Method for evaluating bacterial flora diversity

Abstract

Provided is a method for determining bacterial flora diversity on the basis of skin gas by focusing on the relation between skin gas and bacterial flora diversity. Provided is a method for evaluating bacterial flora diversity, a device for evaluating bacterial flora diversity, and a system for evaluating bacterial flora diversity, on the basis of the amount of at least one bacterial flora-related skin gas by identifying a bacterial flora-related skin gas related to bacterial flora diversity.

Description

Methods for assessing microbiota diversity

[0001] The present invention relates to a method for assessing microbiota diversity based on the amount of microbiota-associated skin gases.

[0002] Volatile substances emitted from the human body surface are called skin gases, also known as skin volatile emissions. Skin gases contribute to body odor. However, sampling and quantifying skin gases have been difficult. However, recent advances in analytical technology have led to attempts to qualitatively analyze skin gas components. As a result, it has been discovered that skin gases contain over 600 compounds. Skin gases are composed of compounds such as aldehydes, acids, ketones, alcohols, hydrocarbons, and esters, with acetic acid, octanoic acid, nonanoic acid, geranylacetone, 6-methylhepten-2-one, nonanal, and decanal being known as major compounds. Skin gases can be classified by their emission pathway, broadly divided into those derived from surface reactions, skin glands (sweat glands, sebaceous glands), and blood. Surface-derived skin gas refers to skin gas that is produced when components secreted from skin glands are converted into volatile compounds and then dissipated on the skin surface by the action of skin resident bacteria, oxygen, ultraviolet rays, and the like. Skin gland-derived skin gas is skin gas emitted from sweat glands and sebaceous glands, and is known to increase due to sweating and sebaceous gland secretion. Blood-derived skin gas is a pathway in which components in the blood produced by metabolism and in vivo reactions volatilize and dissipate directly from the skin, and volatile components carried by blood are mainly dissipated via this pathway. Furthermore, since sweat is composed of plasma, volatile components in blood can also be dissipated from skin glands. Thus, skin gas can be classified into exogenous components based on skin surface reactions and endogenous components originating from the interior of the body. Endogenous components are caused by congenital genetics and chronological aging, and can be used as indicators of these, while exogenous components are caused by environmental and lifestyle factors, including ultraviolet light exposure, and can be used as indicators of these. By examining skin gases, it is expected that health monitoring and medical applications will be possible.Specifically, methods that have been developed include a method for estimating diabetes index values ​​using acetone (Patent Document 1: JP 2020-016448 A), a method for evaluating atopic dermatitis using the amount of acetone (Patent Document 2: JP 2019-219256 A), a method for determining fatigue levels based on the amount of nitrogen gas (Patent Document 3: JP 2019-78585 A), a method for predicting blood glucose levels based on skin gases (Patent Document 4: JP 2017-151063 A), a method for evaluating cancer based on skin gas patterns (Patent Document 5: JP 2021-148517 A), and a method for diagnosing Parkinson's disease based on components selected from medium- to long-chain acylcarnitines and secondary bile acids (Patent Document 6: JP 2017-138141 A).

[0003] Endogenous skin gases are further classified into components derived from metabolism in the body, components derived from ingested substances, and components derived from intestinal bacteria. Metabolic components include ammonia, amines, and acetone. Ingested components include ethanol and acetaldehyde resulting from alcohol consumption, cuminaldehyde resulting from eating curry, diallyl disulfide and allyl methyl sulfide resulting from eating garlic, and nicotine, methylfuran, and 2,5-methylfuran resulting from smoking. Intestinal bacteria-derived components include hydrogen, methane, ethane, and ethylene (Non-Patent Document 1: Journal of the Society on Odor and Fragrance Environment, Vol. 48, No. 6, pp. 410-417).

[0004] JP 2020-016448 A JP 2019-219256 A JP 2019-78585 A JP 2017-151063 A JP 2021-148517 A JP 2017-138141 A

[0005] Journal of Odor and Fragrance Environment, Vol. 48, No. 6, pp. 410-417

[0006] The purpose of the present invention is to provide a method for determining the diversity of intestinal microbiota based on objective indicators that are easy to collect.

[0007] The present inventors focused on the relationship between gut microbiota diversity and skin gases and conducted extensive research into indicators that can simply determine gut microbiota diversity. Surprisingly, they discovered that the amount of a specific skin gas correlates with gut microbiota diversity. Based on this finding, they arrived at the present invention. Specifically, they provide a method for determining microbiota diversity based on the amount of skin gas associated with microbiota diversity. More specifically, the present invention relates to the following: [1] A method for evaluating microbiota diversity, comprising: analyzing collected skin gases to detect microbiota-related skin gases; and determining microbiota diversity based on the correlation between microbiota-related skin gases and microbiota diversity. [2] The method according to item 1, wherein an increase in the amount or concentration of microbiota-related skin gas indicates high microbiota diversity. [3] The method according to item 1 or 2, wherein the microbiota-related skin gas is selected from the group consisting of 2-butanone, ethyl acetate, cyclohexane, tridecane, N,N-dimethylpropanamide, and dichloromethane. [4] The method according to any one of items 1 to 3, wherein the microbiota diversity is Shannon diversity or Faith's phylogenetic diversity. [5] The method according to any one of items 1 to 4, wherein a skin index is determined based on the microbiota diversity. [6] The method according to item 5, wherein the skin index is an index of a skin condition selected from the group consisting of itching, dry skin, and desquamation. [7] A method for evaluating at least one skin index selected from the group consisting of itching, dry skin, and desquamation, comprising: analyzing collected skin gases to detect microbiota-related skin gases; and determining the skin index based on the correspondence between the microbiota-related skin gases and the skin index. [8] A device for evaluating bacterial flora diversity, comprising: an input unit to which data of analysis results from a skin gas analyzer is input; a memory unit that stores a correspondence relationship between the amount of at least one bacterial flora-associated skin gas and the bacterial flora diversity; a processing unit that determines the bacterial flora diversity from the amount of at least one bacterial flora-associated skin gas in the input data of skin gas analysis results and the correspondence relationship stored in the memory unit; and an output unit that outputs the determined bacterial flora diversity.[9] A device for evaluating bacterial biota diversity, comprising: an input unit to which data of the analysis results from a skin gas analyzer is input; a learning unit that is pre-trained using teacher data including at least one type and amount of bacterial biota-related skin gas and the bacterial biota diversity, and that outputs the bacterial biota diversity when input information including the type and amount of the bacterial biota-related skin gas is input; a processing unit that executes processing in the learning unit; and an output unit that outputs the determined bacterial biota diversity.

[10] The device for evaluating bacterial biota diversity according to item 8 or 9, wherein the type of the bacterial biota-related skin gas is selected from the group consisting of 2-butanone, ethyl acetate, cyclohexane, tridecane, N,N-dimethylpropanamide, and dichloromethane.

[11] The device for evaluating bacterial biota diversity according to any one of items 8 to 10, wherein the bacterial biota diversity is Shannon diversity or Faith's phylogenetic diversity.

[12] The evaluation device according to any one of items 8 to 11, wherein the device for evaluating bacterial biota diversity stores in advance in a memory unit a skin correspondence relationship between the bacterial biota diversity and a skin index, the processing unit determines a skin index from the determined diversity and the skin correspondence relationship, and the output unit further outputs the skin index.

[13] The evaluation device according to item 12, wherein the skin index is an index of a skin condition selected from the group consisting of itching, dry skin, and desquamation.

[14] The device for evaluating bacterial biota diversity according to any one of items 8 to 13, wherein the device for evaluating bacterial biota diversity provides a lifestyle improvement plan corresponding to the bacterial biota diversity based on the determined bacterial biota diversity of the subject, wherein: the memory unit further stores a correspondence relationship between the bacterial biota diversity of the subject and a lifestyle improvement corresponding to the bacterial biota diversity; the processing unit determines a lifestyle improvement plan corresponding to the bacterial biota diversity from the determined bacterial biota diversity of the subject and the correspondence relationship stored in the memory unit; and the output unit outputs the determined lifestyle improvement plan.

[15] The device for evaluating microbiota diversity according to Item 14, wherein the lifestyle improvement suggestions are selected from the group consisting of vegetable intake, fruit intake, cheese intake, coffee intake, tea intake, red wine intake, dairy product intake, and (moderate-intensity aerobic) exercise.

[16] A system including the microbiota diversity evaluation device according to any one of items 8 to 15, wherein the microbiota diversity evaluation device is connected to a network, and wherein data of the analysis results from a skin gas analyzer is input to the input unit via the network; and the determined microbiota diversity is output from the output unit via the network.

[17] The system according to item 16, further including a network-connected skin gas analyzer, the skin gas analyzer including: a skin gas sampling unit; a skin gas analysis unit; and a network-connected output unit.

[18] The system according to item 16, further including a network-connected terminal device, the terminal device including: a network connection unit connected to the microbiota diversity evaluation device; and a terminal output unit that outputs at least one selected from the group consisting of the microbiota diversity of the subject and / or lifestyle improvement suggestions output from the output unit of the evaluation device via the network connection unit.

[0008] By measuring the amount of microbiota-associated skin gases, the microbiota diversity can be determined.

[0009] Figure 1 shows an overview of the analysis pipeline in microbiome analysis, from obtaining a base sequence to calculating the bacterial phylogenetic composition. Figure 2 shows a configuration diagram of a microbial biota diversity evaluation device 10 according to the present invention. The process for determining microbial biota diversity is carried out cooperatively by hardware resources such as an input unit 11, a memory unit 12, a processing unit 13, an output unit 14, and a learning unit 15, which are connected via a bus. Figure 3 shows a configuration diagram of a microbial biota diversity evaluation system 20 including the microbial biota diversity evaluation device 10 according to the present invention, which is located on the Internet.

[0010] The present invention relates to a method for evaluating microbiota diversity based on the amount of microbiota-related skin gas among collected skin gases, and to an apparatus and system for evaluating microbiota diversity.

[0011] [Method for Evaluating Microbiota Diversity] The present invention relates to a method for evaluating microbiota diversity, comprising the following steps: analyzing collected skin gases to detect microbiota-related skin gases; and determining the microbiota diversity based on the correspondence between the amount of microbiota-related skin gases and the microbiota diversity. The microbiota diversity can be evaluated based on the amount of microbiota-related skin gases. The evaluation of microbiota diversity may be a determination of skin properties or whether or not a subject suffers from a microbiota-related disease, or may determine the severity of the microbiota-related disease in addition to whether or not a subject suffers from a microbiota-related disease. The method for evaluating microbiota diversity may be referred to as a method for testing skin indicators, a method for testing a microbiota-related disease, a method for determining or distinguishing a microbiota-related disease, or a method for determining the possibility or risk of suffering from a microbiota-related disease. The present invention may further relate to a method for determining the amount of microbiota-related skin gases for diagnosing a microbiota-related disease. Such a method may comprise the steps of analyzing collected skin gases to detect microbiota-related skin gases; and determining the amount of at least one of the microbiota-related skin gases.

[0012] Microbiota diversity refers to the diversity of bacteria in the intestine and is determined by collecting stool, extracting gDNA, and performing 16S rRNA analysis. Microbiota diversity refers to alpha diversity, which indicates the diversity of microorganisms in a single sample, and is more specifically expressed by the Shannon index and Faith phylogenetic diversity. The Shannon index is an index calculated based on the proportion of species in the entire sample, and is higher when the number of species is large and each species is evenly distributed. Faith phylogenetic diversity is diversity that incorporates the length of the phylogenetic tree and takes into account bacterial lineage.

[0013] The diversity of intestinal microbiota has been shown to be related to skin (Example 2), and the greater the diversity, the better the skin condition. Therefore, skin characteristics can be determined from the diversity of microbiota. Examples of skin characteristics include itching, dry skin, and desquamation, and the lower the diversity, the worse these skin characteristics become. From the amount of microbiota-related skin gas, the skin characteristics can be determined through the evaluation of the diversity of microbiota. Skin characteristics can be classified into multiple stages, for example, 2, 3, 4, or 5 stages, for each of itching, dry skin, and desquamation. For example, they can be classified into none, slight, mild, moderate, and severe.

[0014] The symptoms or diseases actually suffered by a subject determined to have low microbiota diversity according to the present invention may be any symptom or condition of a microbiota-related disease, or a condition represented by poor skin condition. For example, the higher the amount of microbiota-related skin gas, the greater the severity of the microbiota-related disease. On the other hand, the lower the amount of microbiota-related skin gas, the less severe the severity of the microbiota-related disease. Similarly, the higher the amount of microbiota-related skin gas, the worse the skin condition may be. On the other hand, the lower the amount of microbiota-related skin gas, the better the skin condition may be.

[0015] The microbiota-associated disease includes at least one disease selected from the group consisting of obesity, frailty, and cognitive decline.

[0016] [Microbiota-Associated Skin Gases] Microbiota-associated skin gases refer to skin gases emitted from the skin that are associated with microbiota diversity. Examples of microbiota-associated skin gases include 2-butanone, ethyl acetate, cyclohexane, tridecane, N,N-dimethylpropanamide, and dichloromethane. Of these skin gases, the amounts of 2-butanone, ethyl acetate, cyclohexane, tridecane, and N,N-dimethylpropanamide have been shown to correlate with Shannon diversity and Face diversity (Example 1). Thus, measuring the amounts of these skin gases can determine the microbiota diversity in the intestine. Furthermore, while the amount of dichloromethane did not correlate with the Shannon index, it did correlate with Face diversity. This makes it possible to determine the microbiota diversity based on Face diversity based on the amount of dichloromethane. The microbiota-associated skin gases may be any combination of two, three, four, five, or all of 2-butanone, ethyl acetate, tridecane, N,N-dimethylpropanamide, dichloromethane, and cyclohexane. When a combination is used, the total amount may be used, or the degree of contribution to the diversity of the bacterial flora may be taken into consideration.

[0017] While it is known that microbiota-related skin gas is contained in some fermented and processed foods, it is only found in a fairly limited range of foods, and therefore it is thought that the microbiota-related skin gas contained in skin gas is produced by intestinal bacteria in the human body.

[0018] Microbiota-associated skin gases may be collected and analyzed by any method. Skin gases can be collected by collecting gases emitted from the human body surface. Skin gas collection may be performed using contact sampling with cotton or a polymeric material (e.g., PDMS), or a wearable device (see JP 2023-99322 A). Furthermore, when collecting skin gases from the hand, the entire hand can be covered with a gas-impermeable bag (e.g., a vinyl bag) and sealed at the wrist. The bag preferably has an openable collection port through which gas can be introduced or removed from the bag. The gas in the bag is preferably air or has been replaced with an inert gas (e.g., nitrogen gas). After a predetermined time has elapsed, the gas in the bag can be transferred to another container (e.g., a storage container) through the collection port. Skin-associated skin gases contained in the storage container can be analyzed by methods well known in the art, such as chromatography and / or mass spectrometry. More preferably, analysis can be performed using gas chromatography-mass spectrometry (GC-MS). The amount of microbiota-related skin gas may be calculated by using the Area% value of the chromatogram itself, by standardizing the skin gas collection method, collection time, and analysis method. In this way, skin gas collection can be performed non-invasively and simply, so it can be collected easily without putting a strain on the body.

[0019] [Association between Individual Microbiota-Associated Skin Gases and Disease] Biological conditions correlated with alpha diversity in gut bacteria include obesity, frailty, and cognitive decline. Specifically, it is known that subjects suffering from obesity, frailty, and cognitive decline have reduced alpha diversity in gut bacteria. Therefore, obesity, frailty, or cognitive decline can be determined from the amount of at least one skin gas selected from the group consisting of 2-butanone, ethyl acetate, cyclohexane, tridecane, N,N-dimethylpropanamide, and dichloromethane, which are skin gases that correlate with alpha diversity.

[0020] The method for evaluating microbiota diversity of the present invention can unambiguously determine microbiota diversity by using a predetermined correspondence relationship between the amount of microbiota-related skin gas and microbiota diversity. The correspondence relationship may be a correspondence relationship between the amount of at least one microbiota-related skin gas and microbiota diversity, or a correspondence relationship between the type and amount of microbiota-related skin gas and microbiota diversity. Using the correspondence relationship, microbiota diversity can be determined from the amount of at least one microbiota-related skin gas, and it can also determine not only microbiota diversity but also skin condition and whether or not a subject suffers from a microbiota-related disease. Furthermore, for microbiota-related diseases, a correspondence relationship between severity and the amount of microbiota-related skin gas can be created in advance. Such a correspondence relationship may be a correspondence table, graph, or correlation equation, or a threshold value can be used. Since the determination of microbiota diversity can be unambiguously determined, it does not require a doctor's judgment and can be considered a so-called non-diagnostic method. The method of the present invention can be used by persons other than medical professionals, such as employees of cosmetics retailers, beauty salons, or testing companies.

[0021] [Device for Evaluating Microbiota Diversity] Another aspect of the present invention may relate to a device for evaluating microbiota diversity (hereinafter referred to as the device for evaluating microbiota diversity). Such a device for evaluating microbiota diversity can determine the microbiota diversity based on the amount of microbiota-related skin gas. Specifically, the device for evaluating microbiota diversity includes: an input unit 11 into which analysis result data from a skin gas analyzer is input; a memory unit 12 that stores a correspondence relationship between the amount of at least one microbiota-related skin gas and the microbiota diversity; a processing unit 13 that determines the microbiota diversity based on the amount of at least one microbiota-related skin gas in the input skin gas analysis result data and the correspondence relationship stored in the memory unit 12; and an output unit 14 that outputs the determined microbiota diversity. For example, the correspondence relationship between the amount of at least one microbiota-related skin gas and the microbiota diversity stored in the memory unit 12 may be a correspondence table, graph, or correlation equation, or may be a relationship between one or more thresholds and the microbiota diversity. The device for evaluating microbiota diversity can also be referred to as a device for testing microbiota diversity.

[0022] The device for evaluating bacterial biota diversity according to the present invention further stores in advance in a storage unit a skin correspondence relationship between bacterial biota diversity and skin properties, the processing unit determines skin properties from the determined bacterial biota diversity and skin correspondence relationship, and the output unit further outputs the skin properties, thereby making it possible to determine skin properties in addition to bacterial biota diversity.

[0023] In another aspect, the present invention may relate to a device for determining a skin index without using microbiota diversity. More specifically, the device for determining a skin index includes the following: an input unit 11 to which data of analysis results from a skin gas analyzer is input; a memory unit 12 that stores a correspondence relationship between the amount of at least one microbiota-related skin gas and the skin index; a processing unit 13 that determines a skin index based on the amount of at least one microbiota-related skin gas in the input data of skin gas analysis results and the correspondence relationship stored in the memory unit 12; and an output unit 14 that outputs the determined skin index. As an example, the correspondence relationship between the amount of at least one microbiota-related skin gas and the skin index stored in the memory unit 12 may be a correspondence table, graph, or correlation equation, or may be a relationship between one or more thresholds and the skin index. The device for determining a skin index may also be referred to as a skin testing device.

[0024] The device for evaluating microbiota diversity according to the present invention further stores in advance in a storage unit a relationship between the microbiota diversity and a disease associated with a microbiota, the processing unit determines a microbiota-associated disease from the determined microbiota diversity and the disease relationship, and the output unit further outputs the microbiota-associated disease, thereby making it possible to determine a microbiota-associated disease in addition to the microbiota diversity.

[0025] In another aspect, the present invention may relate to a device for determining a microbiota-related disease without using microbiota diversity. More specifically, the device for determining a microbiota-related disease includes the following: an input unit 11 to which data of analysis results from a skin gas analyzer is input; a memory unit 12 that stores a correspondence relationship between the amount of at least one microbiota-related skin gas and a microbiota-related disease; a processing unit 13 that determines a microbiota-related disease based on the amount of at least one microbiota-related skin gas in the input data of skin gas analysis results and the correspondence relationship stored in the memory unit 12; and an output unit 14 that outputs the determined microbiota-related disease. As an example, the correspondence relationship between the amount of at least one microbiota-related skin gas and a microbiota-related disease stored in the memory unit 12 may be a correspondence table, graph, or correlation equation, or may be a relationship between one or more thresholds and a skin index. The device for determining a microbiota-related disease may also be referred to as a testing device for a microbiota-related disease.

[0026] The device for evaluating microbiota diversity may further include providing a lifestyle improvement plan corresponding to the determined microbiota diversity in the processing unit 13. In such a device for evaluating microbiota diversity, the memory unit 12 further stores a correspondence relationship between the microbiota diversity of the subject and lifestyle improvements corresponding to the microbiota diversity; the processing unit 13 determines a lifestyle improvement plan corresponding to the microbiota diversity from the determined microbiota diversity of the subject and the correspondence relationship stored in the memory unit 12; and the output unit 14 outputs the determined lifestyle improvement plan. The lifestyle improvement may be at least one selected from the group consisting of vegetable intake, fruit intake, cheese intake, coffee intake, tea intake, red wine intake, dairy product intake, and (moderate-intensity aerobic) exercise, or any combination thereof.

[0027] The processing unit 13 can determine the bacterial flora diversity by reading out the correspondence relationship between the amount of at least one bacterial flora-related skin gas stored in the memory unit 12 and the bacterial flora diversity, and then determining the bacterial flora diversity from the amount of at least one bacterial flora-related skin gas in the skin gas analysis result data input from the input unit 11 and the correspondence relationship. The processing unit 13 may perform a step of extracting the amount of the bacterial flora-related skin gas from the input skin gas analysis result data, or may input data on the amount of the bacterial flora-related skin gas in advance as the skin gas analysis result. The bacterial flora-related skin gas may be any one selected from 2-butanone, ethyl acetate, cyclohexane, tridecane, N,N-dimethylpropanamide, and a mixture of dichloromethane, or any combination thereof. When the correspondence relationship is a correspondence table, the bacterial flora diversity can be determined by reading out the bacterial flora diversity corresponding to the amount of the bacterial flora-related skin gas in the analysis data from the correspondence table. When the correspondence relationship is a correlation equation, the numerical value for the bacterial flora diversity can be determined by substituting the amount of the bacterial flora-related skin gas in the analysis data into the correlation equation. When the correspondence relationship is between one or more thresholds and the microbial biodiversity, the microbial biodiversity can be determined by comparing with each threshold. The determined microbial biodiversity may be stored in the memory unit 12, or output from the memory unit 12 or directly from the processing unit 13 via the output unit 14.

[0028] In another embodiment, instead of determining the bacterial flora diversity based on the correspondence relationship in the processing unit 13, the device for evaluating bacterial flora diversity can use a learning unit 15 that has been pre-trained using information on the amount of at least one bacterial flora-related skin gas and information on the bacterial flora diversity as training data. Such a learning unit 15 is pre-trained so that when data including the amount of at least one bacterial flora-related skin gas is input, the learning unit 15 outputs the bacterial flora diversity. The information on the amount of at least one bacterial flora-related skin gas used when training the learning unit 15 may be information on the type and amount of the bacterial flora-related skin gas. In other words, a learning unit 15 can be used that has been pre-trained using information on the type and amount of the bacterial flora-related skin gas and information on the bacterial flora diversity as training data. Such a learning unit 15 is pre-trained so that when data including the type and amount of the bacterial flora-related skin gas is input, the learning unit 15 outputs the bacterial flora diversity. Specifically, the device for evaluating bacterial flora diversity includes the following: an input unit 11 that inputs data including the amount of at least one bacterial flora-related skin gas from a skin gas analyzer; a learning unit 15 that has been pre-trained using information on the amount of at least one bacterial flora-related skin gas and information on bacterial flora diversity as training data, and that outputs the bacterial flora diversity when data including the amount of at least one bacterial flora-related skin gas input from the input unit 11 is input; and an output unit 14 that outputs the outputted bacterial flora diversity. The device for evaluating bacterial flora diversity may further include a processing unit 13 and a memory unit 12, and the processing unit 13 may control the processing in the learning unit 15, as well as control the input of data including the type and amount of bacterial flora-related skin gas input from the input unit 11 to the learning unit 15, and control the output of the bacterial flora diversity output by the learning unit 15 from the output unit 14. The processing unit 13 may also perform pre-adjustment of the input data of the skin gas analysis results. As an example, the data including the type and amount of microbiota-related skin gas input from the input unit 11 may be the data of the skin gas analysis results itself, in which case the processing unit 13 may perform a step of extracting the amount of microbiota-related skin gas from the input data of the skin gas analysis results.The storage unit 12 may temporarily store data including the type and amount of the microbiota-related skin gas input from the input unit 11 and the microbiota diversity output by the learning unit 15 .

[0029] The input unit 11 includes an interface. The interface may be connected to, for example, an operation unit such as a keyboard or mouse, a communication unit such as a LAN or port, or an external storage device such as a CD-ROM, DVD-ROM, BD-ROM, or memory stick. Data including the type and amount of microflora-related skin gas may be input via the operation unit. Furthermore, instructions for processing in the processing unit 13 can be given from the input unit 11 via the operation unit.

[0030] The storage unit 12 includes a memory device such as RAM, ROM, or flash memory, a fixed disk device such as a hard disk drive, or a portable storage device such as a flexible disk or optical disk. The storage unit 12 may store data and instructions input from the input unit 11. The storage unit 12 stores the correspondence between the type and amount of microbiota-related skin gas and the microbiota diversity. Specifically, the correspondence between the type and amount of skin gas and the microbiota diversity is stored as a correspondence table, graph, correlation equation, or threshold value. The storage unit 12 stores the results of the arithmetic processing performed by the processing unit 13, as well as programs and databases used for various computer processes, and may also store the program of the learning unit 15. The computer program may be installed from a computer-readable recording medium such as a CD-ROM or DVD-ROM, or via the Internet. The computer program is installed in the storage unit 12 using a known setup program or the like.

[0031] The processing unit 13 executes various types of arithmetic processing in accordance with the programs stored in the memory unit 12. The arithmetic processing is performed by a central processing unit (CPU) included in the processing unit 13. This CPU includes functional modules that control the input unit 11, the memory unit 12, the learning unit 15, and the output unit 14, and can perform various types of control. Each of these units may be composed of an independent integrated circuit, microprocessor, firmware, etc. Information generated after each process by the processing unit 13 may be temporarily stored in the memory unit 12, or may be used directly in the next process. The process of the learning unit 15 may be performed by the processing unit 13.

[0032] The output unit 14 is configured to output the bacterial flora diversity generated by performing arithmetic processing in the processing unit 13. The output unit 14 may be an output means such as a display device such as a liquid crystal display that directly displays the results of the arithmetic processing, or a printer, or may be an interface unit for outputting to an external storage device or via a network.

[0033] The learning unit 15 uses a known machine learning technique, such as deep learning, to learn the relationship between input data including the amount of at least one microbiota-related skin gas and information about the microbiota diversity at that time. Information about the amount of at least one microbiota-related skin gas and information about the microbiota diversity at that time are obtained for various subjects, and the learning unit 15 can be trained using this data. Deep learning is machine learning using a multilayer neural network consisting of an input layer, an intermediate layer, and an output layer. A feature vector of the detection information is input to each node in the input layer. Each node in the intermediate layer outputs the sum of values ​​obtained by multiplying each feature vector output from each node in the input layer by a weight, and the output layer outputs the sum of values ​​obtained by multiplying each feature vector output from each node in the intermediate layer by a weight. The learning unit 15 adjusts each weight while learning to minimize the difference between the output value from the output layer and the microbiota diversity information. The input data input to the learning unit 15 is input data relating to the amount of at least one microbiota-related skin gas, and preferably information relating to the type and amount of the microbiota-related skin gas. The microbiota-related skin gas may be any one selected from 2-butanone, ethyl acetate, cyclohexane, tridecane, N,N-dimethylpropanamide, and dichloromethane, or any combination thereof. In one example, the types and amounts of all four types may be input.

[0034] [Microbiota diversity evaluation system] The microbiota diversity evaluation device 10 of the present invention may exist on a network and may constitute a microbiota diversity evaluation system 20 including the microbiota diversity evaluation device 10. The microbiota diversity evaluation system 20 is configured so that data on the results of skin gas analysis is input via the network to the input unit 11 of the microbiota diversity evaluation device 10; and the determined microbiota diversity is output via the network from the output unit 14. Such a microbiota diversity evaluation system 20 may further include a terminal device 30 and / or a skin gas analyzer 40 connected via the network. That is, the microbiota diversity evaluation device 10 may exist on a server, and the input unit 11 and the output unit 14 may each be connected to the network via an interface unit. Furthermore, the learning unit 15 used by the microbiota diversity evaluation device 10 may also be located externally via a server, etc., and the microbiota diversity may be evaluated via communication.

[0035] The skin gas analyzer 40 includes the following: a skin gas sampling unit; a skin gas analysis unit; and a skin gas analyzer output unit, and data on the results of the skin gas analysis can be output via the skin gas analyzer output unit. The skin gas analysis data output in this manner may be provided to the microbiota diversity evaluation device via a network or directly. The skin gas analyzer 40 may be any device capable of analyzing skin gas samples, and a chromatography device, particularly a gas chromatography mass spectrometry (GC / MS) device, may be used.

[0036] The network-connected terminal device 30 may include the following: a network connection unit connected to the microbiota diversity evaluation device 10; and a terminal output unit that outputs, via the network connection unit, at least one selected from the group consisting of the microbiota diversity of the subject output from the output unit 14 of the microbiota diversity evaluation device and lifestyle improvement suggestions according to severity. Data on the results of skin gas analysis may be input to the microbiota diversity evaluation device 10 via the network connection unit.

[0037] Another aspect of the present invention may relate to a program that causes the device 10 to perform the above-mentioned processes. Such a program includes the following instructions to the processing unit 13: read input data including the amount of at least one microbiota-associated skin gas input from the input unit 11, read a correspondence relationship between the amount of at least one microbiota-associated skin gas and the microbiota diversity stored in the memory unit 12, determine the microbiota diversity from the input data and the correspondence relationship, and output the determined microbiota diversity to the output unit 14. Instead of having the processing unit 13 determine the microbiota diversity from the correspondence relationship, the program may input input data to a learning unit 15 that has been pre-trained to output the microbiota diversity when input data related to the amount of at least one microbiota-associated skin gas is input, thereby determining the microbiota diversity.

[0038] All documents mentioned herein are incorporated by reference in their entirety.

[0039] The following examples of the present invention are for illustrative purposes only and do not limit the technical scope of the present invention. The technical scope of the present invention is limited only by the claims. The present invention may be modified, for example, by adding, deleting, or substituting components of the present invention, provided that the modifications do not depart from the spirit of the present invention.

[0040] Example 1: Relationship between skin gas and microbiota diversity Skin gas was collected and analyzed from 57 healthy women aged 25 to 45 years, and feces were also collected. The alpha diversity index, which represents the diversity of the intestinal microbiota, was calculated according to the method described below.

[0041] Skin Gas Analysis A skin gas sampling bag made of a highly gas-barrier material was attached to the hand of a subject who had fasted for breakfast and sealed. The skin gas sampling bag was equipped with a connecting part, and suction was performed through the connecting part, followed by filling with approximately 0.5 L of nitrogen gas. After attachment, the bag was left stationary for 45 minutes to collect skin gas. The collected skin gas sample was transferred from the connecting part to a skin gas storage bag made of a highly gas-barrier material and stored. Skin gas was analyzed using gas chromatography mass spectrometry.

[0042] Determination of α-diversity index: 16S rRNA gene sequencing was performed on each stool sample to reveal the bacterial phylogenetic composition. DNA extraction from stool samples and PCR of 16S rRNA gene sequences were performed according to the method of Murakami et al. (FEMS Microbiol Ecol (2015);91(3): fiv003). First, DNA extracted from the stool sample was used as a template to amplify DNA fragments from the V1-V2 region of the 16S rRNA gene. The PCR products were then sequenced using a paired-end Illumina MiSeq. The bacterial phylogenetic composition was calculated from the resulting sequences according to the QIIME2 (https: / / qiime2.org / ) workflow (Figure 1). First, primer sequences were removed using cutadapt, followed by 3'-terminal deletion, PhiX-derived sequences removal, and quality filtering to obtain high-quality sequences. Afterwards, noise was removed (sequencing error correction), paired-end sequences were merged, and chimeric sequences were removed to obtain representative ASV (Amplicon Sequence Variant) sequences. The bacterial genus of these representative sequences was then identified using the Naive Bayes classifier in QIIME2. In this analysis, the V1-V2 region of the representative sequences obtained by clustering SILVA 132 SSU Ref provided by Silva (https: / / www.arb-silva.de) at a 99% threshold was extracted and used as training data. As indicators of alpha diversity, which indicates the diversity of the intestinal microbiota within each individual, Shannon's diversity, which takes into account both the richness and evenness of bacterial species (Shannon, CEMD Computing: Computers in Medical Practice (1997) 14 (4): 306-17), and Faith's phylogenetic diversity, which is an indicator of the richness of bacterial species that also takes into account the similarity between bacteria (Faith, et al., International Journal of Molecular Sciences (2009) 10 (11): 4723-41), were both calculated using QIIME2.

[0043] The amount of each skin gas in the analysis results of the skin gas collected from each subject was compared with two α diversity indices (Shannon diversity and Faith's phylogenetic diversity) using Spearman's rank correlation coefficient, and a correlation test was performed to calculate the p-value. Skin gas components that showed a significant correlation with α diversity were selected and subjected to simple identification. The results are shown in Table 1 below:

[0044] The amounts of the above-mentioned skin gas components were shown to correlate with Shannon diversity and / or Faith phylogenetic diversity, respectively.

[0045] Example 2: Relationship between microbiota diversity and skin symptoms Forty-seven women (aged 25 to 40) with healthy or sensitive skin underwent facial skin evaluation by a physician, and stool samples were collected. The intestinal bacterial alpha diversity index for each subject was determined using the method described in the section on determining alpha diversity index in Example 1. In addition, a physician evaluated the facial skin characteristics (dryness, scaling, erythema, swelling, papules / vesicles, irritation, and itching) for each subject according to the evaluation and assessment criteria listed in Table 2.

[0046] The evaluation scores for each facial skin condition of each subject and the Shannon diversity index were used to calculate the p-value by Spearman's rank correlation coefficient and a correlation test. The skin conditions for which correlations were found are shown in Table 3 below. Among the skin characteristics, dryness, scaling, and itching were each shown to be correlated with the Shannon index.

Claims

1. A method for assessing microbiota diversity, comprising: analyzing collected skin gases to detect microbiota-related skin gases; and determining the microbiota diversity based on the correspondence between the microbiota-related skin gases and the microbiota diversity.

2. The method of claim 1, wherein an increase in the amount or concentration of microbiota-associated skin gases indicates high microbiota diversity.

3. The method of claim 1, wherein the microbiota-associated skin gas is selected from the group consisting of 2-butanone, ethyl acetate, cyclohexane, tridecane, N,N-dimethylpropanamide, and dichloromethane.

4. The method according to claim 1, wherein the bacterial flora diversity is Shannon diversity or Faith's phylogenetic diversity.

5. The method according to any one of claims 1 to 4, wherein a skin index is determined based on the diversity of the microbiota.

6. The method of claim 5, wherein the skin indicator is an indicator of a skin condition selected from the group consisting of itching, dry skin, and scaling.

7. A method for evaluating at least one skin index selected from the group consisting of itching, dry skin, and desquamation, the method comprising: a step of analyzing collected skin gases to detect microbiota-related skin gases; and a step of determining the skin index based on the correspondence between the microbiota-related skin gases and the skin index.

8. A device for evaluating bacterial flora diversity, comprising: an input unit into which data of analysis results from a skin gas analyzer is input; a memory unit that stores a correspondence relationship between the amount of at least one bacterial flora-related skin gas and the bacterial flora diversity; a processing unit that determines the bacterial flora diversity from the amount of at least one bacterial flora-related skin gas in the input data of skin gas analysis results and the correspondence relationship stored in the memory unit; and an output unit that outputs the determined bacterial flora diversity.

9. A device for evaluating bacterial flora diversity, comprising: an input unit to which data of analysis results from a skin gas analyzer is input; a learning unit that is pre-trained using teacher data including at least one type and amount of bacterial flora-related skin gas and bacterial flora diversity, and that outputs bacterial flora diversity when input information including the type and amount of bacterial flora-related skin gas is input; a processing unit that executes processing in the learning unit; and an output unit that outputs the determined bacterial flora diversity.

10. The device for evaluating bacterial flora diversity described in claim 8 or 9, wherein the type of the bacterial flora-related skin gas is selected from the group consisting of 2-butanone, ethyl acetate, cyclohexane, tridecane, N,N-dimethylpropanamide, and dichloromethane.

11. The device for evaluating microbial biodiversity according to claim 9 or 10, wherein the microbial biodiversity is Shannon diversity or Faith's phylogenetic diversity.

12. The evaluation device according to claim 8 or 9, wherein the microbiota diversity evaluation device pre-stores in a memory unit a correspondence relationship between the microbiota diversity and the skin index, the processing unit determines a skin index from the determined diversity and the skin correspondence relationship, and the output unit further outputs the skin index.

13. The evaluation device according to claim 12, wherein the skin index is an index of a skin condition selected from the group consisting of itching, dry skin, and desquamation.

14. The device for evaluating microbial biodiversity described in claim 8 or 9, wherein the device for evaluating microbial biodiversity provides lifestyle improvement suggestions corresponding to the microbial biodiversity based on the determined microbial biodiversity of the subject, wherein: the memory unit further stores a correspondence relationship between the microbial biodiversity of the subject and lifestyle improvements corresponding to the microbial biodiversity; the processing unit determines lifestyle improvement suggestions corresponding to the microbial biodiversity from the determined microbial biodiversity of the subject and the correspondence relationship stored in the memory unit; and the output unit outputs the determined lifestyle improvement suggestions.

15. The device for evaluating microbial biodiversity described in claim 14, wherein the lifestyle improvement suggestions are selected from the group consisting of vegetable intake, fruit intake, cheese intake, coffee intake, tea intake, red wine intake, dairy product intake, and (moderate intensity aerobic) exercise.

16. A system including the device for evaluating microbiota diversity according to claim 8 or 9, wherein the device for evaluating microbiota diversity is connected to a network, and the system comprises: data on the analysis results from a skin gas analyzer is input to the input unit via the network; and the determined microbiota diversity is output from the output unit via the network.

17. The system of claim 16, further comprising a network-connected skin gas analyzer, the skin gas analyzer comprising: a skin gas sampling unit; a skin gas analysis unit; and a network-connected output unit.

18. The system described in claim 16, further comprising a network-connected terminal device, the terminal device comprising: a network connection unit connected to the microbiota diversity evaluation device; and a terminal output unit that outputs at least one selected from the group consisting of the subject's microbiota diversity and / or lifestyle improvement suggestions output from the output unit of the evaluation device via the network connection unit.

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