 and p2 correspond to the coefficients associated with the predictors (quantity of proteins 1 and 2) defined as the foundation of the clock, and the coefficients pn correspond to the coefficients associated with the n predictors added to the equation to increase the prediction accuracy.

[0132] The intercept and weighting coefficients associated with the expression level of each protein are estimated during model training using the elastic net method implemented in the glmnet package (version 4.1-8). This estimation is based on the input data, namely the number, type, and expression level of proteins present in the stratum corneum of each individual in the model training cohort. It is also influenced by the model parameters, which allow for the exploration of different combinations of the glmnet model's regularization parameter α (lambda) and mixing parameter α (alpha), in order to select the most efficient multiparameter linear equation for determining biological age in the output data.

[0133] The parameter A (lambda) controls the overall intensity of the penalty applied to the model coefficients, while the parameter a (alpha) corresponds to the elastic net mixing parameter. The latter allows modulation of the trade-off between a ridge-type penalty (a = 0), a lasso-type penalty (a = 1), and an intermediate penalty combining the two (0 < a < 1).

[0134] For the establishment of the biological clock, coefficients and the intercept were determined for different groups of proteins selected from the following 34 proteins: COL1A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, SOD1, QPCT, NUDT5, COL1A1, POTSN, ARPC2, GRN, SERPINC1, ATP6AP2, PRSS3, ECM1, DSG1, GNB1, UBE2V1, APOD, KRT5, LMNA, GSN, HEXA, ARHGDIA, GDI2, CFD, PDIA4, YWHAE, KRT9, HIST1 H2AH.

[0135] Tables 4 through 17 define several biological clocks for different numbers of proteins selected from the list. They give the intercept (row 0) and coefficient values ​​for each protein for groups of 1, 2, 3, 4, 5, 6, 7, 9, 10, 12, 15, 20, 26, 31, or 34 proteins (row 1 and following). Each protein is listed in the right-hand column as "Accession ID_name of the gene that codes for the protein in humans."

[0136] [Table 4]

[0137] [Table 5]

[0138] [Table 6]

[0139] [Table 7]

[0140] [Table 8]

[0141] [Table 9]

[0142] [Table 10]

[0143] [Table 11]

[0144] [Table 12]

[0145] [Table 13]

[0146] [Table 14]

[0147] [Table 15]

[0148] [Table 16]

[0149] [Table 17]

[0150] The accuracy of the prediction from the number of proteins was evaluated by Pearson correlation analysis between the predicted age and the biological age.

[0151] Figure 3 shows that, for the biological clock, based on the expression level of three proteins, the reliability of biological age prediction is high (Pearson correlation coefficient r > 0.80). Thus, biological age can be determined from three proteins on the list.

[0152] Additional proteins can be considered to increase reliability. From 4 to all proteins on the list can be selected. With 7 or more proteins, the reliability of the biological age prediction is very high (correlation coefficient of Pearson r>0.95).

[0153] For the establishment of the chronological clock, coefficients and the intercept were determined for different groups of proteins selected from the following 27 proteins: PRDX1, COL1A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, SOD1, NUDT5, COL1A1, POSTN, PLTP, ARPC2, SERPINC1, ATP6AP2, PRSS3, DSG1, ATRN, KRT5, GSN, HEXA, ARHGDIA, GDI2, YWHAE and HIST1 H2AH.

[0154] Tables 18 through 31 define several time clocks for different numbers of proteins selected from the list. They give the intercept (row 0) and coefficient values ​​for each protein for groups of 1, 2, 3, 4, 5, 6, 7, 11, 12, 15, 16, 18, 21, or 27 proteins (row 1 and following). Each protein is listed in the right-hand column as "accession identifier in Uniprot_name of the gene that encodes the protein in humans."

[0155] [Table 18]

[0156] [Table 19]

[0157] [Table 20]

[0158] [Table 21]

[0159] [Table 22]

[0160] [Table 23]

[0161] [Table 24]

[0162] [Table 25]

[0163] [Table 26]

[0164] [Table 27]

[0165] [Table 28]

[0166] [Table 29] -30.

[0167] [Table 30]

[0168] [Table 31]

[0169] The accuracy of the prediction from the number of proteins was assessed by Pearson correlation analysis between predicted age and chronological age.

[0170] Figure 4 shows that for the chronological clock, based on the expression level of 3 proteins, the reliability of chronological age prediction is high (Pearson correlation coefficient r > 0.80). Thus, chronological age can be determined from 3 proteins in the list.

[0171] Additional proteins can be considered to increase reliability. From 4 to all proteins on the list can be selected. With 11 or more proteins, the reliability of the chronological age prediction is very high (Pearson correlation coefficient r > 0.95). List of documents cited

[0172] For the sake of clarity, the following non-patent elements are cited: 1. Abed K., et al. (2023) One-year longitudinal study of the stratum corneum proteome of retinol and all-trans-retinoic acid treated human skin: an orchestrated molecular event. Sci Rep 13, 11 196. 2. Azimi A., et al. (2021). Mass spectrometry-based proteomic analysis of the effect of storage temperature on non-invasively collected samples of human stratum corneum. Prot Clin Appl. 15:e2100005. 3. Friedman J., et al. (2010). “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software, Articles 33 (1): 1-22. 4. Tay KJ, et al. (2023). “Elastic Net Regularization Paths for All Generalized Linear Models.” Journal of Statistical Software, Articles 106 (1): 1-31. 5. Argentieri AM et al. (2024). Proteomic aging clock predicts mortality and risk of common age- related diseases in diverse populations. Nature medicine. 6. Carvalhaes Lago J., et al. (2019). The effect of aging in primary human dermal fibroblasts. PLOS ONE. 7. Han et al. (2005). Inhibitory role of peroxiredoxin II (Prx II) on cellular senescence. FEBS Letters 579, 4897-4902. 8. Lee et al. (2016). Changes in the expression of splicing factor transcripts and variations in alternative splicing are associated with lifespan in mice and humans. Aging Cell, 15, pp 903-913. 9. Papakonstantinou E. et al. (2012). Hyaluronic acid: a key molecule in skin aging. Dermatoendocrinology 4:3, 253-258. 10. Lôpez-Otîn C, et al. (2023). Hallmarks of aging: An expanding universe. Cell. 2023 Jan 19;186(2):243-278.

Claims

Demands

1. A method for determining the protein age of an individual's skin from a sample of stratum corneum, comprising: - the measurement of the expression level of at least three specific proteins from the stratum corneum sample, chosen from the following list of 37 proteins: COL1A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, SOD1, QPCT, NUDT5, COL1A1, POSTN, ARPC2, GRN, SERPINC1, ATP6AP2, PRSS3, ECM1, DSG1, GNB1, UBE2V1, APOD, KRT5, LMNA, GSN, HEXA, ARHGDIA, GDI2, CFD, PDIA4, YWHAE, KRT9, HIST1 H2AH, PRDX1, PLTP, ATRN; - the determination of the protein age of the skin using a multiparametric model defined by a multiparametric linear equation of the form Protein Age = 00 + p1 p1 + p2 p2 +...+ pn pn, where p0 is intercept, p1, p2, ...pn are coefficients, p1, p2, ...pn represent the level of expression of each protein, the number of proteins chosen from the list being equal to n.

2. Method according to claim 1, comprising a prior training step of said multiparameter, wherein the basic constant p0 and the p1, p2, ... pn are weighting coefficients determined by learning.

3. A method according to claim 2, wherein the learning is carried out by the elastic net method.

4. A method according to any one of claims 1 to 3, wherein said protein age is of the biological age type, and wherein said at least three proteins are selected from the following list of 34 proteins: COL1A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, SOD1, QPCT, NUDT5, COL1A1, POSTN, ARPC2, GRN, SERPINC1, ATP6AP2, PRSS3, ECM1, DSG1, GNB1, UBE2V1, APOD, KRT5, LMNA, GSN, HEXA, ARHGDIA, GDI2, CFD, PDIA4, YWHAE, KRT9, HIST1 H2AH.

5. A process according to claim 4, wherein the proteins are: COL1A2, HNRNPA2B1, GANAB, NUDT5, KRT5, YWHAE and HEXA.

6. A method according to any one of claims 1 to 3, wherein said protein age is of the chronological age type, and wherein said at least three proteins are selected from the following list of 27 proteins: PRDX1, COL1 A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, SOD1, NUDT5, COL1 A1, POSTN, PLTP, ARPC2, SERPINC1, ATP6AP2, PRSS3, DSG1, ATRN, KRT5, GSN, HEXA, ARHGDIA, GDI2, YWHAE and HIST1 H2AH.

7. A process according to claim 6, wherein the proteins are: COL1A2, HNRNPA2B1, GANAB, PSMA7, GSTO1, NUDT5, POSTN, SERPINC1, DSG1, KRT5 and HEXA.

8. A method according to claim 4, wherein said protein age is of the biological age type, comprising: - measurement of the expression level of nine proteins specific to the stratum corneum sample: COL1A2, HNRNPA2B1, GANAB, NUDT5, KRT5, YWHAE, PSMA7, SERPINC1 and HEXA; - the determination of the biological age (Age_bio) of the skin using a multiparametric model defined by a multiparametric linear equation of the form Biological Age = pO + p1 p1 + p2 p2 + p3 p3 + p4 p4 + p5 p5 + p6 p6 + p7 p7 + p8 p8 + p9 p9, with p1 corresponding to the expression level of COL1A2, p2 corresponding to the expression level of HNRNPA2B1, p3 corresponding to the expression level of GANAB, p4 corresponding to the expression level of NUDT5, p5 corresponding to the expression level of KRT5, p6 corresponding to the expression level of YWHAE, p7 corresponding to the expression level of PSMA7, p8 corresponding to the expression level of SERPINC1, p9 corresponding to the expression level of HEXA, where the intercept pO has a value of 91 ,1370464900088 where p1 has a value of -0.00401317298712054, p2 has a value of -0.0417935038376829, p3 has a value of -0.0458803799667517, p4 has a value of -0.147933198469151, p5 has a value of 0.014857643266839, p6 has a value of 0,0296399638147097, p7 has a value of -0.0711575967842609, p8 has a value of -0.0951110140646376, p9 has a value of 0.0693549118788835.

9. A method according to claim 4, wherein said protein age is of the biological age type, comprising: - measurement of the expression level of 34 specific proteins from the stratum corneum sample: PSMA7, ARPC2, ATP6AP2, SOD1, SERPINC1, COL1 A1, COL1 A2, CD44, HNRNPA2B1, GRN, PRDX2, GSTO1, DAG1, GANAB, POSTN, ECM1, QPCT, HIST1 H2AH, NUDT5, CFD, LMNA, APOD, GSN, HEXA, KRT5, PDIA4, PRSS3, KRT9, GDI2, ARHGDIA, YWHAE, GNB1, DSG1, and UBE2V1; - the determination of the biological age (Age_bio) of the skin using a multiparametric model defined by a multiparametric linear equation of the form Biological Age = p0 + p1 p1 + p2 p2 + p3 p3 + p4 p4 + p5 p5 + p6 p6 + p7 p7 + p8 p8 + p9 p9 + p10 p10 + p11 p11 + p12 p12 + p13 p13 + p14 p14 + p15 p15 + p16 p16 + p17 p17 + p18 p18 + p19 p19 + p20 p20 + p21 p21 + p22 p22 + p23 p23 + p24 p24 + p25 p25 + p26 p26 + p27 p27 + p28 p28 + p29 p29 + p30 p30 + p31 p31+ p32 p32+ p33 p33+ p34 p34, with p1 corresponding to the expression level of PSMA7, p2 corresponding to the expression level of ARPC2, p3 corresponding to the expression level of ATP6AP2, p4 corresponding to the expression level of SOD1, p5 corresponding to the expression level of SERPINC1, p6 corresponding to the expression level of COL1A1, p7 corresponding to the expression level of COL1A2, p8 corresponding to the expression level of CD44, p9 corresponding to the expression level of HNRNPA2B1, p10 corresponding to the expression level of GRN,p11 corresponding to the expression level of PRDX2, p12 corresponding to the expression level of GSTO1, p13 corresponding to the expression level of DAG1, p14 corresponding to the expression level of GANAB, p15 corresponding to the expression level of POSTN, p16 corresponding to the expression level of ECM1, p17 corresponding to the expression level of QPCT, p18 corresponding to the expression level of HIST1 H2AH, p19 corresponding to the level, of NUDT5 expression, p20 corresponding to the CFD expression level, p21 corresponding to the LMNA expression level, p22 corresponding to the APOD expression level, p23 corresponding to the GSN expression level, p24 corresponding to the HEXA expression level, p25 corresponding to the KRT5 expression level, p26 corresponding to the PDIA4 expression level, p27 corresponding to the PRSS3 expression level, p28 corresponding to the KRT9 expression level, p29 corresponding to the GDI2 expression level, p30 corresponding to the ARHGDIA expression level, p31 corresponding to the YWHAE expression level, p32 corresponding to the GNB1 expression level, p33 corresponding to the DSG1 expression level, p34 corresponding to the UBE2V1 expression level, where the intercept pO has a value of 79.4680521908027 where p1 has a value of -0.0447572537143951, p2 has a value of -0.0310893990991177, p3 has a value of -0.00526910616360357, p4 has a value of -0.0154536611514652,p5 has a value of -0.0251078166283137, p6 has a value of -0.00123780799939871, p7 has a value of -0.00129147564954815, p8 has a value of -0.0112571354761862, p9 has a value of -0.0235927873242526, p10 has a value of -0.00428361067532905, p11 has a value of -0.014156859383358, p12 has a value of -0.0411456223759342, p13 has a value of - 0.00785694846132329, p14 has a value of -0.0551672865234715, p15 has a value of -0.00161450096578245, p16 has a value of -0.00195564946365248, p17 has a value of -0.00261730638760361, p18 has a value of -0.00265332762617516, p19 has a value of -0.0882663376927517, p20 has a value of 0.00224875794993041, p21 has a value of 0.000130851101415848, p22 has a value of 0.0127107649500224, p23 has a value of 0.0165063824751699, p24 has a value of 0.0392367219664753, p25 has a value of 0.0103881509964209, p26 has a value of 0.00372364646634297, p27 has a value of 0.0165315774827813, p28 has a value of 0.00201489153002412,p29 has a value of 0.00431305397957548, p30 has a value of 0.0185028036575522, p31 has a value of 0.0220669277076333, p32 has a value of 0.0485986821523148, p33 has a value of 0.0405096395016236, p34 has a value of 0.0109736173871865.

10. A method according to claim 6 or 7, wherein said protein age is of the chronological age type, comprising: - measurement of the expression level of seven proteins specific to the stratum corneum sample: COL1 A2, HNRNPA2B1, GANAB, NUDT5, GSTO1, SERPINC1, and DSG1, - the determination of the chronological age (Age_chrono) of the skin using a multiparametric model defined by a multiparametric linear equation of the form Chronological Age = p0 + p1 p1 + p2 p2 + p3 p3 + p4 p4 + p5 p5 + p6 p6 + p7 p7, with p1 corresponding to the expression level of COL1A2, p2 corresponding to the expression level of HNRNPA2B1, p3 corresponding to the expression level of GANAB, p4 corresponding to the expression level of NUDT5, p5 corresponding to the expression level of GSTO1, p6 corresponding to the expression level of SERPINC1, p7 corresponding to the expression level of DSG1 where the intercept p0 has a value of 77.8072752355772 where p1 has a value of -0.00338336126413298, p2 has a value of -0.0190120901871719, p3 has a value of -0.0459620434776537, p4 has a value of -0.114398643955703, p5 has a value of -0.0268160371770119, p6 has a value of -0.0147769989797443 and p7 has a value of 0.0531949722591303.

11. A method according to claim 6 or 7, wherein said protein age is of the chronological age type, comprising: - measurement of the expression level of 11 proteins specific to the stratum corneum sample: COL1A2, HNRNPA2B1, GANAB, NUDT5, GSTO1, SERPINC1, DSG1, PSMA7, POSTN, HEXA and KRT5, - the determination of the chronological age (Age_chrono) of the skin using a multiparametric model defined by a multiparametric linear equation of the form Chronological Age = p0 + p1 p1 + p2 p2+ p3 p3+ p4 p4+ p5 p5+ p6 p6 + p7 p7 + p8 p8+ p9 p9+ p10 p10+ p11 p11 , with p1 corresponding to the expression level of COL1A2, p2 corresponding to the expression level of HNRNPA2B1, p3 corresponding to the expression level of GANAB, p4 corresponding to the expression level of NUDT5, p5 corresponding to the expression level of GSTO1, p6 corresponding to the expression level of SERPINC1, p7 corresponding to the expression level of DSG1, p8 corresponding to the expression level of PSMA7, p9 corresponding to the expression level of POSTN, p10 corresponds to the expression level of HEXA, and p11 corresponds to the expression level of KRT5, where the intercept p0 has a value of 87.7607313714849 where p1 has a value of -0.00539528956188052, p2 has a value of -0.0476808808971591 ,p3 has a value of -0.00589752000715537, p4 has a value of -0.216167597972179, p5 has a value of -0.0152812312485426, p6 has a value of -0.144722928657, p7 has a value of 0.0173966503166221, p8 has a value of -0.0544061648836112, p9 has a value of -0.00244095729665806, p10 has a value of 0.103563250165618, p11 has a value of 0.00767579945596927.

12. A method according to claim 6, wherein said protein age is of the chronological age type, comprising: - measuring the expression level of 27 proteins specific to the stratum corneum sample: COL1A2, HNRNPA2B1, GANAB, NUDT5, GSTO1, SERPINC1, PSMA7, POSTN, CD44, ARPC2, ATRN, HIST1 H2AH, COL1 A1, PRDX2, PLTP, SOD1, DAG1, ATP6AP2, PRDX1, DSG1, HEXA, KRT5, YWHAE, ARHGDIA, GSN, PRSS3, GDI2, - the determination of the chronological age (Age_chrono) of the skin using a multiparametric model defined by a multiparametric linear equation of the form Chronological Age = p0 + p1 p1 + p2 p2+ p3 p3+ p4 p4+ p5 p5+ p6 p6 + p7 p7 + p8 p8+ p9 p9+ p10 p10+ p11 p11+ p12 p12+ p13 p13+ p14 p14+ p15 p15+ p16 p16+ p17 p17+ p18 p18+ p19 p19+ p20 p20+ p21 p21 + p22 p22 + p23 p23+ p24 p24+ p25 p25+ p26 p26+ p27 p27, with p1 corresponding to the expression level of COL1A2, p2 corresponding to the level of HNRNPA2B1 expression, p3 corresponding to the expression level of GANAB, p4 corresponding to the expression level of NUDT5, p5 corresponding to the expression level of GSTO1, p6 corresponding p7 corresponds to the expression level of SERPINC1, p8 to the expression level of POSTN, p9 to the expression level of CD44, p10 to the expression level of ARPC2, p11 to the expression level of ATRN, p12 to the expression level of HIST1 H2AH, p13 to the expression level of COL1A1, p14 to the expression level of PRDX2, p15 to the expression level of PLTP, p16 to the expression level of SOD1, p17 to the expression level of DAG1, p18 to the expression level of ATP6AP2, p19 to the expression level of PRDX1, p20 to the expression level of DSG1, p21 to the expression level of HEXA, p22 to the expression level of KRT5, p23 corresponds to the expression level of YWHAE, p24 corresponds to the expression level of ARHGDIA,p25 corresponds to the expression level of GSN, p26 to the expression level of PRSS3, p27 to the expression level of GDI2, where the intercept pO has a value of 75.4220292850186, where p1 has a value of -0.00175401900642419, p2 has a value of -0.0174433918906582, p3 has a value of -0.0443047568242454, p4 has a value of -0.108485046631952, p5 has a value of -0.0359817567656793, p6 has a value of -0.0867027371711276, p7 has a value of - 0.0239783505329508, p8 has a value of -0.000810781057423142, p9 has a value of -0.0297267816485288, p10 has a value of -0.00342485511521615, p11 has a value of -0.00475425193652521, p12 has a value of -0.0018476522553004, p13 has a value of -, 0.00139661433973241, pi 4 has a value of -0.0015248972712091, p15 has a value of - 0.00424124523189471, p16 has a value of -0.0107333361137629, p17 has a value of - 0.00650763321268368, p18 has a value of -0.000537799397525631, p19 has a value of - 0.000308343896009134, p20 has a value of 0.0555410765787683, p21 has a value of 0.030124517455003, p22 has a value of 0.00263692195722626, p23 has a value of 0.00818219468202281, p24 has a value of 0.00654925605234984, p25 has a value of 0.00567909644544739, p26 has a value of 0.0819112709621279, p27 has a value of 0.00281327797423664.

13. A method for assessing the aging of an individual's skin comprising: - the determination at a time T0 of the protein age according to the method of any one of claims 1 to 12, - the determination at a time Tx of the protein age according to the method of one of claims 1 to 12, - if the value (Vx) of the protein age at Tx is greater than the value (Vo) of the protein age at To, skin aging of the individual is proven.

14. A method for assessing the aging of an individual's skin comprising: - the determination of the protein age of the biological age type according to the method of one of claims 4, 5, 8 or 9; - the determination of the protein age of the chronological age type, for example according to the method of one of claims 6, 7, 10 to 12; - if the biological age is greater than the chronological age, premature aging of the individual's skin is proven.

15. A method for evaluating the effect of a product on an individual's skin comprising the following steps: - A To, determination of the biological age of the individual (Age_bioo) from a sample of stratum corneum according to the method of one of claims 4, 5, 8 or 9, and determination of the chronological age of the individual (Age_chronoo) for example according to the method of one of claims 6, 7, 10 to 12; - Determination of the value of the difference between biological age and chronological age at To (ATo) by subtracting chronological age from biological age (ATo = Age_bioo - Age_chronoo); - At Tx, determination of the individual's biological age (Age_bio) x ) from a sample of stratum corneum according to the method of one of claims 4, 5, 8 or 9, and determination of the chronological age of the individual (Chronological_Age) x ) for example according to the method of one of claims 6, 7, 10 to 12; - Determination of the difference between biological age and chronological age at Tx (ATx) by subtracting chronological age from biological age (ATx = Age_bio x - Age_chrono x ); - Comparison of ATo and ATx; - Evaluation of the effect of the product: positive effect when ATx is less than ATo, negative effect or no effect when ATx is greater than ATo.

Images