Detection assistance method, detection assistance apparatus, detection assistance program, and detection assistance system for assisting detection of signs of onset of dementia

Abstract

To detect signs of onset of dementia at an early stage.SOLUTION: A detection assisting method for assisting detection of a sign of onset of dementia includes acquiring a group of measurement data related to an index of a circadian rhythm of a subject, wherein disturbance of the circadian rhythm suggests a sign of onset of dementia.SELECTED DRAWING: Figure 23

Description

[Technical Field]

[0001] This specification discloses a method, a detection assistance device, a detection assistance program, and a detection assistance system for assisting in the detection of signs of the onset of dementia, and a method, a monitoring device, a monitoring program, and a monitoring system for monitoring the function of the suprachiasmatic nucleus. [Background technology]

[0002] Sleep disorders are highly likely to occur in dementia such as Alzheimer's disease and vascular dementia. Patent Document 1 discloses a device control method that acquires information indicating a subject's sleep state and controls a device based on the proportion of the subject's sleep time during a period from the start of a target period to a time in the first half of the target period, the proportion being determined based on the acquired information.

[0003] Patent Document 2 discloses a dementia information output system that includes an acquisition unit that acquires a user's body movement amount, a determination unit that determines the possibility that the user has developed mild dementia or the like based on the degree of fluctuation, which is the degree to which the body movement amount fluctuates over multiple days in each of multiple time periods, and an output unit that outputs dementia information indicating the possibility determined by the determination unit.

[0004] The cause of sleep disorders associated with dementia remains unclear. At least, Non-Patent Document 1 shows that the light entrainment mechanism in circadian rhythm regulation is controlled via the suprachiasmatic nucleus, but that in mice with a systemic deletion of the endothelial nitric oxide synthase (eNOS) gene, no disruption of the light entrainment mechanism occurred. In other words, Non-Patent Document 1 states that eNOS is not involved in circadian rhythm regulation. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2019-170408 [Patent Document 2] International Publication No. 2018 / 150725 [Non-patent literature]

[0006] [Non-Patent Document 1] Lance J. Kriegsfeld3, Deborah L. Drazen, and Randy J. Nelson; Journal of Biological Rhythms, Vol. 16 No. 2, April 2001, 142-148 Summary of the Invention [Problem to be solved by the invention]

[0007] Currently, no treatment has been found that can cure dementia once it has developed. However, several therapeutic drugs for mild cognitive impairment (MCI) and mild dementia that can slow the progression of symptoms are undergoing clinical trials. It is expected that the progression of dementia can be slowed by administering medication before the onset of dementia or in the early stages after onset. For this reason, detecting signs of dementia as early as possible is important in order to delay or stop the progression of cognitive impairment.

[0008] The method described in Patent Document 1 aims to support elderly people in spaces such as elderly care facilities, and estimates the dementia level based on the fact that the actual sleeping time between bedtime and wake-up time is related to the dementia level. However, this method does not detect signs of the onset of dementia.

[0009] Furthermore, Patent Document 2 is based on the assumption that if a subject develops Alzheimer's disease, a circadian sleep disorder will occur, for example, the subject's daytime activity level will decrease, causing the subject to become drowsy even during the day, resulting in shallow sleep at night. However, this method does not include the concept of reduced function of the suprachiasmatic nucleus, which is essential for predicting the onset of Alzheimer's disease, and is based on the premise that circadian sleep disorder is a complication of Alzheimer's disease, and does not assume that dysregulation of circadian rhythms in biological functions occurs before the onset of dementia.

[0010] An object of the present invention is to provide a method, system, device, and computer program for assisting in detecting the onset of dementia based on circadian rhythm dysregulation in order to detect early signs of the onset of dementia. Another object of the present invention is to provide a method, system, device, and computer program for monitoring the function of the suprachiasmatic nucleus. [Means for solving the problem]

[0011] An embodiment of the present invention relates to a detection-aiding method for aiding in the detection of signs of the onset of dementia, comprising obtaining a group of measurement data relating to indicators of circadian rhythm in a subject, wherein a disturbance of said circadian rhythm indicates a sign of the onset of dementia.

[0012] Preferably, the circadian rhythm indicator is a value showing a correlation between at least one selected from autonomic nervous activity, activity level, blood glucose level, and core body temperature, or at least two or more selected from autonomic nervous activity, activity level, blood glucose level, and core body temperature.

[0013] Preferably, the circadian rhythm index is a result of periodic regression analysis obtained by performing periodic regression analysis of the index with 24 hours as one cycle on the group of acquired measurement data.

[0014] Preferably, the result of the periodic regression analysis is at least one selected from the amplitude of the periodic regression curve, the mesar and rhythmicity of the periodic regression curve, or vector information indicating the peak phase and rhythmicity for each of the indices in one cycle obtained by the periodic regression analysis.

[0015] An embodiment of the present invention relates to a detection assistance device for assisting in detecting a symptom of onset of dementia, the detection assistance device including a processing unit that acquires a group of measurement data related to an indicator of a subject's circadian rhythm and outputs a monitoring result generated based on the measurement data, wherein a disturbance of the circadian rhythm indicates a symptom of onset of dementia.

[0016] Preferably, the processing unit further acquires the subject's circadian rhythm from the group of measurement data related to the acquired circadian rhythm indicators, and outputs information indicating signs of the onset of dementia when the acquired circadian rhythm continues for a certain period of time.

[0017] Preferably, the processing unit further outputs, when the acquired circadian rhythm continues for a certain period of time, a label indicating that the subject is in a state where guidance on circadian rhythm control is required.

[0018] The present invention relates to a detection assistance program for assisting in the detection of signs of dementia onset, the program comprising, when executed by a computer, steps of acquiring a group of measurement data relating to indicators of the circadian rhythm of a subject, and generating a monitor based on the group of measurement data. and outputting a result of the analysis, wherein the disruption of the circadian rhythm indicates a sign of the onset of dementia.

[0019] The present invention relates to a detection assistance system for assisting in detecting signs of the onset of dementia, the detection assistance system comprising a measurement device and a detection assistance device, wherein the measurement device measures raw data related to indicators of a circadian rhythm of a subject, generates a group of measurement data related to the indicators of the circadian rhythm of the subject based on the group of measured raw data, and transmits the generated group of measurement data to the detection assistance device, which receives the group of measurement data transmitted by the measurement device and outputs a monitoring result generated based on the group of measurement data, wherein a disturbance in the circadian rhythm indicates a sign of the onset of dementia.

[0020] One embodiment of the present invention relates to a method for monitoring the function of the suprachiasmatic nucleus, comprising obtaining a group of measurement data relating to indices of circadian rhythm in a subject, wherein a disruption of said circadian rhythm indicates a decrease in the function of the suprachiasmatic nucleus.

[0021] One embodiment of the present invention relates to a monitoring device for monitoring the function of the suprachiasmatic nucleus, the monitoring device including a processing unit that acquires a group of measurement data relating to an indicator of a subject's circadian rhythm and outputs a monitoring result generated based on the group of measurement data, wherein a disturbance in the circadian rhythm indicates a decline in the function of the suprachiasmatic nucleus.

[0022] Preferably, the processing unit further acquires the subject's circadian rhythm from the group of measurement data related to the acquired circadian rhythm indicators, and outputs information indicating signs of the onset of dementia when the acquired circadian rhythm continues for a certain period of time.

[0023] Preferably, the processing unit further outputs, when the acquired circadian rhythm continues for a certain period of time, a label indicating that the subject is in a state where guidance on circadian rhythm control is required.

[0024] The present invention relates to a monitoring program for monitoring the function of the suprachiasmatic nucleus, which, when executed by a computer, causes the computer to execute processes including the steps of acquiring a group of measurement data relating to an index of a subject's circadian rhythm and outputting a monitoring result generated based on the group of measurement data, wherein a disturbance of the circadian rhythm suggests a decline in the function of the suprachiasmatic nucleus.

[0025] The present invention relates to a monitoring system for monitoring the function of the suprachiasmatic nucleus, comprising a measurement device and a monitoring device, wherein the measurement device measures raw data relating to an index of a circadian rhythm of a subject, generates a group of measurement data relating to the index of a circadian rhythm of the subject based on the group of measured raw data, and transmits the generated group of measurement data to the monitoring device, which receives the group of measurement data transmitted by the measurement device and outputs a monitoring result generated based on the group of measurement data, and wherein a disturbance in the circadian rhythm indicates a decline in the function of the suprachiasmatic nucleus. [Effects of the Invention]

[0026] According to the present invention, signs of the onset of dementia can be detected earlier. [Brief explanation of the drawings]

[0027] [Figure 1] Phase relationships of biological rhythms [Figure 2] We present an example of quantifying the group rhythms of time-series measurement data of activity, blood glucose level, sympathetic nervous activity, and parasympathetic nervous activity using the cosinor method. [Figure 3] This shows an example of the correlation between circadian rhythm indices displayed using the phase vector display method. The left side of the figure shows the normal correlation between the phases of the indices. The right side of the figure shows a state where the phase relationship is disrupted. [Figure 4] A schematic diagram showing the case where each index of circadian rhythm is normally correlated is shown. [Figure 5] 1 shows the appearance of the detection assistance system and monitoring system. [Figure 6] 2 shows the hardware configuration of a detection assistance device in the first embodiment. [Figure 7] 3 shows the functional configuration of a detection assisting device in the first embodiment. [Figure 8] 4 shows a processing flow of a detection assistance program in the first embodiment. [Figure 9] 4 shows a processing flow of a detection assistance program in the first embodiment. [Figure 10] 4 shows a processing flow of a detection assistance program in the first embodiment. [Figure 11] 10 shows the hardware configuration of a monitoring device in a second embodiment. [Figure 12] 10 shows the functional configuration of a monitoring device in a second embodiment. [Figure 13] 10 shows the flow of processing of a monitoring program in the second embodiment. [Figure 14] 10 shows the flow of processing of a monitoring program in the second embodiment. [Figure 15] 10 shows the flow of processing of a monitoring program in the second embodiment. [Figure 16] (A) shows the results of observation of behavioral rhythms in 10-month-old (middle-aged) eNOS+ / - mice and 10-month-old eNOS- / - mice. (B) shows the results of observation of behavioral rhythms in 17-month-old (old) eNOS+ / - mice and 19-month-old eNOS- / - mice. [Figure 17] The results of observation of behavioral rhythms under 24-hour light-deprivation in mice different from those shown in Figure 16 are shown. (A) shows the results of observation of behavioral rhythms in 10-month-old (middle-aged) eNOS + / − mice and 10-month-old eNOS − / − mice. (B) shows the results of observation of behavioral rhythms in 17-month-old (aged) eNOS + / − mice and 19-month-old eNOS − / − mice. [Figure 18] The results of quantitatively assessing the amplitude strength of circadian rhythms using chi-square periodograms are shown below. (A) shows the results for eNOS+ / - mice, and (B) shows the results for eNOS- / - mice. [Figure 19] The amplitude of behavior at 10 months of age is set to 100, and the average value of the relative amplitude of behavioral rhythm at 17 months of age is shown. [Figure 20] (A) shows the neurovascular unit of a 3-month-old (juvenile) mouse. Per. indicates pericyte, AEF indicates astrocyte end food, End.C. indicates endothelial cell, BL indicates basal lamina, and Lumen indicates vascular lumen. (B) shows an electron micrograph of the neurovascular unit of a 19-month-old eNOS+ / - mouse. (C) shows an electron micrograph of the neurovascular unit of a 19-month-old eNOS- / - mouse with severe vascular damage. (D) shows an electron micrograph of the neurovascular unit of a different 19-month-old eNOS- / - mouse with severe vascular damage. [Figure 21] A schematic diagram of the control mechanisms of the suprachiasmatic nucleus and the paraventricular nucleus of the hypothalamus, the center of the autonomic nervous system, is shown. [Figure 22] An example of a system for detecting suprachiasmatic nucleus function using signals from a Holter electrocardiogram is shown. [Figure 23] 1 shows an example of a cardiac cycle acquired by a Holter monitor. [Figure 24] The sleep phases recorded in the sleep diary for each subject are shown. [Figure 25] The figure shows the circadian rhythm of activity for each subject. The dashed box shows a box plot of the rhythmicity of the subject group. The histogram shows the measurement data, and the curve shows the fitting curve obtained by the cosinor method. [Figure 26] This shows the circadian rhythm of blood glucose levels for each subject. The dashed box shows a box plot of the rhythmicity of the subject group. The histogram shows the measurement data, and the curve shows the fitting curve obtained by the cosinor method. [Figure 27]The graph shows the circadian rhythm of parasympathetic nervous activity for each subject. The dashed box shows a boxplot of the rhythmicity of the subject group. The histogram shows the measurement data, and the curve shows the fitting curve obtained by the cosinor method. [Figure 28] The graph shows the circadian rhythm of sympathetic nervous activity for each subject. The dashed box shows a boxplot of the rhythmicity of the subject group. The histogram shows the measurement data, and the curve shows the fitting curve obtained by the cosinor method. [Figure 29] This shows the relationship between autonomic nervous activity rhythm and age. (A) shows the correlation between age and the percent rhythm of HF (parasympathetic nervous system). (B) shows the correlation between age and the percent rhythm of LFHF (sympathetic nervous system). It was shown that the rhythm of both parasympathetic and sympathetic nervous activity decreases with age. (C) also shows the correlation between the percent rhythm of blood glucose levels and the Pittsburgh Sleep Quality Index (PSQI). [Figure 30] The results of a comprehensive correlation analysis using all the indices examined are shown below. DETAILED DESCRIPTION OF THE INVENTION

[0028] 1. First embodiment The first embodiment disclosed in this specification relates to a detection assistance method (hereinafter sometimes simply referred to as a "detection assistance method"), a detection assistance device, a detection assistance program, and a detection assistance system for assisting in detecting signs of the onset of dementia.

[0029] 1-1. Overview The detection assistance method includes obtaining a set of measurement data relating to an index of a subject's circadian rhythm.

[0030] As used herein, "dementia" includes Alzheimer's disease; vascular dementia; frontotemporal dementia such as Pick's disease; dementia with Lewy bodies; dementia caused by alcohol or drugs; and dementia caused by infectious diseases (spirochetes, HIV virus, prions, etc.). Furthermore, "dementia" includes mild cognitive impairment (MCI) and other conditions. Dementia is preferably mild cognitive impairment, Alzheimer's disease, and vascular dementia. The definition of mild cognitive impairment in this specification follows that published by the Ministry of Health, Labor, and Welfare. Specifically, it refers to a condition that satisfies the following conditions a. to e.: a. There is memory impairment that cannot be explained solely by the effects of age or educational level. b. The patient or a family member complains of forgetfulness. c. Global cognitive function is within normal limits. d. Independent in activities of daily living. e. No dementia. Here, cognitive functions include brain functions such as memory, thinking, orientation, understanding, calculation, learning, language, and judgment.

[0031] As used herein, "signs of the onset of dementia" refers to a state before the onset of dementia, for example, a state in which cognitive function is within a normal range but there is a possibility of developing dementia in the future. Preferably, it refers to a state that does not meet the definition of mild cognitive impairment above, particularly a stage that does not meet the above a. and / or b. As used herein, "signs of the onset of dementia" are suggested by a state in which circadian rhythms are disrupted. Preferably, "signs of the onset of dementia" are suggested by a state in which circadian rhythms are disrupted for a certain period of time or longer. Whether or not the circadian rhythm is disturbed is determined from measurement data obtained from the subject that is related to an index of the circadian rhythm.

[0032] The subject is not limited as long as it is the subject to be examined for the signs of dementia onset.Preferably, the subject satisfies c. to e. of the above definition of mild cognitive impairment, and does not satisfy a. and / or b.Furthermore, it is preferable that the subject does not suffer from psychiatric disorders such as delirium, depression, mental retardation, and schizophrenia.

[0033] There is no age limit for the subject. Preferably, the subject is between 30 and 100 years old. The lower limit of the subject's age is 30, 40, 45, 50, 55, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450, 50 Preferably, the subject is 65, 70, 75, 80, 85, 90, or 95. The upper age limit of the subject is preferably 100, 95, 90, 85, 80, 75, 70, or 65.

[0034] "Measurement data relating to indicators of circadian rhythm" (hereinafter simply referred to as "measurement data") may include a group of raw data of biological information measured by various measurement devices, a group of data after noise removal processing has been performed on the group of raw data, and a group of data after noise removal processing has been further normalized by smoothing or other methods. The measurement data also includes data on the date and time when each piece of raw data was measured. The raw data or measurement data relating to indicators of circadian rhythm is also data relating to indicators of biological rhythms, which will be described later.

[0035] Obtaining a group of measurement data related to indicators of circadian rhythm may include measuring data using various measurement devices described below, or receiving the group of measurement data measured by various measurement devices described below by a terminal with a computing function, such as a personal computer or tablet terminal, or by a person. The group of measurement data may be obtained by a terminal with a computing function via a wired or wireless network, or via a recording medium.

[0036] Circadian rhythm indicators include biorhythm indicators. In other words, the function of the suprachiasmatic nucleus, which is the center of the subject's circadian rhythm, can be estimated from the subject's biorhythm. Biorhythm indicators include autonomic nervous activity, activity level, blood glucose level, blood melatonin concentration, blood cortisol concentration, core body temperature, etc. Preferably, the biorhythm indicator includes at least one selected from autonomic nervous activity, activity level, blood glucose level, and core body temperature. Autonomic nervous activity can be evaluated, for example, from the heart rate variability period. Activity level may include not only activity level but also sleep and wakefulness rhythms. Furthermore, as shown in Figure 1, in healthy individuals, autonomic nervous activity, blood melatonin concentration, blood cortisol concentration, and core body temperature have a constant rhythm with one day (expressed as 24 hours) as one cycle. Sympathetic nervous activity and core body temperature reach their positive peaks between 2:00 PM and 6:00 PM. Parasympathetic nervous activity and blood melatonin level reach their positive peaks between midnight and 6:00 AM, which is the sleep period. Blood cortisol reaches a positive peak around 7am.

[0037] A group of measurement data relating to circadian rhythm indicators is derived from a group of raw biological data measured over time, preferably daily, in a single subject over a predetermined period. The predetermined period is preferably 2 days or more, 5 days or more, 7 days or more, 10 days or more, 14 days or more, 21 days or more, 1 month or more, 2 months or more, 4 months or more, 6 months or more, 1 year or more, 2 years or more, 3 years or more, or 5 years or more. The end time of measurement of the group of raw data is not limited, but can be, for example, until mild cognitive impairment occurs. Furthermore, raw data measurement may continue even after mild cognitive impairment occurs. Raw data measurement may be performed continuously or intermittently over a predetermined period. When raw data is measured intermittently, for example, a cycle of continuous raw data measurement for a certain period and a pause in raw data measurement for a certain period can be repeated. The interval between measurement and pause can be selected depending on the circadian rhythm indicator. For example, measurement and pause can be repeated every 10 minutes, 20 minutes, or 30 minutes. The measurement time and the measurement pause time may be the same or may be different.

[0038] The transmission of raw data or groups of raw data; or measured data or groups of measured data from various measuring devices to a terminal having a computing function may be in real time, or may be divided into data packets transmitted every 10 minutes, 20 minutes, 30 minutes, 1 hour, 2 hours, 3 hours, 4 hours, 6 hours, 8 hours, 12 hours, or 24 hours. The heart rate variability period can be measured using a long-term electrocardiogram recorder such as a Holter electrocardiograph, a wearable device equipped with a heart rate sensor, or the like.

[0039] For example, when using the heart rate variability period as an index of biological rhythms, it is preferable to use a Holter electrocardiogram in terms of data accuracy. For example, the Holter electrocardiogram is used to record electrical signals, which are raw data from the heart, for a predetermined period of 24 hours. The heart rate variability period within a certain period is obtained from the R wave interval (RR interval). Furthermore, the waveform of the obtained variability period is subjected to frequency analysis or waveform separation using a band-pass filter to separate it into waveforms with low-frequency amplitude and waveforms with high-frequency amplitude. When the separated waveform of the variability period has a high proportion of waveforms with high-frequency amplitude, it indicates a state in which parasympathetic nervous activity is dominant, i.e., a sleep state. When the obtained waveform of the variability period has a high proportion of waveforms with low-frequency amplitude, it indicates a state in which sympathetic nervous activity is dominant, i.e., a wakeful state.

[0040] When using activity level as an index of biological rhythms, the subject's activity level is measured. Activity level is measured by detecting body movement as raw data using a wristwatch-type or wearable device with a built-in accelerometer. By measuring body movement continuously over time, it can be detected as fluctuations in activity level. Activity rhythms can be measured from the periodic fluctuations in activity level and the total activity level.

[0041] The activity rhythm may also be monitored by having the subject record the timing of sleep onset and wakefulness. Since activity levels are typically high during wakefulness and low during sleep, having the subject record the time they fall asleep and wakefulness allows the time periods during which activity levels fluctuate to be predicted from the time they fall asleep and wakefulness, without the need to measure activity levels using a measuring device. The period from the time they fall asleep to the time they wakeful is also called the sleep phase. Recording may be done on paper or using an app or similar.

[0042] When blood glucose levels are used as an indicator of biological rhythms, the subject's blood glucose level is measured. Blood glucose levels are measured by inserting an extremely fine tube under the skin to measure the glucose concentration in the interstitial fluid. Alternatively, the glucose concentration in the interstitial fluid can be measured as raw data using infrared or other light from above the skin. Both the placement of a fine tube and measurement using infrared or other light from above the skin using a wristwatch-type or wearable device can continuously measure the glucose concentration in the interstitial fluid. Furthermore, the glucose concentration in the interstitial fluid reflects blood glucose levels.

[0043] Furthermore, blood glucose levels may be monitored by having the subject record the time of meal intake. Since blood glucose levels usually fluctuate depending on the meal, it is possible to predict the time periods during which blood glucose levels fluctuate based on the time periods during which meals are consumed, without measuring blood glucose levels using a measuring device. Recording may be done on paper or using an app or the like.

[0044] When using core body temperature as an indicator of biological rhythms, the subject's core body temperature is continuously measured as raw data using a wearable core body temperature recording device that can be attached to the chest or abdomen (e.g., HERBIO core body thermometer and Murata Manufacturing patch-type core body thermometer).

[0045] The duration and intensity of one cycle of a subject's circadian rhythm can be determined from a group of measurement data related to each biorhythm index. For example, the duration and intensity (amplitude) of one cycle can be quantitatively calculated for each index reflecting sympathetic nervous activity and parasympathetic nervous activity by analyzing the group of measurement data related to each biorhythm index using techniques such as chi-square periodograms, fast Fourier expansion, and machine learning. Furthermore, in healthy individuals, there is a fixed correlation between the phase relationships of sympathetic nervous activity, parasympathetic nervous activity, and activity rhythms. For example, sympathetic nervous activity and parasympathetic nervous activity exhibit rhythms with opposite phases, while behavioral rhythms exhibit a phase that coincides with the rhythm of sympathetic nervous activity. This phase relationship is also useful as an index of biorhythms. Furthermore, the duration of one cycle in humans is approximately 24 to 25 hours. In normal individuals, the cycles of biorhythms, including autonomic nervous activity, do not deviate significantly from this period.

[0046] Whether or not a subject's circadian rhythm is disrupted can be determined from the periodicity of a group of measurement data related to circadian rhythm indicators. The periodicity of each group of measurement data is compared with a corresponding reference range, and when the periodicity of each group of measurement data falls outside the reference range, it can be determined that the subject's circadian rhythm is disrupted.

[0047] The reference range can be determined, for example, from the circadian rhythms of a negative control subject whose circadian rhythm is not disrupted and a positive control subject whose circadian rhythm is disrupted. The reference range can be set from a reference value that can most accurately classify the circadian rhythms of the negative control subject and the circadian rhythms of the positive control subject. Here, the "reference value that can most accurately classify" can be appropriately set based on indicators such as the mean, median, mode, sensitivity, specificity, positive predictive value, and negative predictive value.

[0048] Normally, in a healthy state, the duration of one cycle of the circadian rhythm does not deviate significantly from 24 to 25 hours. Furthermore, if the circadian rhythm indicators do not show a clear periodicity, it can be said that the circadian rhythm is in a state of disruption. Furthermore, if the phase relationship between the indicators of the measurement data group changes, it can also be said that the circadian rhythm is in a state of disruption.

[0049] The reference range may also be determined from the past circadian rhythm of the subject from whom the group of measurement data is obtained.

[0050] In this embodiment, when a disturbance in circadian rhythm is detected, it can be assumed that a symptom of onset of dementia is present. Preferably, when the disturbance in circadian rhythm continues for a certain period of time or more, it can be assumed that a symptom of onset of dementia is present. The certain period of time is, for example, 5 days or more, and preferably when a disturbance in circadian rhythm is detected twice or more with an interval of 1 month or more.

[0051] Because circadian rhythms are closely related to the rhythms of the autonomic nervous system, disturbances in circadian rhythms can be interpreted as disturbances in biological rhythms, including those of the autonomic nervous system.

[0052] The circadian rhythm index may include at least one type of information selected from the peak phase time of the periodic regression curve, the amplitude of the periodic regression curve, the mesar, and the rhythmicity in a periodic regression curve obtained by periodic regression analysis of a group of measurement data related to the circadian rhythm index; vector information indicating the peak phase and rhythmicity for each of the indexes in one cycle obtained by the periodic regression analysis; etc.

[0053] A periodic regression analysis is performed on the acquired measurement data group for each of the indicators, with one cycle being 24 hours, to generate information related to the quality of the subject's circadian rhythm. Generally, one cycle of a human circadian rhythm lasts approximately 24 to 25 hours. In normal individuals, the cycles of biological rhythms, including autonomic nervous system rhythms, do not deviate significantly from this cycle. However, social life rhythms are 24 hours long, regardless of the length of one cycle of an individual's circadian rhythm. For this reason, this specification treats one cycle as 24 hours.

[0054] There are no limitations on periodic regression analysis, as long as it is a method that can obtain a periodic regression curve for each index, with 24 hours as one cycle. The duration of one cycle of the subject's circadian rhythm and the strength (amplitude) of the rhythm can be determined from a group of measurement data on indexes related to each circadian rhythm. For example, the duration of one cycle and the strength (amplitude) of the rhythm can be quantitatively calculated for each group of measurement data on indexes related to circadian rhythm by analysis using chi-square periodogram, fast Fourier expansion, machine learning, etc. One example of a periodic regression analysis method is the cosinor method. Figure 2 shows an example of a periodic regression curve that can be obtained using the cosinor method. The curve drawn with a solid line indicated by the symbol k is the periodic regression curve. The solid line indicated by the symbol l represents the plot of the raw data group. The plots of the periodic regression curve and the measurement data group are synchronized by the acquisition time of the measurement data. The line indicated by the symbol m represents the estimated midline of the periodic regression curve, which represents the median (mesor) of the periodic regression curve. A 24-hour cycle spans the time from the first peak (acrophase) to the second peak phase. In other words, even if a subject's circadian rhythm cycle is not 24 hours, periodic regression analysis normalizes the cycle to 24 hours. The amplitude of the periodic regression curve is expressed as the height from the mesor to the peak position. In other words, the peak phase time of the periodic regression curve, the amplitude of the periodic regression curve, and the mesor obtained by periodic regression analysis can each provide information related to the quality of the circadian rhythm. Furthermore, the periodic regression curve itself can also provide information related to the quality of the circadian rhythm.

[0055] The period regression curve normalizes the subject's circadian rhythm to a 24-hour curve, even if the subject's circadian rhythm is unstable or not 24-hour. This is because the time order that determines social activity is determined by a 24-hour cycle, and this allows us to evaluate the subject's "mismatch" with the social cycle. Therefore, by calculating the percentage rhythm of each subject's index, which indicates the degree of deviation from the period of the actual measurement data group from the period regression curve, we can evaluate how stable and regular the rhythm of each subject's index is, and how well the subject is able to adapt to the socially determined cycle (daily schedule).

[0056] The rhythmicity can be calculated using the following formula: percentage rhythm=100×[(1-RSS)] / TSS] (In the formula, "RSS" stands for residual sum of squares. "TSS" stands for total sum of squares. "x" indicates multiplication. "-" indicates subtraction. " / " indicates division.) Rhythmicity may also constitute an indicator of circadian rhythm.

[0057] At least one reference range selected from the peak phase time of the periodic regression curve, the amplitude of the periodic regression curve, the Messer, and the rhythmicity can be determined, for example, from the circadian rhythms of a negative control subject whose circadian rhythm is not disturbed and a positive control subject whose circadian rhythm is disturbed. The reference range can be set based on a reference value that can most accurately classify the circadian rhythms of the negative control subject and the circadian rhythms of the positive control subject. Here, the "reference value that can most accurately classify" can be appropriately set based on indicators such as the mean, median, mode, sensitivity, specificity, positive predictive value, and negative predictive value.

[0058] Periodic regression analysis can be used to obtain vectors (also called polar plots of peak phases) that indicate the peak time (peak phase) and circadian rhythm stability (rhythmity) for each of the above indices over one cycle. Specifically, the peak phase and rhythmicity of each indices are calculated by fitting and quantifying a group of measurement data for each indices acquired over a 24-hour period using the cosinor method. Furthermore, the peak phase and rhythmicity are calculated as polar coordinates.

[0059] Each vector is represented by a pie chart, with 360° representing 24 hours, as shown in Figure 3. The center of the pie chart indicates the amplitude of the periodic regression curve is 0. The vector is plotted from the origin to the polar coordinate system, and the direction of the vector indicates the phase, i.e., the time of the highest value when the fluctuation was greatest, or the time when the fluctuation reached its peak. The length of the vector indicates the rhythmicity, i.e., the circadian rhythm. The vector information includes elements of vector direction and length, and is generated from percent rhythm obtained by periodic regression analysis and polar coordinates.

[0060] Furthermore, as shown on the left side of Figure 3, when the circadian rhythm is normal, there is a fixed correlation between the phase relationships of sympathetic nervous activity (a), parasympathetic nervous activity (b), activity level (c), blood glucose level (d), and core body temperature (e). For example, sympathetic nervous activity and parasympathetic nervous activity show opposite phases, while blood glucose level, activity rhythm, and core body temperature show phases that match the rhythm of sympathetic nervous activity. On the other hand, the right side of Figure 3 shows the phase of each index in a subject with a disrupted circadian rhythm. Compared to the left side of Figure 3, the phase of parasympathetic nervous activity in particular is significantly shifted. Therefore, this phase relationship, etc., is also useful as an index of circadian rhythm.

[0061] Each vector can be evaluated using the difference in the vector direction of the two indicators. In subjects with normal circadian rhythms, the sympathetic and parasympathetic nervous systems typically exhibit opposite phases, resulting in opposite directions for the sympathetic and parasympathetic nervous system vectors. Furthermore, because the subject's activity level and core body temperature correlate with sympathetic nervous activity, the activity vector overlaps with or at least exhibits the same direction as the sympathetic nervous system vector, while the parasympathetic nervous system vector exhibits the opposite direction. The blood glucose level vector is affected by the timing of eating and drinking. Under normal circumstances, it exhibits the same direction as the activity level vector and sympathetic nervous system vector. However, irregular eating habits alter the vector value, resulting in a change in the phase relationship with other vectors. Furthermore, the blood glucose level vector is correlated with the activity level vector and sympathetic nervous system vector. Therefore, when activity rhythms or sympathetic nervous system activity rhythms exhibit abnormalities, the blood glucose level vector can be used to estimate the cause of circadian rhythm abnormalities in daily life.

[0062] From the heart rate variability cycle, a sympathetic vector indicating the peak phase of the sympathetic nerve activity time and the rhythmicity of the sympathetic nerve activity, and a parasympathetic vector indicating the peak phase of the parasympathetic nerve activity time and the rhythmicity of the parasympathetic nerve activity are generated.

[0063] From the activity amount, an activity amount vector indicating the peak phase of the subject's activity time and the rhythmicity of the activity amount is generated.

[0064] From the blood glucose level, a blood glucose level vector is generated that indicates the peak phase of blood glucose level fluctuations and the rhythmicity of blood glucose level fluctuations.

[0065] From the core body temperature, a core body temperature vector is generated that indicates the peak phase of the core body temperature fluctuation and the rhythmicity of the core body temperature fluctuation.

[0066] When the circadian rhythm is disrupted, the relationship between the directions of these vectors is disrupted, or even if the direction of the vector is not disrupted, if the length of the vector is outside the reference range, preferably shorter than the reference range, it is considered that the circadian rhythm is disrupted.

[0067] For example, when the difference in direction between the sympathetic nerve vector and the parasympathetic nerve vector is outside a reference range, it can be determined that the circadian rhythm is disrupted. When the difference in direction between the sympathetic nerve vector and the parasympathetic nerve vector is within the reference range, it can be determined that the circadian rhythm is not disrupted. In this case, the reference range can be, for example, a range from -150° to -210° relative to the sympathetic nerve vector (which, in time, corresponds to 10 to 14 hours clockwise from the sympathetic nerve vector).

[0068] Furthermore, for example, when the difference in direction between the activity vector and the parasympathetic nerve vector is outside the reference range, it can be determined that the circadian rhythm is disturbed. When the difference in direction between the activity vector and the parasympathetic nerve vector is within the reference range, it can be determined that the circadian rhythm is not disturbed. In this case, the reference range is, for example, -1 The range can be from 50° to -210° (which, in terms of time, corresponds to 10 to 14 hours clockwise from the parasympathetic vector).

[0069] Furthermore, when the difference in direction between the activity vector and the sympathetic nerve vector falls outside a reference range, it can be determined that the circadian rhythm is disrupted. When the difference in direction between the activity vector and the sympathetic nerve vector falls within the reference range, it can be determined that the circadian rhythm is not disrupted. In this case, the reference range can be, for example, from -45° to +45° with respect to the activity vector (which, in time, corresponds to ±3 hours with respect to the sympathetic nerve vector).

[0070] Furthermore, for example, when the difference in direction between the blood glucose vector and the parasympathetic nerve vector is outside a reference range, it can be determined that the circadian rhythm is disrupted. When the difference in direction between the blood glucose vector and the parasympathetic nerve vector is within the reference range, it can be determined that the circadian rhythm is not disrupted. In this case, the reference range can be, for example, from -150° to -210° based on the blood glucose vector (which, when converted to time, is 10 to 14 hours clockwise from the blood glucose vector).

[0071] Furthermore, when the difference in direction between the blood glucose vector and the sympathetic nerve vector is outside the reference range, it can be determined that the circadian rhythm is disrupted. When the difference in direction between the blood glucose vector and the sympathetic nerve vector is within the reference range, it can be determined that the circadian rhythm is not disrupted. In this case, the reference range can be, for example, from -45° to +45° with respect to the blood glucose vector (which, when converted into time, is ±3 hours with respect to the blood glucose vector).

[0072] Furthermore, for example, when the difference in direction between the core body temperature vector and the parasympathetic nerve vector is outside a reference range, it can be determined that the circadian rhythm is disrupted. When the difference in direction between the core body temperature vector and the parasympathetic nerve vector is within the reference range, it can be determined that the circadian rhythm is not disrupted. In this case, the reference range can be, for example, a range from -150° to -210° based on the core body temperature vector (which, in time, corresponds to 10 to 14 hours clockwise from the parasympathetic nerve vector).

[0073] Furthermore, when the difference in direction between the core body temperature vector and the sympathetic nerve vector is outside the reference range, it can be determined that the circadian rhythm is disrupted. When the difference in direction between the blood glucose level vector and the sympathetic nerve vector is within the reference range, it can be determined that the circadian rhythm is not disrupted. In this case, the reference range can be, for example, from -45° to +45° with the activity vector as the reference (which, when converted into time, is ±3 hours with the sympathetic nerve vector as the reference).

[0074] For example, when the magnitude of at least one of the sympathetic nerve vector, the parasympathetic nerve vector, the activity vector, the blood glucose level, and the core body temperature is outside or below the reference range, it can be determined that the circadian rhythm is disrupted. When the magnitude of at least one of the sympathetic nerve vector, the parasympathetic nerve vector, the blood glucose level, and the core body temperature is within or exceeds the reference range, it can be determined that the circadian rhythm is not disrupted.

[0075] The reference range can be determined, for example, from the circadian rhythms of a negative control subject whose circadian rhythm is not disrupted and a positive control subject whose circadian rhythm is disrupted. The reference range can be set from a reference value that can most accurately classify the circadian rhythms of the negative control subject and the circadian rhythms of the positive control subject. Here, the "reference value that can most accurately classify" can be appropriately set based on indicators such as the mean, median, mode, sensitivity, specificity, positive predictive value, and negative predictive value.

[0076] The generated vectors are output, for example, to a display unit of a wearable device worn by the subject so that each vector can be identified. Identifiable is not limited as long as it can be visually identified by a person such as the subject or a person managing the subject's health. For example, each vector can be identified by presenting it in a different color. Furthermore, when each vector is outside the reference range, warning information may be output, for example, indicating that the circadian rhythm is disrupted. The warning information may be text information, a mark such as an exclamation mark, or an alarm sound.

[0077] Furthermore, as shown in Figure 4, it is preferable to evaluate not only the median (mesor) of the periodic regression curve for each individual index of circadian rhythm, such as activity level, blood glucose level, autonomic nervous activity, and core body temperature, but also the balance of these indexes. Such balance can be evaluated by analyzing the correlation between each index for each subject. Therefore, "circadian rhythm index" includes the correlation between each index for each subject. Such correlation can be evaluated, for example, by calculating Spearman's rank correlation coefficient (Pearson's product-moment correlation coefficient).

[0078] A group of measurement data relating to the circadian rhythm index is output as a monitoring result so as to be visible to the subject or others.

[0079] In this embodiment, the disturbance of circadian rhythm indicates a sign of the onset of dementia. Preferably, the disturbance of circadian rhythm continues for a certain period of time. The certain period of time may be, for example, one month or more, two months or more, four months or more, six months or more, nine months or more, or one year or more.

[0080] In this embodiment, to exclude factors other than dementia as factors that may be causing the circadian rhythm of a subject to be disrupted, subjects may be filtered, for example, by their age and / or work style. For example, subjects under a certain age and / or shift workers may be determined to have a disrupted circadian rhythm without showing signs of dementia. The certain ages may be 65, 70, 75, or 80 years old.

[0081] This embodiment may further include outputting a label indicating that the subject is in a state where guidance for circadian rhythm control is required when it is determined that the subject's circadian rhythm is disrupted. The label is not limited as long as it indicates that the subject is in a state where guidance for circadian rhythm control is required. The label may be displayed as text, a symbol such as an exclamation mark, a color, or the like. In order to control the circadian rhythm, guidance to the subject may include, for example, sleeping more at night, spending time in well-lit places during the day, and eating a regular diet.

[0082] 1-2.Detection support system FIG. 5 shows an example of the configuration of a detection assistance system 1000 (hereinafter, sometimes simply referred to as "system 1000") for assisting in the detection of signs of the onset of dementia.

[0083] The system 1000 includes a detection assistance device 10 (hereinafter, sometimes simply referred to as "detection assistance device 10") for assisting in the detection of signs of the onset of dementia, and a measurement device 50. The detection assistance device 10 may be connected to an input device 111 and an output device 112. The measurement device 50 may be a Holter electrocardiograph (for example, a long-term electrocardiogram analyzer DSC-5000 series sold by Nihon Kohden Corporation, or a long-term electrocardiogram analyzer DSC-5000 series sold by Fukuda Denshi Co., Ltd.). Examples of such devices include a wearable device equipped with a heart rate sensor, an activity tracker (e.g., the Actiwatch Spectrum series available from Philips Japan), a blood glucose monitor (e.g., the FreeStyle Libre Pro available from Abbott Japan LLC), and a core body temperature recording device (e.g., the HERBIO core thermometer and the Murata patch-type core thermometer). These devices may also be multi-function devices equipped with multiple measurement functions.

[0084] 1-2-1. Measuring device 50 The hardware configuration and functional configuration of the measuring device 50 will be described below with reference to Figures 6 and 7. However, since the measuring device 50 is a publicly known device, the details of the hardware configuration and functional configuration of the measuring device 50 will follow the specifications of each measuring device 50 provided by the manufacturer.

[0085] (1) Hardware configuration of the measuring device 50 6 shows the hardware configuration of the measurement device 50 used in this embodiment. The measurement device 50 includes a measurement unit 51 that measures raw data of the subject's biological information, and a control unit 52 that controls the measurement device. The control unit 52 includes a CPU (Central Processing Unit) 521 and a communication interface (I / F) 531. The CPU 521 performs arithmetic processing, such as generating a group of measurement data related to an index of the subject's circadian rhythm based on the group of raw data measured by the measurement unit 51. The communication I / F 531 transmits the generated group of measurement data to the detection assistance device 10.

[0086] (2) Functional configuration of the measuring device 50 7 shows the functional configuration of the measurement device 50. The measurement device 50 includes a measurement unit M51, a measurement data generation unit M52, and a measurement data transmission unit M53. The measurement unit M51 corresponds to the control process for the measurement unit 51 performed by the CPU 521 when the measurement unit 51 measures raw data. The measurement data generation unit M52 corresponds to the arithmetic process performed by the CPU 521 to generate measurement data related to an index of the subject's circadian rhythm based on a group of raw data measured by the measurement unit 51. The measurement data transmission unit M53 corresponds to the process performed by the CPU 521 to transmit a group of measurement data to the detection assistance device 10 via the communication I / F 531.

[0087] 1-2-2. Detection auxiliary device 10 This embodiment relates to a detection assistance device 10 for implementing the detection assistance method outlined in 1-1 above. The hardware configuration and functional configuration of the detection assistance device 10 will be described below with reference to Figs. 6 and 7.

[0088] (1) Hardware configuration of the detection assistance device 10 6 shows the hardware configuration of the detection assistance device 10. The detection assistance device 10 may be a general-purpose computer. The detection assistance device 10 is communicatively connected to an input device 111, an output device 112, and a media drive 113. The detection assistance device 10 includes a CPU 101, a memory 102, a ROM (read only memory) 103, a storage device 104, a communication interface (I / F) 105, an input interface (I / F) 106, an output interface (I / F) 107, and a media interface (I / F) 108. The components within the detection assistance device 10 are connected to each other via a bus 109 so as to be able to communicate data with each other.

[0089] The storage device 104 is configured by a hard disk, a semiconductor memory element such as a flash memory, an optical disk, etc. The storage device 104 stores an operating system (OS) 1041, a detection assistance program 1042 (described later), a reference range database (DB) DB1, and a subject information database (DB) DB3. The program 1042 cooperates with the operating system 1041 to cause the computer to function as the detection assistance device 10. The reference range database DB1 stores the reference ranges for each circadian rhythm index. The subject information database DB3 stores information such as the gender, age, and working style of the subject.

[0090] The CPU 101 is also called a processing unit 101 in this embodiment. The reference range database DB1 stores the reference ranges described in 1-1 above.

[0091] The input device 111 is composed of a touch panel, a keyboard, a mouse, a pen tablet, a microphone, etc., and is used to input text or voice to the detection assistance device 10. The input device 111 may be connected from outside the processing unit 101 or may be integrated with the detection assistance device 10.

[0092] The output device 112 is composed of, for example, a display device such as a display, a printer, etc., and outputs various operation windows, detection assistance results, etc. The media drive 113 may be a USB drive, a flexible disk drive, a CD-ROM drive, a DVD-ROM drive, or the like.

[0093] The communication I / F 105 receives groups of raw data from the measurement device 50. The output I / F 107 transmits the results to the output device 112.

[0094] (2) Functional configuration of the detection assist device 10 FIG. 7 shows the functional configuration of the detection assisting device 10.

[0095] The detection assistance device 10 includes a measurement data acquisition means M11, a circadian rhythm estimation means M12, a comparison means M13, and a monitoring result output means M14. The measurement data acquisition means M11, the circadian rhythm estimation means M12, the comparison means M13, and the monitoring result output means M14 correspond to steps S1, S2, S22, and S3, respectively, which will be described later.

[0096] 1-2-3. Detection assistance program processing An example of the processing of the detection assistant program 1042 will be described with reference to FIGS.

[0097] The processing unit 101 receives a processing start command input by the operator from the input device 111, and in step S1, acquires a group of measurement data related to indices of the subject's circadian rhythm. The explanation regarding the group of measurement data in 1-1 above is incorporated herein by reference.

[0098] Next, in step S2, the processing unit 101 acquires the circadian rhythm of the subject, for example, triggered by acquiring the group of measurement data in step S1. The explanation of the method for acquiring the circadian rhythm of the subject described in 1-1 above is incorporated herein by reference.

[0099] Next, the processing unit 101 receives an output start command input by the operator from the input device 111 as a trigger when step S2 is completed, and outputs the subject's circadian rhythm obtained in step S2 to the output device 112 as a monitoring result in step S3.

[0100] In step S4, the processing unit 101 repeats steps S1 to S3 until it receives a command to end processing input by the operator from the input device 111. If the processing unit 101 receives a command to end processing input by the operator in step S4 (in the case of "YES"), it ends the processing. Furthermore, the detection assistance program 1042 includes the process shown in FIG. 9 between step S2 and step S3.

[0101] Triggered by the end of step S2, in step S21, the processing unit 101 acquires the reference range of the circadian rhythm from the reference range database DB1. Subsequently, in step S22, the processing unit 101 determines whether the circadian rhythm of the subject acquired in step S2 is within the reference range. If it is within the reference range (if "YES"), the processing unit 101 proceeds to step S3. If, in step S22, the circadian rhythm of the subject acquired in step S2 deviates from the reference range (if "NO"), the processing unit 101 proceeds to step S23, where information indicating a disturbance in the circadian rhythm is output. The information indicating a disturbance in the circadian rhythm is displayed, for example, as a warning mark on the output device 112. The explanation of the comparison of the circadian rhythm of the subject with the reference range and the cases in which it can be determined that the circadian rhythm has deviated from the reference range, as described in 1-1 above, is incorporated herein by reference.

[0102] Furthermore, in step S24, the processing unit 101 determines whether the circadian rhythm disturbance in the subject has continued for a certain period or more. If the circadian rhythm disturbance in the subject has not continued for a certain period or more (in the case of "NO"), the processing unit 101 proceeds to step S3, where only the circadian rhythm monitoring results are output. If the circadian rhythm disturbance in the subject has continued for a certain period or more (in the case of "YES") in step S23, the processing unit 101 proceeds to step S25, where information indicating signs of dementia onset is output to the output device 112. This output may be output together with the circadian rhythm monitoring results. The information indicating signs of dementia onset may be, for example, information indicating a deviation from normal, such as a text label such as "signs present" or a symbol label such as an exclamation mark. The explanation of the criteria for determining whether the circadian rhythm disturbance in the subject has continued for a certain period or more, as described in 1-1 above, is incorporated herein by reference.

[0103] Furthermore, if the answer is "YES" in step S24, the processing unit 101 may output information indicating signs of dementia in step S25, or alternatively, may output a label indicating that the subject is in a state where guidance on circadian rhythm control is required. The explanation of "a state where guidance on circadian rhythm control is required" is incorporated herein by reference from the explanation in 1-1 above.

[0104] 9, the processing unit 101 may perform steps S51 to S52 shown in Fig. 10 before step S25 in order to exclude factors other than dementia as factors that may be causing the disturbance of the subject's circadian rhythm to continue for a certain period of time. Note that the order in which steps S51 and S52 are performed may be reversed.

[0105] In step S51, the processing unit 101 determines whether the subject's age is equal to or greater than a certain age. The subject's age is stored in the subject information database DB3. If step S51 is "YES," the processing unit 101 proceeds to step S52.

[0106] In step S52, the processing unit 101 determines whether the subject is engaged in shift work. The subject's working style, etc. are stored in the subject information database DB3. If step S52 is "NO", the processing unit 101 proceeds to step S25.

[0107] If step S51 is "NO" or if step S52 is "YES", the processing unit 101 proceeds to step S3, and outputs the monitoring result without outputting information indicating the signs of the onset of dementia.

[0108] The explanations for "above a certain age" and "shift work" are the same as those in 1-1 above.

[0109] 1-3. Recording medium on which the detection assistance program is recorded The detection assistance program 1042 including the processes of steps S1 to S4 and steps S21 to S25, or the processes of steps S1 to S4, steps S21 to S25, and steps S51 to S52 may be recorded on a recording medium.

[0110] That is, the detection assistance program 1042 is stored in a recording medium such as a hard disk, a semiconductor memory device such as a flash memory, an optical disk, etc. The computer program may also be stored in a recording medium connectable via a network, such as a cloud server. The computer program may be provided in a downloadable format or as a program product recorded on a recording medium.

[0111] There are no limitations on the format of the program stored in the recording medium as long as the program can be read by the detection assistance device 10. It is preferable that the program is stored in the recording medium in a non-volatile format.

[0112] 2. Second embodiment A second embodiment disclosed in this specification relates to a monitoring method (hereinafter sometimes simply referred to as a "monitoring method"), a monitoring device, a monitoring program, and a monitoring system for monitoring the function of the suprachiasmatic nucleus.

[0113] 2-1. Overview The monitoring method includes obtaining a set of measurement data relating to an index of a subject's circadian rhythm.

[0114] In this embodiment, the function of the suprachiasmatic nucleus is monitored based on a group of measurement data relating to indicators of the subject's circadian rhythm, which are acquired in the same manner as in the first embodiment. Therefore, the explanation regarding acquisition of measurement data described in 1-1 above can all be applied here.

[0115] In this embodiment, when a disturbance of the circadian rhythm is detected, it may be assumed that the function of the suprachiasmatic nucleus is impaired. Preferably, when the disturbance of the circadian rhythm continues for a certain period of time or longer, it may be assumed that the function of the suprachiasmatic nucleus is impaired.

[0116] 2-2.Monitoring system FIG. 5 shows an example of the configuration of a monitoring system 2000 (hereinafter sometimes simply referred to as "system 2000") for monitoring the function of the suprachiasmatic nucleus.

[0117] The system 2000 includes a monitoring device 20 (hereinafter sometimes simply referred to as "monitoring device 20") for monitoring the function of the suprachiasmatic nucleus, and a measuring device 50. The monitoring device 20 may be connected to an input device 211 and an output device 212. The measuring device 50 is similar to the devices included in the system 1000.

[0118] 2-2-1. Monitoring device 20 This embodiment relates to a monitoring device 20 for implementing the monitoring method outlined in 2-1 above. The hardware configuration and functional configuration of the monitoring device 20 will be described below with reference to Figs.

[0119] (1) Hardware configuration of the monitoring device 20 FIG. 11 shows the hardware configuration of the monitoring device 20. The configuration is basically the same as that of the detection assistance device 10. In the detection assistance device 10, the input device 111, the output device 112, the media drive 113, the CPU 101, the memory 102, the ROM 103, the storage device 104, the communication interface (I / F) 105, the input interface (I / F) 106, the output interface (I / F) 107, the media interface (I / F) 108, and the bus 109 are to be read as an input device 211, an output device 212, a media drive 213, the CPU 201, the memory 202, the ROM 203, the storage device 204, the communication interface (I / F) 205, the input interface (I / F) 206, the output interface (I / F) 207, the media interface (I / F) 208, and the bus 209, respectively, in this embodiment.

[0120] Here, the storage device 204 stores an OS2041, a monitoring program 2042, a reference range database DB2, and a subject information database (DB) DB4 instead of the operating system 1041, the detection assistance program 1042, the reference range database DB1, and the subject information database (DB) DB3 stored in the detection assistance device 10. The monitoring program 2042 works in cooperation with the operating system 2041 to cause the computer to function as the monitoring device 20. The reference range database DB2 stores the reference ranges for each circadian rhythm index. The subject information database DB4 stores information such as the gender, age, and working style of the subject. The CPU 201 is also referred to as a processing unit 201 in this embodiment.

[0121] (2) Functional configuration of the monitoring device 20 FIG. 12 shows the functional configuration of the monitoring device 20.

[0122] The monitoring device 20 includes a measurement data acquisition means M21, a circadian rhythm estimation means M22, a comparison means M23, and a monitoring result output means M24. The measurement data acquisition means M21, the circadian rhythm estimation means M22, the comparison means M13, and the monitoring result output means M24 correspond to steps S11, S12, S122, and S13, respectively, which will be described later.

[0123] 2-2-2. Processing of monitoring programs 13 and 14, an example of the processing of the monitoring program 2042 will be described. The processing performed in steps S11 to S14 of the monitoring program 2042 is the same as steps S1 to S4 described in 1-2-3 above, except that the processing is performed by the processing unit 201 instead of the processing unit 101, the input device 111 is read as the input device 211, and the output device 112 is read as the output device 212. Furthermore, the monitoring program 2042 includes the process shown in FIG. 14 between step S12 and step S13.

[0124] The processing from step S121 to step S124 shown in FIG. 14 is the same as step S21 to step S24, except that the processing is performed by the processing unit 201 instead of the processing unit 101, and the output device 112 is replaced with the output device 212.

[0125] Step S125 corresponds to step S25, but here, the processing unit 201 outputs information indicating a decline in the function of the suprachiasmatic nucleus to the output device 212, instead of information indicating the signs of the onset of dementia. This output may be output together with the circadian rhythm monitoring results.

[0126] The information indicating the functional decline of the suprachiasmatic nucleus may be, for example, a text label such as "Decreased" or a symbolic label such as an exclamation mark, indicating that the condition is different from normal. The explanation of the criteria for determining whether the disturbance of the circadian rhythm in the subject described in 1-1 above has continued for a certain period of time or more is as follows: , which is incorporated herein by reference.

[0127] Furthermore, if the answer is "YES" in step S124, the processing unit 201 may output information indicating a functional decline of the suprachiasmatic nucleus in step S25, or alternatively, instead of outputting information indicating a functional decline of the suprachiasmatic nucleus, output a label indicating that the subject is in a state in which guidance on circadian rhythm control is required. The explanation of "a state in which guidance on circadian rhythm control is required" is incorporated herein by reference from the explanation in 1-1 above.

[0128] 14 is "YES", the processing unit 201 may perform steps S71 to S72 shown in Fig. 15 before step S125 in order to exclude factors other than dementia as factors that may be causing the disturbance of the subject's circadian rhythm to continue for a certain period of time. Note that the order in which steps S71 and S72 are performed may be reversed.

[0129] In step S71, the processing unit 201 determines whether the subject's age is equal to or greater than a certain age. The subject's age is stored in the subject information database DB4. If step S71 is "YES," the processing unit 201 proceeds to step S72.

[0130] In step S72, the processing unit 201 determines whether the subject is engaged in shift work. The subject's working style and the like are stored in the subject information database DB4. If step S72 is "NO", the processing unit 201 proceeds to step S125.

[0131] If step S71 is "NO" or if step S72 is "YES", the processing unit 201 proceeds to step S3, where it outputs the monitoring result without outputting information indicating the signs of the onset of dementia.

[0132] The explanations for "above a certain age" and "shift work" are the same as those in 1-1 above.

[0133] 2-3. Recording media containing the monitoring program The detection assistance program 2042 for performing the processes from step S11 to step S14 and step S121 to step S125, or the processes from step S11 to step S14, step S121 to step S125, and step S71 to step S72 may be recorded on a recording medium. The configuration of the recording medium is as described above in 1-3. [Example]

[0134] The present invention will be described in more detail below with reference to examples, but the present invention should not be construed as being limited to these examples. The animal experiments described in this example were carried out with the approval of the Animal Experiment Committee of Kyoto Prefectural University of Medicine.

[0135] I. Example 1 1. Endothelial Nitric Oxide Synthase Gene Knockout Mice Oxide synthase gene knockout mice (hereinafter referred to as "eNOS- / - mice") were prepared as described in Proc. Mice were purchased from the Jackson Laboratory (Bar Harbor, ME, USA) as described in Natl. Acad. Sci. USA, Vol. 93, pp. 13176-13181, November 1996. The normal group consisted of eNOS+ / - mice, which were heterozygotes carrying wild-type and knockout eNOS genes.

[0136] 2. Animal Maintenance The animals were individually housed in polycarbonate cages (28 × 17 × 12 cm) at a temperature of 22 ± 2°C with a relative humidity of 50% ± 5% and 12-hour lighting from 8:00 to 20:00 (Japan Standard Time). During the rearing period, the mice were kept on a diet (Oriental Yeast, CRF-1 LID6, Tokyo) and Water and water were available ad libitum.

[0137] 3. Activity Observation To observe the behavior of mice, mice were housed in polycarbonate cages equipped with an activity wheel. The cage equipped with the activity wheel was placed in a light-tight chamber with internal lighting. The number of rotations of the activity wheel was continuously monitored by a computer (ClockLab; Actimetrics, USA). The cumulative number of rotations was recorded every minute. Two weeks after the start of the experiment, During this period, the mice were placed under 12 hours of lighting (L:D = 12 hours:12 hours) from 8:00 to 20:00, and then the activity of the mice was observed under 24 hours of darkness.

[0138] 4.Results (1) Comparison of behavioral rhythms between eNOS+ / - and eNOS- / - mice The behavioral rhythms of 10-month-old (middle-aged) eNOS+ / - mice and 10-month-old eNOS- / - mice were compared. The results are shown in Figure 16(A). Figure 16(A) shows the results of keeping mice under 12-hour lighting for two weeks, then switching to 24-hour darkness, and observing their behavior over time. The left side of Figure 16(A) shows the results for eNOS+ / - mice, and the right side shows the results for eNOS- / - mice. When switched to 24-hour darkness, both groups showed a tendency for their activity periods to gradually advance over the course of the day, but their activity remained stable. The transition of the active period in eNOS- / - mice tended to be faster than in eNOS+ / - mice.

[0139] Next, we compared the behavioral rhythms of 17-month-old (aged) eNOS+ / - mice and 17-month-old eNOS- / - mice. The results are shown in Figure 16(B). Figure 16(B) shows the results of keeping mice under 12-hour lighting for two weeks, then switching to 24-hour darkness, and observing the behavior of the mice over time. The left side of Figure 16(B) shows the results for eNOS+ / - mice, and the right side shows the results for eNOS- / - mice. Both groups and However, when the lights were switched off 24 hours a day, the overall activity level decreased. It was more pronounced in mice.

[0140] Figure 17 shows the results of observing the behavioral rhythms of mice different from those shown in Figure 16 under 24-hour light-off conditions. The left side of Figure 17(A) shows the results of 10-month-old eNOS+ / - mice, and the right side shows the results of 10-month-old eNOS- / - mice. As in Figure 16(A), no significant differences were observed between the two groups. The left side of Figure 17(B) shows the results of 17-month-old eNOS+ / - mice, and the right side shows the results of 19-month-old eNOS- / - mice. The results of the 17-month-old eNOS- / - mice showed reduced activity and a broader overall activity time pattern compared to the 17-month-old eNOS+ / - mice.

[0141] (2) Comparison of the amplitude of behavioral rhythms The circadian rhythm was quantitatively evaluated by amplitude strength using a chi-square periodogram. The results are shown in Figure 18. Figure 18(A) shows the results for eNOS+ / - mice, and Figure 18(B) shows the results for eNOS- / - mice. Each graph shows the results for a different mouse. The symbol a indicates The symbol b indicates the amplitude strength of each mouse at 10 months of age, and the symbol b indicates the amplitude strength of each mouse at 17 months of age.

[0142] eNOS+ / - mice showed a slightly decreased amplitude of behavioral rhythms at 17 months of age compared to 10 months of age. The amplitude of the behavioral rhythm was significantly reduced in eNOS- / - mice compared with eNOS+ / - mice at 17 months of age.

[0143] Figure 19 shows the amplitude of the behavioral rhythm at 17 months of age relative to the amplitude of the behavior at 10 months of age, which is set to 100. The amplitude was calculated and averaged between the eNOS+ / - and eNOS- / - mouse groups. The amplitude was significantly lower in the eNOS- / - mouse group (p=0.031 by Student t-test). These results suggest that eNOS-deficient mice exhibit weakened circadian rhythms.

[0144] (3) Electron microscopic observation of the suprachiasmatic nucleus Figure 20 shows the changes in the suprachiasmatic nucleus neurovascular unit (vascular endothelial cells) with age in eNOS- / - mice. Figure 20(A) shows an electron micrograph of the neurovascular unit (composed of pericytes and astrocyte endfeet). Figure 20(A) shows the neurovascular unit of a 3-month-old (juvenile) rat. In Figure 20(A), Per. indicates pericyte, AEF indicates astrocyte end food, and End.C. indicates endothelial cell. BL indicates the basal lamina, and Lumen indicates the vascular lumen. At 3 months of age, the tissue structure consisting of each cell is maintained normally. A schematic diagram of the behavioral rhythm is shown on the right side of the electron microscope photograph. 3 The behavioral rhythm of eNOS- / - mice at 6 months of age was normal, and the pericytes, basal lamina, and blood The layer structure of the endothelial cells that make up the tube lumen and the shape of the lumen were clearly maintained. Figure 20(B) shows electron micrographs of the neurovascular unit of a 19-month-old eNOS+ / - mouse. While the lumen shape is clearly maintained, the layered structure of the pericytes, basal lamina, and endothelial cells that make up the vascular lumen is unclear compared to Figure 20(A). Furthermore, the behavioral pattern showed a difference between active and resting periods, but the active phases became intermittent. These are all physiological changes associated with aging. Figures 20(C) and 20(D) show electron micrographs of the neurovascular unit of different 19-month-old eNOS- / - mice. In both images, the neurovascular unit showed a distorted lumen shape, and the layered structure of the pericytes, basal lamina, and endothelial cells that make up the vascular lumen was unclear.

[0145] These results suggest that eNOS- / - mice experience destruction of blood vessels in the suprachiasmatic nucleus, particularly the neurovascular unit, with aging, and that the severity of this destruction correlates with the disruption of circadian rhythms. It became.

[0146] It has been reported that mice with suprachiasmatic nucleus lesions generally show disruption of circadian rhythms. Therefore, the vascular endothelial cell damage observed in eNOS- / - mice with aging may be due to the It has been suggested that this leads to dysfunction of the suprachiasmatic nucleus, causing disruption of the circadian rhythm. Furthermore, it has been reported that the sleep-wake rhythm is disrupted in dementia. In this study, the disruption of behavioral rhythm was observed in eNOS- / - mice, suggesting that the circadian rhythm is disrupted. It has been suggested that disruption of this system is associated with the onset of dementia.

[0147] Non-patent document 1 states that no disruption of circadian rhythm was observed in eNOS- / - mice. However, in Non-Patent Document 1, experiments were conducted using 6-month-old mice. 6-month-old mice correspond to the age of 30 in humans. However, since the onset of Alzheimer's disease in humans is often in the 65-year-old age group or older, it is thought that the experimental model described in Non-Patent Document 1 did not adequately reflect the pathological condition in humans.

[0148] As shown in Figure 21, the suprachiasmatic nucleus controls the paraventricular nucleus of the hypothalamus, which is the center of the autonomic nervous system. Therefore, dysfunction of the suprachiasmatic nucleus is thought to cause disturbances in biological rhythms, including the rhythm of the autonomic nervous system.

[0149] Therefore, it is thought that the function of the suprachiasmatic nucleus can be evaluated by monitoring biological rhythms, including those of the autonomic nervous system.

[0150] II. Example 2 Figure 22 shows an example of a system for detecting suprachiasmatic nucleus function, which uses signals from a Holter electrocardiograph to measure heart rate, which best reflects the state of the autonomic nervous system. Electrical signals from the heart are recorded using a Holter electrocardiograph for 24 hours over two weeks. The heart rate fluctuation cycle within a certain period of time is obtained from the interval between R waves (RR interval). Furthermore, the waveform of the obtained fluctuation cycle is analyzed. A frequency analysis is performed to separate the low-frequency amplitude and the high-frequency amplitude. When the proportion of high-frequency amplitude in the acquired waveform of the fluctuation cycle is high, it indicates a state in which the parasympathetic nervous system is dominant, i.e., a sleep state. When the proportion of low-frequency amplitude in the acquired waveform of the fluctuation cycle is high, it indicates a state in which the sympathetic nervous system is dominant.

[0151] An example of analysis is shown in Figure 23. The left side of Figure 23 is an example of the autonomic nervous system functioning regularly, and a certain periodicity is also observed in the behavioral rhythm. The right side of Figure 23 is an example of a case where there is a disturbance in the rhythm of the autonomic nervous system, and a disturbance is also observed in the behavioral rhythm.

[0152] III. Example 3 Twenty-one female hospital nurses (one day shift, 20 working three-shift shifts; ages 22 to 52 (33.67 ± 1.9 years); years of service 1 to 30 years (11.2 ± 2.09 years)) were monitored for activity, blood glucose levels, sympathetic nervous activity, and parasympathetic nervous activity, and data were collected. Periodic regression analysis was performed on the data to calculate rhythmicity, meandering, and amplitude. Sleep phases were also recorded using sleep diaries. Figure 24 shows the sleep phases of each subject over a two-week period. Because many of the subjects worked shifts, their sleep phases shifted between one and three times per week. Figure 25 shows the activity data (histograms) for each subject over a five-day period and fitting curves obtained using the cosinor method. The dashed box plots indicate the rhythmicity. Figure 26 shows the blood glucose level measurement data (represented as a histogram) for each subject over five days, along with the fitting curves obtained using the cosinor method. The dashed boxes show box plots of rhythmicity. Figure 27 shows the parasympathetic nervous activity measurement data (represented as a histogram) for each subject over five days, along with the fitting curves obtained using the cosinor method. The dashed boxes show box plots of rhythmicity. Figure 28 shows the sympathetic nervous activity measurement data (represented as a histogram) for each subject over five days, along with the fitting curves obtained using the cosinor method. The dashed boxes show box plots of rhythmicity. The numbers in each box plot indicate the subject number. As shown in each box plot, there was considerable variation in rhythmicity for activity level, blood glucose level, parasympathetic nervous activity, and sympathetic nervous activity. Furthermore, it was revealed that subjects with low activity levels or low blood glucose rhythmicity do not necessarily have low parasympathetic or sympathetic nervous activity. This suggests that it is necessary not only to evaluate the rhythmicity of individual indices, but also to examine the correlation between indices. Figure 29 shows the relationship between autonomic nervous activity rhythm and age. (A) shows the relationship between age and HF (parasympathetic nervous system). (B) shows the correlation between age and LFHF (sympathetic nerve) rhythmicity (percent rhythm). (C) shows the correlation between the percent rhythm of blood glucose levels and the PSQI (Pittsburgh Sleep Quality Index). It was shown that the rhythm of both parasympathetic and sympathetic nervous activity decreases with age. show. Figure 30 shows the results of a comprehensive correlation analysis using all the indices examined. As shown in Figure 30, for example, correlations were found between age and parasympathetic rhythmicity, and between age and sympathetic rhythmicity. In addition, correlations were found between blood glucose amplitude and MEQ (Morningness-Eveningness Questionnaire), between blood glucose rhythmicity and MEQ, between blood glucose amplitude and PSQI (Pittsburgh sleep quality index), and between blood glucose rhythmicity and PSQI. Although a correlation was observed between the MEQ and PSQI, no correlation was observed between the other indices. The Epworth Sleepiness Scale (ESS) is a questionnaire-based measure of subjective symptoms. This is an internationally widely used assessment method in which participants fill out a questionnaire and the results are converted into a score, evaluating the characteristics (constitution) of their own biological clock, known as chronotype, as well as sleep quality and daytime sleepiness. For example, if subjective symptoms such as daytime sleepiness and poor nighttime sleep quality due to day-night reversal are observed, these can be considered accompanying symptoms suggesting a circadian rhythm malfunction associated with dementia, and the assessment using the physiological function rhythm index mentioned above can be reinforced. [Explanation of symbols]

[0153] 10. Detection aids 101 Processing section 1000 Detection Aid System 50 Measuring Equipment 20 Monitoring Device 201 Processing section 2000 Monitoring System

Claims

[Claim 1] obtaining a group of measurement data relating to indicators of circadian rhythm of the subject, wherein a disturbance in the circadian rhythm indicates a symptom of the onset of dementia; A detection assistance method for assisting in detecting signs of the onset of dementia, comprising:

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