Determining the protein age of an individual's skin

Abstract

The present invention relates to biological clocks for measuring skin aging by measuring the level of protein expression in a sample of stratum corneum from an individual. [Fig. 1]

Description

Title of the invention: Determination of the protein age of an individual's skin technical field

[0001] The present disclosure falls within the field of protein clocks which make it possible to measure aging from biological markers (genetic, epigenetic, protein, telomeric etc.) from various biological samples. Previous technique

[0002] In contact with the environment, the skin is the largest human organ. Its aging is the result of intrinsic factors, but also extrinsic factors linked to its exposure to various agents (toxins, solar radiation, pollutants, stress, etc.) grouped under the name "exposome." Researchers therefore aim to develop methods to assess the condition of the skin, identify early signs of disease, and propose appropriate therapeutic solutions.

[0003] The assessment of the biological age of the skin is a major issue, allowing the impact of cosmetic and medical treatments to be measured.

[0004] Measuring the biological age of the skin makes it possible to monitor 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 the biological age of the skin 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 from the blood plasma proteome of thousands of individuals. However, although a set of protein factors have 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 established a biological clock from stratum corneum proteins.

[0009] The inventors have selected a limited number of proteins found in both fibroblast secretion and stratum corneum to calculate the biological age of the skin.

[0010] Based on a comprehensive quantitative proteomic analysis conducted on cutaneous fibroblast secretions from 18 individuals aged between 19 and 72 years, they selected 210 proteins present in the stratum corneum. Protein expression levels were correlated with the age of the individuals using a machine learning approach. The inventors then selected 27 proteins whose expression level correlated with the chronological age of the individuals.

[0011] In order 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 makes it possible, using a sample of stratum corneum from an individual, to measure the skin's biological age and thus assess the deviation from their chronological age, i.e., the acceleration of their skin aging. Brief description of the invention

[0012] The present application relates to a method for determining the protein age of an individual's skin from a sample of stratum corneum, comprising: - measuring the level of expression of at least three specific proteins of 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, HIST1H2AH, 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 = [30 + [31 pl + [32 p2 +...+ [3n pn, where [30 is the intercept, [31,[32, ...[3n are coefficients, pl,p2,...pn represent the level of expression of each protein, the number of proteins chosen from the list being equal to n.

[0013] In one embodiment, said protein age is of the biological age type, and in which said at least three proteins are 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, HIST1H2AH.

[0014] 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 HIST1H2AH.

[0015] According to another aspect, the present application relates to a method for evaluating the aging of an individual's skin comprising: - the determination at a time To of the protein age, - the determination at a time Tx of the protein age, - if the value (Vx) of the protein age at Tx is greater than the value (Vo) of the protein age at To, an aging of the individual's skin is proven.

[0016] According to another aspect, the present application relates to 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.

[0017] According to another aspect, the present application relates to a method for evaluating the effect of a product on the skin of an individual comprising the following steps: - A To, determination of the biological age of the individual (Age_bio0) from a sample of stratum comeum, and determination of the chronological age of the individual (Age_chrono0); - Determination of the value of the difference between biological age and chronological age at To (AT0) by subtracting chronological age from biological age (AT0 = Age_bio0 -Age_chrono0); - A Tx, determination of the biological age of the individual (Age_biox) from a sample of stratum comeum, and determination of the chronological age of the individual (Age_chronox); - Determination of the value of the difference between biological age and chronological age at Tx (ATX) by subtracting chronological age from biological age (ATX = Age_biox -Age_chronox); - Comparison of AT0 and ATX; - Evaluation of the effect of the product: positive effect when ATX is less than AT0, negative effect or no effect when ATX is greater than AT0. Brief description of the drawings Fig. 1

[0018] [Fig. 1] Sequence diagram of the algorithm used for the design of biological clock models Fig. 2

[0019] [Fig.2] Sequence diagram of the algorithm used for the design of chronological clock models Fig. 3

[0020] [Fig. 3] Graph A: Pearson correlation coefficient 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 = COL1A2, 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 = COL1A2, 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, COL1A1, POSTN, ARPC2, SERPINC1, ATP6AP2, ECM1, DSG1, GNB1, UBE2V1, APOD, KRT5, GSN, HEXA, ARHGDIA, CFD and YWHAE ; 31P = COL1A2, 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 HIST1H2AH;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, HIST1H2AH. For example, the vertical bar representing an intersection of 5 proteins corresponds to the proteins present only in the 20, 26, 31 and 34 protein clocks as indicated by the connected black dots located with respect to the vertical bar and the sets concerned by this intersection. Fig. 4

[0021] [Fig.4] Graph A: Pearson correlation coefficient as a function of Number of predictors (proteins) used for the chronological clock model. Figure B: Upset diagram of comparisons of 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 dataset 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; 11P= COL1A2, HNRNPA2B1, GANAB, PSMA7, GSTO1, NUDT5, POSTN, SERPINC1, DSG1, KRT5 and HEXA; 12P= COL1A2, 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 HIST1H2AH ; 18P = COL1A2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, NUDT5, COL1A1, POSTN, ARPC2, SERPINC1, DSG1, ATRN, KRT5, GSN, HEXA, ARHGDIA and YWHAE ; 21P = COL1A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, NUDT5, COL1A1, POSTN, PLTP, ARPC2, SERPINC1, DSG1, ATRN, KRT5, GSN, HEXA, ARHGDIA, YWHAE et HIST1H2AH ; 27P = 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 HIST1H2AH. ; Description of the invention

[0022] The present invention proposes a method for determining the protein age of an individual's skin from a sample of stratum comeum, by measuring the level of expression of chosen proteins and using a multiparametric model.

[0023] 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 comeum, 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 chronological age of an individual's skin from a sample of stratum comeum, through the measurement of the level of expression of chosen proteins and the use of a multiparametric model.

[0025] 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 the invention, it is used as a reference point for comparison with biological age, which can differ depending on various health and aging factors.

[0026] 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 protein expression, 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.

[0027] Thus, individuals of the same calendar age, i.e. chronological age, may have a different biological age depending on intrinsic factors (genetics, epigenetics...) and on extrinsic factors (UV exposure, pollution, tobacco...).

[0028] 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 comeocytes. This layer plays a crucial role in protecting the skin against external aggressions by 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.

[0029] This may include, for example, the stratum corneum of the face, hands, arms, legs or any other part of the surface of the human body.

[0030] The expression level of a protein 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 the age of the individual. In the context of the invention, the expression level of specific proteins in the stratum corneum can be used to assess skin aging.

[0031] 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, HIST1H2AH, 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 = [30 + [31 pl + [32 p2 +...+ [3n pn, where [30 is the intercept, [31, [32, ... [3n are coefficients, pl, p2, ... pn represent the level of expression of each protein, the number of proteins chosen from the list being equal to n.

[0032] [30 corresponds to the intercept, that is to say represents the value of the dependent variable Age when all protein predictors (pl, p2, .. .pn) are equal to zero (intercept). [30 is therefore a basic constant.

[0033] The coefficients [31, [32 correspond to the coefficients associated with the predictors (quantity of proteins 1 and 2) defined as the basis of the clock and the coefficients [3n correspond to the coefficients associated with the n predictors added to the equation. [31, [32, ... [3n can also be called weighting coefficients.

[0034] In one embodiment, the method includes a preliminary step of training said multiparametric linear equation, in which the intercept PO and the . end are weighting coefficients determined by learning.

[0035] Typically, learning is carried out using the elastic net method.

[0036] 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 where the explanatory variables are numerous or highly correlated.

[0037] The Lasso method favors the selection of variables 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.

[0038] 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.

[0039] 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.

[0040] The level of protein expression can be quantified by mass spectrometry.

[0041] Other protein quantification techniques can be considered, by going From the simplest immunological techniques to the most 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, and NanoString protein assays). These techniques are well-known to any professional seeking to measure the expression level of a protein in a sample.

[0042] The proteins cited in this application 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.

[0043] [Tables 1]

[0044]

[0045]

[0046] PESE23 732119 P22526 014637 024318 P78427 QiiiiE PSG44Î. 0.26769 QSUCK9 PS2452 QSSS5 U^l “il P23?æ Foieœ O7S787 O§5O Q25518 iioii 962x75 075882 Q13464 PS5B9£^ P13647 PŒ539S PQ6865 pssss Pæ745 P13667 PS2258 PSSS27 Codagen 3ipha-2(i) Chain Ol^OæOihSissssssssli® Hetephogeneousnoïie as ribonudeop««teins AÿSl felt a(sha-g^üsosidasE A3 Peoteasofse subunit sipha.»?....................................................................... GSüïathsonë S-transfa rasomega-1 Süp=rsxid= àsmytase [Cu-Zn] A SP- su^ r py rophosphata se Historical HSA type 1-H Oh?iSSîb;iiSSSSSSSSSS| Phosphos ipid transfer probe Sn iiOîO:S?^OS^SS:ïæOi®0^ Grain^ns Renia receptor This is a biar even if it lends its 1 i^sSSsgiæOilïïïïïïïïïiiiiiiiiiiij Guan^e nucJes^be-bSn-ding prêts in G(i| / G(S's / GfT} suruh it fee ta-i oooiiiiiiiiiiiiiiiiiiiiiiiiiiii Uhiquitin-conjugatingenzyme E2 variant i Keratin, type iScytaskeietai 5 Geisoiia Rho GDP-dssocsstson inh^or 1 Corn piement facto r D 14-3- 3 protest options iSI® SS® Hc®s sapiens COE2A2 ;WNRNPA2B1 liioiiiiii Homo sspsens GAHS;" Homo sapsns GSTO1 isHSiOiSèèèOssïï IsOsltlthl Homo sapsns EOOi Homo sspsens jN:U3“5 Homo sapiens iHSTHPHHH Hcsmss sspjsrss GRH Biiiiéiii Homo sapiens iATPSAPS Homo sapiens ;ECM1 Homo sap s os ô?-;6i losiiiiii Homo sapiens :U3E2V1 Homo sapiens iKRTS: lioiiiili Homo sapiens SS H iiiifiiiii Homo sapiens OSSI GDSSAB sapiens iCFD ligiiossBgiisA iilOiilli Homo sapiens VWHAE For the purposes of this application, the stratum comeum 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 D-squame stripping. Slightly invasive techniques can be used such as cyanoacrylate skin surface stripping, microdermabrasion or curettage. 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.

[0047] 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, HIST1H2AH.

[0048] Thus, in particular, said method for determining protein age makes it possible to determine the biological age of an individual's skin from a sample of stratum comeum, and includes the measurement of the level of expression of at least three proteins 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, HIST1H2AH.

[0049] “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.

[0050] In particular, 3 proteins are selected from the list. These are, for example, COL1A2, HNRNPA2B1 and GANAB.

[0051] In particular, 4 proteins are selected from the list. These are, for example, COL1A2, HNRNPA2B1, GANAB and NUDT5.

[0052] In particular, 5 proteins are selected from the list. These include, for example, COL1A2, HNRNPA2B1, GANAB, NUDT5, and KRT5.

[0053] In particular, 6 proteins are selected from the list. These include, for example, COL1A2, HNRNPA2B1, GANAB, NUDT5, KRT5 and YWHAE.

[0054] In particular, 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.

[0055] In particular, 9 proteins are selected from the list. These include, for example, COL1A2, HNRNPA2B1, GANAB, NUDT5, KRT5, YWHAE, HEXA, SERPINC1 and PSMA7.

[0056] In particular, 10 proteins are selected from the list. These include, for example, COL1A2, HNRNPA2B1, GANAB, NUDT5, KRT5, YWHAE, HEXA, SERPINC1, PSMA7 and POSTN.

[0057] In particular, 12 proteins are selected from the list. These include, for example, C0L1A2, HNRNPA2B1, GANAB, NUDT5, KRT5, YWHAE, HEXA, SERPINC1, PSMA7, POSTN, CD44 and DSG1.

[0058] In particular, 15 proteins are selected from the list. These include, for example, COL1A2, HNRNPA2B1, GANAB, PSMA7, GSTO1, NUDT5, POSTN, SERPINC1, ATP6AP2, DSG1, GNB1, KRT5, GSN, HEXA, YWHAE.

[0059] In particular, 20 proteins are selected from the list. These include, for example, COL1A2, PRDX2, HNRNPA2B1, GANAB, PSMA7, GSTO1, DAG1, NUDT5, POSTN, ARPC2, SERPINC1, ATP6AP2, ECM1, DSG1, GNB1, APOD, KRT5, GSN, HEXA, and YWHAE.

[0060] In particular, 26 proteins are selected from the list. These include, for example, COL1A2, 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.

[0061] In particular, 31 proteins are selected from the list. These include, for example, COL1A2, 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 HIST1H2AH.

[0062] In particular, the 34 proteins are selected from the list.

[0063] 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 comeum, 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, HIST1H2AH; - 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 = [30 + [31 pl + [32 p2 +...+ [3n pn, where [30 is the intercept, [31, [32, ..., [3n are coefficients, pl, p2, ..., pn represent the expression level of each protein, the number of proteins chosen from the list being equal to n.

[0064] According to one embodiment, the protein age is of the chronological age type and said at least three proteins are chosen from the following list of 27 proteins: PRDX1, C0L1A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GST01, DAG1, SOD1, NUDT5, COL1A1, POSTN, PLTP, ARPC2, SERPINC1, ATP6AP2, PRSS3, DSG1, ATRN, KRT5, GSN, HEXA, ARHGDIA, GDI2, YWHAE and HIST1H2AH.

[0065] Thus, in particular, said method for determining protein age makes it possible to determine the chronological age of an individual's skin from a sample of stratum comeum, and includes measuring the level of expression of at least three proteins 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 HIST1H2AH.

[0066] “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.

[0067] In particular, 3 proteins are selected from the list. These are, for example, COL1A2, HNRNPA2B1 and GANAB.

[0068] In particular, 4 proteins are selected from the list. These are, for example, COL1A2, HNRNPA2B1, GANAB and NUDT5.

[0069] In particular, 5 proteins are selected from the list. These include, for example, COL1A2, HNRNPA2B1, GANAB, GSTO1 and KRT5.

[0070] In particular, 6 proteins are selected from the list. These include, for example, COL1A2, HNRNPA2B1, GANAB, GSTO1, NUDT5 and DSG1.

[0071] In particular, 7 proteins are selected from the list. These include, for example, COL1A2, HNRNPA2B1, GANAB, GSTO1, KRT5, NUDT5, SERPINC1 and DSG1.

[0072] In particular, 11 proteins are selected from the list. These include, for example, COL1A2, HNRNPA2B1, GANAB, PSMA7, GSTO1, NUDT5, POSTN, SERPINC1, DSG1, KRT5, and HEXA. Thus, in a particular embodiment, the proteins are: COL1A2, HNRNPA2B1, GANAB, PSMA7, GSTO1, NUDT5, POSTN, SERPINC1, DSG1, KRT5, and HEXA.

[0073] 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.

[0074] In particular, 15 proteins are selected from the list. These include, for example, COL1A2, HNRNPA2B1, GANAB, PSMA7, GSTO1, NUDT5, POSTN, ARPC2, SERPINC1, ATRN, KRT5, GSN, HEXA, ARHGDIA and YWHAE.

[0075] In particular, 16 proteins are selected from the list. These include, for example, COL1A2, HNRNPA2B1, GANAB, PSMA7, GSTO1, NUDT5, POSTN, ARPC2, SERPINC1, ATRN, KRT5, GSN, HEXA, ARHGDIA, YWHAE and HIST1H2AH.

[0076] In particular, 18 proteins are selected from the list. These include, for example, C0L1A2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, NUDT5, COL1A1, POSTN, ARPC2, SERPINC1, DSG1, ATRN, KRT5, GSN, HEXA, ARHGDIA and YWHAE.

[0077] In particular, 21 proteins are selected from the list. These include, for example, COL1A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, NUDT5, COL1A1, POSTN, PLTP, ARPC2, SERPINC1, DSG1, ATRN, KRT5, GSN, HEXA, ARHGDIA, YWHAE and HIST1H2AH.

[0078] In particular, the 27 proteins are selected from the list.

[0079] 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 comeum, said protein age being of the chronological age type, comprising: - 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 HIST1H2AH; - 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 = [30 + [31 pl + [32 p2 +...+ [3n pn, where [30 is the intercept, [31, [32, [33.. .[3n are coefficients, pl, p2, .... pn represent the level of expression of each protein, the number of proteins chosen from the list being equal to n.

[0080] Another aspect of the present 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.

[0081] For the purposes of this application, x can be in weeks, months, or years, and Tx is greater than To. This applies to all the methods described in this application.

[0082] Thus, the present 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.

[0083] If the Vx value of the protein age at Tx is less than the Vo value of the protein age at To, a rejuvenation of the individual's skin is proven.

[0084] 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 evident when the biological age is greater than the chronological age. The greater the difference, the more pronounced the premature aging.

[0085] 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.

[0086] If the biological age is less than the chronological age (Age_bio < Age_chrono), no premature aging of the individual's skin is proven.

[0087] Chronological age is the actual age of the 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 comeum as described above.

[0088] In particular, this relates to the assessment of facial skin aging.

[0089] 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 comeum using a previously described method; - A Tx, determination of the protein age of the individual from a sample of stratum comeum using a previously described method; - Comparison of protein age at To and protein age at Tx; - Evaluation of the product's effect.

[0090] A positive effect of the product is demonstrated when the protein age at Tx is less 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.

[0091] 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_bio0) from a sample of stratum comeum from a previously described process, and determination of the chronological age of the individual (Age_chrono0); - Determination of the value of the difference between biological age and chronological age at To (AT0) by subtracting chronological age from biological age (AT0 = Age_bio0 -Age_chrono0); - A Tx, determination of the biological age of the individual (Age_biox) from a sample of stratum comeum from a previously described process, and determination of the chronological age of the individual (Age_chronox); - Determination of the value of the difference between biological age and chronological age at Tx (ATX) by subtracting chronological age from biological age (ATX = Age_biox -Age_chronox); - Comparison of AT0 and ATX; - Evaluation of the product's effect.

[0092] A positive effect of the product is demonstrated when ATX is less than AT0. A negative effect or no effect of the product is demonstrated when ATX is greater than AT0.

[0093] 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_bio0) from a sample of stratum comeum from a previously described process, and determination of the chronological age of the individual (Age_chrono0); - Determination of the value of the difference between biological age and chronological age at To (AT0) by subtracting biological age from chronological age (AT0 = Age_chrono0 - Age_bio0) - A Tx, determination of the biological age of the individual (Age_biox) from a sample of stratum comeum from a previously described process, and determination of the chronological age of the individual (Age_chronox); - 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_chronox - Age_biox) - Comparison of AT0 and ATX; - Evaluation of the product's effect.

[0094] A positive effect of the product is demonstrated when ATX is greater than AT0. A negative effect or no effect of the product is demonstrated when ATX is less than AT0.

[0095] In these methods, chronological age can be obtained from the time elapsed since the date of birth of the individual, or from the process for determining the chronological age of an individual from a sample of stratum comeum as described above.

[0096] In particular, this can be a method for evaluating the effect of a cosmetic or therapeutic product.

[0097] 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.

[0098] Thus, in particular, the positive effect is a skin rejuvenation effect.

[0099] A therapeutic product may be a product for the treatment of a skin condition such as atopic dermatitis, eczema or acne.

[0100] The cosmetic or therapeutic product may be a product for local application to the skin. Local application is application to the targeted area.

[0101] The cosmetic or therapeutic product may thus be in the form of: cream, emulsion, oil, serum.

[0102] The cosmetic or therapeutic product may be a product with a remote action. This is, for example, a product to be ingested for a remote action on the skin. It may have a direct or indirect effect on the skin.

[0103] The cosmetic or therapeutic product may 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).

[0104] In particular, this can be a method of evaluating the negative effect of a product on the skin, for example, skin harmfulness.

[0105] The negative or harmful effect therefore corresponds to skin aging.

[0106] A harmful product can, for example, be a cigarette, a food product, pollutants from skin exposure or not, pesticides, drugs, UV exposure, radioactivity. Examples

[0107] 1. Algorithmic machine learning solution

[0108] Using the R software (R version 4.4.1 (2024-06-14 ucrt)), the user launches the following commands sequentially: 1. Installation of the packages and libraries glmnet (version: 4.1-8) and data.table (version: 1.16.0) 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.

[0109] 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.

[0110] This process is schematically illustrated in [Fig. 1] and [Fig. 2]. The sequence diagrams describe the process of training an Elastic Net model in R to predict biological ([Fig. 1]) or chronological ([Fig. 2]) age from protein data. The user loads the libraries and data, prepares the training matrices, and configures cross-validation to optimize the parameters μmin and γmin. Cross-validation is an iterative machine learning process that selects the optimal parameters (μmin and γmin) that minimize validation error. The final model is fitted with these parameters, and the results (coefficients and graphs) are displayed to the user for analysis.

[0111] 2. Measurement of protein expression levels

[0112] By way of example and proof of concept, the inventors quantified by mass spectrometry the protein extracts secreted by fibroblasts from skin biopsies of 18 individuals aged between 19 and 72 years. Among the proteins identified, they selected a set of 210 proteins which are found in the human “Stratum Corneum” and used as input data to develop chronological and biological clocks.

[0113] The same algorithmic sequence can be applied to other protein quantification methodologies.

[0114] Materials and methods of mass spectrometry analysis

[0115] After collection of the secretomes 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). The peptides were separated on a C18 column (75 µm x 500 mm; Acclaim Pepmap RSLC, C18, 2 µm, 100 Angstrom) 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 by DataAnalysis (Bruker) version 4.4 to generate a .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: ESLQUAD-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.

[0116] 3. Determination of the weighting coefficient p

[0117] From the tables of quantification of stratum corneum proteins, the solutions The Leaming machine algorithms detailed in the sequence diagrams allow us to establish the coefficients of the multiparametric equations (Age = [30 + [31pl + [32p2 + ... + |3npn]) which determine the age (chronological or biological) of individuals.

[30] corresponds to the intercept, that is, it represents the value of the dependent variable Age when all the protein predictors (pl, p2, ..., pn) are equal to zero (intercept). The coefficients [31,

[32] correspond to the coefficients associated with the predictors (quantity of proteins 1 and 2) defined as the foundation of the clock, and the coefficients [3n] correspond to the coefficients associated with the n predictors added to the equation to increase the prediction accuracy.

[0118] For the establishment of the biological clock, the 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, HIST1H2AH.

[0119] Tables 2 to 15 below 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 indicated in the right-hand column in the form "Accession ID_name of the gene that codes for the protein in humans".

[0120] [Tables2] Value Factor 079.6437061391811 (Intercept) 1 -0.099601895319648 P22626_HNRNPA2B1

[0121] [Tableaux3] Value factor 0 81.7459225917021 (Intercept) 1 -0.0650824783510436 P22626_HNRNPA2B1 2 -0.0839115776055179 Q14697..GANAB

[0122] [Tableaux4] Value factor 0102.162457156466 (Intercept) 1 -0.00426071182788079 P08123_COL1 A2 2 -0.0555086475421377 P22626_HNRNPA2B1 3 -0.0482397061780051 Q14697_GANAB

[0123] [Tableaux5] Value factor 0102.533646179489 (Intercept) 1 -0.00390829400853871 P08123_COL1 A2 2-0.047452122002888 P22626_HNRNPA2B1 3 -0.0387327052024269 Q14697..GANAB 4 -0.135455453065059 Q9UKK9_NUDT5

[0124] [Tableauxô] Value 087.4828645708921 1 -0.00286185935023952 2-0.0326503700950315 3-0.0871305137016255 4-0.127918931451434 50.0236126584032138 factor (Intercept) P08123.COL1A2 P22626..HNRNPA2B1 Q14697_GANAB Q9UKK9_NUDT5 P13647_KRT5

[0125] [Tableaux?] Value 091.9837094249468 1 -0.00424446485636704 2 -0.0279782523651577 3-0.111675335254189 4-0.150178622628235 50.0142523819182876 60.0618549163120401

[0126] [Tableaux8] factor (Intercept) P08123_COL1A2 P22626_HNRNPA2B1 Q14697_GANAB Q9UKK9_NUDT5 P13647_KRT5 P62258...YWHAE Value 091.1370464900088 1 -0.00401317298712054 2-0.0417935038376829 3 -0.0458803799667517 4-0.0711575967842609 5-0.147933198469151 6-0.0951110140646376 70.014857643266839 80.0693549118788835 90.0296399638147097

[0127] [Tableaux9] factor (Intercept) P08123_C01_1 A2 P22626.HNRNPA2B1 Q14697..GANAB O14818_PSMA7 Q9UKK9_NUDT5 P01008_SERPINC1 P13647..KRT5 P06865_HEXA P62258_YWHAE Value 0 88.0660692247643 1 -0.00329225169765943 2 -0.0365932537437426 3-0.0473271249395534 4-0.0654451388720747 5-0.159691341263395 6 -0.00299957325080309 7-0.0923058050949601 80.0163018110164198 90.0750580822573527 10 0.0298266556490232 factor (Intercept) P08123_COL1A2 P22626.HNRNPA2B1 Q14697„GANAB 014818 PSMA7 Q9UKK9_NUDT5 Q15063.POSTN P01008...SERPINC1 P13647_KRT5 P06865_HEXA P62258_YWHAE

[0128] [Tableaux 10] Value factor 089.0139868974001 (Intercept) 1 -0.0025470553590878 P08123.....COL1A2 2-0.0471757366521333 P22626_HNRNPA2B1 3 -0.0373827386229756 P16070...CD44 4-0.0446851010829998 Q14697_GANAB 5-0.00461876332165496 014818_PSMA7 6-0.11395482242.3212 Q9UKK9_NUDT5 7-0.0011340430206303 Q15063_POSTN 8 -2.89306288980602e-05 P01008_SERPINC1 90.0346821696912609 Q02413_DSG1 10 0.00768203991715158 P13647_KRT5 11 0.0498154793244906 P06865...HEXA 12 0.0062786141750248 P62258 YWHAE Value 083.1216276572845 1 -0.00284754687747865 2-0.0385486921710152 3-0.0519209020163135 4-0.0710184256637248 5-0.00116888073116661 6-0.154180229140995 7 -0.00398078051830848 8-0.0315270195542569 9-0.0175071869124187 100.0279533151800621 11 0.0819507500119415 120.0130562348585095 13 0.00549202071971032 140.0660075369942179 150.0167586789477149 factor (Intercept) P08123_COL1 A2 P22626..HNRNPA2B1 Q14697_GANAB O14818_PSMA7 P78417_GSTO1 Q9UKK9.NUDT5 Q15063...POSTN P01008_SERP!NC1 075787. ATP6AP2 Q02413_DSG1 P62873_GNB1 P13647_KRT5 P06396_GSN P06865..HEXA P62258_YWHAE

[0130] [Tableauxl2] Value 081.6377757296422 1 -0.00245859147022444 2 -0.00242702040814707 3-0.0294723261679414 4-0.061938133040444 5 -0.0724765475151934 6 -0.00534966690225695 7-0.00313334544801781 8-0.142585696322822 9-0.00398082214917212 10 -0.00707618932288178 11 -0.0377502830568843 12-0.0143964887945186 13 -0.000682414408317421 14 0.0310554567883759 150.0747248119996075 16 3.01262575430017e-05 170.0140567466782832 18 0.00938836393266213 190.0609302242619602 200.0211272348813849 factor P08123_COL1A2 P32119...PRDX2 P22626...HNRNPA2B1 Q14697_GANAB 014818. PSMA7 P78417_GSTO1 Q14118_DAG1 Q9UKK9..NUDT5 Q15063_POSTN O15144..ARPC2 P01008 SERPINC1 O75787..ATP6AP2 Q16610_ECM1 Q02413_DSG1 P62873_GNB1 P05090..APOD P13647_KRT5 P06396..GSN P06865 HEXA P62258_YWHAE

[0131] [Tableaux 13] Value 081.1191525331127 1 -0.001504379721482 2-0.0162127535614885 3 -0.0248790730996888 4-0.00415551943918926 5-0.0675073236386269 6-0.0646549831521704 7-0.0160062639115462 8 -0.00429447885977388 9-0.00182352489778984 10-0.117211441875212 11 -0.00121605600585948 12-0.00304413652099937 13-0.0181912716531402 14-0.0463058805034638 15-0.0114513140739217 16 -0.00108967608097841 170.0342218967510262 180.0669999148092152 190.000310539063914946 200.00178651302536261 21 0.0146239111624225 220.0153166801900526 230.0540645004680165 240.0153354850008871 250.00138015569827238 260.0242841943268812 factor (Intercept) P08123..COL1A2 P32119. PRDX2 P22626_HNRNPA2B1 P16070_CD44 Q14697.GANAB O14818..PSMA7 P78417_GSTO1 Q14118_DAG1 Q16769..QPCT Q9UKK9_NUDT5 P02452_COL1A1 Q15063_POSTN O15144_ARPC2 P01008_SERPfNC1 O75787_ATP6AP2 Q16610 ECM1 Q02413..DSG1 P62873_GNB1 Q13404..UBE2V1 P05090_APGD P13647...KRT5 P06396.GSN P06865_HEXA P52565 ARHGDfA P00746.CFD P62258_YWHAE

[0132] [Tableaux 14] Value 081.3114154110425 1-0.00115736550838263 2-0.0129227166930763 3-0.022070991881263 4-0.0164403139218727 5-0.0485995607387381 6-0.0360000655340731 7-0.032125765302211 8-0.00576721022783841 9-0.00944980750941406 10-0.0857655527331545 11 -0.000889039216981527 12-0.00131516098128907 13-0.0185168786792591 14-0.00446057213973241 15-0.027394386447532 16-0.00245105424334386 170.00273533853859874 18-0.00144188863441863 190.0470779228653684 200.0202899935338382 21 0.00652130393384996 220.0114141412703676 230.00905795269507888 240.0112282340324406 250.0334492304289912 260.00404012297823653 270.00439190827529571 280.00134420043582692 290.0209081741182165 300.00149412091949205 31 -0.0102216818532832 factor lOBëOliiiiiiiis P08123.COL1A2 P32119_PRDX2 P22626_HNRNPA2B1 P16070_CD44 Q14697 GANAB O14818_PSMA7 P78417_GSTO1 Q14118...DAG1 P00441...SOD1 Q9UKK9...NUDT5 P02452_CQL1A1 Q15063...POSTN O15144...ARPC2 P28799_GRN P01008..SERPINC1 075787.ATP6AP2 P35030_PRSS3 Q16610 ECM1 Q02413_DSG1 P62873_GNB1 Q13404 UBE2V1 P05090..APOD P13647 ..KRT5 P06396..GSN P06865_HEXA P52565..ARHGDIA P50395_GDI2 P13667_PDIA4 P62258_YWHAE P35527_KRT9 Q96KK5..HIST1 H2AH Value factor 079.4680521908027 (Intercept) 1-0.00129147564954815 P08123..COL1A2 2-0.014156859383358 P32119..PRDX2 3 -0.0235927873242526 P22626..HNRNPA2B1 4-0.0112571354761862 P16070_CD44 5-0.0551672865234715 Q14697 GANAB 6-0.0447572537143951 014818 PSMA7 7-0.0411456223759342 P78417..GSTO1 8 -0.00785694846132329 Q14118_DAG1 9-0.0154536611514652 P00441..SOD1 10-0.00261730638760361 Q16769_QPCT 11 -0.0882663376927517 Q9UKK9_NUDT5 12 -0.00123780799939871 P02452_COL1A1 13 -0.00161450096578245 Q15063_POSTN 14-0.0310893990991177 015144 ARPC2 15-0.00428361067532905 P28799..GRN 16-0.0251078166283137 P01008_SERPlNC1 17 -0.00526910616360357 O75787_ATP6AP2 180.0165315774827813 P35030..PRSS3 19 -0.00195564946365248 Q16610..ECM1 200.0405096395016236 Q02413...DSG1 210.0485986821523148 P62873..GNB1 22 0.0109736173871865 Q13404 UBE2V1 23 0.0127107649500224 P05090_APOD 240.0103881509964209 P13647..KRT5 250.000130851101415848 P02545...LMNA 26 0.0165063824751699 P06396_GSN 270.0392367219664753 P06865 HEXA 280.0185028036575522 P52565_ARHGDIA 290.00431305397957548 P50395_GDI2 30 0.00224875794993041 P00746..CFD 31 0.00372364646634297 P13667_PDIA4 . 32 0.0220669277076333 P62258_YWHAE 33 0.00201489153002412 P35527_KRÎ9 34-0.00265332762617516 Q96KK5_HIST1 H2AH

[0134]

[0135]

[0136]

[0137] The accuracy of the prediction from the number of proteins was evaluated by Pearson correlation analysis. 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. 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 (Pearson correlation coefficient r > 0.95). To establish the chronological clock, coefficients and intercepts were determined for different groups of proteins selected from among the 27 proteins following: PRDX1, C0L1A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GST01, DAG1, SOD1, NUDT5, COL1A1, POSTN, PLTP, ARPC2, SERPINC1, ATP6AP2, PRSS3, DSG1, ATRN, KRT5, GSN, HEXA, ARHGDIA, GDI2, YWHAE and HIST1H2AH.

[0138] Tables 16 to 29 below 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 indicated in the right-hand column in the form "accession identifier in Uniprot_name of the gene that encodes the protein in humans".

[0139] [Tables 16] Value Factor 0109.033740662871 (Intercept) 1 -0.0106560293114135 P08123_COL1 A2

[0140] [Tableauxl7] Value 0 98.4172003804887 1 -0.00711735926370587 2-0.0726376815185393 factor (Intercept) P08123_COL1A2 P22626._.HNRNPA2B1

[0141] [Tables l8] Value factor 0 97.0163417336036 (Intercept) 1 -0.00649088547120006 P08123_COL1 A2 2 -0.054073744792476 P22626. HNRNPA2B1 3 -0.0585705236996428 Q14697_GANAB

[0142] [Tableauxl9] Value factor 097.3738210810089 (Intercept) 1 -0.00592365284513611 P08123_COL1A2 2 -0.0415104750623347 P22626_HNRNPA2B1 3 -0.0437149480322568 Q14697_GANAB 4 -0.207507670232684 Q9UKK9...NUDT5 Value 0 97.0218019713528 1 -0.00573423220731352 2 -0.0293576239096092 3-0.0362169547667119 4 -0.0389272735373222 5-0.201694730414072 factor (Intercept) P08123..COL1A2 P22626_HNRNPA2B1 Q14697.GAN AB P78417..GSTO1 Q9UKK9..NUDT5

[0144] [Tableaux21] Value 0 76.9594476275495 1 -0.00345217892151715 2-0.0198196985326436 3-0.037874232554851 4 -0.0226728837867966 5-0.108081006467899 60.0372793269610556 factor (Intercept) P08123_COL1 A2 P22626_HNRNPA2B1 Q14697_GANAB P78417.GSTO1 Q9UKK9_NUDT5 Q02413. DSG1

[0145] [Tableaux22] Value 077.8072752355772 1 -0.00338336126413298 2-0.0190120901871719 3 -0.0459620434776537 4-0.0268160371770119 5-0.114398643955703 6 -0.0147769989797443 70.0531949722591303 factor (Intercept) P08123_CGL1A2 P22626..HNRNPA2B1 Q14697. GAN AB P78417 GSTO1 Q9UKK9_NUDT5 P01008...SERPINC1 Q02413..DSG1 Value 087.7607313714849 1 -0.00539528956188052 2-0.0476808808971591 3 -0.00589752000715537 4-0.0544061648836112 5 0.0152812312485426 6-0.216167597972179 7 -0.00244095729665806 8-0.144722928657 9 0.0173966503166221 10 0.00767579945596927 11 0.103563250165618 factor (Intercept) P08123_COL1A2 P22626...HNRNPA2B1 Q14697_GANAB O14818..PSMA7 P78417_GSTO1 Q9UKK9.NUDT5 Q15063_POSTN P01008_SERPINC1 Q02413_DSG1 P13647_KRT5 P06865_HEXA

[0147] [Tableaux24] Value factor 087.1353654117625 (Intercept) 1 -0.00514515474793084 P08123_COL1 A2 2 -0.0427416622425003 P22626 HNRNPA2B1 3 -0.000759938673672224 P16070_CD44 4-0.0146281532089039 Q14697..GANAB 5-0.0374566711326902 O14818_PSMA7 6 -0.0201182921260469 P78417...GSTO1 7 -0.183259689169476 Q9UKK9_NUDT5 8 -0.000761512663975501 Q15063 ..POSTN 9 -0.0991047576936396 P01008_SERPINC1 100.037003470975181 Q02413_DSG1 11 0.0018812294764175 P13647_KRT5 12 0.0714178347600534 P06865 ..HEXA Value 0 89.2839850488793 1 -0.0054122668931869 2-0.022092299137788 3 -0.0158653559434786 4-0.0514520677580239 5-0.0744960382782992 6-0.161232095947221 7 -0.000532042292456355 8 -0.0522080044998347 9-0.177555262186028 10-0.011705627633927 11 0.00574374041370702 12 0.00216058301504183 130.0766169511795171 140.01813491049168 15 0.0355413936779299

[0149] [Tableaux26] Value 0 89.1880630920811 1 -0.00559861101038669 2-0.0313252493634374 3-0.0321009987406931 4-0.0494380607164389 5-0.0572702971240949 6-0.154602310216408 7 -0.00129465244092755 8-0.0623151421003076 9-0.164296409352544 10-0.014081728385851 11 0.00608918182664105 12 0.00894391720113634 13 0.0781958819049908 140.0367836062200953 150.028626392789955 16 0.0305781709779963 factor (Intercept) P08123_COL1A2 P22626..HNRNPA2B1 Q14697_GANAB O14818_PSMA7 P78417_GSTO1 Q9UKK9_NUDT5 Q15063..POSTN O15144..ARPC2 P01008_SERPINC1 O75882_ATRN P13647_KRT5 P06396..GSN P06865_HEXA P52565_ARHGDIA P62258_YWHAE factor (Intercept) P08123_COL1 A2 P22626...HNRNPA2B1 Q14697_GANAB O14818._PSMA7 P78417..GSTO1 Q9UKK9_NUDT5 Q15063...POSTN O15144_ARPC2 P01008..SERPINC1 O75882_ATRN P13647_KRT5 P06396_GSN P06865_HEXA P52565..ARHGDIA P62258_YWHAE Q96KK5...HIST1 H2AH Value factor 081.5937635183989 (Intercept) 1 -0.00370555500916098 P08123_COL1 A2 2-0.0203562939351941 P22626_HNRNPA2B1 3 -0.00528738264867511 P16070_CD44 4-0.0390134050257166 Q14697_GANAB 5-0.0546684421190883 014818 ...PSMA7 6-0.0584581187156886 P78417...GSTO1 7-0.139670789298635 Q9UKK9...NUDT5 8 -0.000781509455266436 P02452_COL1A1 9 -0.00219546217357469 Q15063_POSTN 10 -0.0492323521736127 015144. ARPC2 11 -0.155638106804304 P01008_SERPINC1 120.0378698969497666 Q02413_DSG1 13 -0.00802627502856816 O75882_ATRN 14 0.00619990158171485 P13647... KRT5 150.0152768295597922 P06396_GSN 160.0669739877791102 P06865...HEXA 170.0438890013208878 P52565...ARHGDIA 180.0231776050434048 P62258...YWHAE

[0151] [Tableaux28] Value factor 082.1811045367048 (Intercept) 1 -0.00364363320795898 2 -0.00792440178250936 3 -0.0236983464062788 4 -0.0102673608308416 5 -0.0447589393178473 6 -0.0500931936054933 7 -0.0573087678196513 8 -0.124293370196238 9-0.00110177629467195 10-0.00237931218266936 11 0.00391657698844103 12 -0.0605954505923213 13 -0.14893371660883 140.0352389887390698 15 -0.0125137190010621 16 0.00594580124046078 170.0210390087039575 180.0638861924993241 190.0616253471461243 200.0236680929786541 21 0.014576870426468 P08123_COL1 A2 P32119_PRDX2 P22626_HNRNPA2B1 P16070_CD44 Q14697_GANAB O14818_PSMA7 P78417_GSTO1 Q9UKK9...NUDT5 P02452_COL1A1 Q15063...POSTN P55058_PLTP 015144.ARPC2 P01008_SERPINC1 Q02413...DSG1 O75882_ATRN P13647_KRT5 P06396_GSN P06865_HEXA P52565_ARHGDIA P62258...YWHAE Q96KK5_HIST1 H2AH

[0152] [Tableaux29] Value factor 075.4220292850186 (Intercept) 1 -0.000308343896009134 Q06830_PRDX1 2-0.00175401900642419 P08123 COL1A2 3-0.0015248972712091 P32119...PRDX2 4-0.0174433918906582 P22626_HNRNPA2B1 5-0.0297267816485288 P16070_CD44 6-0.0443047568242454 Q14697_.GANAB 7-0.0239783505329508 O14818_PSMA7 8-0.0359817567656793 P78417_GSTO1 9-0.00650763321268368 Q14118..DAG1 10-0.0107333361137629 P00441_SOD1 11-0.108485046631952 Q9UKK9_NUDT5 12-0.00139661433973241 P02452_CGL1A1 13-0.000810781057423142 Q15063.. POSTN 14 -0.00424124523189471 P55058..PLTP 15-0.00342485511521615 O15144_ARPC2 16-0.0867027371711276 P01008...SERPINC1 17 -0.000537799397525631 075787 ATP6AP2 180.0819112709621279 P35030..PRSS3 190.0555410765787683 Q02413_DSG1 20-0.00475425193652521 O75882_ATRN 21 0.00263692195722626 P13647...KRT5 220.00567909644544739 P06396_GSN 230.030124517455003 P06865_HEXA 240.00654925605234984 P52565_ARHGDIA 250.00281327797423664 P5O395_GDI2 260.00818219468202281 P62258_YWHAE 27-0.0018476522553004 Q96KK5...HIST1 H2AH .

[0153] The accuracy of the prediction from the number of proteins was evaluated by Pearson correlation analysis.

[0154] Figure 4 shows that for the chronological clock, based on the expression level of three proteins, the reliability of chronological age prediction is high (Pearson correlation coefficient r > 0.80). Thus, chronological age can be determined from three proteins in the list.

[0155] Additional proteins can be taken into account to obtain greater reliability. From 4 to all of the proteins in the list can be selected. From 11 proteins onwards, the level of reliability of the prediction of chronological age is very high (Pearson correlation coefficient r>0.95). List of documents cited

[0156] For the avoidance of doubt, the following non-patent element(s) is / 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, 11196. 2. Azimi A., et al. (2021). Mass spectrometry-based proteomic analysis of the effect of storage température 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 Ail 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 rôle 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 molécule in skin aging. Dermato-endocrinology 4:3, 253-258.

Claims

Demands

1. A method for determining the protein age of an individual's skin from a sample of stratum corneum, comprising: - measuring the expression level of at least three specific proteins from the stratum corneum sample, chosen from the following list of 37 proteins: C0L1A2, PRDX2, HNRNPA2B1, CD44, GANAB, PSMA7, GSTO1, DAG1, SOD1, QPCT, NUDT5, C0L1A1, POSTN, ARPC2, GRN, SERPINC1, ATP6AP2, PRSS3, ECM1, DSG1, GNB1, UBE2V1, APOD, KRT5, LMNA, GSN, HEXA, ARHGDIA, GDI2, CFD, PDIA4, YWHAE, KRT9, HIST1H2AH, 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 = [30 + [31 pl + [32 p2 +...+ [3n pn, where [30 is the intercept, [31,[32, . ..[3n are coefficients, pl,p2,...pn represent the level of expression of each protein, the number of proteins chosen from the list being equal to n.

2. A method according to claim 1, 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, HIST1H2AH.

3. The method according to claim 2, wherein the proteins are: COL1A2, HNRNPA2B1, GANAB, NUDT5, KRT5, YWHAE and HEXA.

4. A method according to claim 1, 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, 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 HIST1H2AH.

5. The method according to claim 4, wherein the proteins are: C0L1A2, HNRNPA2B1, GANAB, PSMA7, GSTO1, NUDT5, POSTN, SERPINC1, DSG1, KRT5 and HEXA.

6. Method for evaluating the aging of an individual's skin comprising: - determining the protein age at a time To according to the method of one of the preceding claims, - determining the protein age at a time Tx according to the method of one of the preceding claims, - if the value (Vx) of the protein age at Tx is greater than the value (Vo) of the protein age at To, aging of the individual's skin is confirmed.

7. Method for evaluating the aging of an individual's skin comprising: - determining the protein age of the biological age type according to the method of claim 2 or 3; - determining the protein age of the chronological age type, for example according to the method of claim 4 or 5; - if the biological age is greater than the chronological age, premature aging of the individual's skin is confirmed.

8. Method for evaluating the effect of a product on the skin of an individual comprising the following steps: - At To, determination of the biological age of the individual (Age_bio0) from a sample of stratum corneum according to the method of claim 4 or 5, and determination of the chronological age of the individual (Age_chrono0) for example according to the method of claim 4 or 5; - Determination of the value of the difference between the biological age and the chronological age at To (AT0) by subtracting the chronological age from the biological age (AT0 = Age_bio0 - Age_chrono0); - A Tx, determination of the biological age of the individual (Age_biox) from a sample of stratum corneum according to the method of claim 2 or 3, and determination of the chronological age of the individual (Age_chronox) for example according to the method of claim 4 or 5; - Determination of the value of the difference between biological age and chronological age at Tx (ATX) by subtracting chronological age from biological age (ATX = Age_biox - Age_chronox); - Comparison of AT0 and ATX; - Evaluation of the effect of the product: positive effect when ATX is less than AT0, negative effect or no effect when ATX is greater than AT0.

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