Markers for skin metabolic phenotyping and uses thereof

Abstract

This invention belongs to the field of biodetection technology, specifically disclosing a novel skin typing method, biomarkers based on skin surface metabolomics typing, and their applications. Based on population-level skin surface metabolite measurement data and through multi-dimensional scientific analysis, this application ultimately discovered that the skin samples of the subjects formed two distinct clusters. This demonstrates that differences in metabolomics clusters based on skin typing objectively exist. Furthermore, the two distinct clusters based on the aforementioned skin surface metabolomics data are named "Metabolic Type 1" or "Metabolic Type 2." Simultaneously, these two typing methods also exhibit differences in various skin condition indicators (e.g., sebum levels, actual age, wrinkles, freckles, etc.). By using the typing method provided in this application to study skin surface metabolomics substances, one or more metabolites can be used as biomarkers to identify the specific skin typing level and guide adjustments to the desired skin condition. This application overcomes the limitations of existing methods that define skin typing solely based on dry, oily, etc., providing more comprehensive guidance for practice.

Description

Technical Field

[0001] This invention belongs to the field of biological detection technology, specifically relating to biomarkers for skin metabolic typing and their applications. Background Technology

[0002] Most existing studies on skin classification divide human skin into three types—dry, oily, and moist—based on single characteristics such as oil and moisture content (Byrd, Belkaid et al. 2018). For example, some define dry skin as having a stratum corneum moisture content of less than 10%, low sebum secretion, dryness, flaking, fine texture, dullness, a dark complexion, and a tendency to develop fine wrinkles and pigmentation. This classification method has low resolution, fails to provide clear biomarkers, and does not delve into the phenotypic differences among these three skin types. Therefore, it cannot provide guidance on how to regulate skin phenotypic characteristics by intervening in skin surface metabolites.

[0003] Previous studies on skin surface metabolites have mainly focused on disease populations, such as those with acne, psoriasis, and atopic dermatitis (Zeng, Wen et al. 2017, Pappas, Kendall et al. 2018, Danby, Andrew et al. 2022). For the more general population of healthy individuals (at least those with skin without obvious pathological features), there are still few studies on their skin surface metabolites, especially a lack of comprehensive research on water-soluble and lipid-soluble substances on the skin surface.

[0004] Skin surface metabolites are low-molecular-weight compounds on the skin surface, primarily derived from skin cells, microbial metabolic processes, and the external environment (Elpa, Chiu et al. 2021, Chen, Zhao et al. 2022). The collection of these compounds is called the skin surface metabolome (Chen, Chen et al. 2024). These compounds not only serve as major nutrients shaping the skin microbiome but can also be metabolized into bioactive molecules, potentially altering the host's skin phenotypic characteristics (Chen, Chen et al. 2024). The skin surface metabolome is also an important source of biomarkers for disease and cosmetic applications, highlighting its importance in dermatological research. Therefore, characterizing the metabolite fingerprint of the skin surface can provide valuable insights into microbe-host interactions and skin biology, ultimately leading to a stage where we can utilize important skin surface metabolites to modulate skin phenotypes or use the skin metabolome as a diagnostic tool for skin or systemic diseases.

[0005] The human skin surface metabolome exhibits strong individual variability. Faced with such complex diversity, analyzing the structural patterns of skin surface metabolites can significantly reduce data complexity. Stratification, defined as "enterotypes," has been applied to gut microbiota and can effectively describe the structure of the gut microbiota community, positively impacting clinical practice (Costea, Hildebrand et al. 2018).

[0006] Therefore, this application analyzes the composition and components of the surface skin metabolome, aiming to obtain a pattern of skin surface metabolome material information for skin surface omics research. Summary of the Invention

[0007] This application collects data on the surface metabolites of facial skin in a population and analyzes it from multiple dimensions. Ultimately, it was found that the facial skin samples of the subjects formed two different clusters. That is, the above study found that the differences in metabolomics clusters based on skin subtypes objectively exist. At the same time, based on the two obvious clusters in the skin surface metabolomics data of the above subjects, these two clusters are named "Metabolic Type 1 / 2" (DERMET-1 / 2).

[0008] Therefore, the first objective of this application is to provide a new classification of skin types.

[0009] Furthermore, the two clusters exhibited different trends in skin condition and individual status. For example, Wilcoxon rank-sum tests on multiple clinical phenotypes showed that the actual age and skin age of the DERMET-1 group were significantly lower than those of the DERMET-2 group, and the distribution of DERMET-1 type in the population decreased with increasing age and skin age. In addition, the skin brightness (L) of the DERMET-1 group was significantly higher than that of the DERMET-2 group, the skin yellowness (b) of the DERMET-1 group was significantly lower than that of the DERMET-2 group, and the density of melasma, freckles, average depth of nasolabial folds, and average depth of periorbital wrinkles on the face of the DERMET-1 group were all significantly lower than those of the DERMET-2 group.

[0010] Therefore, a second objective of the present invention is to provide an attribution status index based on a new skin type classification.

[0011] This application analyzed whether subjects with two different metabolites had different metabolite characteristics. The results showed that the DERMET-1 subjects (n=90) had relatively high metabolite diversity (Shannon index), with more than 200 lipids significantly higher than those in the DERMET-2 subjects.

[0012] By studying metabolomics substances on the skin surface, data from one or more metabolites can be used to identify the specific skin subtype under which the skin is classified. Indicators specific to that skin subtype can then guide adjustments to achieve the desired skin condition. These metabolomics substances provide a rich source of information, aiding in a deeper understanding of skin conditions and differentiating between various skin subtypes. These biochemical substances can be used as biomarkers for diagnosing skin conditions and guiding treatment practices. For example, based on individual needs, differences in metabolomics can be used as useful biomarkers to determine whether the treatment chosen by the subject is effective, i.e., to assess treatment efficacy.

[0013] Therefore, the third objective of this invention is to provide a method for classifying skin types in different population groups.

[0014] The fourth objective of this invention is to determine how to apply the classification of skin types in different populations to guide different skin conditions, skin diseases, or systemic diseases.

[0015] In summary, biomarkers involved in metabolomics and their content differences can be used as objective measures to record and judge individual status, skin condition, etc., and can also be used to guide the completion of treatment plans in practice.

[0016] More specifically, this application provides a method for preparing a product for diagnosing the skin health status of a subject using the aforementioned biomarker, comprising the following steps:

[0017] S1: Collect metabolite samples from the subject's skin surface;

[0018] S2: Detect the content of markers in the metabolite sample on the skin surface;

[0019] S3: The skin type, skin condition, or individual condition can be directly determined by the content of the markers in step S2.

[0020] Preferably, in step S3, skin type is determined based on the following markers: TG(17:0_18:0_18:0), TG(8:0_16:1_18:2), DG(16:1_18:0), TG(16:0_17:0_18:0), DG(16:0_17:1), DG(16:1_16:1), TG(16:0_19:1_20:2), DG(16:1_18:1), DG(15:0_16:1), TG(14:1_14:1_16:1), TG(14:1_16:1_16:2), DG(16:0_16:1), DG(15:0_17:0).

[0021] More preferably, in step S3, the skin type is determined to be DERMET-1 based on the following markers: TG (17:0_18:0_18:0), concentration ≥1.20; TG (8:0_16:1_18:2), concentration ≥1.1; TG (16:0_17:0_18:0), concentration ≥5.5; TG (14:1_14:1_16:1), concentration ≥12; TG (14:1_16:1_16:2), concentration ≥3, where all concentrations are in pmol (picomolar).

[0022] More preferably, in step S3, the skin type is determined to be DERMET-1 based on the following markers: TG (17:0_18:0_18:0), concentration range 1.20~39; TG (8:0_16:1_18:2), concentration range 1.1~37.2; TG (16:0_17:0_18:0), concentration range 5.5~105.6; TG (14:1_14:1_16:1), concentration range 12~200; TG (14:1_16:1_16:2), concentration range 3~46.

[0023] More preferably, in step S3, the skin type is determined to be DERMET-2 based on the following markers: TG (17:0_18:0_18:0), concentration below 1.10; TG (8:0_16:1_18:2), concentration below 1.03; TG (16:0_17:0_18:0), concentration below 5.0; TG (14:1_14:1_16:1), concentration below 10; TG (14:1_16:1_16:2), concentration below 2.5, where all concentrations are in pmol.

[0024] That is, by measuring the concentration range of a person's skin metabolomics data, it is possible to determine which skin type (DERMET-1 or DERMET-2 group) they belong to. Furthermore, this application verifies the accuracy of this measurement by reverse verification, and both methods can achieve high accuracy.

[0025] The above-mentioned skin surface metabolites and their concentration ranges can be used for individual skin typing, and are also applicable to large-scale population testing.

[0026] Terminology Explanation:

[0027] Skin typing: Population typing (stratification) is an effective method that helps to better understand complex biological issues such as human physical and mental health. Applying this method to skin surface metabolomics data, the process of dividing populations into groups based on different metabolite compositions is called skin typing.

[0028] Among them, DERMET-1 is named based on a distinct cluster in the skin surface metabolomics of this application, and the corresponding distinct cluster is named DERMET-2. After studying the individual information of the two distinctly different clusters, the two subtypes are distinguished by at least the following indicators:

[0029] Classification sebum Maximum nasolabial fold depth (NLF) Density of frown lines Type 1 range <![CDATA[5~88μg / cm 2 ]]> 0～0.039 0～0.025 Type 2 range <![CDATA[0~4μg / cm 2 ]]> 0.04～0.045 0.026～0.112 Classification Porphyrin ratio (T-zone) Skin age age Type 1 range 0.0003～0.0158 26～54 20～51 Type 2 range 0～0.0002 55～68 52～60

[0030] Skin condition refers to the health status and appearance of the skin at a specific time. It encompasses multiple aspects, including skin structure, function, appearance, and sensation, all of which collectively determine the overall condition of the skin. Some key components of skin condition include: skin type, such as dry, oily, combination, and normal; skin barrier function; skin elasticity and firmness; skin pigmentation; skin texture and pore size; skin hydration status; skin diseases and lesions; skin microbiome; and skin sensation and response.

[0031] Individual state refers to the comprehensive performance of a person's physiological, psychological, and social functions at a specific point in time. This concept encompasses multiple levels, including health status, emotional state, cognitive function, and social adaptability. Individual state can be influenced by a variety of factors, including heredity, environment, lifestyle, social relationships, and personal experiences.

[0032] An individual's chronological age: An individual's chronological age typically refers to the total number of years a person has lived from birth to the present, also known as their biological age. This age can be determined by calculating the time difference between their birth date and the current date. Chronological age is an objective measure based on the passage of time and is not affected by an individual's health condition, lifestyle, or other subjective factors.

[0033] Skin age, also known as dermal age, refers to the actual health condition and degree of aging of the skin, and it differs from an individual's chronological age (chronological age). Skin age reflects the apparent age or texture age of the skin and can determine a person's appearance. Some people may have an older chronological age, but due to proper skincare, their skin may appear younger. Therefore, skin age is an important factor affecting appearance.

[0034] Skin age is typically assessed by observing characteristics such as skin elasticity, radiance, wrinkles, and age spots. A younger skin age means the skin is closer to its optimal state, exhibiting good elasticity, radiance, smoothness, and fewer wrinkles and age spots. Younger skin age is often associated with youth, health, and vitality, indicating good skin cell renewal and repair functions, and effective resistance to environmental damage.

[0035] Sebum: Sebum is a complex oily mixture containing waxes, triglycerides, free fatty acids, and phospholipids and cholesterol esters produced by epithelial cell metabolism. Sebum is oily and moisturizes the skin, preventing dryness and cracking. The fatty acids in sebum can kill bacteria and resist fungi and viruses, acting as a natural barrier for the skin. Furthermore, within normal secretion ranges, sebum also plays a protective and moisturizing role for the skin.

[0036] Maximum Nasolabial Fold Depth (NLF): This refers to the depth of the lines extending from the sides of the nose to the corners of the mouth, the typical location of the nasolabial folds on the face. This depth can be used as an indicator of skin aging, as it typically deepens with age. Nasolabial folds are primarily related to skin aging and excessive facial expressions; they are depressions on the skin's surface caused by aging skin tissue and collagen loss. In the field of cosmetic medicine, the depth of nasolabial folds is sometimes used to guide treatments, such as improving their appearance through fillers or surgery. The maximum depth of nasolabial folds varies from person to person and is related to a variety of factors, including genetics, lifestyle habits, and environmental factors.

[0037] Frown line density: Frown line density refers to the number and density of wrinkles in a specific area between the eyebrows. It is an important indicator of skin aging. As we age and our skin deteriorates, collagen and elastin fibers decrease, skin elasticity reduces, and the density of frown lines tends to increase.

[0038] Porphyrin Ratio (T-zone): Porphyrins are a class of large heterocyclic compounds formed by the interconnection of the α-carbon atoms of four pyrrole subunits via methylene bridges (=CH-). The porphyrin ratio (T-zone) refers to the relative abundance of porphyrins detected in the T-zone of the face (i.e., the area formed by the forehead, nose, and chin). The facial T-zone is rich in sebaceous glands, and the sebum secreted by these glands provides a suitable environment for the growth of microorganisms such as Propionibacterium acnes. Since Propionibacterium acnes produces porphyrins during its metabolism, the T-zone has become an important area for detecting porphyrins to assess skin health.

[0039] Compared with the prior art, the beneficial effects of this application are as follows:

[0040] This application collects data on skin surface metabolites from a population and conducts scientific analysis across multiple dimensions. The analysis reveals that the skin samples from the subjects formed two distinct clusters, demonstrating that differences in metabolomics clusters based on skin type objectively exist. These two clear clusters, identified from the skin surface metabolomics data, are named "Metabolic Type 1" or "Metabolic Type 2." Furthermore, these two types exhibit differences in various skin condition indicators (e.g., sebum levels, actual age, wrinkles, freckles, etc.). This application develops a novel skin type classification and, based on this classification, a method for determining skin condition using biomarkers. By using the classification provided in this application to study skin surface metabolomics substances, one or more metabolites can be used as biomarkers to identify the specific skin type and guide adjustments to achieve the desired skin condition. This application addresses the shortcomings of existing models that define skin types solely based on dry, oily, etc., providing more comprehensive guidance for practice. Attached Figure Description

[0041] Figure 1 This section illustrates the emergence of DERMET-1 and DERMET-2 subtypes and the differences in metabolite composition between them. Specifically: a. Principal coordinate analysis (PCoA) based on Bray-Cutis dissimilarity; b. Box plot showing the alpha diversity (Shannon index) of metabolites in DERMET-1 and DERMET-2; c. Bar chart illustrating the differences in metabolite composition between DERMET-1 and DERMET-2.

[0042] Figure 2 The actual age (a) and visual age (b) of two types of DERMETs;

[0043] Figure 3 The bar chart shows the composition ratio of the two DERMET-s in different age groups and the sensory age group;

[0044] Figure 4 The clinical presentations of the two DERMETs were compared using box plots. Detailed Implementation

[0045] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0046] Unless otherwise specified, the experimental methods used in the embodiments of this invention are conventional methods; unless otherwise specified, the materials and reagents used are commercially available.

[0047] Example 1: Origin and Classification of Skin Subtypes DERMET-1 and DERMET-2

[0048] 1. Skin typing sample collection: This study included 170 healthy female subjects (randomly selected, aged 20 to 60 years) and collected metabolite samples from their cheeks for targeted metabolomics and targeted lipidomics detection to comprehensively evaluate the water-soluble and lipid-soluble substances on the cheek skin surface of each subject in this cohort.

[0049] Specific processing procedures and steps: Through two D100 skin tape was applied five times to the adjacent area of ​​the ipsilateral cheek of healthy subjects to obtain two parallel samples from each subject. These two samples, one for the targeted metabolomics group and the other for the targeted lipid group, were used to analyze the water-soluble and lipid-soluble substances. A total of 1187 metabolites were detected from samples of 170 healthy subjects.

[0050] 2. Based on the Bray-Cutis distance of metabolite data between samples, clustering was performed using the Partition Around Centroid (PAM) algorithm. The 170 subjects formed two clusters, named DERMET-1 and DERMET-2, respectively, with n=90 for the former and n=80 for the latter.

[0051] 3. Calculate the metabolite diversity for each subject and perform Wilcoxon rank-sum test to compare the metabolite diversity between the two DERMETs subjects.

[0052] 4. Skin typing analysis and results: Based on the facial skin surface metabolite data of this cohort, multidimensional cluster analysis and principal coordinate analysis (PCoA) were applied. The results showed that facial skin samples from 170 subjects formed two distinct clusters; that is, based on the skin surface metabolomics data of 170 Chinese individuals, two clear clusters existed, which were named "Metabolic Type 1 / 2" (DERMET-1 / 2). Figure 1 As shown, where:

[0053] a. Principal coordinate analysis (PCoA) based on Bray-Cutis dissimilarity shows the clustering of 170 samples from faces. The box plot in the upper right corner shows the mean distance within the corresponding group in yellow or green. The red horizontal line represents the mean distance between groups. Permutation multivariate analysis of variance (PERMANOVA) was calculated using the adonis function in the vegan package to determine the dissimilarity between the two groups. b. Box plot shows the alpha diversity (Shannon index) of DERMET-1 and DERMET-2 based on metabolites (*** indicates p<0.001, Wilcoxon rank-sum test). c. Bar plot of metabolite composition differences between DERMET-1 and DERMET-2. Orange bars: metabolites enriched in the DERMET-1 group. Green bars: metabolites enriched in the DERMET-2 group.

[0054] Next, we analyzed whether the subjects with the two metabolites had different metabolite characteristics. A t-test was performed for each metabolite, and the fold change between groups for each metabolite was calculated. Differential metabolites were screened using p < 0.05, fold change > 1.2, or fold change < 0.83 as criteria. The results showed that the two groups had different metabolite characteristics. The DERMET-1 group had more than 200 lipids significantly higher than the DERMET-2 group, while the DERMET-2 group had 5 water-soluble substances significantly higher than the DERMET-1 group.

[0055] These metabolites not only serve as biomarkers for predicting individual skin metabolites but also possess the potential to regulate skin phenotypic characteristics. This provides a basis for modulating skin phenotype by regulating skin surface metabolites.

[0056] Specifically, 134 lipids showed a fold change of 4-6, while 125 lipids showed a fold change of less than 4. Thirteen lipids showed a fold difference greater than 6 times compared to DERMET-2 skin: TG(17:0_18:0_18:0)(triglycerides_TG(17:0_18:0_18:0)), TG(8:0_16:1_18:2)(triglycerides_TG(8:0_16:1_18:2)), DG(16:1_18:0)(diglycerides_DG(16:1_18:0)), TG(16:0_17:0_18:0)(triglycerides_TG(16:0_17:0_18:0)), DG(16:0_17:1)(diglycerides_DG(16:0_17:1)), DG(16:1_16:1)(diglycerides_DG(16:1_16:1)), TG (16:0_19:1_20:2)(triglycerides_TG(16:0_19:1_20:2)), DG(16:1_18:1)(diacylglycerides_DG(16:1_18:1)), DG(15:0_16:1)(diacylglycerides_DG(15:0_16:1)), TG(14:1_14:1_16:1)(triglycerides_TG(14:1_14:1_16:1)), TG(14:1_16:1_16:2)(triglycerides_TG(14:1_16:1_16:2)), DG(16:0_16:1)(diacylglycerides_DG(16:0_16:1)), DG(15:0_17:0)(diacylglycerides_DG(15:0_17:0)). The DERMET-2 subjects (n=80) had relatively low metabolite diversity, with five water-soluble substances significantly higher than those in the DERMET-1 subjects: 1,7-Dimethylxanthine, Methyl-L-alaninate, N-acetylaspartate, Phosphocholine, and Theophylline.

[0057] Next, the study analyzed whether the two metabolotypes of the subjects had different skin phenotype characteristics. For example... Figures 2 to 4 As shown, Figure 2 ab. Box plots compare the actual age (a) and visual age (b) of the two DERMETs (** indicates p < 0.01, ** indicates p < 0.001, Wilcoxon rank-sum test). Figure 3 ab. The bar chart shows the composition ratio of the two DERMET-s in different age groups and the sensory age group. Figure 4ad. Box plots compared the clinical presentation of the two DERMETs (** indicates p<0.01, ** indicates p<0.001, Wilcoxon rank-sum test).

[0058] The results showed that DERMET-1 subjects had higher sebum levels and were significantly younger than DERMET-2 subjects. Furthermore, the facial visual age of DERMET-1 subjects was significantly lower than that of DERMET-2 subjects, meaning that the former's facial skin appeared older, specifically manifested in fewer nasolabial folds, frown lines, etc. Consistent with the above, the prevalence of DERMET-2 gradually increased with both chronological and perceived age, and younger subjects (20-30 years old) exhibited only the DERMET-1 type.

[0059] Based on skin surface metabolomics data, this study revealed the existence of two different metabolites (DERMET-1 / 2) in the population, with different marker metabolites.

[0060] In clinical manifestations, skin with different metabolic types has significantly different phenotypic characteristics. Specifically, DERMET-1 skin looks younger, while DERMET-2 skin looks older, has more wrinkles, and less oil, etc.

[0061] Example 2: Individual measurements were performed based on the skin typing methods DERMET-1 and DERMET-2 provided in this application, and the reliability of the method and the accuracy of the population were statistically analyzed.

[0062] 1. Refer to the test method of Example 1, that is, through two D100 skin tape was applied five times to the adjacent area of ​​the ipsilateral cheek of healthy subjects to obtain two parallel samples from each subject. The water-soluble and lipid-soluble components of these two samples were used for the proposed targeted metabolomics and lipidomics assays, respectively. Ten female volunteers were randomly selected for testing (their original individual information and skin condition were recorded). Biomarker indicators and concentrations (pmol) after testing metabolomics substances according to this application are as follows:

[0063]

[0064]

[0065] Based on the above test indicators and concentrations, skin DERMET-1 or DERMET-2 typing was performed, and the original collected information was checked and found to be consistent with the registered status.

[0066] The above indicates that skin typing can be easily performed using the aforementioned biomarkers in metabolomics, and the results are largely consistent with reality. That is, by detecting biomarkers to obtain skin typing, a series of effective information reflecting an individual's or skin condition can be obtained.

[0067] 2. Referring to the testing method in Example 1, data were collected from a wider range of individuals (sample size greater than 80). The data range and measurement accuracy are shown in the table below (all units are pmol):

[0068] Calculation formula:

[0069] DERMET-1 accuracy % = Number of Type 1 individuals in this range / Total number of Type 1 individuals × 100%;

[0070] DERMET-1 accuracy % = Number of Type 2 individuals in this range / Total number of Type 2 individuals × 100%;

[0071]

[0072]

[0073] As shown in the table above, based on the concentrations of metabolomics substances measured individually, they can be assigned to the corresponding skin subtypes DERMET-1 and DERMET-2. Compared with the recorded data of the original individual samples, the accuracy is high. Therefore, this method can be used for specific subtyping and data processing to obtain the corresponding skin subtype status, and has the potential to be used for subsequent regulation of skin phenotypic characteristics. For example, the effectiveness of skin treatment methods can be judged by monitoring skin metabolite subtypes and their status, providing a basis for regulating skin phenotype by modulating skin surface metabolites.

[0074] Obviously, the above embodiments of the present invention are merely examples to clearly illustrate the technical solution of the present invention, and are not intended to limit the specific implementation of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the claims of the present invention should be included within the protection scope of the claims of the present invention.

Claims

1. A biomarker based on skin metabolomics typing DERMET-1 or DERMET-2, characterized in that, The biomarker is used to determine whether the skin belongs to DERMET-1 or DERMET-2; or to characterize an individual's condition or skin condition; the biomarker is one or more of the following substances in skin metabolomics: Fat-soluble substances: TG (17:0_18:0_18:0), TG (8:0_16:1_18:2), DG (16:1_18:0), TG (16:0_17:0_18:0), DG (16:0_17:1), DG (16:1_16:1), TG (16:0_19:1_20:2), DG (16:1_18:1), DG (15:0_16:1), TG (14:1_14:1_16:1), TG (14:1_16:1_16:2), DG (16:0_16:1), G (15:0_17:0); Water-soluble substances: 1,7-dimethylxanthine, L-alanine methyl ester, N-acetylaspartic acid, phosphocholine, theophylline.

2. The marker according to claim 1, characterized in that, The indicators of individual condition or skin condition are one or more of the following: individual's actual age, skin age, skin tone brightness, skin yellow value, density of melasma, density of freckles, average depth of nasolabial folds, and average depth of periorbital wrinkles.

3. The method for preparing a product for diagnosing the skin health status of a subject using the biomarker of claim 1 or 2, characterized in that, Includes the following steps: S1: Collect metabolite samples from the subject's skin surface; S2: Detect the content of markers in the sample; S3: The skin type, skin condition, or individual condition can be directly determined by the content of the markers in step S2.

4. The method according to claim 3, characterized in that, In step S3, skin type is determined based on the following markers: TG(17:0_18:0_18:0), TG(8:0_16:1_18:2), DG(16:1_18:0), TG(16:0_17:0_18:0). DG(16:0_17:1), DG(16:1_16:1), TG(16:0_19:1_20:2), DG(16:1_18:1) DG(15:0_16:1), TG(14:1_14:1_16:1), TG(14:1_16:1_16:2), DG(16:0_16:1), DG(15:0_17:0).

5. The method according to claim 3 or 4, characterized in that, In step S3, the skin type is determined to be DERMET-1 based on the following markers: TG (17:0_18:0_18:0), concentration above 1.20; TG (8:0_16:1_18:2), concentration above 1.1; TG (16:0_17:0_18:0), concentration above 5.5; TG (14:1_14:1_16:1), concentration above 12; TG (14:1_16:1_16:2), concentration above 3. All concentrations are in pmol.

6. The method according to claim 5, characterized in that, In step S3, the skin type is determined to be DERMET-1 based on the following markers: TG (17:0_18:0_18:0), concentration range 1.20~39; TG (8:0_16:1_18:2), concentration range 1.1~37.2; TG (16:0_17:0_18:0), concentration range 5.5~105.6; TG (14:1_14:1_16:1), concentration range 12~200; TG (14:1_16:1_16:2), concentration range 3~46.

7. The method according to claim 3, characterized in that, In step S3, the skin type is determined to be DERMET-2 based on the following markers: TG (17:0_18:0_18:0), concentration below 1.10; TG (8:0_16:1_18:2), concentration below 1.03; TG (16:0_17:0_18:0), concentration below 5.0; TG (14:1_14:1_16:1), concentration below 10; TG (14:1_16:1_16:2), concentration below 2.

5.

8. The application of the marker according to claim 1 in detecting skin typing, characterized in that, First, a skin surface metabolite sample is collected, and then the substances in the skin surface metabolite sample are detected. The substances in the skin surface metabolite sample are selected from at least one of the following: compounds generated from amino acid metabolism, compounds generated from lipid metabolism, compounds generated from carbohydrate metabolism, and mixtures thereof.

9. The application according to claim 8, characterized in that, The markers are used for skin typing in large populations.

10. The application according to claim 8, characterized in that, The biomarkers are used to conduct individual skin testing and to regulate skin type and skin condition by modulating skin surface metabolites.

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