Analysis device and analysis method

Abstract

This analysis device 1 comprises a light emission unit 11 for emitting measurement light I including light in the 900 nm wavelength band toward a subject S under measurement, a light detection unit 12 for acquiring spectral data for reflected light R from the subject S under measurement, a data processing unit 21 for subjecting the spectral data to noise removal processing, a first determination unit 22 that stores a PLS regression model relating to the prediction of the amount of neutral fat in the subject S under measurement and determines the amount of neutral fat in the subject S under measurement by applying the PLS regression model to the spectral data that has been subjected to noise removal processing, and a second determination unit 23 that stores data indicating correlation with the amount of neutral fat in the subject S under measurement and determines the amount of brown fat tissue or beige fat in the subject S under measurement on the basis of said data and the amount of neutral fat determined by the first determination unit 22.

Description

Technical Field

[0001] The present disclosure relates to an analytical apparatus and an analytical method.

Background Art

[0002] Brown adipose tissue (BAT) is adipose tissue that has the property of burning fat and consuming energy through a specific uncoupling protein 1 (UCP1). Recent research reports that brown adipose tissue affects the metabolism of glucose and lipids throughout the body and insulin sensitivity. There are also reports that brown adipose tissue is involved in the metabolic control of the whole body through secreted substances and nerves and plays a role as an endocrine organ. If it becomes possible to control the weight and activity of brown adipose tissue, prevention and improvement of metabolic diseases such as metabolic syndrome are expected.

[0003] Currently, as a means for measuring brown adipose tissue in the human body, positron emission tomography (FDG-PET) using fluorodeoxyglucose, a glucose analogue, is the mainstream. As a non-invasive method for measuring human brown adipose tissue, for example, there is a measurement method described in Patent Document 1. In this measurement method, time-resolved spectroscopy (TRS) is used, and brown adipose tissue is evaluated based on the total amount of hemoglobin in the measurement site.

[0004] In recent years, it has been found that human brown adipose tissue is mainly composed of beige fat. Beige fat is formed by the appearance of UCP1 in white fat and has the same properties as brown adipose tissue. As non-invasive methods for measuring beige fat, for example, there are methods described in Non-Patent Documents 1 and 2. All of these methods are based on the reflection spectrum of the measurement site.

[0005] In Non-Patent Document 1, it is disclosed that the process of beigeing of white fat is detected based on the ratio of reflection intensities at wavelengths of 550 nm and 680 nm and the slope of the spectrum in the wavelength range of 570 nm to 630 nm. In Non-Patent Document 2, the volume fraction of fat and water is determined such that the difference between the measured diffuse reflection spectrum obtained in the wavelength range of 1050 nm to 1350 nm and the reflection spectrum modeled from a lookup table based on Monte Carlo simulation is minimized, and the process of beigeing of white fat is detected from the said volume fraction.

Prior Art Documents

Patent Documents

[0006]

Patent Document 1

Non-Patent Documents

[0007]

Non-Patent Document 1

Non-Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0008] In order to promote clinical applications such as research on brown adipose tissue in humans, treatment using brown adipose tissue, and disease prevention, a method more specialized in the analysis of brown adipose tissue and beige fat is required. For example, if it becomes possible to not only relatively evaluate brown adipose tissue and beige fat, but also perform quantitative evaluation of brown adipose tissue or beige fat, it is considered that this could form the basis for future research and application of brown adipose tissue.

[0009] The present disclosure has been made to solve the above problems, and an object thereof is to provide an analyzer and an analysis method capable of performing quantitative evaluation of brown adipose tissue or beige fat.

Means for Solving the Problems

[0010] In the course of intensive research on the above problems, the applicant of the present application focused on the spectral characteristics of brown adipose tissue and beige fat, and found that brown adipose tissue and beige fat have light absorption characteristics not seen in white fat. Further, when cold stimulation was applied to brown adipose tissue and beige fat, the light absorption characteristics of brown adipose tissue and beige fat changed with the application of the stimulation. It was found that there is a certain correlation between the amount of change in this light absorption characteristic and the amount of change in neutral fat obtained from biochemical analysis. Therefore, the applicant of the present application obtained the knowledge that quantitative evaluation of brown adipose tissue or beige fat can be easily performed by combining the analysis of the amount of neutral fat based on the spectral data of the object to be measured and the regression model for predicting the amount of neutral fat in the object to be measured, and completed the content of the present disclosure.

[0011] An analyzer according to an aspect of the present disclosure includes a light irradiation unit that irradiates a measurement object with measurement light including light in a wavelength band of 900 nm, a light detection unit that detects reflected light from the measurement object and acquires spectral data of the reflected light in the measurement object, a data processing unit that performs noise removal processing on the spectral data acquired by the light detection unit, a first determination unit that holds a PLS regression model related to prediction of the amount of neutral fat in the measurement object and determines the amount of neutral fat in the measurement object by applying the spectral data subjected to noise removal processing to the PLS regression model, and a second determination unit that holds data indicating a correlation with the amount of neutral fat in the measurement object and determines the amount of brown adipose tissue or beige fat in the measurement object based on the data and the amount of neutral fat determined by the first determination unit.

[0012] In this analyzer, spectral data of the reflected light in the measurement object is acquired, and noise removal processing is performed on the acquired spectral data. In the spectral data after the noise removal processing, differences occur in the absorption characteristics of the light in the wavelength band of 900 nm depending on the presence or absence of brown adipose tissue, beige fat, and white fat in the measurement object. Therefore, by applying the spectral data subjected to noise removal processing to a previously created PLS regression model, the amount of neutral fat in the measurement object can be determined. By comparing the determination result of the amount of neutral fat with the data indicating the correlation with the amount of neutral fat in the measurement object, the absolute value of the amount of brown adipose tissue or beige fat in the measurement object can be determined.

[0013] The PLS regression model may be a model based on the value of the absorption peak of fat in the spectral data subjected to noise removal processing. By using the PLS regression model based on the value of the absorption peak of fat, it is possible to improve the determination accuracy of the amount of neutral fat in the measurement object.

[0014] The first determination unit may determine the presence or absence of brown adipose tissue, beige fat, and white fat in the object to be measured based on the presence or absence of an absorption peak of water in the 900 nm wavelength band in the spectral data subjected to noise removal processing. In brown adipose tissue and beige fat, there is a tendency for an absorption peak of water to be observed in the 900 nm wavelength band, while in white fat, there is a tendency for no absorption peak of water to be observed in the 900 nm wavelength band. By determining the presence or absence of brown adipose tissue, beige fat, and white fat in the object to be measured based on the presence or absence of an absorption peak of water in the 900 nm wavelength band, the amount of information obtained by the analysis can be further increased.

[0015] The second determination unit holds data indicating the correlation with the amount of neutral fat in the object to be measured to which a stimulus has been applied, and based on the data and the amount of neutral fat determined by the first determination unit, determines the amount of brown adipose tissue or beige fat in the object to be measured to which the stimulus has been applied. In this case, the amount of brown adipose tissue or beige fat in the object to be measured to which the stimulus has been applied can be accurately determined. By significantly determining the change in the amount of brown adipose tissue or beige fat in the object to be measured before and after the application of the stimulus, it becomes possible to further expand the scope of application of the analysis.

[0016] The analysis method according to one aspect of the present disclosure includes a light irradiation step of irradiating the object to be measured with measurement light including light in the 900 nm wavelength band, a light detection step of detecting reflected light from the object to be measured and acquiring spectral data of the reflected light in the object to be measured, a data processing step of performing noise removal processing on the spectral data acquired in the light detection step, a first determination step of determining the amount of neutral fat in the object to be measured by applying the spectral data subjected to noise removal processing to a PLS regression model for predicting the amount of neutral fat using the PLS regression model, and a second determination step of determining the amount of brown adipose tissue or beige fat in the object to be measured based on the data indicating the correlation with the amount of neutral fat in the object to be measured and the amount of neutral fat determined in the first determination step.

[0017] In this analysis method, spectral data of reflected light in the object to be measured is acquired, and noise removal processing is performed on the acquired spectral data. In the spectral data after noise removal processing, differences in the absorption characteristics of light in the 900 nm wavelength band occur depending on the presence or absence of brown adipose tissue, beige fat, and white fat in the object to be measured. Therefore, by applying the spectral data subjected to noise removal processing to a pre-created PLS regression model, the amount of neutral fat in the object to be measured can be determined. By comparing the determination result of the amount of neutral fat with the data indicating the correlation with the amount of neutral fat in the object to be measured, the absolute value of the amount of brown adipose tissue or beige fat in the object to be measured can be determined.

[0018] As the PLS regression model, a model based on the value of the absorption peak of fat in the spectral data subjected to noise removal processing may be used. By using the PLS regression model based on the value of the absorption peak of fat, it is possible to improve the determination accuracy of the amount of neutral fat in the object to be measured.

[0019] In the first determination step, based on the presence or absence of the absorption peak of water in the 900 nm wavelength band in the spectral data subjected to noise removal processing, the presence or absence of brown adipose tissue, beige fat, and white fat in the object to be measured may be determined. In brown adipose tissue and beige fat, there is a tendency for an absorption peak of water to be observed in the 900 nm wavelength band, and in white fat, there is a tendency for no absorption peak of water to be observed in the 900 nm wavelength band. By determining the presence or absence of brown adipose tissue, beige fat, and white fat in the object to be measured based on the presence or absence of the absorption peak of water in the 900 nm wavelength band, the amount of information obtained by the analysis can be further increased.

[0020] In the second determination step, based on the data showing the correlation with the amount of neutral fat in the measurement object to which the stimulus is applied, and based on this data and the amount of neutral fat determined in the first determination step, the amount of brown adipose tissue or beige fat in the measurement object to which the stimulus is applied may be determined. In this case, the amount of brown adipose tissue or beige fat in the measurement object to which the stimulus is applied can be accurately determined. By being able to significantly determine the change in the amount of brown adipose tissue or beige fat in the measurement object before and after the application of the stimulus, it becomes possible to further expand the scope of application of the analysis.

Effect of the Invention

[0021] According to the present disclosure, quantitative evaluation of brown adipose tissue or beige fat can be performed.

Brief Description of the Drawings

[0022]

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DETAILED DESCRIPTION OF THE INVENTION

[0023] Hereinafter, with reference to the drawings, preferred embodiments of an analyzer and an analysis method according to one aspect of the present disclosure will be described in detail.

[0024] FIG. 1 is a block diagram showing the configuration of an analyzer according to an embodiment of the present disclosure. The analyzer 1 shown in FIG. 1 is configured as a device for measuring the absolute value of the amount of brown adipose tissue or the absolute value of the amount of beige adipose tissue in the object to be measured S. The analyzer 1 enables discrimination between brown adipose tissue, beige adipose tissue, and white adipose tissue, which has been difficult with conventional positron emission tomography (PET) examinations and thermography, and easily realizes quantitative evaluation of brown adipose tissue or beige adipose tissue. The object to be measured S is, for example, a biological tissue of a human or an animal. The object to be measured S may be a tissue in a living body or a tissue obtained by cutting out a part of the living body.

[0025] Brown adipose tissue is a type of adipose tissue that has the property of burning fat and consuming energy through a specific uncoupling protein, UCP1. Its cell origin is muscle progenitor cells. Morphological characteristics include containing multilocular lipid droplets and being rich in mitochondria. The main sites of brown adipose tissue in humans are between the scapulae in neonates and around the kidneys in adults. Beige fat is white fat in which UCP1 appears and undergoes browning, acquiring properties similar to those of brown adipose tissue. The main sites of beige fat in humans are the supraclavicular fossa and paravertebral regions. Its cell origin is preadipocytes. Morphological characteristics, similar to those of brown adipose tissue, include containing multilocular lipid droplets and being rich in mitochondria. White fat mainly has the physiological functions of energy storage and release. The main sites of white fat in humans are subcutaneous throughout the body and around internal organs. Its cell origin is preadipocytes. Morphological characteristics include having unilocular lipid droplets. The main constituent of white fat is neutral fat.

[0026] Examples of the application of analyzer 1 include, for example, the medical field and the sports field. In the medical field, applications for the treatment and prevention of, for example, diabetes and dyslipidemia are conceivable. Brown adipose tissue is known to be highly associated with insulin sensitivity and lipid metabolism. By comparing the amount of brown adipose tissue or beige fat in patients with diabetes or dyslipidemia with that in healthy individuals and increasing the amount of brown adipose tissue or beige fat, treatment and prevention independent of drugs are expected.

[0027] By measuring the amount of brown adipose tissue or beige adipose tissue, it is also possible to prevent and improve metabolic diseases such as metabolic syndrome. By adding data on the amount of brown adipose tissue or beige adipose tissue to the physical examinations conducted at schools and the like, it becomes possible to prevent diseases and manage body weight in the younger generation. By examining the relationship between the increase in visceral fat after middle and old age and the amount of brown adipose tissue or beige adipose tissue, the development of research on aging can also be expected. It has also been reported that mice with enhanced functions of certain types of brown adipose tissue are long-lived. In the development of supplements and the like that promote the increase and activation of brown adipose tissue or beige adipose tissue, application as an evaluation means for the developed products can also be considered.

[0028] In the sports field, for example, by measuring the amount of brown adipose tissue or beige adipose tissue before and after training, it becomes possible to evaluate training programs and provide new weight loss programs. When weight management that only reduces fat without reducing muscle mass is required, as a third option in addition to diet and training, an approach by increasing the amount of brown adipose tissue or beige adipose tissue can be considered. It is expected that a monitor for the amount of brown adipose tissue or beige adipose tissue will assist in body weight control.

[0029] Hereinafter, the configuration of the analyzer 1 will be described. As shown in FIG. 1, the analyzer 1 includes a probe 2 and a calculation unit 3. The analyzer 1 is connected to a display device 4 so as to be able to communicate information with each other. The display device 4 is a monitor, a touch panel display, or the like. The display device 4 receives the analysis result information from the analyzer 1 and displays the information.

[0030] Probe 2 has a light irradiation unit 11 and a light detection unit 12. Probe 2 is connected to the arithmetic unit 3 so as to be able to communicate information with each other. Probe 2 may be a handy type of probe that houses the light irradiation unit 11 and the light detection unit 12 in a small housing. Probe 2 may be a wireless type of probe capable of wireless communication with the arithmetic unit 3. In this case, the degree of freedom of the measurement posture in the analyzer 1 can be increased. For example, by fixing a wireless type of probe to a part of the body, it becomes possible to perform measurements during meals or exercise, and it also becomes easier to acquire data on changes over time throughout the day.

[0031] The light irradiation unit 11 is a part that irradiates the measurement object S with the measurement light I including light in the 900 nm wavelength band. As the light source constituting the light irradiation unit 11, for example, a halogen light source, an LD, an LED, an SLD, etc. can be used. The wavelength band of the measurement light I is, for example, 900 nm to 1000 nm. This wavelength band includes 920 nm to 930 nm where the absorption peak of fat exists and 960 nm to 970 nm where the absorption peak of water exists.

[0032] The light detection unit 12 is a part that detects the reflected light R from the measurement object S and acquires the spectral data of the reflected light R in the measurement object S. As the detection element constituting the light detection unit 12, for example, a CCD array, a CMOS array, a PD array, etc. can be used. The light detection unit 12 outputs information indicating the detection result to the arithmetic unit 3. The light detection unit 12 may have an integrating sphere. In this case, by diffusely reflecting the reflected light R from the measurement object S inside the integrating sphere, uniform diffuse reflection spectral data of the reflected light R can be acquired.

[0033] The calculation unit 3 is a part that performs various calculations based on the information from the light detection unit 12. Physically, the calculation unit 3 is a computer system including memories such as RAM and ROM, a processor (calculation circuit) such as a CPU, a communication interface, and a storage unit such as a hard disk. Examples of such a computer system include a personal computer, a cloud server, and smart devices (such as a smartphone and a tablet terminal). The calculation unit 3 functions as a controller of the analyzer 1 by executing a program stored in the memory by the CPU of the computer system.

[0034] Functionally, the calculation unit 3 has a data processing unit 21, a first determination unit 22, and a second determination unit 23. The data processing unit 21 is a part that performs noise removal processing on the spectral data acquired by the light detection unit 12. Examples of the noise removal processing include second derivative such as Savitzky-Golay differentiation. The data processing unit 21 outputs the spectral data after the noise removal processing to the first determination unit 22.

[0035] The first determination unit 22 is a part that determines the amount of neutral fat in the object to be measured S. When the first determination unit 22 receives the spectral data after the noise removal processing from the data processing unit 21, it determines the presence or absence of brown adipose tissue, beige fat, and white fat in the object to be measured S based on the presence or absence of the absorption peak of water in the 900 nm wavelength band in the spectral data.

[0036] Figures 2 and 3 are diagrams showing an example of the spectrum of the reflected light. Figure 2 shows the second derivative spectrum of the group of objects to be measured (control group) without stimulation. Figure 3 shows the second derivative spectrum of the group of objects to be measured (stimulated group) with stimulation. Here, the stimulation is a cold stimulation. In the control group, 5 rats bred at room temperature of 24 °C for 28 days were prepared, and in the stimulated group, 5 rats bred at room temperature of 4 °C for 28 days were prepared.

[0037] From the results shown in FIGS. 2 and 3, it can be seen that regardless of the application of stimulation, in brown adipose tissue and beige fat, there is an absorption peak P1 of water (a peak with a negative value) near wavelengths of 960 nm to 970 nm. Also, from the results shown in FIGS. 2 and 3, it can be seen that regardless of the application of stimulation, in white fat, there is no absorption peak P1 of water (a peak with a negative value) near wavelengths of 960 nm to 970 nm. From these results, it can be understood that based on the presence or absence of the absorption peak of water in the 900 nm band in the spectral data, the presence or absence of brown adipose tissue, beige fat, and white fat in the object S to be measured can be determined.

[0038] When determining the amount of neutral fat in the object S to be measured, the first determination unit 22 holds a PLS regression model regarding the prediction of the amount of neutral fat in the object S to be measured. The PLS regression model here is a model based on the value of the absorption peak of fat in the spectral data subjected to noise removal processing. When the first determination unit 22 receives the spectral data after noise removal processing from the data processing unit 21, it determines the amount of neutral fat in the object S to be measured by applying the spectral data to the PLS regression model. The first determination unit 22 generates information indicating the determination result of the amount of neutral fat in the object S to be measured and outputs it to the second determination unit 23.

[0039] FIG. 4 is a diagram showing an example of the predicted value of the amount of neutral fat in brown adipose tissue from the PLS regression model. In the figure, the horizontal axis represents the actually measured value of the amount of neutral fat, and the vertical axis represents the predicted value of the amount of neutral fat. From the results in FIG. 4, the relationship between the amount of neutral fat in brown adipose tissue predicted by PLS regression and the actual amount of neutral fat in brown adipose tissue can be grasped. In FIG. 4, the coefficient of determination for calibration (model creation data) is R 2 = 0.75, and the coefficient of determination for validation (verification data) is R 2 = 0.73.

[0040] Figure 5 is a diagram showing an example of the predicted value of the amount of neutral fat in beige fat from the PLS regression model. In this figure, the horizontal axis represents the measured value of the amount of neutral fat, and the vertical axis represents the predicted value of the amount of neutral fat. From the results of Figure 5, the relationship between the amount of neutral fat in beige fat predicted by PLS regression and the actual amount of neutral fat in beige fat can be grasped. In Figure 5, the coefficient of determination of calibration (model creation data) is R 2 = 0.73, and the coefficient of determination of validation (verification data) is R 2 = 0.53.

[0041] The second determination unit 23 is a part that determines the amount of brown adipose tissue or the amount of beige fat in the object to be measured S. When determining the amount of brown adipose tissue or the amount of beige fat in the object to be measured S, the second determination unit 23 holds data showing the correlation with the amount of neutral fat in the object to be measured S. When the second determination unit 23 receives information indicating the determination result of the amount of neutral fat in the object to be measured S from the first determination unit 22, based on the data and the amount of neutral fat determined by the first determination unit 22, it determines the amount of brown adipose tissue or the amount of beige fat in the object to be measured S. The second determination unit 23 generates information indicating the determination result of the amount of brown adipose tissue or the amount of beige fat in the object to be measured S and outputs it to the display device 4.

[0042] Figure 6 is a diagram showing the correlation between the amount of neutral fat and the amount of brown adipose tissue in the control group. In this figure, the horizontal axis represents the amount of neutral fat, and the vertical axis represents the amount of brown adipose tissue. From the results shown in Figure 6, it can be seen that there is a certain correlation between the amount of neutral fat and the amount of brown adipose tissue in the control group. The coefficient of determination of the correlation in this figure is R 2 = 0.72. By referring to this correlation, the absolute value of the amount of brown adipose tissue can be determined based on the amount of neutral fat determined by the first determination unit 22.

[0043] FIG. 7 is a diagram showing the correlation between the amount of visceral fat and the amount of beige fat in the control group. In this figure, the horizontal axis represents the amount of visceral fat, and the vertical axis represents the amount of beige fat. From the results shown in FIG. 7, it can be seen that there is a certain correlation between the amount of visceral fat and the amount of beige fat in the control group. The coefficient of determination of the correlation in this figure is R 2 = 0.57. By referring to this correlation, the absolute value of the amount of beige fat can be determined based on the amount of visceral fat determined by the first determination unit 22.

[0044] The second determination unit 23 may hold data showing the correlation with visceral fat in the measurement object S to which the stimulus is applied. In this case, the second determination unit 23 determines the amount of brown adipose tissue or the amount of beige fat in the measurement object S to which the stimulus is applied based on the data and the amount of visceral fat determined by the first determination unit 22. The second determination unit 23 generates information indicating the determination result of the amount of brown adipose tissue or the amount of beige fat in the measurement object S to which the stimulus is applied, and outputs it to the display device 4. The second determination unit 23 may determine both the amount of brown adipose tissue and the amount of beige fat in the measurement object S, or may determine only one of them.

[0045] FIG. 8 is a diagram showing the correlation between the amount of visceral fat and the amount of brown adipose tissue in the stimulation group. In this figure, the horizontal axis represents the amount of visceral fat, and the vertical axis represents the amount of brown adipose tissue. From the results shown in FIG. 8, it can be seen that there is also a certain correlation between the amount of visceral fat and the amount of brown adipose tissue in the stimulation group. The coefficient of determination of the correlation in this figure is R 2 = 0.9. By referring to this correlation, the absolute value of the amount of brown adipose tissue can be determined based on the amount of visceral fat determined by the first determination unit 22.

[0046] FIG. 9 is a diagram showing the correlation between the amount of visceral fat and the amount of beige fat in the stimulation group. In this figure, the horizontal axis represents the amount of visceral fat, and the vertical axis represents the amount of beige fat. From the results shown in FIG. 9, it can be seen that there is also a certain correlation between the amount of visceral fat and the amount of beige fat in the stimulation group. The coefficient of determination of the correlation in this figure is R 2It is 0.73. By referring to this correlation, the absolute value of the beige fat amount can be determined based on the neutral fat amount determined by the first determination unit 22.

[0047] Subsequently, an analysis method according to an embodiment of the present disclosure will be described.

[0048] FIG. 10 is a flowchart showing the analysis method in this embodiment. In this embodiment, the implementation of the analysis method using the above-described analyzer 1 is exemplified. As shown in FIG. 10, this analysis method includes a light irradiation step (step S01), a light detection step (step S02), a data processing step (step S03), a first determination step (step S04), and a second determination step (S05).

[0049] In the light irradiation step, the probe 2 of the analyzer 1 is set on the object to be measured S, and measurement light I including light in the 900 nm wavelength band is irradiated from the light irradiation unit 11 of the probe 2 toward the object to be measured S. The measurement light I irradiated on the object to be measured S is reflected by the object to be measured S and becomes reflected light R. In the light detection step, the reflected light R from the object to be measured S is detected by the light detection unit 12 of the probe 2, and spectral data of the reflected light R in the object to be measured S is acquired. When the light detection unit 12 has an integrating sphere, the spectral data obtained by the light detection unit 12 becomes uniform diffuse reflection spectral data of the reflected light R.

[0050] In the data processing step, noise removal processing is performed on the spectral data acquired by the light detection unit 12. Here, as the noise removal processing, differential processing such as second-order differentiation or Savitzky-Golay differentiation is applied to the spectral data acquired by the light detection unit 12.

[0051] In the first determination step, based on the presence or absence of the absorption peak of water in the 900 nm wavelength band in the spectral data subjected to the noise removal process, the presence or absence of brown adipose tissue, beige fat, and white fat in the measurement object S is determined. Also, in the first determination step, a PLS regression model related to the prediction of the neutral fat amount is used, and by applying the spectral data subjected to the noise removal process to the PLS regression model, the neutral fat amount in the measurement object S is determined. As the PLS regression model, a model based on the value of the absorption peak P2 of fat in the spectral data subjected to the noise removal process is used.

[0052] In the second determination step, using data showing the correlation with the neutral fat amount in the measurement object S, based on the data and the neutral fat amount determined in the first determination step, the amount of brown adipose tissue or beige fat in the measurement object S is determined. When the measurement object S to which a stimulus such as cold stimulation is applied is the analysis target, in the second determination step, data showing the correlation with the neutral fat amount in the measurement object S to which the stimulus is applied is used. In the second determination step, based on the data and the neutral fat amount determined by the first determination unit 22, the amount of brown adipose tissue or beige fat in the measurement object S to which the stimulus is applied is determined.

[0053] As described above, in the analyzer 1 and the analysis method, the spectral data of the reflected light R in the measurement object S is acquired, and the acquired spectral data is subjected to a noise removal process. In the spectral data after the noise removal process, differences occur in the light absorption characteristics in the 900 nm wavelength band depending on the presence or absence of brown adipose tissue, beige fat, and white fat in the measurement object S. Therefore, by applying the spectral data subjected to the noise removal process to a previously created PLS regression model, the neutral fat amount in the measurement object S can be determined. By comparing the determination result of the neutral fat amount with the data showing the correlation with the neutral fat amount in the measurement object S, the absolute value of the amount of brown adipose tissue or beige fat in the measurement object S can be determined.

[0054] In this embodiment, the PLS regression model is a model based on the value of the absorption peak of fat in the spectral data subjected to noise removal processing. By using the PLS regression model based on the value of the absorption peak P2 of fat, it is possible to improve the accuracy of determining the amount of neutral fat in the object S to be measured.

[0055] In this embodiment, the first determination unit 22 determines the presence or absence of brown adipose tissue, beige fat, and white fat in the object S to be measured based on the presence or absence of the absorption peak P1 of water in the 900 nm wavelength band in the spectral data subjected to noise removal processing. In brown adipose tissue and beige fat, there is a tendency for the absorption peak P1 of water to be observed in the 900 nm wavelength band, and in white fat, there is a tendency for the absorption peak P1 of water not to be observed in the 900 nm wavelength band (see FIGS. 2 and 3). By determining the presence or absence of brown adipose tissue, beige fat, and white fat in the object S to be measured based on the presence or absence of the absorption peak of water in the 900 nm wavelength band, in addition to the absolute value of the amount of brown adipose tissue or beige fat, it is possible to obtain the analysis results regarding the presence or absence of brown adipose tissue, beige fat, and white fat. Therefore, the amount of information obtained by the analysis can be further increased. Further, for example, when it is determined that brown adipose tissue or beige fat does not exist, the process of determining the absolute value of the amount of brown adipose tissue or beige fat is not performed, thereby shortening the time required for the process.

[0056] In this embodiment, the second determination unit 23 holds data indicating the correlation with the amount of neutral fat in the object S to be measured to which a stimulus has been applied, and based on the data and the amount of neutral fat determined by the first determination unit 22, determines the amount of brown adipose tissue or beige fat in the object S to be measured to which a stimulus has been applied. In this case, the amount of brown adipose tissue or beige fat in the object S to be measured to which a stimulus has been applied can be accurately determined. By being able to significantly determine the change in the amount of brown adipose tissue or beige fat in the object S to be measured before and after the application of the stimulus, it becomes possible to further expand the scope of application of the analysis.

[0057] The present disclosure is not limited to the above-described embodiments. For example, the analyzer 1 may include a determination unit that determines the amount of change in brown adipose tissue or beige fat based on the change in the value of the fat absorption peak P2 (see FIGS. 2 and 3) in the spectrum data subjected to noise removal processing.

[0058] FIG. 11(a) is a diagram showing the change over time in the value of the fat absorption peak in brown adipose tissue in the control group and the stimulation group. FIG. 11(b) is a diagram showing the change over time in the amount of neutral fat in brown adipose tissue in the control group and the stimulation group. The results in this figure were obtained based on biochemical analysis. The fat absorption peak in brown adipose tissue exists near a wavelength of 924 nm.

[0059] From the results of FIG. 11(a), it was confirmed that in the two data for 14 days and 28 days, the fat absorption peak significantly decreased due to the continuation of cold stimulation. Also, from the results of FIG. 11(b), it was confirmed that the amount of neutral fat in brown adipose tissue in the stimulation group was lower than the amount of neutral fat in brown adipose tissue in the control group in all periods of data. Therefore, it was confirmed that the amount of decrease in the fat absorption peak tended to coincide with the amount of decrease in neutral fat obtained from biochemical analysis.

[0060] FIG. 12(a) is a diagram showing the change over time in the value of the fat absorption peak in beige fat in the control group and the stimulation group. FIG. 12(b) is a diagram showing the change over time in the amount of neutral fat in beige fat in the control group and the stimulation group. The results in this figure were obtained based on biochemical analysis. The fat absorption peak in beige fat exists near a wavelength of 926 nm.

[0061] From the results of Fig. 12(a), it was confirmed that in the data excluding the 7-day data, the absorption peak of fat significantly decreased due to the continuation of cold stimulation. Also, from the results of Fig. 12(b), although the reduction rate was smaller compared to brown adipose tissue, it was found that the amount of neutral fat in the beige fat in the stimulation group was lower than the amount of neutral fat in the beige fat in the control group in the three data of 7 days, 14 days, and 28 days. Therefore, it was confirmed that the reduction amount of the fat absorption peak tended to be consistent with the reduction amount of neutral fat obtained from biochemical analysis.

Description of Symbols

[0062] 1…Analyzer, 11…Light irradiation unit, 12…Light detection unit, 21…Data processing unit, 22…First judgment unit, 23…Second judgment unit, I…Measurement light, R…Reflected light, S…Object to be measured, P1…Absorption peak of water, P2…Absorption peak of fat.

Claims

1. A light irradiation unit that irradiates a measurement object with measurement light including light having a wavelength of 920 nm to 930 nm at which an absorption peak of fat exists and light having a wavelength of 960 nm to 970 nm at which an absorption peak of water exists. A light detection unit that detects reflected light from the measurement object and acquires spectral data of the reflected light in the measurement object. A data processing unit that performs noise removal processing on the spectral data acquired by the light detection unit. A first determination unit that holds a PLS regression model related to prediction of the amount of neutral fat in a measurement object, and determines the amount of neutral fat in the measurement object by applying the spectral data subjected to the noise removal processing to the PLS regression model. An analyzer comprising: a second determination unit that holds data indicating a correlation with the amount of neutral fat in a measurement object, and determines the amount of brown adipose tissue or beige fat in the measurement object based on the data and the amount of neutral fat determined by the first determination unit.

2. The analyzer according to claim 1, wherein the PLS regression model is a model based on the value of the absorption peak of fat in the spectral data subjected to the noise removal processing.

3. The analyzer according to claim 1 or 2, wherein the first determination unit determines the presence or absence of brown adipose tissue, beige fat, and white fat in the measurement object based on the presence or absence of an absorption peak of water having a wavelength of 960 nm to 970 nm in the spectral data subjected to the noise removal processing.

4. The analyzer according to any one of claims 1 to 3, wherein the second determination unit holds data indicating a correlation with the amount of neutral fat in a measurement object to which a stimulus has been applied, and determines the amount of brown adipose tissue or beige fat in the measurement object to which the stimulus has been applied based on the data and the amount of neutral fat determined by the first determination unit.

5. A light irradiation step of irradiating a measurement object with measurement light including light having a wavelength of 920 nm to 930 nm at which an absorption peak of fat exists and light having a wavelength of 960 nm to 970 nm at which an absorption peak of water exists. A light detection step of detecting reflected light from the measurement object and acquiring spectral data of the reflected light in the measurement object. A data processing step of performing noise removal processing on the spectral data acquired in the light detection step. A first determination step of determining the amount of neutral fat in the object to be measured by applying the spectral data subjected to the noise removal process to a PLS regression model using the PLS regression model for predicting the amount of neutral fat; An analysis method comprising: a second determination step of determining the amount of brown adipose tissue or beige fat in the object to be measured based on data showing a correlation with the amount of neutral fat in the object to be measured and the amount of neutral fat determined in the first determination step. **Claim 6** The analysis method according to claim 5, wherein, as the PLS regression model, a model based on the value of the absorption peak of fat in the spectral data subjected to the noise removal process is used. **Claim 7** In the first determination step, based on the presence or absence of the absorption peak of water at wavelengths of 960 nm to 970 nm in the spectral data subjected to the noise removal process, the presence or absence of brown adipose tissue, beige fat, and white fat in the object to be measured is determined. The analysis method according to claim 5 or 6. **Claim 8** In the second determination step, based on data showing a correlation with the amount of neutral fat in the object to be measured to which a stimulus has been applied and the amount of neutral fat determined in the first determination step, the amount of brown adipose tissue or the amount of beige fat in the object to be measured to which the stimulus has been applied is determined. The analysis method according to any one of claims 5 to 7.

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