Biomarker for mild health disorder

Abstract

To provide a biomarker for mild health disorder that may lead to decreased productivity.SOLUTION: The present invention provides a biomarker for mild health disorder comprising a combination of hsa-miR-26b-3p, hsa-miR-130a-3p, hsa-miR-92a-3p, hsa-miR-423-5p, hsa-miR-182-5p, and hsa-let-7e-5p.SELECTED DRAWING: None

Description

[Technical Field]

[0001] The present invention relates to biomarkers of mild illness. [Background technology]

[0002] In recent years, in order to realize a society in which people live long and healthy lives, there has been an increasing emphasis not only on the treatment and prevention of diseases, but also on the early detection and care of illnesses before they develop into diseases.

[0003] Various compositions have been proposed for improving disorders at a stage before they develop into diseases (for example, Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2007-302577 Summary of the Invention [Problem to be solved by the invention]

[0005] On the other hand, the inventors have defined "mild illness" as mild physical and mental illness that is subjectively felt by an individual and that may reduce productivity before it develops into a disease. However, no biomarkers for such mild disorders have been established.

[0006] The present invention has been made in view of the above circumstances, and an object of the present invention is to provide a biomarker for mild disorders that may reduce productivity. [Means for solving the problem]

[0007] The present inventors have discovered that the above-mentioned problems can be solved by using a specific miRNA as a marker, and have completed the present invention. Specifically, the present invention provides the following.

[0008] (1) hsa-miR-26b-3p, hsa-miR-130a-3p, hsa-miR-92a-3p, hsa-miR-423-5p, hsa-miR-182-5p, and hsa-let-7e-5p A biomarker for mild discomfort, comprising a combination of

[0009] (2) hsa-miR-26b-3p, hsa-miR-130a-3p, hsa-miR-92a-3p, hsa-miR-3615, hsa-miR-4732-3p, hsa-miR-27b-5p, hsa-miR-148a-3p, hsa-miR-874-3p, hsa-miR-6842-3p, hsa-miR-142-3p, hsa-miR-127-3p, hsa-miR-454-5p, hsa-miR-374a-3p, hsa-miR-1301-3p, hsa-miR-431-5p, hsa-miR-4446-3p, hsa-miR-194-5p, hsa-miR-885-5p, hsa-miR-92b-3p, hsa-miR-6511a-3p, hsa-miR-1307-5p, hsa-miR-1180-3p, hsa-miR-374b-5p, hsa-miR-142-5p, hsa-miR-16-5p, hsa-miR-20b-5p, hsa-miR-378a-3p, hsa-miR-99b-5p, hsa-miR-18a-5p, hsa-miR-6741-3p, hsa-miR-301b-3p, and hsa-miR-744-5p A biomarker for mild illness, comprising one or more selected from the group consisting of: [Effects of the Invention]

[0010] The present invention provides biomarkers for minor disorders that may reduce productivity. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of the present invention will be described in detail, but the present invention is not particularly limited thereto.

[0012] (1) Biomarkers of mild illness The biomarkers for mild illness according to the present invention include two types of biomarker groups, as described below. Hereinafter, these biomarker groups are also collectively referred to as "biomarkers of the present invention." Both of the two types of biomarker groups are composed of miRNA biomarkers, and were newly discovered by the present inventors as marker groups associated with mild disorders.

[0013] In the present invention, "mild discomfort" refers to a type of mild physical and mental discomfort that is subjectively felt by an individual at a stage before it develops into a disease. Such mild illness can impair the energy and concentration needed to carry out daily life and work, and can reduce productivity (the amount of output achieved through daily life and work, etc.).

[0014] More specifically, "mild illness" refers to a state in which the level of stress is low to moderate but not severe, and includes one or more of the symptoms that derive from that state (psychological stress reaction, irritability, depression, fatigue, physical complaints, loss of vitality, etc.).

[0015] The presence or absence and severity of mild illness can be identified using the "Simple Occupational Stress Questionnaire" (Kato Masaaki, Ministry of Labor, 1999 "Research on the Prevention of Work-Related Illnesses," Research Report on Stress in the Workplace and Its Health Effects, 2000). For example, the assessment can be made based on the subject's responses to the 29 items in "Area B," which is a subjective stress assessment defined in the questionnaire.

[0016] As a specific method for evaluating the presence or absence and degree of mild discomfort, the method shown in the Examples can be used.

[0017] In the present invention, the term "microRNA (miRNA)" encompasses non-coding RNAs of 15 to 25 bases that are involved in the translational repression of mRNA. miRNAs are usually transcribed as hairpin-like RNA precursors, which are then cleaved by a dsRNA cleaving enzyme with RNase III activity and then incorporated into the RNA-induced silencing complex (RISC).

[0018] (1-1) Biomarker group according to the first aspect The group of biomarkers according to the first embodiment includes all combinations of the following six types of biomarkers: hsa-miR-26b-3p hsa-miR-130a-3p hsa-miR-92a-3p hsa-miR-423-5p hsa-miR-182-5p hsa-let-7e-5p

[0019] The present inventors have found that by detecting all six of the above biomarkers, it is possible to determine with high accuracy the presence or absence of a mild disorder in an individual and the degree of progression thereof. Although symptoms of mild illness are similar to symptoms of depression, for example, the group of biomarkers according to the first aspect can make it possible to distinguish between mild illness and depression.

[0020] In one aspect of the present invention using the biomarker group according to the first aspect, the combination of biomarkers is not particularly limited as long as it includes all six of the above biomarkers, and biomarkers other than the six above may or may not be further combined. Examples of biomarkers other than the six types mentioned above include one or more biomarkers included in the group of biomarkers according to the second aspect described below.

[0021] (1-2) Biomarker group according to the second aspect The group of biomarkers according to the second aspect includes one or more biomarkers selected from the group consisting of the following 32 types of biomarkers. The following biomarkers may be used singly or in combination of two or more types. hsa-miR-26b-3p hsa-miR-130a-3p hsa-miR-92a-3p hsa-miR-3615 hsa-miR-4732-3p hsa-miR-27b-5p hsa-miR-148a-3p hsa-miR-874-3p hsa-miR-6842-3p hsa-miR-142-3p hsa-miR-127-3p hsa-miR-454-5p hsa-miR-374a-3p hsa-miR-1301-3p hsa-miR-431-5p hsa-miR-4446-3p hsa-miR-194-5p hsa-miR-885-5p hsa-miR-92b-3p hsa-miR-6511a-3p hsa-miR-1307-5p hsa-miR-1180-3p hsa-miR-374b-5p hsa-miR-142-5p hsa-miR-16-5p hsa-miR-20b-5p hsa-miR-378a-3p hsa-miR-99b-5p hsa-miR-18a-5p hsa-miR-6741-3p hsa-miR-301b-3p hsa-miR-744-5p

[0022] The present inventors have found that mild illness in an individual can be determined with high accuracy by detecting one or more of the above 32 biomarkers. None of the above biomarkers have been reported to be associated with mood disorders (depression, etc.). Therefore, the biomarker group according to the second aspect can distinguish between mild illness and mood disorders (depression, etc.) whose symptoms are similar to those of mild illness.

[0023] In one embodiment of the present invention using the group of biomarkers according to the second embodiment, there is no particular limitation as long as one or more of the 32 types of biomarkers mentioned above are used, and biomarkers other than the 32 types mentioned above may or may not be further combined.

[0024] (2) Biomarker detection By detecting the biomarkers of the present invention in any sample, the presence or absence of mild disorders and the degree of progression of the disorders in the individual from which the sample was derived can be determined with high accuracy.

[0025] In the present invention, "detection of a biomarker" includes detecting fluctuations (increases, decreases) in the expression level of a biomarker (for example, expression level over time) and detecting the presence or absence of expression of a biomarker.

[0026] If all of the biomarker group according to the first aspect (six types of biomarkers) are detected in a sample, the individual from whom the sample was derived can be determined to be suffering from mild illness.

[0027] If one or more of the biomarker group (32 types of biomarkers) according to the second aspect are detected in a sample, the individual from whom the sample was derived can be determined to be suffering from a mild disorder.

[0028] In the present invention, the term "specimen" includes any sample derived from a living organism and an extract thereof. Examples of samples include body fluids (blood, serum, plasma, urine, saliva, sweat, cerebrospinal fluid, tissue exudate, etc.), tissues (brain tissue, nerve tissue, skin tissue, etc.), cells (brain cells, nerve cells, etc.), stool, hair, etc. Sample extracts include, for example, nucleic acids (RNA, DNA, etc.). Of the above, blood is preferred, and plasma is more preferred, in view of ease of preparation and handling.

[0029] In the present invention, the term "individual from which a specimen is derived" includes any organism from which the specimen is collected. Specific living organisms include humans, non-human mammals (monkeys, dogs, cats, cows, horses, sheep, goats), rodents (mice, rats, etc.), and the like.

[0030] The method for detecting a biomarker is not particularly limited, and includes any method that can determine whether or not a biomarker of the present invention is present in a sample, and if so, its expression level. Specific methods include the polymerase chain reaction (PCR) method and the microarray method.

[0031] For example, by comparing the presence or absence of expression of the biomarkers of the present invention in a sample and the expression level thereof in the sample with the results in a control (e.g., a sample from a healthy individual), the presence or absence and the degree of progression of mild disorders in the individual from which the sample was derived can be determined.

[0032] (3) Application of the biomarker of the present invention The biomarker of the present invention functions as a biomarker for mild disorders, and therefore can be used not only to determine whether an individual has mild disorders by detecting it, but also for the following purposes.

[0033] (3-1) Method for diagnosing minor illness in an individual The present invention encompasses methods for diagnosing mild disorders, comprising the steps of detecting a biomarker of the present invention in a sample collected from an individual, and diagnosing the individual with mild disorders based on the results.

[0034] (3-2) Screening method for therapeutic substances for mild disorders The present invention encompasses a method for screening for a therapeutic substance for mild disorders, which comprises the steps of administering any candidate substance to an animal and selecting the candidate substance using as an index the change in the amount of the biomarker of the present invention resulting from said administration.

[0035] For example, for a biomarker that is negatively (or positively) correlated with mild disorders, a candidate substance that increases (or decreases) the amount of the biomarker can be selected as a therapeutic substance for mild disorders.

[0036] The animals used in the screening method are not particularly limited, as long as they are non-human. [Example]

[0037] The present invention will be explained in more detail below with reference to examples, but the present invention is not limited to these examples.

[0038] An intervention test was conducted on subjects using the following method, and miRNAs that were strongly associated with mild illness were identified based on the expression levels of miRNAs before and after the test.

[0039] (1) Evaluation of mild illness using the "Simple Occupational Stress Questionnaire" The subjects in this example were evaluated for mild illness using the "Simple Occupational Stress Questionnaire" at each time point before and after the intervention test described below. The details of the method are as follows.

[0040] The "Simple Occupational Stress Questionnaire" is a questionnaire created by a group consisting of the Stress Measurement Research Group of the "Study Group on the Prevention of Work-Related Illnesses" of the Ministry of Labor of Japan in 1999 (Kato Masaaki, Ministry of Labor, 1999 "Study on the Prevention of Work-Related Illnesses", Research Report on Stress in the Workplace and Its Health Effects, 2000). In this example, an excerpt from the "Area B" questionnaire of the "Short Questionnaire for Occupational Stress" was used. This questionnaire contains 29 questions related to mental health, etc., and each evaluation item is scored based on the answers.

[0041] The evaluation items using the questionnaire are as follows: Vibrancy: Survey No. 1-3 Irritability: Questionnaire No. 4-6 Fatigue: Questionnaire No. 7-9 Anxiety: Questionnaire No. 10-12 Depression: Questionnaire No. 13-18 Physical complaints: Questionnaire No. 19-29

[0042] The responses of "1" and "2" in No. 1 to 3, and "3" and "4" in No. 4 to 18 were treated as "psychological stress." The responses of "3" and "4" for Nos. 19 to 29 were treated as "physical stress."

[0043] In this example, the scores of the answers to the questions in the questionnaire were added up and used as the score for each evaluation item for data analysis.

[0044] (2) Intervention trials Functional ingredients contained in various fruit juices and vegetable extracts are expected to have the effect of alleviating mild disorders. Therefore, by identifying the correlation between the expression levels of biomarkers before and after ingestion of the functional ingredients and each symptom of mild disorders, it may be possible to identify biomarkers related to mild disorders. Therefore, three types of intervention studies were conducted using various fruit juices and vegetable extracts, and samples were collected from subjects (63 people in total) at each time point before and after the intervention, and data analysis was performed.

[0045] The subjects were randomly divided into two groups: a placebo group (one receiving a sample that did not contain fruit juice or vegetable extracts) and a test group (one receiving a sample that contained fruit juice or vegetable extracts), and were asked to take each sample for 12 weeks.

[0046] Plasma was collected from the subjects at each time point before and after the start of the intervention study, and miRNA was extracted using a commercially available kit. PCR was performed using the miRNAs obtained from each intervention study using the "Truseq Small RNA kit" (Illumina, California, USA). The resulting PCR products were size-selected by electrophoresis to obtain libraries. Next, next-generation sequencing was performed using "MiniSeq" (Illumina, California, USA), and the raw read counts of each sample were analyzed using the DESeq method. The total read counts of each sample were then corrected to 1 million. The data set obtained by the above operations was subjected to the following data analyses.

[0047] (3) Data analysis-1: Simple correlation analysis The correlation between the scores for each evaluation item of mild ill health (vigor, irritability, fatigue, anxiety, depression, psychological stress, somatic complaints, or physical stress) and the expression levels of 71 types of blood miRNAs (number of reads of each miRNA per million reads) detected in more than half of the samples before and after intervention was evaluated by calculating the Pearson product-moment correlation coefficient.

[0048] Based on the evaluation results, the following 30 miRNAs with correlation coefficients of |0.200| or higher were selected. hsa-miR-92a-3p hsa-miR-484 hsa-miR-4446-3p hsa-miR-378a-3p hsa-miR-26b-3p hsa-miR-182-5p hsa-miR-151a-5p hsa-miR-127-3p hsa-miR-99b-5p hsa-miR-885-5p hsa-miR-454-5p hsa-miR-431-5p hsa-miR-374a-3p hsa-miR-29a-3p hsa-miR-128-3p hsa-let-7f-5p hsa-let-7e-5p hsa-let-7d-3p hsa-let-7a-5p hsa-miR-6741-3p hsa-miR-301b-3p hsa-miR-18a-5p hsa-miR-744-5p hsa-miR-423-5p hsa-miR-320a hsa-miR-1301-3p hsa-miR-4433b-5p hsa-miR-194-5p hsa-miR-144-5p hsa-miR-130a-3p

[0049] Of the 30 miRNAs listed above, 17 miRNAs that met all of the following criteria were selected (Table 1, the numbers indicate correlation coefficients). - Statistical differences were observed in all evaluation items except for "anxiety" and "depression." No reported examples of miRNAs related to depression were found.

[0050] The 17 types of miRNAs mentioned above are as follows: hsa-miR-92a-3p hsa-miR-4446-3p hsa-miR-378a-3p hsa-miR-26b-3p hsa-miR-127-3p hsa-miR-99b-5p hsa-miR-885-5p hsa-miR-454-5p hsa-miR-431-5p hsa-miR-374a-3p hsa-miR-6741-3p hsa-miR-301b-3p hsa-miR-18a-5p hsa-miR-744-5p hsa-miR-1301-3p hsa-miR-194-5p hsa-miR-130a-3p

[0051] [Table 1]

[0052] (4) Data analysis-2: Integrated analysis using multivariate regression analysis and Mann-Whitney U test Multivariate regression analysis was performed using the following method, and the Mann-Whitney U test was performed based on the results.

[0053] (4-1) Multivariate regression analysis First, the scores of each evaluation item of mild ailments (vigor, irritability, fatigue, anxiety, depression, and physical complaints) were set as dependent variables. The expression levels (number of reads) of 193 types of miRNAs detected in more than half of the samples before intervention were set as explanatory variables. Based on the above-mentioned objective variables and explanatory variables, an orthogonal partial least squares regression analysis (OPLS-R analysis) was performed using the multivariate analysis software SIMCA (Umetrics, Umea, Sweden). The statistical significance of the regression model was confirmed, and the coefficient of determination, R 2 Models with a value of 0.5 or higher were set as models capable of predicting various evaluation items. As a result, we successfully constructed models for the evaluation items "anxiety" and "depression," and selected several miRNAs with VIP (Variable Importance of Projection) values ​​of 1.0 or higher, indicating that they significantly contributed to the construction of the models.

[0054] Next, to evaluate the statistical differences between the low and high scores of "anxiety" and "depression" among the selected miRNAs, the population was divided into tertiles based on the scores. Specifically, the n numbers of the low and high groups were adjusted to be at least one-third (n = 21) of the total (n = 63). Additionally, the same population was divided into two quantiles and adjusted into a low group and a high group.

[0055] (4-2) Mann-Whitney U test For each evaluation item, the statistical difference between the low and high groups for the expression level (number of reads) of each miRNA was evaluated using the Mann-Whitney U test (GraphPad, Inc., San Diego, CA, USA). We identified miRNAs that showed statistical differences between the low and high groups in the tertile method, and miRNAs that showed statistical differences between the low and high groups in the bi-quantile method, and selected multiple miRNAs for each of "anxiety" and "depression" that showed differences in both the groups divided by the tertile method and the groups divided by the bi-quantile method.

[0056] From the above miRNAs, 11 types of miRNAs were selected that overlapped with both "anxiety" and "depression" (Table 2, numbers in parentheses indicate P values. -: P≧0.05, *: P<0.05, **: P<0.01, ***: P<0.001).

[0057] The 11 types of miRNAs mentioned above are as follows: hsa-miR-92b-3p hsa-miR-423-5p hsa-miR-6511a-3p hsa-miR-92a-3p hsa-let-7e-5p hsa-let-7c-5p hsa-miR-451a hsa-miR-1307-5p hsa-miR-26b-3p hsa-miR-130a-3p hsa-miR-182-5p

[0058] It was found that some of the above 11 types of miRNAs include miRNAs that have been reported to be associated with depression. Although some of the symptoms of mild ailment are similar to those of depression, in this example, in order to distinguish between mild ailment and depression, we focused on the following six miRNAs, which have not been reported to be associated with depression, as potential markers of mild ailment (in Table 2, the miRNA names are marked with an asterisk at the end). hsa-miR-92b-3p hsa-miR-6511a-3p hsa-miR-92a-3p hsa-miR-1307-5p hsa-miR-26b-3p hsa-miR-130a-3p

[0059] [Table 2]

[0060] (5) Data Analysis-3: Mann-Whitney U Test Using the same pre-intervention samples as in "Data Analysis-2" above, we examined whether statistical differences were observed in evaluation items other than "anxiety" and "depression" using the following method.

[0061] Without performing multivariate regression analysis (OPLS-R analysis), subjects were divided into low and high groups based on their scores using the tertile or bi-quartile method for each evaluation item (vigor, irritability, fatigue, anxiety, depression, and physical complaints). Next, the statistical differences between the two groups for the expression levels (number of reads) of 193 types of miRNA were evaluated using the Mann-Whitney U test.

[0062] For each evaluation item, the following 38 miRNAs were selected, which showed differences in both the groups divided by the tertile method and the groups divided by the bistile method. hsa-miR-1180-3p hsa-miR-139-5p hsa-miR-17-5p hsa-miR-27b-5p hsa-miR-30a-5p hsa-miR-3615 hsa-miR-874-3p hsa-let-7f-5p hsa-miR-107 hsa-miR-142-3p hsa-miR-148a-3p hsa-miR-19a-3p hsa-miR-19b-3p hsa-miR-4732-3p hsa-miR-6741-3p hsa-miR-6842-3p hsa-let-7c-5p hsa-let-7e-5p hsa-miR-1307-5p hsa-miR-130a-5p hsa-miR-205-5p hsa-miR-26b-3p hsa-miR-375 hsa-miR-378a-3p hsa-miR-423-5p hsa-miR-451a hsa-miR-502-3p hsa-miR-627-5p hsa-miR-6511a-3p hsa-miR-92a-3p hsa-miR-92b-3p hsa-miR-181b-5p hsa-miR-182-5p hsa-miR-142-5p hsa-miR-16-5p hsa-miR-186-5p hsa-miR-20b-5p hsa-miR-374b-5p

[0063] Of the 38 miRNAs listed above, 14 miRNAs that met all of the following criteria were selected. - Statistical differences were observed in all evaluation items except for "anxiety" and "depression." No reported examples of miRNAs related to depression were found.

[0064] The 14 types of miRNAs mentioned above are as follows: hsa-miR-1180-3p hsa-miR-27b-5p hsa-miR-3615 hsa-miR-874-3p hsa-miR-142-3p hsa-miR-148a-3p hsa-miR-4732-3p hsa-miR-6741-3p hsa-miR-6842-3p hsa-miR-1307-5p hsa-miR-142-5p hsa-miR-16-5p hsa-miR-20b-5p hsa-miR-374b-5p

[0065] (6) Data analysis-4: Identification of biomarker candidate miRNAs and their combined analysis Using the following method, miRNAs that are candidates for biomarkers of mild disorders were identified based on the results of the above "Data Analysis-1" to "Data Analysis-3," and a combined analysis was performed.

[0066] First, based on the results of "Data Analysis-1" to "Data Analysis-3" above, six types of miRNAs that overlapped in all analysis results among the "miRNAs before selection (Data Analysis-1: 30 types, Data Analysis-2: 11 types, Data Analysis-3: 38 types)" were selected as candidates for biomarkers of mild illness. The six types of miRNAs mentioned above are as follows: hsa-miR-92a-3p hsa-miR-26b-3p hsa-miR-182-5p hsa-let-7e-5p hsa-miR-423-5p hsa-miR-130a-3p

[0067] We examined whether the presence or absence of mild illness could be determined when all six types of miRNAs were combined. Specifically, we used "SPSS Statistics 29.0.2" (IBM Japan, Tokyo, Japan) to calculate the "AUC (Area Under Curve)" value of the "Receiver Operating Characteristic (ROC)" curve, which represents the relationship between "sensitivity" (true positive rate) and "1-specificity" (false positive rate). After performing logistic regression analysis for each miRNA (including combinations), ROC curves were created for the scores of each evaluation item (divided into 2 quartiles: Low group and High group).

[0068] The results are shown in Table 3 (the values ​​indicate AUC values). A higher AUC value means higher accuracy in determining the presence or absence of mild illness. As shown in Table 3, when all six miRNAs were combined, the AUC values ​​were higher for all evaluation items compared to when each miRNA was used alone. In particular, the AUC values ​​were 0.7 or higher for four evaluation items (irritability, fatigue, anxiety, and depression), demonstrating that these are highly accurate biomarkers for mild illness.

[0069] [Table 3]

Claims

1. hsa-miR-26b-3p, hsa-miR-130a-3p, hsa-miR-92a-3p, hsa-miR-423-5p, hsa-miR-182-5p, and hsa-let-7e-5p Biomarkers of mild illness, including a combination of:

2. hsa-miR-26b-3p, hsa-miR-130a-3p, hsa-miR-92a-3p, hsa-miR-3615, hsa-miR-4732-3p, hsa-miR-27b-5p, hsa-miR-148a-3p, hsa-miR-874-3p, hsa-miR-6842-3p, hsa-miR-142-3p, hsa-miR-127-3p, hsa-miR-454-5p, hsa-miR-374a-3p, hsa-miR-1301-3p, hsa-miR-431-5p, hsa-miR-4446-3p, hsa-miR-194-5p, hsa-miR-885-5p, hsa-miR-92b-3p, hsa-miR-6511a-3p, hsa-miR-1307-5p, hsa-miR-1180-3p, hsa-miR-374b-5p, hsa-miR-142-5p, hsa-miR-16-5p, hsa-miR-20b-5p, hsa-miR-378a-3p, hsa-miR-99b-5p, hsa-miR-18a-5p, hsa-miR-6741-3p, hsa-miR-301b-3p, and hsa-miR-744-5p A biomarker for mild illness comprising one or more selected from the group consisting of:

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