KR20230033922A - Method for predicting skin age using microbiome - Google Patents

Abstract

The present specification provides a skin age predicting method, which comprises the steps of: (1) collecting a skin microorganism sample from the skin of a subject; (2) quantitatively analyzing the microorganisms by amplifying the sequences of two or more types of microorganisms selected from proteobacteria phylum, cutibacterium sp. genus, and rothia sp. genus and bacillus sp. genus in the collected skin microorganism samples; and (3) predicting the skin age of the subject from the quantitative ratio of the two or more selected microorganisms. According to the skin age predicting method according to the present disclosure, the skin age can be predicted more reliably and accurately by utilizing the quantitative ratio of specific skin microorganisms.

Description

Method for predicting skin age using microbiome {METHOD FOR PREDICTING SKIN AGE USING MICROBIOME}

The present disclosure relates to a method for predicting skin age using a microbiome.

Since human skin aging progresses with individual variation, even people of the same biological age may have different degrees of skin aging. According to dermatologists, human skin age can be determined through enlarged visual features and the length, width, and thickness of skin folds. Based on these visual characteristics and personal experiences, dermatologists use the concept of skin age to infer skin conditions. Skin age is defined not according to the age of the object, but according to the age of the skin feature. Therefore, in order to predict skin age, factual data such as skin characteristics collected from many people and their professional analysis by experts are required.

On the other hand, such skin age may be judged differently according to the subjective feeling of the person observing the skin of the subject, and objective criteria for determining the skin age have not been established, making it difficult to quantify the skin age. That is, objective and quantified evaluation, such as the age of the subject's skin, has a difficulty in that it cannot be easily judged without professional analysis by experts.

Accordingly, studies to more objectively predict skin age are increasing worldwide. From this point of view, a technique for measuring skin age based on a more objective criterion, that is, ensuring reliability and accuracy, is required.

On the other hand, various microorganisms form a community inside and outside the body of humans and animals. This microbial community (microbiota) is naturally located inside and outside the body of humans and animals, and the characteristics of the microbial community are determined according to the part of the body where the microorganism is generated. Certain microbes have a beneficial effect on the skin, such as maintaining weak acidity or protecting the skin from infection by harmful bacteria that are not originally present on the skin or are present in small numbers.

The microbiome is the microbiome that exists in the human body, and refers to all the microorganisms and their genetic information coexisting in the human body. Recently, research has revealed that microorganisms living in our body have a very large influence on the human body, and it is regarded as the second genome, and microbiome research is being conducted in various fields.

Republic of Korea Patent Publication No. 10-2020-0121692

An object of the present disclosure is to provide a more accurate and highly reliable skin age prediction method using skin microbial information.

In order to achieve the above object, one embodiment of the present invention comprises (1) collecting a skin microorganism sample from the skin of a subject;

(2) Two or more microorganisms selected from among Proteobacteria, Cutibacterium sp. , Rothia sp. , and Bacillus sp. in the collected skin microbial sample Quantitatively analyzing the microorganism by amplifying the sequence of; and

(3) predicting the skin age of the subject from the quantitative ratio of the two or more selected microorganisms;

Including, it provides a skin age prediction method.

In addition, another embodiment of the present invention is a sample collection unit for obtaining a skin microorganism sample from the skin of the subject;

Two or more species selected from Proteobacteria, Cutibacterium sp. , Rothia sp. , and Bacillus sp. in the skin microbial sample obtained from the sampling unit Microorganism analysis unit for quantitatively analyzing the microorganisms by amplifying the sequence of the microorganisms; and

an age prediction unit for predicting a skin age of a subject from a quantitative ratio of two or more types of microorganisms analyzed by the microorganism analysis unit;

It provides a skin age prediction kit comprising a.

According to the method for predicting skin age according to the present disclosure, skin age can be predicted more reliably and accurately by utilizing a quantitative ratio of specific skin microorganisms.

1 is a graph showing the correlation between microorganisms of the genus Proteobacteria, Cutibacterium sp. , Rothia sp. , and Bacillus sp. and skin age, respectively.
Figure 2 is a graph showing the correlation between the log scale value (Log ₂ CP) of the quantitative ratio of microorganisms of the phylum Proteobacteria and the genus Cutibacterium ( Cutibacterium sp. ) and skin age.
3 is a graph showing error values of predicted ages by the linear regression models of Examples 1 to 4 according to an embodiment of the present invention.
4 is a graph showing the correlation between predicted age and actual age by the linear regression model of Examples 3 and 4 according to an embodiment of the present invention and the amount of sebum, respectively.
5 is a graph showing the correlation between predicted age and actual age by the linear regression model of Examples 1 and 4 according to an embodiment of the present invention and the amount of sebum, respectively.

As described above, a method for objectively and accurately predicting skin age has not yet been standardized. Accordingly, as a result of repeated studies on accurate skin age prediction, it was found that skin age can be predicted more accurately and objectively by utilizing the weight ratio of specific microorganisms present in the skin.

Hereinafter, the present invention will be described in detail.

A skin age prediction method according to an aspect of the present invention quantitatively analyzes a specific microorganism in a skin microbial sample collected from the skin of a subject, and utilizes a quantitative ratio between two or more types of microorganisms using the quantitative analysis result value to determine the skin age of the subject. predict

According to a specific embodiment, the skin age prediction method according to an aspect of the present invention may include the following steps (1) to (3).

(1) collecting a skin microorganism sample from the skin of a subject;

(2) Two or more microorganisms selected from among Proteobacteria, Cutibacterium sp. , Rothia sp. and Bacillus sp. in the collected skin microbial sample Quantitatively analyzing the microorganism by amplifying the sequence of; and

(3) predicting the skin age of the subject from the quantitative ratio of the two or more selected microorganisms.

In one embodiment, the step (1) is a step of collecting a skin microbial sample, and the part for collecting a skin microbial sample from the skin of the subject is not particularly limited, and may be, for example, the face, specifically the cheek.

In one embodiment, the skin microbial sample in step (1) may be collected through a method such as tape stripping, scraping, or swabbing, but the skin containing a plurality of microorganisms Any known method capable of collecting a microbial sample may be used, but is not limited thereto.

In one embodiment, the skin microbial sample includes a plurality of microorganisms, and the step (2) is a step of quantitatively analyzing the microorganisms in the skin microbial sample, extracting DNA from the skin microbial sample containing the plurality of microorganisms The microbial sequence can be amplified and analyzed using the following quantitative PCR or next generation sequencing (NGS) method.

In one embodiment, in the step (2), Proteobacteria (Proteobacteria) phylum, Rothia genus ( Rothia sp. ) and Bacillus genus ( Bacillus sp. ) in the collected skin microbial sample One or more microorganisms selected from ; And Cutibacterium ( Cutibacterium sp. ) By amplifying the sequence of the microorganism of the genus, the microorganism can be quantitatively analyzed.

In one embodiment, in the step (2), Proteobacteria phylum, Rothia sp. , Bacillus sp. and Cutibacterium genus in the collected skin microorganism sample ( Cutibacterium sp. ) By amplifying the sequence of the microorganisms, the microorganisms can be quantitatively analyzed.

In one embodiment, the step (3) is a step of predicting the skin age of the subject from the quantitative ratio of the microorganisms quantitatively analyzed in step (2), wherein the Cutibacterium sp. Representing a negative correlation, the proteobacteria (Proteobacteria) phylum, Rosia genus ( Rothia sp. ) and Bacillus genus ( Bacillus sp. ) To show a positive correlation with skin age of one or more microorganisms selected from It is possible to predict the skin age of the subject using the method.

In one embodiment, in the step (3), the quantitative ratio of the microorganisms may be normalized by converting to a log scale of one or more of the following:

(i) log ₂ CP=log ₂ (( Cutibacterium sp. )/(Proteobacteria))

(ii) log ₂ RC=log ₂ (( Rothia sp. )/( Cutibacterium sp. ))

(iii) log ₂ BC=log ₂ (( Bacillus sp. )/( Cutibacterium sp. )).

Here, ( Cutibacterium sp. ), (Proteobacteria), ( Rothia sp. ) and ( Bacillus sp. ) are the results of quantitative analysis of microorganisms of the genus Cutibacterium, microorganisms of the phylum Proteobacteria, microorganisms of the genus Rosia and microorganisms of the genus Bacillus, respectively. means

In one embodiment, in step (3), the skin age of the subject may be predicted using one or more of the following linear regression models:

(i) y = -1.9318*log ₂ CP + 40.4767;

(ii) y = -0.7695*log ₂ CP + 1.6986*log ₂ RC + 55.7376;

(iii) y = -0.8064*log ₂ CP + 1.0166*log ₂ RC + 1.0567*log ₂ BC + 61.8824.

In one embodiment, when the quantitative analysis result of the microorganisms of the genus Rosia and the microorganisms of the genus Bacillus is missing in the step (2), the skin age of the subject is determined by using the linear regression model of (i) in the step (3) can predict

In one embodiment, when the result of the quantitative analysis of microorganisms of the genus Bacillus is missing in step (2), the skin age of the subject can be predicted in step (3) using the linear regression model of (ii).

In one embodiment, when there is no microorganism whose quantitative analysis result value is missing in step (2), the skin age of the subject can be predicted in step (3) using the linear regression model of (iii).

As described above, according to the method for predicting skin age according to one aspect of the present invention, it is possible to more objectively and accurately predict skin age by utilizing the weight ratio of specific microorganisms that have a high correlation with skin age. It can be usefully used to provide customized cosmetics for individual skin types.

In another aspect, the present invention includes a sample collection unit for obtaining a skin microorganism sample from the skin of a subject; Two or more species selected from Proteobacteria, Cutibacterium sp. , Rothia sp. , and Bacillus sp. in the skin microbial sample obtained from the sampling unit Microorganism analysis unit for quantitatively analyzing the microorganisms by amplifying the sequence of the microorganisms; and an age prediction unit for predicting the skin age of the subject from the quantitative ratio of the two or more types of microorganisms analyzed by the microorganism analysis unit.

With respect to skin microbial sample collection in the sample collection unit, microbial quantitative analysis in the microbial analysis unit, and skin age prediction in the age prediction unit, (1) skin microbial sample collection step of the skin age prediction method, (2) ) Microorganism quantitative analysis step and (3) skin age prediction step as described in detail.

Hereinafter, the contents of the present invention will be described in more detail through examples and test examples. However, these examples and test examples are only presented to understand the contents of the present invention, and the scope of the present invention is not limited to these examples and test examples, and modifications and substitutions commonly known in the art and insertion, etc., which are also included in the scope of the present invention.

[Test Example 1] Confirmation of correlation between actual age and specific microorganisms

Skin microbial samples were collected from the cheeks of 180 Korean women (56 women in their 20s, 46 women in their 30s, 44 women in their 4th years, 8 women in their 50s, and 26 women in their 60s). Proteobacteria (Proteobacteria), Cutibacterium ( Cutibacterium sp. ), Rosia ( Rothia sp. ), Bacillus ( Bacillus sp. )) were quantitatively analyzed to confirm the correlation between actual age and microorganisms.

Specifically, the skin microbial analysis was conducted in two steps: obtaining a skin microbial sample and analyzing the microbial community. A 5 x 4 cm ² area of the subject's left or right cheek was swabbed with a sterilized cotton swab to collect skin microorganisms 2-3 times. The collected skin microbial samples were stored at -80 ° C until use. After extracting total microbial genomic DNA from the sample, primers targeting the V4-V5 region of the bacterial 16S rRNA gene (16S-518F: TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG CCA GCA GCY GCG GTA AN, 16S-926R: GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG ACA GCC GTC AAT TCN TTT RAG T) was used to amplify the target sequence through PCR. The amplified 16S rRNA gene was sequenced using the Illumina MiSeq platform.

The nucleotide sequence information obtained in the sequencing step was analyzed using the QIIME 2 (Quantitative 114 Insights Into Microbial Ecology) pipeline (ver. 2020.8) (Caporaso, JG et al. , "QIIME allows analysis of high-throughput community sequencing data" , 397 Nat. Methods 2010; 7 (5), 335). After removing the primer sequence from the base sequence information, filtering the sequence with a quality score of 20 or less using the Deblur Plugin, cutting the merged sequence into 375 bp, and Amplicon Sequencing Variants corresponding to 100% OTU clustering (ASVs) information was obtained. For the acquired ASVs, taxonomic analysis was performed using the GreenGenes database (ver. 13_5), and mitochondria and chloroplasts were removed to obtain skin microbial community information. The specific microorganisms in each subject's microbial community were quantified as the ratio of the presence of specific microorganisms to the total microorganisms. Through a correlation analysis between the percentage of microorganisms obtained and the actual age of the subject, four microorganisms with a high absolute value of the correlation coefficient ( r ) were selected at a statistically significant level ( P < 0.05).

The results are shown in Figure 1, and from the results of Figure 1, the microorganisms of the genus Cutibacterium tend to decrease as the skin ages, that is, as the actual age increases, showing a negative correlation ( r = -0.4197) It can be seen that there is a positive correlation (Proteobacteria: r = 0.3541; Rosia: r = 0.3769; genus Bacillus: r = 0.428).

[Example 1] Conversion of microbial quantification ratio to log scale

Using the quantitative analysis values of microorganisms of the genus Proteobacteria, Cutibacterium sp. , Rothia sp. , and Bacillus sp. , analyzed in Test Example 1, the following Microbial quantification ratios were normalized by transforming to a logarithmic scale.

(i) log ₂ CP=log ₂ (( Cutibacterium sp. )/(Proteobacteria))

(ii) log ₂ RC=log ₂ (( Rothia sp. )/( Cutibacterium sp. ))

(iii) log ₂ BC=log ₂ (( Bacillus sp. )/( Cutibacterium sp. ))

(Here, ( Cutibacterium sp. ), (Proteobacteria), ( Rothia sp. ) and ( Bacillus sp. ) are microorganisms of the genus Cutibacterium, microorganisms of the phylum Proteobacteria, microorganisms of the genus Rosia and Bacillus, respectively, in Test Example 1. It means the result of quantitative analysis of microorganisms in the genus.)

[Test Example 2] Confirmation of correlation between skin age and microbial ratio

The correlation between the log scale value of the quantitative ratio (C/P) of microorganisms of the genus Proteobacteria (P) and Cutibacterium (C) calculated in Example 1 and skin age was confirmed, and the results are shown in FIG. 2 and shown in Table 1 below.

C alone P alone Log ₂ CP r -0.4197 0.3541 -0.4412 P -value 4.68E-09 1.08E-06 7.06E-10

From the results of FIG. 2 and Table 1, the quantitative ratio (C / P) of microorganisms of the genus Cutibacterium negatively correlated with skin age to the microorganisms of the phylum Proteobacteria positively correlated with skin age was calculated on a log scale When converted to Test Example 1, it was confirmed that the absolute value of the correlation coefficient increased compared to the case where each of the microorganisms belonging to the genus Proteobacteria or Cutibacterium was used as in Test Example 1.

[Examples 2 to 5] Establishment of linear regression model using microbial quantitative ratio

The following linear regression models were established using the logarithmic scale of each microbial quantification ratio calculated in Example 1.

(i) y = -1.9318*log ₂ CP + 40.4767;

(ii) y = -0.7695*log ₂ CP + 1.6986*log ₂ RC + 55.7376;

(iii) y = -0.8064*log ₂ CP + 1.0166*log ₂ RC + 1.0567*log ₂ BC + 61.8824

In the case of (i), a total of 178 subjects were modeled, excluding 2 subjects in which no microorganisms of the genus Rosia were detected, and in the case of (ii), a total of 108 subjects, excluding 72 subjects in which no microorganisms of the genus Rosia were detected, were modeled. In the case of (iii), missing values of microorganisms of the genus Rosia and microorganisms of the genus Bacillus were replaced with log ₂ RC and log ₂ BC minimum values, respectively, and all 180 subjects were modeled.

At this time, the linear regression model of (i) was Example 2, and the linear regression model of (ii) was Example 3, and the linear regression model of (iii) was Example 4.

In addition, in the case of Example 5, as a mixed model of (i), (ii) and (iii), specifically, when the microorganisms of the genus Rosia and the genus Bacillus are missing, the linear regression model of (i) is used, Example 5 was a mixed model in which the linear regression model of (ii) was used when the Bacillus genus microorganism was missing, and the linear regression model of (iii) was used when there was no missing value.

[Test Example 3] Confirmation of Error in Skin Age Prediction by Linear Regression Model

14 males and females in their 20s (1 male, 13 females), 24 males and females in their 30s (8 males, 16 females), 26 males and females in their 40s who were not used to establish the skin age prediction linear regression model through Examples 1 to 5 (1 male, 25 females) Using skin microorganism samples from a total of 64 people, the degree of error in predicting the age of the linear regression models of Examples 2 to 5 was measured. At this time, 64 skin microbial samples were collected and analyzed by the same method as the skin microbial sample collection in Test Example 1, and the results are shown in FIG. 3 and Tables 2 and 3 below.

Error degree (absolute value) Example 2 7.50 ± 5.63 Example 3 12.52 ± 9.54 Example 4 14.85 ± 10.86 Example 5 7.10 ± 5.84

Example 2 Example 5 number of people % number of people % Within ±1 year 3 4.69 6 9.38 Within ±5 years 29 45.31 31 48.44 Within ±10 years 45 70.31 47 73.44 ±10 years old or older 19 29.69 17 26.56

From the results of FIG. 3 and Tables 2 and 3, it was confirmed that the age prediction by the linear regression model of Example 2 and the model of Example 5, which is a mixed model of Examples 2, 3 and 4, had a relatively low degree of error. , in particular, it was confirmed that the age prediction by Example 5, which is a mixed model, had the lowest degree of error.

[Test Example 4] Correlation analysis between skin age and sebum amount predicted by linear regression model (model establishment data)

Skin age and sebum amount ( sebum) was analyzed. At this time, the amount of sebum was measured on the cheek using a Sebumeter after waiting for more than 15 minutes in a constant temperature and humidity room after washing the face ( μ g / cm ² ) was used.

The results are shown in Figure 4 and Table 4 below.

real age Example 2 Example 3 Example 4 Example 5 Sebum r -0.6275 -0.4345 -0.3867 -0.5005 -0.5034 P 2.22E-16 1.13E-07 3.04E-06 4.74E-10 3.61E-10

From the results of FIG. 4 and Table 4, the skin age predicted by the linear regression model of Examples 2 to 5 also shows a negative correlation in the amount of sebum, like the actual age, and in particular, the linear regression model of Examples 4 and 5 It was confirmed that the correlation coefficient between the predicted skin age and sebum amount was the highest.

[Test Example 5] Correlation analysis between skin age and sebum amount predicted by linear regression model (external data)

14 males and females in their 20s (1 male, 13 females), 24 males and females in their 30s (8 males, 16 females), 26 males and females in their 40s who were not used to establish the skin age prediction linear regression model through Examples 1 to 5 Using 64 skin microorganism samples (1 male, 25 females), the correlation between actual age and skin age predicted by the linear regression model of Examples 2 to 5 and sebum amount was analyzed. At this time, the amount of sebum was measured on the cheek using a Sebumeter after waiting for more than 15 minutes in a constant temperature and humidity room after washing the face ( μ g / cm ² ) was used.

The results are shown in Figure 5 and Table 5 below.

real age Example 2 Example 3 Example 4 Example 5 Sebum r -0.2866 -0.2752 -0.1136 -0.1190 -0.3026 P 2.17E-02 2.78E-02 3.71E-01 3.49E-01 1.51E-02

From the results of FIG. 5 and Table 5, the skin age predicted by the linear regression model of Examples 2 to 5 also shows a negative correlation in the amount of sebum, just like the actual age. It was confirmed that the predicted skin age showed a similar level of correlation with the actual age. In particular, in the case of the skin age predicted by the linear regression model of Example 5, it was confirmed that the correlation coefficient with the sebum amount was higher than the actual age at a statistically significant level ( P < 0.05).

Claims (18)

(1) collecting a skin microorganism sample from the skin of a subject;
(2) Two or more microorganisms selected from among Proteobacteria, Cutibacterium sp. , Rothia sp. and Bacillus sp. in the collected skin microbial sample Quantitatively analyzing the microorganism by amplifying the sequence of; and
(3) predicting the skin age of the subject from the quantitative ratio of the two or more selected microorganisms;
Including, skin age prediction method. The method of claim 1, wherein in step (2),
A method for predicting skin age by amplifying microbial sequences using quantitative PCR or next generation sequencing (NGS) methods. The method of claim 1, wherein in step (2),
Proteobacteria (Proteobacteria) phylum, Rosia genus ( Rothia sp. ) and Bacillus genus ( Bacillus sp. ) in the collected skin microbial sample; And Cutibacterium genus ( Cutibacterium sp. ) By amplifying the sequence of the microorganism to quantitatively analyze the microorganism, skin age prediction method. The method of claim 1, wherein in step (3),
The Cutibacterium genus ( Cutibacterium sp. ) Microorganisms exhibiting a negative correlation with skin age, the Proteobacteria (Proteobacteria) phylum, Rosia genus ( Rothia sp. ) and Bacillus genus ( Bacillus sp. ) selected from A method for predicting skin age, wherein one or more microorganisms predict a skin age of a subject using a positive correlation with skin age. According to claim 1,
In the step (3), the quantitative ratio of the microorganisms is normalized by converting to one or more of the following log scales, skin age prediction method:
(i) log ₂ CP=log ₂ (( Cutibacterium sp. )/(Proteobacteria));
(ii) log ₂ RC=log ₂ (( Rothia sp. )/( Cutibacterium sp. ));
(iii) log ₂ BC=log ₂ (( Bacillus sp. )/( Cutibacterium sp. ));
Here, ( Cutibacterium sp. ), (Proteobacteria), ( Rothia sp. ) and ( Bacillus sp. ) are the results of quantitative analysis of microorganisms of the genus Cutibacterium, microorganisms of the phylum Proteobacteria, microorganisms of the genus Rosia and microorganisms of the genus Bacillus, respectively. means The method of claim 5, wherein in step (3),
A skin age prediction method, wherein the skin age of a subject is predicted using one or more of the following linear regression models:
(i) y = -1.9318*log ₂ CP + 40.4767;
(ii) y = -0.7695*log ₂ CP + 1.6986*log ₂ RC + 55.7376;
(iii) y = -0.8064*log ₂ CP + 1.0166*log ₂ RC + 1.0567*log ₂ BC + 61.8824 According to claim 6,
When the quantitative analysis result of the microorganisms of the genus Rosia and the microorganisms of the genus Bacillus are missing in the step (2), the skin age of the subject is predicted using the linear regression model of (i) in the step (3), skin age prediction method. According to claim 6,
When the quantitative analysis result of the microorganism of the genus Bacillus is missing in the step (2), the skin age of the subject is predicted using the linear regression model of (ii) in the step (3). Method for predicting skin age. According to claim 6,
If there is no microorganism whose quantitative analysis result value is missing in step (2), skin age prediction method of predicting the skin age of the subject using the linear regression model of (iii) in step (3). a sample collection unit that obtains a skin microorganism sample from the skin of a subject;
Two or more species selected from the genus Proteobacteria, the genus Cutibacterium sp. , the genus Rothia sp. , and the genus Bacillus sp. in the skin microbial sample obtained from the sampling unit Microorganism analysis unit for quantitatively analyzing the microorganisms by amplifying the sequence of the microorganisms; and
an age prediction unit for predicting a skin age of a subject from a quantitative ratio of two or more types of microorganisms analyzed by the microorganism analysis unit;
Containing, skin age prediction kit. 11. The method of claim 10, wherein the microorganism analysis unit,
A skin age prediction kit that amplifies microbial sequences using quantitative PCR or next generation sequencing (NGS) methods. 11. The method of claim 10, wherein the microorganism analysis unit,
At least one microorganism selected from the genus Proteobacteria, Rothia sp., and Bacillus sp. in the skin microbial sample; And Cutibacterium ( Cutibacterium sp. ) A skin age prediction kit for quantitatively analyzing the microorganism by amplifying the sequence of the genus microorganism. 11. The method of claim 10, wherein the age prediction unit,
The Cutibacterium genus ( Cutibacterium sp. ) Microorganisms exhibiting a negative correlation with skin age, the Proteobacteria (Proteobacteria) phylum, Rosia genus ( Rothia sp. ) and Bacillus genus ( Bacillus sp. ) selected from A skin age prediction kit for predicting the skin age of a subject by using that one or more microorganisms show a positive correlation with skin age. According to claim 10,
The quantitative ratio of the two or more microorganisms is normalized by converting to one or more of the following log scales, skin age prediction kit:
(i) log ₂ CP=log ₂ (( Cutibacterium sp. )/(Proteobacteria));
(ii) log ₂ RC=log ₂ (( Rothia sp. )/( Cutibacterium sp. ));
(iii) log ₂ BC=log ₂ (( Bacillus sp. )/( Cutibacterium sp. ));
Here, ( Cutibacterium sp. ), (Proteobacteria), ( Rothia sp. ) and ( Bacillus sp. ) are the results of quantitative analysis of microorganisms of the genus Cutibacterium, microorganisms of the phylum Proteobacteria, microorganisms of the genus Rosia and microorganisms of the genus Bacillus, respectively. means The method of claim 14, wherein the age prediction unit,
A skin age prediction kit that predicts the skin age of a subject using one or more of the following linear regression models:
(i) y = -1.9318*log ₂ CP + 40.4767;
(ii) y = -0.7695*log ₂ CP + 1.6986*log ₂ RC + 55.7376;
(iii) y = -0.8064*log ₂ CP + 1.0166*log ₂ RC + 1.0567*log ₂ BC + 61.8824. According to claim 15,
When the quantitative analysis result of the microorganisms of the genus Rosia and the microorganisms of the genus Bacillus is missing in the microorganism analysis unit, the age prediction unit predicts the skin age of the subject using the linear regression model of (i) Skin age prediction kit. According to claim 15,
Skin age prediction kit, wherein the age prediction unit predicts the skin age of the subject using the linear regression model of (ii) when the value of the quantitative analysis of the microorganisms of the genus Bacillus is missing in the microorganism analysis unit. According to claim 15,
When there is no microorganism whose quantitative analysis result value is missing in the microorganism analysis unit, the age prediction unit predicts the skin age of the subject using the linear regression model of (iii), skin age prediction kit.

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