Method for acquiring stress determination data, stress determination marker, and use thereof

Abstract

To provide a method capable of conveniently and accurately determining stress.SOLUTION: The present invention provides a method for acquiring stress determination data, the method including measuring a stress determination marker that includes at least one selected from pyruvic acid, nicotinamide, Ile, N-acetyl-β-alanine, 2AB, choline, Val, uracil, xanthine, estrone, inosine, adenosine, and Leu in a biological sample derived from a subject, or includes a combination of at least one selected from propionic acid, spermine, 3-methylbutanoic acid, 4-methyl-2-oxopentanoic acid, diethanolamine, putrescine, and body temperature together with at least one of the above.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a method for acquiring data for stress assessment, a marker for stress assessment, and uses thereof. [Background technology]

[0002] Recently, the number of people complaining of mental health problems has been increasing, and the number of potential patients is said to be approximately 3 to 10 times the number of reported patients. Furthermore, the number of patients developing mental illnesses due to mental stress is also increasing, with depression in particular causing significant social losses. Therefore, there is a need for prevention and early intervention for disorders caused by mental stress.

[0003] Patent Document 1 describes that stress can be assessed using α-amylase present in a subject's saliva as an indicator. Patent Document 2 describes that if a subject's salivary cortisol concentration change over a given period of time deviates from the concentration change range of a healthy individual, it can be determined that the subject is possibly suffering from chronic stress. Patent Document 3 describes that the level of stress can be assessed based on the subject's vital signs, such as heart rate, body temperature, blood pressure, and sweat rate. Patent Document 4 describes a stress reduction support program that encourages actions that lead to stress reduction based on estimated stress values ​​such as the user's pulse wave and heart rate, and data on decision-making bias. Patent Document 5 describes that chronic stress levels can be detected from measured expression levels of metabolites such as 5-oxoproline, glycerophosphocholine, S-methylcysteine, phosphorylcholine, ethanolamine phosphate, and taurine. Non-Patent Document 1 describes that stress in humans can be assessed by measuring salivary substances such as cortisol, α-amylase, chromogranin A, and secretory immunoglobulin A (sIgA). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2006-345869 [Patent Document 2] Japanese Patent Application Laid-Open No. 2000-275248 [Patent Document 3] Japanese Patent Application Laid-Open No. 2012-249797 [Patent Document 4] Japanese Patent Publication No. 2022-161309 [Patent Document 5] Japanese Patent Application Publication No. 2023-104238 [Non-patent literature]

[0005] [Non-Patent Document 1] Michio Niimi, Journal of Kagawa Prefectural University of Health Sciences, Vol. 9, pp. 1-8 Summary of the Invention [Problem to be solved by the invention]

[0006] However, conventional stress assessment techniques have the problem that their accuracy is not sufficiently high.

[0007] The present invention has been made in view of the above, and an object of the present invention is to provide a method for easily and accurately determining stress. [Means for solving the problem]

[0008] The present invention provides the following [1] to

[16] . [1] A method for obtaining data for stress assessment, comprising measuring a marker for stress assessment containing at least one selected from the following (1) to (13) in a biological sample derived from a subject: (1) Pyruvate (2) Nicotinamide (3) Isoleucine (4) N-acetyl β-alanine (5) 2-aminobutyric acid (6) Colin (7) Valin (8) Uracil (9) Xanthine (10) Estrone (11) Inosine (12) Adenosine (13) Leucine [2] The method according to [1], wherein the marker for stress assessment further comprises at least one selected from the group consisting of the following (14) to (20): (14) Propionic acid (15) Spermin (16) 3-Methylbutanoic acid (17) 4-Methyl-2-oxopentanoic acid (18) Diethanolamine (19) Putrescine (20) Body temperature [3] The method according to [1] or [2], wherein the marker for determining stress level to be measured includes at least (1). [4] The method according to [1] or [2], wherein the measurement of the stress assessment marker is carried out using a substance capable of specifically binding to the stress marker to be measured. [5] The method according to [4], wherein the substance capable of specifically binding to the stress level determination marker to be measured is an aptamer, or a microarray in which the antibody or aptamer is immobilized on a support. [6] The method according to [1] or [2], wherein the sample is selected from the group consisting of saliva, dental plaque, blood, and urine. [7] The method according to [1] or [2], wherein the measurement is carried out by at least one of mass spectrometry and immunoassay. [8] A method for obtaining data for functional evaluation of a subject's stress coping behavior, comprising measuring a marker for stress assessment comprising at least one selected from (1) to (13) above in a sample collected from the subject who has engaged in stress coping behavior, or comprising a combination of at least one selected from (1) to (13) above and at least one selected from (14) to (20) above. [9] A stress assessment system for carrying out the method according to claim 1 or 2, comprising a data processing means for preparing data for stress assessment of the subject from the measured values ​​of stress assessment markers in a sample isolated from the subject, the markers including at least one selected from (1) to (13) above, or a combination of at least one selected from (1) to (13) above and at least one selected from (14) to (20) above.

[10] A stress management system comprising a data processing means for measuring a stress assessment marker containing at least one selected from (1) to (13) above, or a combination of at least one selected from (1) to (13) above and at least one selected from (14) to (20) above, in a sample isolated from a subject who has engaged in stress coping behavior by the method of claim 1 or 2, and for preparing data for functional evaluation of the subject's stress coping behavior from the obtained measurement values.

[11] The stress management system according to

[10] , further comprising a means for providing recommended coping behaviors that present recommended stress coping behaviors based on the functional evaluation data.

[12] At least one or more selected from the above (1) to (13), or A combination of at least one selected from the above (1) to (13) and at least one selected from the above (14) to (20), A marker for determining stress levels.

[13] A kit for determining stress levels, comprising a substance capable of specifically binding to the marker described in

[12] .

[14] The kit according to

[13] , wherein the substance capable of specifically binding to the marker is an antibody, an aptamer, or a microarray in which the antibody or aptamer is immobilized on a support.

[15] An antibody or aptamer for determining stress levels that can specifically bind to the marker described in

[12] .

[16] A microarray for determining stress levels, in which the antibody or aptamer for determining stress according to

[15] is immobilized on a support. [Effects of the Invention]

[0009] According to the present invention, a method for easily and accurately determining mental and psychological stress (hereinafter sometimes simply referred to as "stress") experienced by a subject is provided. The present invention makes it possible to predict stress, which can lead to stress prevention and early detection and treatment of stress-related diseases, thereby contributing to maintaining good health. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is an explanatory diagram showing an outline of one embodiment of the stress management system of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0011] [1: Stress assessment marker] The marker for assessing stress includes at least one selected from the group consisting of the following (1) to (13):

[0012] (1) Pyruvate (C3H4O3) [ka]

[0013] (2) Nicotinamide (C6H6N2O) [ka]

[0014] (3) Isoleucine (Ile, C6H 13 No. 2) [ka]

[0015] (4) N-acetyl β-alanine [ka]

[0016] (5) 2-aminobutyric acid (2AB) [ka]

[0017] (6) Choline (C5H 14 NO) [ka]

[0018] (7) Valine (C5H) 11 No. 2) [ka]

[0019] (8) Uracil (C4H4N2O2) [ka]

[0020] (9) Xanthine (C5H4N4O2) [ka]

[0021] (10) Estrone (C 18 H 22 O2) [ka]

[0022] (11) Inosine (C 10 H 12 N4O5) [ka]

[0023] (12) Adenosine (C 10 H13 N5O4) [ka]

[0024] (13) Leucine (Leu, CH 13 No. 2) [ka]

[0025] The marker for stress assessment may further include at least one selected from the group consisting of the following (14) to (20):

[0026] (14) Propionic acid (C3H6O2) [ka]

[0027] (15) Spermin (C 10 H 26 N4) [ka]

[0028] (16) 3-Methylbutanoic acid (CH 10 O2) [ka]

[0029] (17) 4-Methyl-2-oxopentanoic acid [ka]

[0030] (18) Diethanolamine (CH 11 No. 2) [ka]

[0031] (19) Putrescine (C4H 12 N2) [ka]

[0032] (20) Body temperature reading The temperature reading is the subject's axillary temperature reading.

[0033] As the marker, it is also preferable to combine one or more types selected from (1) to (13), or one or more types selected from (1) to (13) and one or more types selected from (14) to (20), with one or more types selected from the following components: 1-Methylnicotinamide Adenine Guanine Hypoxanthine Hydroxyproline Dehydroepiandrosterone sulfate (DHEA-S) β-alanine N6,N6,N6-trimethyllysine ·glutamine Glucosamine Creatine Threonine Creatinine Gamma-aminobutyric acid (GABA) Serine (Ser) Glutamic acid (Glu) 11-deoxycortisol N,N-dimethylglycine Guanosine Carnitine Agmatine Cysteine Alanine (Ala) Nicotinic acid Succinic acid Ethanolamine phosphate Tetrahydrocortisol ·cholesterol Tetrahydrocortisone 11-deoxycorticosterone Sedoheptulose 7-phosphate (S7P) Aspartic acid (Asp) Glyceric acid Adenosine monophosphate (AMP) γ-Butyrobetaine 3-phenylbutyric acid Tyrosine (Tyr) Glycyl-leucine (Gly-Leu) N1,N8-diacetylspermidine Dehydroepiandrosterone (DHEA) o-Acetylcarnitine Pregnenolone Malonic acid Cytosine Spermidine 5-aminovaleric acid ·testosterone 1,3-Diaminopropane Citrulline

[0034] [2: Antibodies, aptamers, microarrays, and kits for stress assessment] Each of the above markers can be measured using a substance capable of binding to the marker (each compound) (preferably a substance capable of specifically binding to the marker).

[0035] -Substances that can bind to markers (antibodies, aptamers)- The substance capable of binding to a marker may be any substance having affinity for the marker, such as an antibody or a compound. Examples of antibodies include polyclonal antibodies and monoclonal antibodies. Antibodies can be prepared by conventional methods and may be human antibodies or antibodies from mammals other than humans. Compounds may be either low molecular weight or high molecular weight compounds, and may be either organic or inorganic compounds. Examples include aptamers, luminescent substances (fluorescent substances, chemiluminescent substances), dyes, pigments, color-developing or color-developing reagents, immunostaining reagents, enzymes, and radioisotopes. Aptamers may be either natural aptamers or artificial aptamers. Artificial aptamers can be prepared by conventional methods. The substance capable of binding to a marker is preferably an antibody or an aptamer.

[0036] When the substance capable of binding to a marker is an antibody, the amount of the marker in a test sample can be measured by binding the antibody to the marker and then reacting with a secondary antibody that binds to the antibody (primary antibody). A labeling substance (e.g., the above-mentioned luminescent substance, radioisotope, or enzyme) may be bound to the secondary antibody. When the substance capable of binding to a marker is a color-developing or color-reproducing substance, the amount of the marker can be measured by binding the substance to the marker to cause a color-developing or color-reproducing reaction and confirming the degree of color-development or color-reproducing.

[0037] -Microarray- Substances capable of binding to markers may be immobilized on a carrier (substrate) to form a microarray. Examples of the support include solids of any shape, such as dishes, plates (e.g., microtiter plates), wells, strips, chips, beads, and membranes. The material of the carrier is not particularly limited, and examples include inorganic materials such as glass and organic materials such as nitrocellulose. The surface of the carrier may be coated or modified as desired. Microarrays can be prepared by arranging and immobilizing the above-mentioned substances in an aligned or random manner on a carrier using conventional methods. Immobilization can be achieved by physical adsorption, covalent bonding using functional groups, or the like. For immobilization, devices such as a microarrayer or spotter may be used.

[0038] -kit- The amount of a marker can be measured using a kit containing at least one substance capable of binding to the marker. The substance capable of binding to the marker is as described above. The kit may contain at least one substance, or a combination of two or more of these substances (e.g., a combination of substances capable of binding to multiple compounds as markers). The kit may further contain reagents or tools for detecting the marker. Examples include a secondary antibody, a coloring or color-developing reagent, a sample container (e.g., a container for holding the collected sample), gum, a mouthwash container, mouthwash (e.g., water such as distilled water, 3 to 4 mL per use), test paper, a device for applying a spot to the test paper, and an instruction manual. The secondary antibody is as described above. When saliva is used as a sample, the gum may be any gum (e.g., paraffin gum) commonly used for collecting stimulated saliva. The inclusion of gum facilitates the collection of saliva as a sample. The inclusion of a mouthwash, a mouthwash container, a test paper, a device for applying a spot, and a detection device allows saliva to be easily collected as a mouthwash, as described below. The detection device may be any device (e.g., a device capable of detecting the amount of marker in a sample (e.g., a sample applied to a test paper using a spotting tool) (e.g., a device capable of detecting the amount of marker by a method described below). The reagents and tools included in the detection kit may be enough for a single collection or multiple collections, and in the case of multiple collections, they may be divided into individual portions.

[0039] [3. How to assess stress] The above markers can be used to assess stress in a subject. That is, by measuring the above markers in a sample collected from a subject, the stress of the subject can be assessed or data for assessing stress can be obtained.

[0040] -Subjects- The subject is usually a human, but may also be a non-human animal (e.g., laboratory animals (mice, rats, guinea pigs, hamsters, rabbits, etc.) or pets (dogs, cats, birds, etc.)). The subject may be either a minor or an adult, regardless of gender. In this specification, an adult generally refers to a person aged 20 or over, and a minor generally refers to a person under 20 years of age.

[0041] -sample- The sample is not particularly limited as long as it is a biological sample that can be collected from a subject, and examples include body fluids such as saliva, dental plaque, blood (whole blood, plasma, serum, etc.), urine, sweat, and tears. Of these, saliva, dental plaque, blood, and urine are preferred, saliva and dental plaque are more preferred, and saliva is even more preferred. These can be collected non-invasively and at any time, reducing the burden on the subject.

[0042] The saliva sample may be unstimulated saliva, stimulated saliva, or mouthwash. Stimulated saliva can be collected by chewing paraffin gum. Unstimulated saliva can be collected by collecting saliva that is naturally secreted at rest. Mouthwash can be collected by spitting out a mouthwash solution (e.g., distilled water) after holding it in the mouth.

[0043] - Measuring the amount of markers in the sample - The amount of a marker contained in a sample can be measured (quantified, detected) using a substance that binds to the marker. Measurement methods include, for example, immunoassays (immunoelectrophoresis, single radial immunodiffusion (SRID), radioimmunoassay (RIA), enzyme-linked immunosorbent assay (EIA), latex immunoassay (LIA), fluorescence immunoassay (FIA), enzyme-linked immunosorbent assay (ELISA), sandwich ELISA, electrochemiluminescence immunoassay (ECLIA), Western blot, and radioimmunoassay (RIA)), mass spectrometry, color reaction, and colorimetric methods. Among these, mass spectrometry and immunoassays are preferred.

[0044] Various mass spectrometers can be used for measurements by mass spectrometry, including GC-MS, LC-MS, FAB-MS, EI-MS, CI-MS, FD-MS, MALDI-MS, ESI-MS, HPLC-MS, FT-ICR-MS, CE-MS, ICP-MS, Py-MS, and TOF-MS.

[0045] An example of measuring the content of a component in a biological sample using an antibody or aptamer is shown below. First, the antibody or aptamer is adsorbed to a carrier using a known method. The sample is diluted as necessary and then added and incubated. Next, a secondary antibody conjugated with a fluorescent substance, chemiluminescent substance, or enzyme is added and incubated. Detection can be performed by adding the respective substrate and measuring the visible light produced by the fluorescent or chemiluminescent substance or enzyme reaction.

[0046] An example of measuring the amount of a marker in a biological sample using the above-mentioned microarray is shown below. First, a sample is added to the microarray, and the marker in the sample is allowed to bind to the substance bound to the array. Next, a secondary antibody is added and incubated. Detection can be performed by visualizing the marker using a label bound to the secondary antibody (e.g., by measuring visible light from the label).

[0047] When measuring components in a sample using mass spectrometry (e.g., CE-MS), the components can be identified and measured by comparing them with known data, such as "standard compounds" and "publicly available databases (KEGG, HMDB, NIST 11 library)." If no known data exists for a component, the unknown component can be identified by isolating it using preparative HPLC or similar methods and then analyzing it using a nuclear magnetic resonance (NMR) spectrometer.

[0048] Stress can be assessed from the measured amount of a marker and useful data for assessment can be obtained by using the amount or ratio of the marker as an index, but it is preferable to compare it with a control value. The control value is preferably determined in advance as a cutoff value. For example, when the control value is the amount of a marker in a sample collected from a healthy subject, the assessment can be made as follows: Markers (1)-(2) pyruvate, nicotinamide: If the amount of the marker is higher than the control value, it is determined that stress is high. On the other hand, if the amount of the marker is lower than the control value, it is determined that stress risk is low. Markers (3) to (13) are isoleucine, N-acetyl-β-alanine, 2-aminobutyric acid, choline, valine, uracil, xanthine, estrone, inosine, adenosine, and leucine. If the amount of a marker is lower than the control value, it is determined that stress is high. On the other hand, if the amount of a marker is higher than the control value, it is determined that stress risk is low. The cutoff value can be calculated by statistical analysis (e.g., logistic regression analysis) based on the results of stress tests such as the new version of the STAI (State-Trait Anxiety Inventory, Japan Psychological Testing Association) and the Simple Occupational Stress Questionnaire (Ministry of Health, Labour and Welfare) and the concentrations of each marker.

[0049] As used herein, stress assessment refers to predicting and diagnosing whether a subject is experiencing stress. As used herein, stress refers to mental stress, i.e., stress caused by psychosocial stressors (e.g., interpersonal relationships, work, or family problems). The accuracy of assessment using the assessment method is, for example, 50% or more, 60% or more, 70% or more, 80% or more, or 90% or more. Therefore, the assessment of the present invention is useful as a preliminary method prior to diagnosis by a physician.

[0050] [4. Application of stress assessment: Evaluation of stress coping behavior and stress management] The above-mentioned method of assessment can be used not only to assess the stress state of a subject, but also to evaluate the stress coping behavior of the subject and for stress management.

[0051] When a subject performs stress coping behavior and then performs a stress assessment using the above marker, if the stress level is determined to be low, the coping behavior can be evaluated as appropriate, whereas if the coping behavior is inappropriate, the coping behavior can be evaluated as inappropriate. By using this method, the subject can determine the appropriateness of the coping behavior. Furthermore, by performing another coping behavior based on the results and then performing the assessment again, an appropriate coping behavior can be identified.

[0052] [5. Stress Assessment System / Stress Management System] [5.1 Stress Assessment System] The above method may be carried out using a determination system including a data processing means for processing data for determining stress in a subject from the measured amount of the marker in a sample isolated from the subject. The measured amount can be measured using a substance capable of binding to the marker, and a measurement unit for obtaining the measured amount may be included as part of the determination system. The data processing means can store, for example, measured values ​​and correlations with stress (e.g., determination criteria such as control values, past determination result data for the subject, and a mathematical model for determination), and can perform a determination process for the subject's stress level based on these and generate data. If necessary, additional information related to the determination, such as changes from past determination results, may also be generated. The determination system may further include an input means for inputting the measured amount of the marker in a sample isolated from the subject and an output means for outputting data generated by the processing means. Note that the determination system typically has a control unit for controlling each unit included in the system. The determination system can be realized using various electronic devices, such as mobile devices (e.g., smartphones, mobile phones, tablet devices, and laptops), dedicated general-purpose computers, electronic devices equipped with computers, and, if necessary, peripheral devices such as networks (e.g., email, the Internet), and printers and scanners.

[0053] [5.2 Stress Management System] In the above-described stress assessment system, if the data processing means generates data for functional evaluation of the subject's stress coping behavior, it can be used to evaluate stress coping behavior. The system can also be used as a stress management system by further including a recommended coping behavior providing means for presenting recommended stress coping behaviors based on the functional evaluation data. The recommended coping behavior providing means preferably provides recommended coping behaviors based on information such as the subject's stressors and past stress coping behaviors by performing cognitive behavioral therapy using artificial intelligence. The stress management system of the present invention can propose recommended stress coping behaviors to the subject based on the results of stress assessment, for example, by checking the presence or absence of markers in saliva as a biological sample, and can also present problems with previous stress coping behaviors, if necessary. This allows the subject to try the proposed stress coping behaviors, which contributes to stress reduction and improved stress tolerance, preventing the onset of mental illness, and maintaining a healthy daily life (Figure 1). [Example]

[0054] The present invention will be described in more detail below with reference to examples. The following examples are provided to suitably explain the present invention, but are not intended to limit the present invention.

[0055] 1. Marker detection 1.1 Subject Classification We selected 16 men and women aged 20 to 49 who were regular office workers working full-time day shifts and who met the following criteria: a high-stress group (7 men, 9 women, mean age 36.7±6.8 years, age range 25-47 years) and a healthy group (7 men, 9 women, mean age 37.3±7.5 years, age range 26-48 years).

[0056] -Selection criteria for the high stress group- (1) The results of administering the new version of the STAI (State-Trait Anxiety Inventory, Japan Psychological Testing Association) show that the percentile is 50 or higher on the state anxiety scale (how you feel right now) or the trait anxiety scale (how you generally feel on a daily basis; neuropsychiatric and depression patients generally score highly). (2) The results of the Simple Occupational Stress Questionnaire (Ministry of Health, Labour and Welfare) must meet the criteria set out in the manual, using either the total score or the raw score conversion table.

[0057] -Selection criteria for healthy subjects- (1) After administering the new version of the STAI, the percentile for either the state anxiety scale or the trait anxiety scale is less than 50. (2) The results of the occupational stress questionnaire survey, using the total score or the raw score conversion table, do not meet the criteria described in the manual. -Exclusion criteria common to all groups- (1) Persons who have been diagnosed with a mental disorder (2) Persons with intellectual disabilities (3) Those whose work system is telecommuting only (4) People with dentures (including implants) (5) Smokers (6) Subjects who have taken antibiotics within the past month (7) Persons undergoing orthodontic treatment (8) Persons who have been diagnosed with severe cardiovascular disease, liver disease, kidney disease, digestive disease, respiratory disease, endocrine disorder, metabolic disorder, or alcoholism (9) Persons receiving ongoing treatment with medicines (excluding OTC topical medications) (10) People diagnosed with severe periodontal disease (11) Persons who have been diagnosed with dental caries that require treatment (12)) Those who have had a tooth extracted within one week (13) Subjects with bleeding stomatitis (14) Pregnant or breastfeeding individuals who intend to become pregnant by the day of the test (15) A person who is deemed unsuitable for other reasons [1.3 Analysis of components in mouthwash and temperature measurement]

[0058] The subjects in each group underwent temperature measurements (axillary temperature was measured using a thermometer), the Uchida-Kraepelin test (3 minutes), a working memory test (3 minutes), and mouthwash collection.

[0059] -Collection and pretreatment of mouthwash fluid- The subjects rinsed their mouths with 3 mL of sterilized purified water for 10 seconds, then spat the entire amount into a special container. Samples were collected four times: once at rest; once after the Kraepelin test; once five minutes after the Kraepelin test; and once after the working memory test.

[0060] The collected samples (untreated) were pretreated as follows before analysis. After collection in a 25 mL centrifuge tube (IWAKI, Centrifuge Tube Mini), vortexing was performed. 200 μL of the sample was transferred to two 1.5 mL microtubes for analysis of known stress markers (chromogranin A, sIgA). The remaining sample was transferred to a 5 mL microtube for analysis of water-soluble metabolites and steroid hormones. After dispensing, the samples were promptly stored at -80°C. The 5 mL microtubes were then transferred to a refrigerator and left to thaw. After vortexing (2,500 rpm, 30 seconds), centrifuged (13,000 × g, 5 minutes, 4°C), and dispensed (200 μL of the upper layer was dispensed into four 1.5 mL microtubes, and the remaining volume was transferred to a 1.5 mL microtube). After dispensing, the samples were promptly stored at -80°C and used as samples.

[0061] -Marker analysis method- The water-soluble metabolites, steroid hormones, and known stress markers (chromogranin A, secretory IgA (sIgA)) in the above samples were analyzed by the following methods.

[0062] (1) Detection of water-soluble metabolites (Sample preparation) The dispensed sample (1.5 mL microtube) was transferred from the deep freezer to a refrigerator and left to thaw. After thawing, the sample was vortexed at 2,500 rpm for 30 seconds to achieve uniformity. After vortexing, 180 μL was transferred to an ultrafiltration filter (Ultrafree MCPLHCC, molecular weight cutoff 5,000 Da, Japan Millipore) and centrifuged at 9,100 × g, 4°C, for approximately 2–3 hours until the entire volume was filtered. The filtrate was then dried in a centrifugal concentrator at 40°C for approximately 2–3 hours and stored at -80°C. After storage, 60 μL of the internal standard mix solution (200 μM) was added to the sample immediately before measurement. The resulting solution was thoroughly mixed at 2,500 rpm for 30 seconds and used as the sample solution.

[0063] To control analytical accuracy, a quality control (QC) sample was prepared by mixing equal volumes of the mouthwash collected from each subject in the high-stress and healthy groups. Fifteen samples were randomly selected from the 128 total samples (collected four times from a total of 32 subjects). 10 μL aliquots of each sample (10 μL x 15 samples = 150 μL) were taken and mixed to homogenize. After mixing, 180 μL was transferred to an ultrafiltration filter and centrifuged (9,100 × g, 4°C, approximately 2–3 hours) until the entire volume was filtered. The filtrate was dried in a centrifugal concentrator (40°C) for approximately 2–3 hours. 60 μL of internal standard mix solution (200 μM) was added, and the mixture was thoroughly mixed (2,500 rpm, vortex for 30 seconds). This mixture was used as the QC sample for all batches. After preparation, the samples were stored at -80°C.

[0064] (analysis) Water-soluble metabolites in the samples were analyzed by capillary electrophoresis-mass spectrometry (CE-MS). The internal standards used were L-methionine sulfone (Methionine sulfone, Alfa Aesar), D-camphor-10-sulfonic acid sodium salt (CSA, Fujifilm Wako Pure Chemical Industries), 2-morpholinoethanesulfonic acid monohydrate (MES, Dojindo Laboratories), 3-aminopyrrolidine dihydrochloride (3-aminopyrrolidine, Sigma-Aldrich), and 1,3,5-benzonetricarboxylic acid (Trimesate). The analysis was performed using an Agilent 1600 Capillary Electrophoresis system (Agilent Technologies) and an Agilent 6220 TOF LC / MS system (Agilent Technologies) under the following conditions.

[0065] [Cation] Devise:Agilent CE-TOFMS system No.1 -HPCE- Capillary:Fused silica id50mm×100cm Buffer: 1M Formate Voltage: Positive, 30kV Temperature: 20℃ Injection:Pressure injection 50mbar,5sec Preconditioning:5min at 30mM Ammonium formate,pH9.0,5min at Milli-Q and 5min at run buffer -TOFMS- Polarity: Positive Capillary voltage: 4000V Fragmenter:75V Skimmer:50V Oct RFV:500V Drying gas:N2,10L / min Drying gas temp.:300℃ Nebulizer gas presser:7psig Sheath liquid:50% MeOH / Water Containing 0.01μM Hexakis(2,2-difluorothoxy)phosphazene Flow rate:10mL / min Mass range:50-1,000m / z Scan mode:Scan Scan rate:1.5cycles / sec Lock mass:2MeOH 13 C isotope [M+H] + 66.063061,Hexakis phosphazene[M+H] + 622.028963

[0066] [Anion] Device:Agilent CE-TOFMS system No.2 -HPCE- Capillary:COSMO(+),i.d.50mm×105cm Buffer:50mM Ammonium acetate,pH8.5 Voltage:Negative,30kV Temperature:20℃ Injection:Pressure injection 50mbar,30sec(approximately 30nL) Preconditioning:2min at 50mM Ammonium acetate,pH3.4 and 5min at run buffer -TOFMS- Polarity:Negative Capillary voltage: 3500V Fragmenter: 100V Skimmer: 50V Oct RFV:500V Drying gas: N2, 10L / min Drying gas temperature: 300℃ Nebulizer gas presser: 7 psig Sheath liquid:5mM Ammonium acetate in 50% MeOH / Water containing 0.01 μM Hexakis(2,2-difluorothoxy)phosphazene Flow rate: 10 mL / min Mass range: 50-1,000 m / z Scan mode:Scan Scan rate: 1.5 cycles / sec Lock mass: 2[CH3COOH] 13 C isotope [MH] - 120.03841,Hexakis(2,2-difluorothoxy)phosphazene+CH3COOH[MH] - 680.035541 ESI needle: Platinum

[0067] The peak area values ​​of each metabolite were obtained from the obtained data (the data for analysis was adjusted using file conversion software: dotMZ (ver. 1.5.4.4)), and the relative area values ​​to the internal standards (cation: methionine sulfone, anion: CSA) were calculated. The quantitative values ​​were calculated based on the following formula (peak identification and quantitative value conversion software: MasterHands (ver. 2.17.3.18) developed by Keio University was used).

number

[0068] (2) Detection of steroid hormones (Sample preparation) 200 μL of clinical sample (mouthwash) was centrifuged at 25°C, concentrated to dryness, and stored at -80°C. A 96-well solid-phase extraction plate was then conditioned with 1 mL of 0.1% formic acid / MeOH, followed by 1 mL of 0.1% formic acid water. The sample was returned to room temperature, and 30 μL of 0.1% formic acid water, 330 μL of 0.1% formic acid / MeOH, 10 μL of stock H2O, and 30 μL of MeOH were added and mixed thoroughly (Vortex, approximately 1 minute). After centrifugation (13,000 rpm, 10 minutes, 4°C), the supernatant was transferred to a 1.5 mL Eppendorf tube. 1 mL of 0.1% formic acid water was added, vortexed briefly, and loaded onto the 96-well solid-phase extraction plate. The plate was then washed with 1 mL of 0.1% formic acid water and 1 mL of 0.1% formic acid / 15% ethanol. The mixture was then eluted with 200 μL of MeOH and then 200 μL of IPA. The eluate was dried using an SPE Dry 96, and 40 μL of MeOH was added and mixed thoroughly to prepare a measurement sample.

[0069] (analysis) The steroid hormones in the samples were analyzed by liquid chromatography-mass spectrometry (LC-MS / MS). The analysis was performed using surrogate substances for each component (4-androstene-3,17-dione (2,2,4,6,6,16,16-D7), 17β-estradiol-16,16,17-D3, corticosterone-d8, cortisone (2,2,4,6,6,12,12-D7), progesterone-d9, testosterone-2,2,4,6,6-d5, cortisol-9,11,12,12-d4, dehydroepiandrosterone-D5, aldosterone-9,11,11,12-D4, dehydroepiandrosterone-D5-3-sulfate sodium) as standard substances. The following compounds were used: 11-Deoxycortisol-2,2,4,6,6-D5, 11-Deoxycorticosterone-2,3,4-13C3, 5α-Dihydroteststerone-D3, estrone-2,3,4-13C3, pregnenolone-13C2D2, 11-Deoxycortisol-2,2,4,6,6-D5, 11-Deoxycorticosterone-2,3,4-13C3. High-performance liquid chromatography (HPLC) was performed using a Nexera LC system (Shimadzu Corporation) and a mass spectrometer (MS / MS: SHIMADZU LCMS8060 (Shimadzu Corporation)) under the following conditions:

[0070] [LC-MS / MS measurement conditions] -UHPLC- Nexera LC System Separation column: Kinetex Biphenyl (2.6 μm, 2.1 mm x 100 mmL, 00D-4622-AN) Column temperature: constant at 30°C Mobile phase: Solution A: Milli-Q water containing 0.15 mM NH4F Solution B: MeOH containing 0.15mM NH4F Gradient conditions: as shown in Table 1

[0071] [Table 1]

[0072] Injection volume: 10 μL (+15 μL Milli-Q water co-injection) (needle stroke set to 50 mm) Sample cooler temperature: 4℃

[0073] -MS / MS- Ionization mode: Pos. / Neg. switching Positive / negative ionization switching time: 5 msec Channel switching time (pause time): 1msec Number of MRM transitions: 41 (31 positive, 10 negative) MRM Dwell time (data acquisition time): 31-199 msec (set for each transition) Interface temperature: 400℃ Heat block temperature: 500℃ Desolvation tube (DL) temperature: 150°C Heating gas flow rate: 10L / min Drying gas flow rate: 10L / min Nebulizer gas flow rate: 3L / min

[0074] Peak area values ​​were calculated from the obtained data (using the measurement and peak analysis system LabSolutions (ver. 5.2.2) (Shimadzu Corporation) and Excel 2013 (Microsoft) as software for calculating quantitative values), and the relative area values ​​for the internal standards (surrogate substances for each component) were calculated, and quantitative values ​​were calculated based on the following formula.

number

[0075] (3) Detection of known marker substances The levels of chromogranin A, sIgA, and cortisol were measured by ELISA.

[0076] [2. Confirmation of the relationship between stress and markers] As a result of the processing in item 1 above, the detected components were compared between the high-stress group and the healthy group using significance tests (unpaired t-test or Mann-Whitney U test), logistic regression analysis, multivariate analysis, and machine learning.

[0077] [2.1 Significance test] -Analysis procedure- Significance tests were performed using the Python language (Python Software Foundation) according to the following procedure. Data normality was tested using the Shapiro-Wilk test. If normality was confirmed, a parametric test (Welch's t-test) was performed; if normality was not confirmed, a nonparametric test (Mann-Whitney U test) was performed. The obtained p-values ​​were corrected for multiple comparisons (Benjamini-Hochberg method), the q-values ​​were calculated, and a volcano plot was drawn.

[0078] -Results / Discussion- Analysis of data from all four samples (128 samples in total) revealed that there were 36 metabolites that were significantly different between the high-stress and healthy groups (p<0.05), with some showing p<0.03 and p<0.01 (Table 2).

[0079] [Table 2]

[0080] [2.2 Machine Learning] For machine learning, a predictive model was constructed using logistic regression using the Python language (Python Software Foundation). The predictive model was constructed using the following procedure.

[0081] -Analysis procedure- (1) Preparation of analysis data If 50% or more were zero (missing), the data was excluded from the analysis, and if the quantitative value was zero (missing), it was replaced with 1 / 5 of the minimum value of that metabolite.

[0082] (2) Variable transformation The following three patterns were performed: no transformation; logarithmic transformation (log); and Box-Cox transformation (bc).

[0083] (3) Standardization (Z-score)

[0084] (4) Search for the optimal combination of features Exhaustive search was performed to extract useful features. Leave-one-out cross validation (LOOCV) was performed to search for optimal combinations of up to three metabolites.

[0085] We also checked whether prediction accuracy would improve by taking the ratio between components (one variable for two components, two variables for three components) for combinations of two or three components. In analyses using ratio data, the Box-Cox transformation cannot be used for negative values, so the Yeo-Johnson transformation (yj) was used instead.

[0086] -Results / Discussion- The quantitative values ​​and ratios of each component or combination shown in Tables 3 to 6 all showed an AUC above a certain level. This AUC was calculated using Out-Of-Fold (OOF) and represents the predictive performance for unseen data. These results suggest that the present invention can easily and accurately determine stress levels.

[0087] [Table 3]

[0088] [Table 4]

[0089] [Table 5]

[0090] Table 6

Claims

1. A method for obtaining data for stress assessment, comprising measuring a marker for stress assessment, the marker including at least one selected from the following (1) to (13), in a biological sample derived from a subject: (1) Pyruvate (2) Nicotinamide (3) Isoleucine (4) N-acetyl β-alanine (5) 2-aminobutyric acid (6) Choline (7) Valine (8) Uracil (9) Xanthine (10) Estrone (11) inosine (12) Adenosine (13) leucine

2. The method according to claim 1, wherein the marker for stress assessment further comprises at least one selected from the group consisting of the following (14) to (20): (14) Propionic acid (15) Spermin (16) 3-methylbutanoic acid (17) 4-methyl-2-oxopentanoic acid (18) Diethanolamine (19) putrescine (20) Body temperature

3. The method according to claim 1 or 2, wherein the markers for determining stress level to be measured include at least (1).

4. 3. The method according to claim 1, wherein the measurement of the marker for assessing stress is carried out using a substance capable of specifically binding to the stress marker to be measured.

5. The method according to claim 4, wherein the substance capable of specifically binding to the marker for determining stress level to be measured is an aptamer, or a microarray in which the antibody or aptamer is immobilized on a support.

6. 3. The method according to claim 1, wherein the sample is selected from the group consisting of saliva, dental plaque, blood, and urine.

7. 3. The method according to claim 1, wherein the measurement is carried out by at least one of mass spectrometry and immunoassay.

8. A method for obtaining data for functional evaluation of stress coping behavior of a subject, the method comprising measuring a marker for stress assessment in a sample collected from the subject who has engaged in stress coping behavior, the marker comprising at least one selected from the following (1) to (13), or a combination of at least one selected from the following (1) to (13) and at least one selected from the following (14) to (20): (1) Pyruvate (2) Nicotinamide (3) Isoleucine (4) N-acetyl β-alanine (5) 2-aminobutyric acid (6) Choline (7) Valine (8) Uracil (9) Xanthine (10) Estrone (11) inosine (12) Adenosine (13) leucine (14) Propionic acid (15) Spermin (16) 3-methylbutanoic acid (17) 4-methyl-2-oxopentanoic acid (18) Diethanolamine (19) putrescine (20) Body temperature

9. A stress assessment system for carrying out the method according to claim 1 or 2, comprising a data processing means for preparing data for stress assessment of a subject from measured values ​​of a marker for stress assessment, the marker comprising at least one selected from the following (1) to (13), or a combination of at least one selected from the following (1) to (13) and at least one selected from the following (14) to (20), in a sample isolated from the subject: (1) Pyruvate (2) Nicotinamide (3) Isoleucine (4) N-acetyl β-alanine (5) 2-aminobutyric acid (6) Choline (7) Valine (8) Uracil (9) Xanthine (10) Estrone (11) inosine (12) Adenosine (13) leucine (14) Propionic acid (15) Spermin (16) 3-methylbutanoic acid (17) 4-methyl-2-oxopentanoic acid (18) Diethanolamine (19) putrescine (20) Body temperature

10. A stress management system comprising a data processing means for measuring a marker for stress assessment in a sample isolated from a subject who has engaged in stress coping behavior by the method of claim 1 or 2, the marker comprising at least one selected from the following (1) to (13), or a combination of at least one selected from the following (1) to (13) and at least one selected from the following (14) to (20), and for preparing data for functional evaluation of the subject's stress coping behavior from the obtained measurement values. (1) Pyruvate (2) Nicotinamide (3) Isoleucine (4) N-acetyl β-alanine (5) 2-aminobutyric acid (6) Choline (7) Valine (8) Uracil (9) Xanthine (10) Estrone (11) inosine (12) Adenosine (13) leucine (14) Propionic acid (15) Spermin (16) 3-methylbutanoic acid (17) 4-methyl-2-oxopentanoic acid (18) Diethanolamine (19) putrescine (20) Body temperature

11. 11. The stress management system according to claim 10, further comprising a recommended coping behavior providing means for presenting recommended stress coping behaviors based on the function evaluation data.

12. At least one selected from the following (1) to (13), or A combination of at least one selected from the following (1) to (13) and at least one selected from the following (14) to (20): A marker for determining stress levels. (1) Pyruvate (2) Nicotinamide (3) Isoleucine (4) N-acetyl β-alanine (5) 2-aminobutyric acid (6) Choline (7) Valine (8) Uracil (9) Xanthine (10) Estrone (11) inosine (12) Adenosine (13) leucine (14) Propionic acid (15) Spermin (16) 3-methylbutanoic acid (17) 4-methyl-2-oxopentanoic acid (18) Diethanolamine (19) putrescine (20) Body temperature

13. A kit for determining stress level, comprising a substance capable of specifically binding to the marker according to claim 12.

14. The kit according to claim 13 , wherein the substance capable of specifically binding to the marker is an antibody, an aptamer, or a microarray in which the antibody or aptamer is immobilized on a support.

15. An antibody or aptamer for determining stress levels, capable of specifically binding to the marker described in claim 12.

16. A microarray for determining a stress level, in which the antibody or aptamer for determining stress according to claim 15 is immobilized on a support.

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