Method for evaluating sleep quality

Abstract

To elucidate the relationship between enteric bacteria and brain waves during sleep, and to provide a method for evaluating sleep quality.SOLUTION: Provided is a method for evaluating sleep quality based on a causal relationship between the level of Alistipes and the duration of deep non-REM sleep, and between the level of Sellimonas and the duration of light non-REM sleep.SELECTED DRAWING: None

Description

Technical Field

[0001] The present invention relates to a method for evaluating the quality of sleep.

Background Art

[0002] Sleep occupies nearly one-third of a day and has a function as a homeostasis mechanism to recover physical and brain fatigue (Garbarino S et al., Commun Biol. 2021;4:1304). For example, in terms of physical functions, sleep is involved in the regulation of almost all systems in the body, such as the autonomic nervous system (Tavares L et al., Methodist Debakey Cardiovasc J. 2021;17:49-52), cardiovascular system (Covassin N et al., Sleep Med Clin. 2016;11:81-9), immune system (Irwin MR, Sleep Med Rev. 2012;16:231-41), and metabolic system (Magee L, Sleep Med Rev. 2012;16:231-41). In terms of brain function, sleep plays an important role in cognitive ability (Van Dongen HP et al., Sleep. 2003;26:117-26), memory consolidation (Tononi G et al., Neuron. 2014;81:12-34), mood regulation (Lieberman HR et al., Biol Psychiatry. 2005;57:422-9), etc. Sleep is not simply a state of rest uniformly. Even during sleep, brain activities change in various ways. Sleep is broadly classified into REM (Rapid eye movement) sleep and non-REM (Non-rapid eye movement) sleep based on the electroencephalogram state. Furthermore, non-REM sleep is divided into light non-REM sleep (stages 1 and 2: N1, N2) and deep non-REM sleep (stage 3: N3) (Ackermann S et al., Curr Neurol Neurosci Rep. 2014;14:430 / Hori T et al., Psychiatry Clin Neurosci. 2001;55:305-10). During non-REM sleep, brain activities and the autonomic nervous system are in a resting state, and a decrease in heart rate, respiratory rate, and blood pressure is observed (Pagani M et al., Circ Res. 1986;59:178-93). On the other hand, REM sleep is a light sleep state, and the electroencephalogram is very similar to that during wakefulness.Therefore, this REM sleep is thought to be important for memory consolidation and intellectual development (Nofzinger EA et al., Brain Res. 1997;770:192-201 / Maquet P et al., Nature. 1996;383:163-6). From the perspective of autonomic nerve activity, during this REM sleep period, nerve activity increases to the same extent as during wakefulness, and even more so (Knudsen K et al., Lancet Neurol. 2018;17:618-28).

[0003] Sleep, which is important for the recovery of physical and brain functions, has been regarded as a global problem of sleep deprivation in recent years. In particular, according to a survey by the Organization for Economic Cooperation and Development (OECD), the sleep duration of Japanese people is the lowest among 33 countries, and it has been revealed that Japan is one of the countries with the most serious sleep deprivation in the world (OECD database: https: / / www.oecd.org / health / health-data.htm). Furthermore, according to the "National Health and Nutrition Survey" conducted by the Ministry of Health, Labour and Welfare in 2019, although the government recommends 6 to 8 hours of sleep, 37.5% of men and 40.6% of women have less than 6 hours of sleep (Japan: National Institute of Health and Nutrition NIHN; 2020). Sleep deprivation has been reported to be associated with an increased risk of various health problems such as cardiovascular diseases, diabetes, metabolic syndrome, and depression (Tobaldini E et al., Nat Rev Cardiol. 2019;16:213-24 / Reutrakul S et al., Metabolism. 2018;84:56-66 / Agrawal S et al., CNS Neurol Disord Drug Targets. 2022). Also, not only the sleep duration but also the duration of REM sleep and non-REM sleep is important. For example, it has been reported that when the proportion of REM sleep duration to the total sleep duration is less than 15%, the risk of death due to cardiovascular diseases and other causes increases (Leary EB et al., JAMA Neurol. 2020;77:1241-51). In addition, the proportion of non-REM sleep N3 is correlated with daytime sleepiness, exercise performance, and problem-solving performance, and it has been reported to be important for overall daytime activities (Dijk DJ, J Clin Sleep Med. 2009;5:S6-15 / McCarter SJ et al., Sleep Med Rev. 2022;64:101657). Furthermore, it has also been reported that when the ratio of non-REM sleep N3 decreases, the tendency to be anxious and depressed becomes stronger (Motomura Y., PLoS One. 2013;8:e56578).

[0004] Sleep is strongly influenced by internal and external environments such as the circadian rhythm, light environment, and feeding, and among these, the gut microbiota is one of them (Non-Patent Document 1). Approximately 40 trillion and over 100 species of gut bacteria inhabit the intestines of mammals, and this population is called the gut microbiota. A symbiotic relationship exists between the gut microbiota and the host. The gut microbiota utilizes indigestible nutrients that the host could not fully digest and absorb for growth and proliferation, and the host utilizes the metabolites produced when the gut microbiota ferments and decomposes indigestible nutrients for physiological functions (Marchesi JR et al., Gut. 2016;65:330-9 / Koh A et al., Cell. 2016;165:1332-45 / Blaak EE et al., Benef Microbes. 2020;11:411-55). Here, it has been reported that there is an interaction between the state of the brain, the function of the intestinal tract, and the gut microbiota, and this interrelationship is called the brain-gut interaction or the brain-gut axis (Mayer EA et al., Annu Rev Med. 2022;73:439-53). As an example of the brain affecting the function of the intestinal tract, irritable bowel syndrome (IBS), which is known as a stress-related disease, can be cited. IBS is a disease in which abdominal pain and discomfort continue despite no abnormalities in the intestinal tract, and constipation, diarrhea, etc. occur repeatedly. When the brain feels anxiety or stress, the intestinal tract receives the signal hypersensitively, causing abnormal peristaltic movements in the intestinal tract and resulting in abdominal pain, diarrhea, and constipation. It has also been reported that a vicious cycle occurs in which the stimulus is transmitted to the brain, increasing pain and anxiety, and causing further abnormalities in peristaltic movements (Coss-Adame E et al., Curr Gastroenterol Rep. 2014;16:379). On the other hand, as an example of the intestine affecting brain function, reports of basic research using germ-free mice can be cited. It has been reported that in germ-free mice without gut bacteria, the response to stress is greater than that of normal mice, and the expression level of brain-derived neurotrophic factor (BDNF) is also decreased.Furthermore, it has been shown that when the gut microbiota of normal mice is transplanted into these germ-free mice, the stress response can be suppressed to the same extent as that of normal mice (Sudo N et al., J Physiol. 2004;558:263-75). In addition, as the relationship between the gut microbiota and brain function, it is also associated with memory formation, cognitive function, mental health, and circadian rhythm, and it has been reported that these brain-gut interactions are connected through the vagus nerve and the circulatory system (Sherwin E et al., Science. 2019;366 / Chu C et al., Nature. 2019;574:543-8 / Lu J et al., PLoS One. 2018;13:e0201829 / Mohle L et al., Cell Rep. 2016;15:1945-56).

[0005] Given that the gut microbiota interacts with various brain functions, it is quite conceivable that the gut microbiota may also affect sleep. In fact, in mice whose gut microbiota was removed by antibiotics, it was confirmed that the time of non-REM sleep during the inactive period decreased compared to normal mice, and the time of non-REM sleep and REM sleep during the active period increased. That is, it has been reported that the rhythm of sleep and wakefulness is lost (Non-Patent Document 1, Non-Patent Document 2). Also, in rats, when prebiotics were continuously administered from the weaning period, it was reported that the diversity of the gut microbiota increased in the adult period, and the decrease in non-REM sleep time could be suppressed even when sleep was disturbed by an electric shock (Non-Patent Document 3). In humans, it has also been reported that when adults with latent symptoms of depression, anxiety, and insomnia ingested probiotics, the sleep score in the Pittsburgh Sleep Quality Index improved along with changes in the composition of the gut microbiota (Non-Patent Document 4). In addition, when medical students who were assumed to be under chronic stress ingested probiotic tablets, a decrease in the genus Bifidobacterium and an increase in the genera Streptococcus and Lachnospira were observed, and at the same time, an improvement in the sleep score of the Pittsburgh Sleep Quality Index and a shortening of the deep sleep latency (the time from falling asleep to reaching the first N3 stage) were also reported (Non-Patent Document 5). In survey reports, it has also been reported that there is a correlation between the sleep time by questionnaire and the abundance ratio of various gut bacteria (Non-Patent Document 6), and a correlation between the sleep efficiency measurement using an actiwatch and the diversity of the gut microbiota and the abundance ratio of gut bacteria (Non-Patent Document 7).

[0006] As described above, many reports have been made on the relationship between the gut microbiota and sleep. However, there are few reports discussing the relationship between sleep and the gut microbiota from the perspective of electroencephalograms, and furthermore, there are no reports clarifying the causal relationship.

Prior Art Documents

Non-Patent Documents

[0007]

Non-Patent Document 1

[0008] An object of the present invention is to evaluate the quality of sleep by analyzing the bacterial composition of the gut microbiota sample. [Means for Solving the Problems]

[0009] The present inventors attempted to clarify the relationship between gut bacteria and brain waves using various statistical analysis methods from the database of gut microbiota and brain wave measurements during sleep, and as a result of repeated considerable creative considerations, a causal relationship was found between the proportion of the genus Alistipes and the time of deep non-REM sleep, and between the proportion of the genus Sellimonas and the time of light non-REM sleep. Based on these findings, the present inventors completed the present invention.

[0010] The present invention includes, for example, the inventions shown in the following [1] to

[10] .

[0011] [1] A method for screening a subject regarding the quality of sleep, comprising the following steps: (a) Analyzing the bacterial composition of the intestinal microbiota sample derived from the subject; (b) Comparing the ratio of the genus Alistipes and / or the genus Celeromonas in the intestinal bacteria with the ratio of the corresponding bacteria in an arbitrary population; (c) Based on the comparison, screening whether the subject is included in either a group with a relatively long deep non-REM sleep time or a group with a relatively short deep non-REM sleep time, or whether the subject is included in either a group with a relatively long light non-REM sleep time or a group with a relatively short light non-REM sleep time; A method comprising the above. [2] The step (c) in the above [1] is as follows: (c-1) Determining that a group with a relatively high ratio of the genus Alistipes by the comparison is a group with a long deep non-REM sleep time, or determining that a group with a relatively low ratio of the genus Alistipes by the comparison is a group with a short deep non-REM sleep time; and / or (c-2) Determining that a group with a relatively high ratio of the genus Celeromonas by the comparison is a group with a long light non-REM sleep time, or determining that a group with a relatively low ratio of the genus Celeromonas by the comparison is a group with a short light non-REM sleep time The method according to [1], comprising the above. [3] A method for evaluating the quality of sleep of a subject, comprising the following steps: (a) Analyzing the bacterial composition of the intestinal microbiota sample derived from the subject; (b) Comparing the ratio of the genus Alistipes and / or the genus Celeromonas in the intestinal bacteria with the ratio of the corresponding bacteria in an arbitrary population; (c) Based on the comparison, evaluating that the time of deep non-REM sleep of the subject may be longer or shorter than the time of deep non-REM sleep of any group, or that the time of light non-REM sleep of the subject may be longer or shorter than the time of light non-REM sleep of any group; A method comprising the above. [4] The step (c) is as follows: (c-1) When the ratio of the genus Allistipes of the subject is higher than the ratio of the genus Allistipes of any group by the comparison, evaluating that the time of deep non-REM sleep of the subject may be long, or when the ratio of the genus Allistipes of the subject is lower than the ratio of the genus Allistipes of any group by the comparison, evaluating that the time of deep non-REM sleep of the subject may be short; and / or (c-2) When the ratio of the genus Serimonus of the subject is higher than the ratio of the genus Serimonus of any group by the comparison, evaluating that the time of light non-REM sleep of the subject may be long, or when the ratio of the genus Serimonus of the subject is lower than the ratio of the genus Serimonus of any group by the comparison, evaluating that the time of light non-REM sleep of the subject may be short The method according to [3], comprising the above. [5] The method according to [1] or [3], wherein deep non-REM sleep is non-REM sleep N3 and light non-REM sleep is non-REM sleep N2. [6] A kit for evaluating the sleep quality of a subject, comprising a detection reagent for detecting the genus Allistipes and / or the genus Serimonus. [7] The kit according to [6], which is used in the method according to [1] or [3]. [8] The kit according to [6], wherein the detection reagent is a nucleic acid that hybridizes with the nucleic acid sequence of the genus Allistipes and / or the genus Serimonus. [9] The kit according to [8], wherein the nucleic acid that hybridizes with the nucleic acid sequence of the genus Allistipes and / or the genus Serimonus is a nucleic acid that hybridizes with the region of 16S rRNA.

[10] The kit described in [7], where deep non-REM sleep is non-REM sleep N3 and light non-REM sleep is non-REM sleep N2.

Advantages of the Invention

[0012] The method and evaluation kit of the present invention can potentially be used to evaluate the quality of sleep by analyzing gut microbiota specimens and determining the proportions of the genus Alistipes and / or the genus Celeromonas.

Brief Description of the Drawings

[0013]

Figure 1

Figure 2

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Modes for Carrying Out the Invention

[0014] The present invention will be described in detail below.

[0015] In one embodiment, the subject of the present invention is a mammal. Also, in one embodiment, the subject of the present invention is a human.

[0016] In one embodiment, the quality of sleep in the present invention refers to the length of time of light non-REM sleep and deep non-REM sleep. Also, in one embodiment, light non-REM sleep includes non-REM sleep N2, and / or deep non-REM sleep includes non-REM sleep N3. In one embodiment, when it is determined that the time of deep non-REM sleep and / or non-REM sleep N3 may be short, it can be presumed that deep sleep is insufficient. In one embodiment, the length of time of light non-REM sleep and deep non-REM sleep means that in a population of subjects consisting of two groups, the time of light non-REM sleep or deep non-REM sleep in one group is relatively longer or shorter than the time of light non-REM sleep or deep non-REM sleep in the other group. In one embodiment, the length of time of light non-REM sleep and deep non-REM sleep means that the time of light non-REM sleep or deep non-REM sleep of a subject is longer or shorter than the time of light non-REM sleep or deep non-REM sleep of any group.

[0017] In this specification, non-REM sleep N1, N2, and N3 are also referred to as non-REM sleep stages 1, 2, and 3.

[0018] Intestinal bacteria are bacteria that inhabit the digestive tract. The intestinal microbiota is a population formed by each intestinal bacterium inhabiting and being divided into regions within the digestive tract. Also, the intestinal microbiota may be referred to as intestinal bacterial flora, intestinal microbiota, intestinal microbiome, etc.

[0019] In one embodiment, the sample of the intestinal microbiota in the present invention is feces.

[0020] Examples of methods for analyzing the composition of bacteria from specimens derived from the intestinal flora used in the present invention include, for example, culturing using selective media, metabolome analysis, proteomics analysis, 16S rRNA analysis, shotgun metagenome analysis, and whole genome analysis. However, it is not limited thereto as long as the ratio of any bacteria can be compared with any population.

[0021] In one embodiment, the analysis in the present invention is performed by analyzing a shotgun library prepared from the metagenomic DNA of bacteria contained in the intestinal flora. In one embodiment, the analysis is performed by a next-generation sequencer.

[0022] In one embodiment, the analysis of the present invention is performed by analyzing a library prepared by amplifying a region containing the variable region of 16S rRNA of bacteria contained in the intestinal flora. Also, in one embodiment, the variable region is a region containing the V3-V4 variable region. In one embodiment, the analysis is performed by a next-generation sequencer.

[0023] Examples of next-generation sequencers that can be used in the present invention include, for example, GS Junior (Roche), Genome Sequencer FLX System (454 Life Sciences), MiSeq (Illumina), NovaSeq (Illumina), and Ion Proton System (Thermo Fisher Scientific), etc. However, it is not limited thereto as long as it is a device capable of analyzing the intestinal flora composition or 16S rRNA library.

[0024] In the present invention, any bacterium to be analyzed belongs to the genus Alistipes and / or the genus Sellimonas. Examples of bacteria of the genus Alistipes include, for example, Alistipes onderdonkii, Alistipes finegoldii, Alistipes indistinctus, Alistipes shahii, Alistipes putredinis, Alistipes timonensis, Alistipes ihumii, Alistipes senegalensis, Alistipes finegoldii CAG:68, Alistipes putredinis CAG:67, Alistipes hominis, Alistipes muris, and Alistipes montrealensis. However, as long as the bacterium belongs to the genus Alistipes, the analysis target is not limited to these. Examples of bacteria of the genus Sellimonas include, for example, Sellimonas intestinalis, Sellimonas catena, Sellimonas caecigallum, and Sellimonas monacensis. However, as long as the bacterium belongs to the genus Sellimonas, the analysis target is not limited to these.

[0025] In the analysis targeting the composition of the genus Alistipes and / or the genus Sellimonas, any detection reagent can be used. The detection reagent in the present invention is not particularly limited as long as it can detect the genus Alistipes and / or the genus Sellimonas. In one embodiment, the detection reagent in the present invention is a selective culture medium that can selectively culture the genus Alistipes and / or the genus Sellimonas. In another embodiment, the detection reagent in the present invention is a detector for bacterial metabolites of the genus Alistipes and / or the genus Sellimonas. In another embodiment, the detection reagent in the present invention is an antibody that selectively binds to proteins of the genus Alistipes and / or the genus Sellimonas. In another embodiment, the detection reagent in the present invention is a nucleic acid that specifically hybridizes with a nucleic acid sequence of the genus Alistipes and / or the genus Sellimonas. As the nucleic acid sequence of the genus Alistipes and / or the genus Sellimonas, sequences known to those skilled in the art can be used.

[0026] In one embodiment, a nucleic acid that specifically hybridizes with a nucleic acid sequence of the genus Allistipes and / or the genus Ceriomonas is a nucleic acid that hybridizes with a region of 16S rRNA of the genus Allistipes and / or the genus Ceriomonas, preferably a nucleic acid that hybridizes with a region containing a variable region of 16S rRNA, and more preferably a nucleic acid that hybridizes with a region containing the V3-V4 region of 16S rRNA.

[0027] In one embodiment, in an analysis targeting the composition of the genus Allistipes and / or the genus Ceriomonas, a quantitative PCR method using a nucleic acid that specifically hybridizes with a nucleic acid sequence of the genus Allistipes and / or the genus Ceriomonas can be used.

[0028] In one embodiment, in the present invention, in addition to the genus Allistipes and / or the genus Ceriomonas, any other arbitrary bacteria can be simultaneously targeted for analysis.

[0029] In one embodiment, any population for comparing the ratio of any bacteria in the gut bacteria of a subject is a population including the subject from whom the sample is derived. In another embodiment, any population for comparing the ratio of bacteria is a population having a similar race, place of residence, food culture, physical characteristics, and / or health status to the subject. Also, in one embodiment, any population for comparing the ratio of bacteria is a population of 10 or more individuals, preferably 100 or more individuals, and more preferably 500 or more individuals.

[0030] In one embodiment, the step of comparing the ratio of the genus Allistipes and / or the genus Ceriomonas in the gut bacteria with the ratio of the bacteria in any population means the step of comparing the ratio of the genus Allistipes and / or the genus Ceriomonas in the gut bacteria with the average value of the ratio of the bacteria in any population.

[0031] In one embodiment, the significance level for determining that the proportion of any bacterium in the gut bacteria of the subject is high or low compared to any population is 10%, preferably 5%.

[0032] In one embodiment, when the proportion of the genus Alistipes in the gut microbiota sample is high or low compared to any population, it can be determined that the subject related to the sample may have a long or short deep non-REM sleep time. Also, in one embodiment, when the proportion of the genus Alistipes in the gut microbiota sample is high or low compared to any population, it can be determined that the subject related to the sample may have a long or short non-REM sleep N3 time. Further, in yet another embodiment, when the proportion of the genus Alistipes in the gut microbiota sample is low compared to any population, it can be determined that the subject related to the sample may not have enough deep sleep.

[0033] In one embodiment, when the proportion of the genus Serimonas in the gut microbiota sample is high or low compared to any population, it can be determined that the subject related to the sample may have a long or short light non-REM sleep time. Also, in one embodiment, when the proportion of the genus Serimonas in the gut microbiota sample is high or low compared to any population, it can be determined that the subject related to the sample may have a long or short non-REM sleep N2 time.

[0034] <The screening method of the present invention> The present invention includes a method for screening a subject regarding the quality of sleep.

[0035] In one embodiment, the screening method of the present invention comprises the following steps: (a) analyzing the bacterial composition of a gut microbiota sample derived from the subject; (b) comparing the proportion of the genus Alistipes and / or the genus Serimonas in the gut bacteria with the proportion of the corresponding bacteria in any population; (c) Based on the comparison, screening the group to which the subject belongs, whether it is the group with a relatively long deep non-REM sleep time or the group with a relatively short deep non-REM sleep time, or whether it is the group with a relatively long light non-REM sleep time or the group with a relatively short light non-REM sleep time; which is a method including this. Here, the step (c) is as follows: (c-1) Determining the group with a relatively high proportion of Allistipes as the group with a long deep non-REM sleep time by the comparison, or determining the group with a relatively low proportion of Allistipes as the group with a short deep non-REM sleep time; and / or (c-2) Determining the group with a relatively high proportion of Cerrimonas as the group with a long light non-REM sleep time by the comparison, or determining the group with a relatively low proportion of Cerrimonas as the group with a short light non-REM sleep time, which may be included.

[0036] Also, in one embodiment, the screening method of the present invention is the following steps: (a) Analyzing the bacterial composition of the intestinal microbiota sample derived from the subject; (b) Comparing the proportion of Allistipes in the intestinal bacteria with the proportion of the bacteria in an arbitrary population; and (c’) When the proportion of Allistipes is high by the comparison, screening the subject as being included in the group with a possible long deep non-REM sleep time, or when the proportion of Allistipes is low by the comparison, screening the subject related to the sample as being included in the group with a possible short deep non-REM sleep time; which is a method including this. It is.

[0037] Also, in one embodiment, the screening method of the present invention is the following steps: (a) Analyzing the bacterial composition of the intestinal microbiota sample derived from the subject; (b) Comparing the proportion of the genus Serimonas in the intestinal bacteria with that of the bacteria in an arbitrary population, and, (c’) When the proportion of the genus Serimonas is high according to the comparison, screening the subject as being included in a group that may have a long light non-REM sleep time, or when the proportion of the genus Serimonas is low according to the comparison, screening the subject related to the sample as possibly being included in a group with a long or short light non-REM sleep time, A method comprising: is provided.

[0038] In one embodiment, the screening method of the present invention comprises: The following steps: (a) Analyzing the bacterial composition of the intestinal microbiota sample derived from the subject; (b) Comparing the proportion of the genus Alistipes and / or the genus Serimonas in the intestinal bacteria with that of the bacteria in an arbitrary population, and (c-1’) When the proportion of the genus Alistipes is high according to the comparison, screening the subject as being included in a group that may have a long non-REM sleep N3 time, or when the proportion of the genus Alistipes is low according to the comparison, screening the subject related to the sample as possibly being included in a group with a short non-REM sleep N3 time, and / or (c-2’) When the proportion of the genus Serimonas is high according to the comparison, screening the subject as being included in a group that may have a long non-REM sleep N2 time, or when the proportion of the genus Serimonas is low according to the comparison, screening the subject related to the sample as possibly being included in a group with a short non-REM sleep N2 time, A method comprising: is provided.

[0039] Also, in one embodiment, the screening method of the present invention comprises: The following steps: (a) Analyzing the bacterial composition of the intestinal microbiota sample derived from the subject; (b) Comparing the proportion of the genus Alistipes in the intestinal bacteria with that of the bacteria in an arbitrary population, and, (c’) When the proportion of the genus Alistipes is high as a result of the comparison, screening the subject as being included in a group that may have a long non-REM sleep N3 time, or when the proportion of the genus Alistipes is low as a result of the comparison, screening the subject related to the specimen as being included in a group that may have a short non-REM sleep N3 time, A method comprising: is provided.

[0040] Also, in one embodiment, the screening method of the present invention is The following steps: (a) Analyzing the bacterial composition of the intestinal microbiota specimen derived from the subject, (b) Comparing the proportion of the genus Cerrimonadaceae in the intestinal bacteria with that of the bacteria in an arbitrary population, and, (c’) When the proportion of the genus Cerrimonadaceae is high as a result of the comparison, screening the subject as being included in a group that may have a long non-REM sleep N2 time, or when the proportion of the genus Cerrimonadaceae is low as a result of the comparison, screening the subject related to the specimen as being included in a group that may have a short non-REM sleep N2 time, A method comprising: is provided.

[0041] <The evaluation method of the present invention> The present invention includes a method for evaluating the sleep quality of a subject.

[0042] In one embodiment, the evaluation method of the present invention is The following steps: (a) Analyzing the bacterial composition of the intestinal microbiota specimen derived from the subject, and (b) Comparing the proportion of the genus Alistipes and / or the genus Cerrimonadaceae in the intestinal bacteria with that of the bacteria in an arbitrary population, and (c) Based on the comparison, evaluating that the time of deep non-REM sleep of the subject may be longer or shorter than that of any group, or that the time of light non-REM sleep of the subject may be longer or shorter than that of any group; A method comprising is provided. Here, the step (c) includes the following: (c-1) When the proportion of the genus Allistipes of the subject is higher than that of any group according to the comparison, evaluating that the time of deep non-REM sleep of the subject may be long, or when the proportion of the genus Allistipes of the subject is lower than that of any group according to the comparison, evaluating that the time of deep non-REM sleep of the subject may be short; and / or (c-2) When the proportion of the genus Serimonas of the subject is higher than that of any group according to the comparison, evaluating that the time of light non-REM sleep of the subject may be long, or when the proportion of the genus Serimonas of the subject is lower than that of any group according to the comparison, evaluating that the time of light non-REM sleep of the subject may be short. may be included.

[0043] Also, in one embodiment, the evaluation method of the present invention includes the following steps: (a) Analyzing the bacterial composition of the gut microbiota sample from the subject; (b) Comparing the proportion of the genus Allistipes in the gut bacteria with that of any group; and (c’) When the proportion of the genus Allistipes of the subject is higher than that of any group according to the comparison, evaluating that the time of deep non-REM sleep of the subject related to the sample may be long, or when the proportion of the genus Allistipes of the subject is lower than that of any group according to the comparison, evaluating that the time of deep non-REM sleep of the subject related to the sample may be short. A method comprising is provided.

[0044] Also, in one embodiment, the evaluation method of the present invention is the following steps: (a) A step of analyzing the bacterial composition of the gut microbiota sample derived from the subject; (b) A step of comparing the proportion of the genus Serimonas in the gut bacteria with the proportion of the bacteria in an arbitrary population; and (c’) A step of evaluating that when the proportion of the genus Serimonas of the subject is higher than the proportion of the genus Serimonas of an arbitrary population by the comparison, the time of light non-REM sleep of the subject related to the sample may be long, or when the proportion of the genus Serimonas of the subject is lower than the proportion of the genus Serimonas of an arbitrary population by the comparison, the time of light non-REM sleep of the subject related to the sample may be short; A method comprising is provided.

[0045] In one embodiment, the evaluation method of the present invention is the following steps: (a) A step of analyzing the bacterial composition of the gut microbiota sample derived from the subject; (b) A step of comparing the proportion of the genus Alistipes and / or the genus Serimonas in the gut bacteria with the proportion of the bacteria in an arbitrary population; and (c-1’) A step of evaluating that when the proportion of the genus Alistipes of the subject is higher than the proportion of the genus Alistipes of an arbitrary population by the comparison, the time of non-REM sleep N3 of the subject related to the sample may be long, or when the proportion of the genus Alistipes of the subject is lower than the proportion of the genus Alistipes of an arbitrary population by the comparison, the time of non-REM sleep N3 of the subject related to the sample may be short; and / or (c-2’) A step of evaluating that when the proportion of the genus Serimonas of the subject is higher than the proportion of the genus Serimonas of an arbitrary population by the comparison, the time of non-REM sleep N2 of the subject related to the sample may be long, or when the proportion of the genus Serimonas of the subject is lower than the proportion of the genus Serimonas of an arbitrary population by the comparison, the time of non-REM sleep N2 of the subject related to the sample may be short. A method comprising is as follows.

[0046] Also, in one embodiment, the evaluation method of the present invention includes the following steps: (a) Analyzing the bacterial composition of the gut microbiota sample from the subject; (b) Comparing the proportion of the genus Alistipes in the gut bacteria with the proportion of the bacteria in an arbitrary population; and (c’) When the proportion of the genus Alistipes in the subject is higher than the proportion of the genus Alistipes in an arbitrary population as a result of the comparison, evaluating that the time of non-REM sleep N3 of the subject related to the sample may be long; or when the proportion of the genus Alistipes in the subject is lower than the proportion of the genus Alistipes in an arbitrary population, evaluating that the time of non-REM sleep N3 of the subject related to the sample may be short. A method comprising is as follows.

[0047] Also, in one embodiment, the evaluation method of the present invention includes the following steps: (a) Analyzing the bacterial composition of the gut microbiota sample from the subject; (b) Comparing the proportion of the genus Serimonas in the gut bacteria with the proportion of the bacteria in an arbitrary population; and (c’) When the proportion of the genus Serimonas in the subject is higher than the proportion of the genus Serimonas in an arbitrary population as a result of the comparison, evaluating that the time of non-REM sleep N2 of the subject related to the sample may be long; or when the proportion of the genus Serimonas in the subject is lower than the proportion of the genus Serimonas in an arbitrary population, evaluating that the time of non-REM sleep N2 of the subject related to the sample may be short. A method comprising is as follows.

[0048] <Evaluation Kit of the Present Invention> The present invention includes a kit for evaluating the sleep quality of a subject.

[0049] The evaluation kit of the present invention includes any detection reagent for detecting the genus Allistipes and / or the genus Celimonas. The detection reagent included in the evaluation kit of the present invention is not particularly limited as long as it can detect the genus Allistipes and / or the genus Celimonas. In one embodiment, the detection reagent included in the evaluation kit of the present invention is a selective culture medium that can selectively culture the genus Allistipes and / or the genus Celimonas. In another embodiment, the detection reagent included in the evaluation kit of the present invention is a detector for bacterial metabolites of the genus Allistipes and / or the genus Celimonas. In another embodiment, the detection reagent included in the evaluation kit of the present invention is an antibody that selectively binds to proteins of the genus Allistipes and / or the genus Celimonas. In another embodiment, the detection reagent included in the evaluation kit of the present invention is a nucleic acid that specifically hybridizes with a nucleic acid sequence of the genus Allistipes and / or the genus Celimonas.

[0050] In one embodiment, the evaluation kit of the present invention is an evaluation kit including a detection reagent for the genus Allistipes and / or the genus Celimonas used in the above screening method or evaluation method. is. In one embodiment, the evaluation kit of the present invention has the following steps: (a) Analyzing the bacterial composition of the intestinal microbiota sample from the subject; (b) Comparing the proportion of the genus Allistipes and / or the genus Celimonas in the intestinal bacteria with the proportion of the bacteria in any population; and (c) Based on the comparison, screening whether the subject is included in the group with a relatively long deep non-REM sleep time or the group with a relatively short deep non-REM sleep time, or whether the subject is included in the group with a relatively long light non-REM sleep time or the group with a relatively short light non-REM sleep time. including Step (c) is as follows: (c-1) Determining, based on the comparison, that a group with a relatively high proportion of Allistipes is a group with a long deep non-REM sleep time, or determining that a group with a relatively low proportion of Allistipes is a group with a short deep non-REM sleep time; and / or (c-2) Determining, based on the comparison, that a group with a relatively high proportion of Ceriomonass is a group with a long light non-REM sleep time, or determining that a group with a relatively low proportion of Ceriomonass is a group with a short light non-REM sleep time A method comprising An evaluation kit containing a detection reagent for Allistipes and / or Ceriomonass, which is used as described. In one embodiment, the evaluation kit of the present invention comprises the following steps: (a) Analyzing the bacterial composition of the gut microbiota sample from the subject; and (b) Comparing the proportion of Allistipes and / or Ceriomonass in the gut bacteria with the proportion of the bacteria in an arbitrary population; and (c) Based on the comparison, evaluating that the deep non-REM sleep time of the subject may be longer or shorter than that of an arbitrary population, or that the light non-REM sleep time of the subject may be longer or shorter than that of an arbitrary population Here, step (c) is as follows: (c-1) When the proportion of Allistipes in the subject is higher than the proportion of Allistipes in an arbitrary population based on the comparison, evaluating that the deep non-REM sleep time of the subject may be long, or when the proportion of Allistipes in the subject is lower than the proportion of Allistipes in an arbitrary population, evaluating that the deep non-REM sleep time of the subject may be short; and / or (c-2) When the ratio of the genus Serimonas in the subject is higher than the ratio of the genus Serimonas in an arbitrary population as a result of the comparison, evaluating that the time of light non-REM sleep of the subject may be long, or when the ratio of the genus Serimonas in the subject is lower than the ratio of the genus Serimonas in an arbitrary population as a result of the comparison, evaluating that the time of light non-REM sleep of the subject may be short A method comprising An evaluation kit containing a detection reagent for the genus Allistipes and / or the genus Serimonas, which is used therein.

[0051] Also, in one embodiment, the evaluation kit of the present invention Comprises the following steps: (a) A step of analyzing the bacterial composition of the intestinal flora specimen derived from the subject (b) A step of comparing the ratio of the genus Allistipes in the intestinal bacteria with the ratio of the bacteria in an arbitrary population, and (c’) When the ratio of the genus Allistipes in the subject is higher than the ratio of the genus Allistipes in an arbitrary population as a result of the comparison, evaluating that the time of deep non-REM sleep of the subject related to the specimen may be long, or when the ratio of the genus Allistipes in the subject is lower than the ratio of the genus Allistipes in an arbitrary population as a result of the comparison, evaluating that the time of deep non-REM sleep of the subject related to the specimen may be short An evaluation kit containing a detection reagent for the genus Allistipes, which is used therein.

[0052] Also, in one embodiment, the evaluation kit of the present invention Comprises the following steps: (a) A step of analyzing the bacterial composition of the intestinal flora specimen derived from the subject (b) A step of comparing the ratio of the genus Serimonas in the intestinal bacteria with the ratio of the bacteria in an arbitrary population, and (c’) When the ratio of the genus Serimonus of the subject is higher than the ratio of the genus Serimonus of any population as a result of the comparison, evaluating that the time of light non-REM sleep of the subject related to the specimen may be long; or when the ratio of the genus Serimonus of the subject is lower than the ratio of the genus Serimonus of any population, evaluating that the time of light non-REM sleep of the subject related to the specimen may be short. An evaluation kit containing a detection reagent for the genus Serimonus, which is used therein.

[0053] In one embodiment, the evaluation kit of the present invention comprises the following steps: (a) Analyzing the bacterial composition of the intestinal microbiota specimen from the subject; (b) Comparing the ratios of the genus Alistipes and / or the genus Serimonus in the intestinal bacteria with the ratios of the corresponding bacteria in any population; and (c) Based on the comparison, evaluating that the time of deep non-REM sleep of the subject may be longer or shorter than the time of deep non-REM sleep of any population, or that the time of light non-REM sleep of the subject may be longer or shorter than the time of light non-REM sleep of any population, wherein step (c) comprises: (c-1) When the ratio of the genus Alistipes of the subject is higher than the ratio of the genus Alistipes of any population as a result of the comparison, evaluating that the time of deep non-REM sleep of the subject may be long; or when the ratio of the genus Alistipes of the subject is lower than the ratio of the genus Alistipes of any population, evaluating that the time of deep non-REM sleep of the subject may be short; and / or (c-2) When the ratio of the genus Serimonus of the subject is higher than the ratio of the genus Serimonus of any population as a result of the comparison, evaluating that the time of light non-REM sleep of the subject may be long; or when the ratio of the genus Serimonus of the subject is lower than the ratio of the genus Serimonus of any population, evaluating that the time of light non-REM sleep of the subject may be short. An evaluation kit containing a nucleic acid that specifically hybridizes with a nucleic acid sequence of the genus Allistipes and / or the genus Celeromonas, for use in is as follows.

[0054] Also, in one embodiment, the evaluation kit of the present invention comprises the following steps: (a) Analyzing the bacterial composition of the gut microbiota sample from the subject; (b) Comparing the proportion of the genus Allistipes in the gut bacteria with the proportion of the bacteria in any population; and (c) When the proportion of the genus Allistipes in the subject is higher than the proportion of the genus Allistipes in any population as a result of the comparison, evaluating that the time of deep non-REM sleep of the subject related to the sample may be long; or when the proportion of the genus Allistipes in the subject is lower than the proportion of the genus Allistipes in any population as a result of the comparison, evaluating that the time of deep non-REM sleep of the subject related to the sample may be short. An evaluation kit containing a nucleic acid that specifically hybridizes with a nucleic acid sequence of the genus Allistipes, for use in is as follows.

[0055] Also, in one embodiment, the evaluation kit of the present invention comprises the following steps: (a) Analyzing the bacterial composition of the gut microbiota sample from the subject; (b) Comparing the proportion of the genus Celeromonas in the gut bacteria with the proportion of the bacteria in any population; and (c’) When the proportion of the genus Celeromonas in the subject is higher than the proportion of the genus Celeromonas in any population as a result of the comparison, evaluating that the time of light non-REM sleep of the subject related to the sample may be long; or when the proportion of the genus Celeromonas in the subject is lower than the proportion of the genus Celeromonas in any population as a result of the comparison, evaluating that the time of light non-REM sleep of the subject related to the sample may be short. An evaluation kit containing a nucleic acid that specifically hybridizes with a nucleic acid sequence of the genus Celeromonas, for use in is as follows.

[0056] In one embodiment, the evaluation kit of the present invention comprises the following steps: (a) Analyzing the bacterial composition of the gut microbiota sample from the subject; (b) Comparing the proportion of the genus Alistipes and / or the genus Celeribacter in the gut bacteria with the proportion of the bacteria in any population; and (c-1’) When the proportion of the genus Alistipes in the subject is higher than the proportion of the genus Alistipes in any population by the comparison, evaluating that the time of non-REM sleep N3 of the subject related to the sample may be long; or when the proportion of the genus Alistipes in the subject is lower than the proportion of the genus Alistipes in any population by the comparison, evaluating that the time of non-REM sleep N3 of the subject related to the sample may be short; and / or (c-2’) When the proportion of the genus Celeribacter in the subject is higher than the proportion of the genus Celeribacter in any population by the comparison, evaluating that the time of non-REM sleep N2 of the subject related to the sample may be long; or when the proportion of the genus Celeribacter in the subject is lower than the proportion of the genus Celeribacter in any population by the comparison, evaluating that the time of non-REM sleep N2 of the subject related to the sample may be short, an evaluation kit comprising a nucleic acid that specifically hybridizes with a specific sequence of the genus Alistipes and / or the genus Celeribacter and is used in the process. That's it.

[0057] Also, in one embodiment, the evaluation kit of the present invention comprises the following steps: (a) Analyzing the bacterial composition of the gut microbiota sample from the subject; (b) Comparing the proportion of the genus Alistipes in the gut bacteria with the proportion of the bacteria in any population; and (C’) When the ratio of Alicyclobacillus in the subject is higher than the ratio of Alicyclobacillus in any population as a result of the comparison, a step of evaluating that the time of non-REM sleep N3 of the subject related to the specimen may be long, or when the ratio of Alicyclobacillus in the subject is lower than the ratio of Alicyclobacillus in any population, a step of evaluating that the time of non-REM sleep N3 of the subject related to the specimen may be short, An evaluation kit containing a nucleic acid that specifically hybridizes with a nucleic acid sequence of the genus Alicyclobacillus, which is used thereof.

[0058] Also, in one embodiment, the evaluation kit of the present invention has the following steps: (a) A step of analyzing the bacterial composition of the intestinal microbiota specimen derived from the subject, (b) A step of comparing the ratio of the genus Serimonas in the intestinal bacteria with the ratio of the bacteria in any population, and (c’) When the ratio of the genus Serimonas in the subject is higher than the ratio of the genus Serimonas in any population as a result of the comparison, a step of evaluating that the time of non-REM sleep N2 of the subject related to the specimen may be long, or when the ratio of the genus Serimonas in the subject is lower than the ratio of the genus Serimonas in any population, a step of evaluating that the time of non-REM sleep N2 of the subject related to the specimen may be short, An evaluation kit containing a nucleic acid that specifically hybridizes with a specific sequence of the genus Serimonas, which is used thereof.

[0059] Specific examples are provided here for further understanding of the present invention, but these are for illustrative purposes only and do not limit the present invention.

Example

[0060] <Means> (Subject of investigation) This invention was implemented using data from the "Comprehensive Research Survey (Healthy Living Survey) for the Construction of an Integrated Database of Diet, Gut Microbiome, and Health Information" conducted at Hokkaido University of Information Sciences. The "Healthy Living Survey" targeted healthy Japanese men and women aged 20 to 80, excluding those with severe cerebrovascular diseases, heart diseases, liver diseases, kidney diseases, digestive diseases, and infectious disease patients requiring notification. It was conducted twice a year, in summer and winter, for each subject. And the "Healthy Living Survey" was implemented in 2019 and 2020 as part of the Strategic Innovation Creation Program (SIP) project. In this invention, the data from the summer of 2019 was used for analysis. Among the data included in the summer of 2019, analysis was performed using the data of 642 people for "Subject Attributes", "Body Composition Measurement", "Heart Rate and Brain Waves during Sleep", "Microbial Composition", and "Diet Survey" (reference URL: https: / / gr-sharingdbs.biosciencedbc.jp / shd0001-v4).

[0061] (Statistical Analysis Method) Statistical analysis was performed using GraphPad Prism (version 9.5.1, GraphPad Software Inc., USA) and the free software "R" (version 4.3.1, CRAN).

[0062] ·Exclusion Criteria for Analysis Among the above-mentioned dataset, those with missing values in any one of them were excluded from the analysis. Also, for the gut microbiota dataset, bacteria with a value of 0 for all subjects were considered as not having any subjects with that bacterium and were excluded from the analysis. Based on these exclusion criteria, 601 sets of data were used for analysis. The average value ± standard error of the physical characteristics of 601 subjects are shown in Table 1.

Table 1

[0063] ·Hierarchical Cluster Analysis Hierarchical cluster analysis was performed using R. In performing hierarchical cluster analysis, as preprocessing of the data, the EEG dataset was read as a csv file into a data frame, and the data frame was standardized using the scale function. The standardized data frame was used for hierarchical cluster analysis. First, the distance between samples was calculated using the dist function included in the stats package. Note that the Euclidean method was used for calculating the distance. Next, the ward.D algorithm for cluster merging was specified using the hclust function included in the stats package, and the calculation was performed. The dendrogram of the subjects and EEG was created using the plot function included in the gplots package, and the results of the hierarchical cluster analysis were represented as a heatmap using the heatmap.2 function. The results of the dendrogram and heatmap of the subjects created by the hierarchical cluster analysis were confirmed, and the subjects were classified into five clusters using the cutree function included in the stats package. The cluster results separated by the cutree function were changed to the factor type using the factor function and combined with the EEG data frame using the cbind function. Finally, the results were output in csv format using the write.csv function, and analysis of variance was performed using the obtained csv file.

[0064] ·One-way analysis of variance and non-parametric tests Based on the cluster results obtained by hierarchical cluster analysis, the mean values of each item of EEG, gut microbiota, dietary records, and body composition were calculated for each cluster, and it was tested with Prism whether there were significant changes between the clusters. For the data of each item, first, the normal distribution of the data was examined using the D’Agostino-Pearson test, and the variance of the data was examined using Bartlett’s test. When the data was normally distributed and had equal variance, one-way analysis of variance was used, and Tukey's multiple comparison test was performed as a post hoc test. When the data was non-normally distributed or had unequal variance, the non-parametric Kruskal-Wallis test was used, and Dunn's multiple comparison test was performed as a post hoc analysis. The p-value was calculated for each test, and p < 0.05 was considered significant to find the characteristics of each cluster.

[0065] · Multiple regression analysis EEG items and gut bacteria with significant differences between clusters were extracted by one-way ANOVA and non-parametric tests. Using the EEG items as explanatory variables and each gut bacterium as the target variable, multiple regression analysis was performed by the forced entry method using Prism. As data processing before performing the Prism test, the extracted EEG items and gut bacteria were converted into a csv file as a dataset. Then, it was read as a csv file in R to form a data frame, and the data frame was standardized using the scale function. The standardized data frame was output in csv format using the write.csv function, and multiple regression analysis was performed using the obtained csv file. Note that the model with only EEG items as explanatory variables is defined as Model 1. In the above-mentioned one-way ANOVA and non-parametric tests, significant differences between clusters were also found in items such as age, height, weight, and blood pressure. That is, these items may act as confounding factors and affect gut bacteria. Therefore, the model with age, gender, BMI, systolic blood pressure, and diastolic blood pressure as confounding factors adjusted in the explanatory variables of Model 1 was defined as Model 2. Furthermore, the total energy intake, water-soluble dietary fiber intake, insoluble dietary fiber intake, which are considered to directly affect the gut microbiota, and sodium intake and potassium intake, which are nutrients related to blood pressure, with significant differences between clusters, were adjusted as confounding factors, and the resulting model was defined as Model 3. Note that VIF was calculated for multicollinearity, and it was confirmed that the value of VIF did not exceed 5.

[0066] · Causal exploration Causal exploration was performed using the variables used in Model 3 of multiple regression analysis with a Gaussian Bayesian network to examine the causal relationships between variables. The standardized data frame described above that was written to a csv file was used. First, as prior information regarding gender and age, since it is unlikely that gut bacteria affect gender, such as gut bacteria → gender, constraints were added so that the link gut bacteria → gender would not appear. The constraints were applied using the tiers2dblacklist function included in the bnlearn package. Next, sub-datasets were created from the dataset to be used by the bootstrap method, and a hill-climbing structural learning algorithm was implemented for each sub-dataset to learn a directed acyclic graph (DAG). The number and direction of the links that appeared in the DAG were recorded, and after calculating the appearance rate of the links for the entire sub-dataset, those with a significant appearance rate were adopted as a reliability index to form a causal relationship model. The above structural learning was performed using the boot.strength function included in the bnlearn package, and a graph was created using the strength.plot function. Next, parameter estimation was performed using the bn.fit function included in the bnlearn package. The maximum likelihood estimation method was used for the estimation method.

[0067] <Results> The subjects were cluster-classified based on the electroencephalogram data during sleep. The results are shown in Figure 1. At this time, when the electroencephalogram data was visualized as a heatmap, five locations with significant differences in values were found among the items of the electroencephalogram data. Therefore, the clusters were divided into five, and cluster classification was performed at the dotted line locations shown in Figure 1. Extract the characteristic items from the electroencephalogram of each cluster, create a bar graph, and show it in Figure 2. In Cluster 1, the appearance time of intermediate awakenings was significantly longer and the sleep latency (the time required from going to bed to falling asleep) was longer than in other clusters. In Cluster 2, the appearance times of non-REM sleep N1 and N2 were significantly longer than in other clusters. In Cluster 3, the deep sleep latency was significantly longer and the appearance time of REM sleep was longer than in other clusters. In Cluster 4, the sleep time was significantly shorter than in Clusters 1, 2, and 3. Also, although no significant difference was found, the sleep time of Cluster 4 was shorter than that of Cluster 5, and Cluster 4 had the shortest sleep time. In Cluster 5, the appearance time of non-REM N3 was significantly longer than in other clusters.

[0068] From the above, it was confirmed that different characteristics appeared in the electroencephalogram for each cluster.

[0069] Next, it was confirmed whether there were differences in physical characteristics and dietary items among the clusters. Table 2 shows whether the male-female ratio differed in each cluster. Only in Cluster 3 were there more male subjects than female subjects. Also, the summary of the physical characteristics of each cluster is shown in the upper part of Figure 3. The age was significantly higher in Cluster 3 than in other clusters. The height and weight were significantly higher in Cluster 3 than in Clusters 1 and 5, and in terms of BMI, Cluster 3 was significantly higher than Clusters 2 and 5. The systolic blood pressure was significantly higher in Cluster 3 than in Clusters 1, 2, and 5, and the diastolic blood pressure was significantly higher in Cluster 3 than in Cluster 5.

Table 2

[0070] From the above, it was confirmed that the physical characteristics also differed significantly among the clusters.

[0071] Regarding the dietary items, we examined whether there were significant differences between clusters. As for the energy intake, it was found that Cluster 3 had significantly more energy intake than Clusters 1 and 4. Also, regarding blood pressure, for which significant differences were observed in physical characteristics, we examined the intake of sodium and potassium, which may affect blood pressure, but no significant differences were found between clusters. Furthermore, we also examined the intake of water-soluble dietary fiber and insoluble dietary fiber, which may affect the gut microbiota, but no significant differences were found between clusters.

[0072] Next, regarding the gut microbiota, we extracted the gut bacteria for which significant differences were observed between clusters and showed them in Fig. 4 as a bar graph. The gut bacteria for which significant differences were observed were four species: Sutterella, Sellimonas, Odoribacter, and Alistipes. For the genus Sutterella, the abundance ratio in Cluster 3 was significantly higher than that in Clusters 1 and 2. For the genus Sellimonas, the abundance ratio in Cluster 2 was significantly higher than that in Clusters 3 and 4. For the genus Odoribacter, the abundance ratio in Cluster 5 was significantly higher than that in Cluster 2, and for the genus Alistipes, the abundance ratio in Cluster 5 was significantly higher than that in Cluster 4.

[0073] Each cluster showed characteristic brain waves during sleep, and characteristic changes in gut bacteria were also observed. Therefore, to explore the relationship between brain waves during sleep and gut bacteria, multiple regression analysis was performed with brain waves during sleep as the explanatory variable and characteristic gut bacteria as the objective variable. Here, as described above, considering that there are also significant differences in physical characteristics among clusters, it is possible that the differences in physical characteristics, rather than brain waves during sleep, affected gut bacteria. That is, it is necessary to examine the relationship between brain waves and gut microbiota after adjusting for physical characteristics as confounding factors. Furthermore, there were also significant differences in total energy intake among clusters in terms of dietary items. Although no significant difference was found, dietary fiber may affect gut bacteria, and sodium and potassium may affect blood pressure, which is one of the physical characteristics with significant differences. Therefore, it is also necessary to adjust for these dietary items as confounding factors. Considering the above, Model 1 was the multiple regression analysis without adjusting for confounding factors. Model 2 was the one with physical characteristics adjusted as confounding factors. Model 3 was the one with both physical characteristics and dietary items adjusted as confounding factors, and the analysis was performed in three model cases. The results are shown in Table 3. Model 1 is the unadjusted model, Model 2 is the model adjusted by physical characteristics, and Model 3 is the model adjusted by physical characteristics and dietary records. In the genus Sutterella, a significant association was found with non-REM sleep N3 in all models. In the genus Sellimonas, a significant association was found with non-REM sleep N2 in all models. In the genus Odoribacter, there was a tendency for an association with non-REM sleep N1 in all models. In the genus Alistipes, a significant association was found with non-REM sleep N3 and deep sleep latency in all models.

Table 3

[0074] From the above, even after adjusting for physical characteristics and dietary items, a relationship was found between brain waves during sleep and gut bacteria.

[0075] In regression analysis, it is possible to know whether there is a correlation, but it is not possible to know whether there is a causal relationship. Therefore, causal exploration was carried out to examine what kind of causal relationship exists between brain waves and gut bacteria. In order to carry out causal exploration taking into account the confounding factors described above, the variables used in Model 3 were utilized. The results of the causal exploration are shown in Fig. 5. In addition, causally unrelated relationships were added as constraints, and a list thereof is shown in Table 4. From the results, the causal relationships found between the brain waves during sleep and gut bacteria were "genus Alistipes → non-REM N3" and "genus Sellimonas → non-REM N2". Furthermore, the results of parameter estimation are shown in Table 5. The coefficient indicating the relationship of the link "genus Alistipes → non-REM sleep N3" was 0.113, and the coefficient indicating the relationship of the link "genus Sellimonas → non-REM sleep N2" was 0.099. From the above, it is suggested that the higher the abundance ratio of the genus Alistipes in the gut microbiota, the more frequently non-REM sleep N3 appears, and there is a possibility of getting the deepest sleep, and the higher the abundance ratio of the genus Sellimonas in the gut microbiota, the more frequently non-REM sleep N2 appears, and there is a possibility of getting light sleep.

Table 4

Table 5-1

Table 5-2

Industrial Applicability

[0076] The evaluation method of the present invention is expected to be useful for evaluating the quality of sleep. In addition, the screening method of the present invention is expected to be useful for screening a population for the quality of sleep. In addition, the evaluation kit of the present invention is expected to be useful for evaluating the quality of sleep using the method of the present invention.

Claims

1. 1. A method for screening a subject for sleep quality, comprising the steps of: (a) analyzing the bacterial composition of a gut microbiota sample from the subject; (b) comparing the proportion of Alistipes and / or Sellimonas in the intestinal bacteria with the proportion of said bacteria in a population; (c) based on the comparison, screening whether the subject is included in a group having a relatively long deep non-REM sleep time or a group having a relatively short deep non-REM sleep time, or whether the subject is included in a group having a relatively long light non-REM sleep time or a group having a relatively short light non-REM sleep time; A method comprising:

2. The step (c) is as follows: (c-1) determining, by the comparison, that a group having a relatively high proportion of Alistipes spp. is a group having a long time of deep non-REM sleep, or determining, by the comparison, that a group having a relatively low proportion of Alistipes spp. is a group having a short time of deep non-REM sleep; and / or (c-2) determining, by the comparison, that a group having a relatively high proportion of Serimonas genus is a group having a long period of light non-REM sleep, or determining, by the comparison, that a group having a relatively low proportion of Serimonas genus is a group having a short period of light non-REM sleep; The method of claim 1 , comprising:

3. 1. A method for assessing sleep quality in a subject, comprising the steps of: (a) analyzing the bacterial composition of a gut microbiota sample from the subject; (b) comparing the proportion of Alistipes and / or Serimonas in the Enterobacteria with the proportion of said bacteria in a given population; (c) assessing, based on the comparison, that the subject's deep non-REM sleep time is likely to be longer or shorter than a population's deep non-REM sleep time, or that the subject's light non-REM sleep time is likely to be longer or shorter than a population's light non-REM sleep time; A method comprising:

4. The step (c) is as follows: (c-1) assessing that the subject's deep non-REM sleep time is likely to be long if the proportion of Alistipes in the subject is higher than the proportion of Alistipes in an arbitrary population by the comparison, or assessing that the subject's deep non-REM sleep time is likely to be short if the proportion of Alistipes in the subject is lower than the proportion of Alistipes in an arbitrary population; and / or (c-2) evaluating the subject's light non-REM sleep time as being likely to be long if the proportion of Serimonas genus in the subject is higher than the proportion of Serimonas genus in an arbitrary population by the comparison, or evaluating the subject's light non-REM sleep time as being likely to be short if the proportion of Serimonas genus in the subject is lower than the proportion of Serimonas genus in an arbitrary population by the comparison; The method of claim 3 , comprising:

5. 4. The method according to claim 1 or 3, wherein the deep non-REM sleep is non-REM sleep N3 and the light non-REM sleep is non-REM sleep N2.

6. A kit for evaluating the sleep quality of a subject, comprising a detection reagent for detecting the genus Alistipes and / or the genus Serimonas.

7. A kit according to claim 6 for use in the method according to claim 1 or claim 3.

8. The kit of claim 6, wherein the detection reagent is a nucleic acid that hybridizes with a nucleic acid sequence of the genus Alistipes and / or Serimonas.

9. The evaluation kit according to claim 8 , wherein the nucleic acid that hybridizes with a nucleic acid sequence of the genus Alistipes and / or Serimonas is a nucleic acid that hybridizes with a region of 16s rRNA.

10. The kit according to claim 7, wherein the deep non-REM sleep is non-REM sleep N3 and the light non-REM sleep is non-REM sleep N2.

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