Body composition estimation program and information processing device

Abstract

The present invention provides a body composition estimation program and information processing device that estimate body composition based on measurable values ​​without invasiveness. [Solution] Terminal 1 includes a lean body mass estimation means for estimating lean body mass based on the user's age, gender, height, and weight; a muscle mass estimation means for estimating muscle mass based on the user's age, gender, height, and weight; a sarcopenia determination means for determining the likelihood of sarcopenia based on at least one of lean body mass and muscle mass; a survival rate evaluation means 104 for evaluating the survival rate based on at least one of lean body mass and muscle mass; and a standard index calculation means 105 for calculating a standard index for insurance enrollment based on at least one of lean body mass and muscle mass.

Description

Technical Field

[0001] The present invention relates to a body composition estimation program and an information processing device.

Background Art

[0002] As a conventional technique, an information processing device that can obtain body composition or health information based on body composition even when there is no body composition meter has been proposed (for example, see Patent Document 1).

[0003] The information processing device disclosed in Patent Document 1 includes a health diagnosis data acquisition unit that acquires health diagnosis data, a body composition estimation unit that estimates body composition based on the health diagnosis data, a health information acquisition unit that acquires health information based on the estimated body composition, and an output unit that outputs the body composition or health information. This information processing device obtains the correlation between health diagnosis data and body composition by performing multiple regression analysis with multiple types of health diagnosis data as explanatory variables and a specific body composition as the target variable. Note that the multiple regression equation is prepared for each age and gender. Also, as an example of body composition, the fat-free mass and muscle mass are estimated.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] The above information processing device obtains body composition from health diagnosis data, but it is necessary to receive a health diagnosis before obtaining the body composition, which imposes a burden on those who need human assistance when undergoing blood sampling and various measurements.

[0006] An object of the present invention is to provide a body composition estimation program and an information processing device that estimate body composition based on measurable values without invasion. [Means for solving the problem]

[0007] One aspect of the present invention provides the following body composition estimation program and information processing device to achieve the above objective.

[0008] [1] Computers, A body composition estimation program designed to function as an estimation tool for estimating lean body mass based on the user's age, gender, height, and weight. [2] The body composition estimation program according to claim 1, wherein the estimation means estimates muscle mass based on the user's age, gender, height, and weight. [3] The body composition estimation program according to claim 1, further configured as a determination means for determining the likelihood of sarcopenia based on the lean body mass. [4] The body composition estimation program according to claim 2, further comprising a determination means for determining the likelihood of sarcopenia based on at least one of lean body mass and muscle mass. [5] The body composition estimation program according to claim 1, further configured to function as an evaluation means for evaluating survival rate based on lean body mass. [6] The body composition estimation program according to claim 2, further comprising an evaluation means for evaluating survival rate based on at least one of lean body mass and muscle mass. [7] The body composition estimation program according to claim 1, further comprising a calculation means for calculating a standard index for insurance enrollment based on the lean body mass. [8] The estimation means estimates the lean body mass based on the following formula: Fat-free mass = α1 × age + β1 × weight + γ1 × height 2 +δ1×gender+ε1 The body composition estimation program described in [1] above, wherein the coefficients in the formula are α1=-0.036, β1=0.261, γ1=10.361, δ1=5.845, ε1=1.135, and gender is male=1, female=0. [9] The estimation means estimates the muscle mass based on the following formula: Muscle mass = α² × age + β² × weight + γ² × height 2 +δ2×gender+ε2 The body composition estimation program described in [2] above, wherein the coefficients in the formula are α2=-0.033, β2=0.245, γ2=9.600, δ2=5.708, ε2=1.548, and gender is male=1, female=0.

[10] An information processing device having an estimation means for estimating lean body mass based on the user's age, gender, height, and weight. [Effects of the Invention]

[0009] According to the invention of claim 1, 2, 8, 9, or 10, body composition can be estimated based on non-invasive, measurable numerical values. According to the invention of claim 3 or 4, it is possible to determine whether or not sarcopenia is present. According to the invention of claim 5 or 6, the survival rate can be evaluated. According to the invention of claim 7, it is possible to calculate the standard index for insurance enrollment. [Brief explanation of the drawing]

[0010] [Figure 1] Figure 1 is a schematic diagram showing an example of the system configuration according to the embodiment. [Figure 2] Figure 2 is a block diagram showing an example of the configuration of a terminal according to the embodiment. [Figure 3] Figure 3 shows an example of the structure of measurement data. [Figure 4] Figure 4 shows an example of how estimated value information is structured. [Figure 5] Figure 5 is a graph showing the correspondence between lean body mass estimated by the above formula and lean body mass measured by a body composition analyzer. [Figure 6] Figure 6 is a graph showing the correspondence between the FFMI estimated by the above formula and the FFMI measured by a body composition analyzer. [Figure 7] Figure 7 is a graph showing the correspondence between lean body mass estimated by the above formula and lean body mass measured by a body composition analyzer. [Embodiments of the Invention]

[0011] [Embodiment] (System Configuration) FIG. 1 is a schematic diagram showing a configuration example of a system according to an embodiment.

[0012] The system includes a terminal 1a as an information processing device such as a smartphone or a PC (Personal Computer) operated by a user 3 who is a measurement target, a terminal 1b as an information processing device such as a PC or a smartphone operated by a user 4 who diagnoses and analyzes the user 3, and a measuring instrument 2 that measures the height, weight, etc. of the user 3. Note that the measuring instrument 2 measures at least height and weight, and may measure other information. Also, the measuring instrument 2 may prepare devices for height measurement and weight measurement separately. Further, the measuring instrument 2 transmits the measured information to the terminal 1a or 1b, but the user 3 or 4 may manually input the measured value to the terminal 1a or 1b.

[0013] In the above system, the terminal 1a or 1b estimates the fat-free mass and muscle mass of the user 3 using the information measured by the measuring instrument 2 and information such as the age and gender of the user 3. The estimated fat-free mass and muscle mass are displayed on the display unit of the terminal 1a or 1b and are used to diagnose and analyze the health of the user 3.

[0014] (Configuration of Information Processing Device) FIG. is a block diagram showing a configuration example of the terminal 1a according to an embodiment. Note that the terminal 1b has the same configuration as the terminal 1a, and thus the description thereof is omitted.

[0015] Terminal 1a comprises a control unit 10 consisting of a CPU (Central Processing Unit) and the like that controls each part and executes various programs, a storage unit 11 consisting of a storage medium such as flash memory that stores information, a communication unit 12 that communicates with the outside via a network, a display unit 13 consisting of an LCD (Liquid Crystal Display) and the like that that displays images and characters, and an operation unit 14 consisting of switches, a touch panel and the like that that receives input operations from user 3.

[0016] The control unit 10 functions as a measurement value acquisition means 100, lean body mass estimation means 101, muscle mass estimation means 102, sarcopenia determination means 103, survival rate evaluation means 104, reference index calculation means 105, etc., by executing the body composition estimation program 110 described later.

[0017] The measurement value acquisition means 100 communicates with the measuring instrument 2 via the communication unit 12 to acquire measurement values ​​such as the height and weight of the user 3 measured by the measuring instrument 2 and stores them in the storage unit 11 as measurement value information 111. Alternatively, the measurement value acquisition means 100 may acquire measurement values ​​based on input operations of the user 3 or 4 to the operation unit 14.

[0018] The lean body mass estimation means 101 estimates the lean body mass of user 3 based on the measured value information 111 and the user's age and gender, which are entered separately. The estimation method will be described later.

[0019] The muscle mass estimation means 102 estimates the muscle mass of user 3 based on the measurement information 111 and the user's age and gender, which are entered separately. The estimation method will be described later.

[0020] The sarcopenia determination means 103 determines the likelihood of user 3 having sarcopenia (pre-sarcopenia, dynapenia) based on the lean body mass estimated by the lean body mass estimation means 101 and / or the muscle mass estimated by the muscle mass estimation means 102. The determination method will be described later.

[0021] The survival rate evaluation means 104 evaluates the survival rate of user 3 based on the lean body mass estimated by the lean body mass estimation means 101. The evaluation method will be described later.

[0022] The standard index calculation means 105 calculates a standard index for user 3's insurance enrollment based on the lean body mass estimated by the lean body mass estimation means 101. The calculation method will be described later.

[0023] The memory unit 11 stores a body composition estimation program 110 that causes the control unit 10 to operate as the means 100-105 described above, measurement information 111, estimated value information 112, judgment information 113, survival rate information 114, reference index information 115, etc.

[0024] Figure 3 shows an example of the configuration of the measurement information 111.

[0025] The measurement information 111 includes a user ID for identifying the user, the user's name, age, gender, height, and weight.

[0026] Figure 4 shows an example of the configuration of the estimated value information 112.

[0027] The estimated value information 112 includes a user ID, lean body mass estimated by lean body mass estimation means 101, and muscle mass estimated by muscle mass estimation means 102.

[0028] (Operation of information processing device) Next, the operation of this embodiment will be explained by dividing it into (1) measurement operation, (2) estimation operation, and (3) other operations.

[0029] (1) Measurement operation First, user 3 operates terminal 1a and registers at least their name, age, and gender as user information for the estimation program. The measurement value acquisition means 100 of terminal 1a registers the information entered in the name, age, and gender fields of the measurement value information 111.

[0030] Next, user 3 measures their height and weight using measuring device 2. Measuring device 2 transmits the measured height, weight, and other information to terminal 1a (or 1b).

[0031] The measurement value acquisition means 100 of terminal 1a acquires measurement values ​​such as the user's height and weight measured by the measuring instrument 2 via the communication unit 12 and stores them in the storage unit 11 as measurement value information 111. The measurement value acquisition means 100 may also acquire measurement values ​​based on input operations of the user 3 to the operation unit 14.

[0032] (2) Estimated behavior The lean body mass estimation means 101 estimates the lean body mass of user 3 based on the measured value information 111 and the user's age and gender, which are entered separately. For estimation, the following formula is used, which was obtained by performing a multiple regression analysis of the lean body mass of 564 subjects (77.2 ± 7.4 years old, 175 males, 389 females) from age, gender, height, and weight.

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[0033] Figure 5 is a graph showing the correspondence between lean body mass estimated by the above formula and lean body mass measured by a body composition analyzer.

[0034] Here, the error rates for lean body mass and each coefficient are as follows. Note that the error rate is calculated from (predicted value / actual value) / actual value × 100. Fat-free mass: 0.2±5.6 α1:-0.01(Error rate 5.7±5.9%)~-0.06(Error rate-4.7±5.5%) β1: 0.22 (error rate -5.4±5.3%) ~ 0.3 (error rate -5.7±5.9%) γ1:9.5 (error rate -5.0±5.3%)~11 (error rate 4.2±5.8%) δ1:0.001 (error rate -3.5±7.7%)~13 (error rate 4.9±9.4%) ε1:0.001 (error rate -2.7±5.5%)~3(error rate 5.3±5.9%)

[0035] The Spearman rank correlation coefficient between the estimated and observed values ​​is 0.944, and the p-value is ≤0.001, indicating a strong correlation.

[0036] Furthermore, verification using external data (179 subjects (74.7 ± 6.2 years old, 31 males, 148 females)) also showed a strong correlation, with a Spearman rank correlation coefficient of 0.918 and a p-value of ≤0.001.

[0037] Furthermore, the muscle mass estimation means 102 estimates the muscle mass of user 3 based on the measured value information 111 and the age and gender of user 3, which are entered separately. For estimation, similar to lean body mass, the following formula is used, which was obtained by performing multiple regression analysis on the muscle mass of 564 subjects (77.2 ± 7.4 years old, 175 males, 389 females) from age, gender, height, and weight.

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[0038] Figure 7 is a graph showing the correspondence between lean body mass estimated by the above formula and lean body mass measured by a body composition analyzer.

[0039] Here, the error rates for muscle mass and each coefficient are as follows. Note that the error rate is calculated from (predicted value / actual value) / actual value × 100. Muscle mass: 0.2 ± 5.5 α2:-0.01(Error rate 5.3±5.8%)~-0.06(Error rate-5.7±5.5%) β2: 0.21 (error rate -4.9±5.3%) ~ 0.28 (error rate 5.4±5.9%) γ2:9.0 (error rate -3.6±5.4%)~10 (error rate 2.8±5.7%) δ2:2 (error rate -2.3±6.4%) ~ 8 (error rate 1.8±6.2%) ε2:0.0001(error rate -4.1±5.4%)~3(error rate 4.4±5.7%)

[0040] The Spearman rank correlation coefficient between the estimated and observed values ​​is 0.944, and the p-value is ≤0.001, indicating a strong correlation.

[0041] Furthermore, verification using external data (179 subjects (74.7 ± 6.2 years old, 31 males, 148 females)) also showed a Spearman rank correlation coefficient of 0.918 and a p-value of ≤0.001, indicating a strong correlation.

[0042] (3) Other operations The sarcopenia determination means 103 determines the likelihood of user 3 having sarcopenia (pre-sarcopenia, dynapenia) based on the lean body mass estimated by the lean body mass estimation means 101 and / or the muscle mass estimated by the muscle mass estimation means 102.

[0043] The sarcopenia determination method 103 calculates the FFMI (Fat Free Mass Index) based on the following formula. Since FFMI has been reported to potentially serve as a surrogate marker for SMI (Skeletal Muscle Mass Index, which is the sum of the muscle mass of the limbs divided by the square of the height) for screening for low muscle mass in sarcopenia, the sarcopenia determination method 103 determines the likelihood of sarcopenia based on FFMI. The determination is made by comparing FFMI with a predetermined threshold.

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[0044] Figure 6 is a graph showing the correspondence between the FFMI estimated by the above formula and the FFMI measured by a body composition analyzer.

[0045] The Spearman rank correlation coefficient between the estimated and observed values ​​is 0.805, and the p-value is ≤0.001, indicating a strong correlation.

[0046] Furthermore, verification using external data (179 subjects (74.7 ± 6.2 years old, 31 males, 148 females)) also showed a Spearman rank correlation coefficient of 0.837 and a p-value of ≤0.001, indicating a strong correlation.

[0047] Furthermore, the survival rate evaluation means 104 evaluates the survival rate of user 3 by comparing the lean body mass (muscle mass and / or FFMI) estimated by the lean body mass estimation means 101 with a predetermined threshold. Specifically, for example, the relationship between lean body mass and mortality has been statistically reported using the Cox proportional hazards model in studies of lean body mass in Asians (Obesity (Silver Spring). 2023 Dec;31(12):3043-3055.). The cutoff points for lean body mass index (FFMI) used to classify cancer patients in China in terms of time to death are <14.14 kg / m2 for women and <16.31 kg / m2 for men, and it is based on the fact that a low FFMI score can be a strong predictor of mortality in elderly cancer patients, using these as thresholds (Nutrition. 2022 Feb:94:111508.). Furthermore, the fact that a decrease in FFMI due to BIA independently predicts cancer survival and is associated with a decline in quality of life (Clin Nutr. 2021 Jun;40(6):3901-3907.), and that a decrease in lean body mass (FFM) is associated with decreased survival, worsening clinical outcomes, decreased quality of life, and increased treatment toxicity in cancer patients (Clin Nutr. 2012 Aug;31(4):435-47.), can also be used as evidence. Furthermore, decreased muscle mass (AST / ALT and Cr / CysC*100) has been reported to be associated with an increased risk of death at 1, 2, and 3 years in community-acquired pneumonia in the elderly (BMC Geriatr. 2022 Nov 19;22(1):880. Doi: 10.1186 / s12877-022-03626-y). Since the threshold for AST / ALT at which the risk of death increases is 1.48 and the threshold for Cr / CysC*100 is 64.64, thresholds may be set by verifying the correspondence between these thresholds and the muscle mass estimated by the muscle mass estimation means 102.

[0048] Furthermore, the reference index calculation means 105 calculates a reference index for user 3's insurance enrollment based on the lean body mass (muscle mass, FFMI, and / or survival rate evaluated by the survival rate evaluation means 104) estimated by the lean body mass estimation means 101. This utilizes the fact that lean body mass (muscle mass and / or FFMI) is related to health status and survival rate, as described above. Specifically, for example, in a study of Japanese people, the dose-response relationship between FFMI and mortality showed an inverse dose-response relationship between mortality and FFMI for both men and women (J Am Med Dir Assoc. 2020 Jun;21(6):726-733.e4.). For Japanese men, a FFMI of 17.5 kg / m2 or less, and for Japanese women, 15.4 kg / m2 or less, is one indicator of an increased risk of death (J Am Med Dir Assoc. 2020 Jun;21(6):726-733.e4.).

[0049] (Effects of the embodiment) According to the embodiment described above, lean body mass and muscle mass are estimated from age, sex, height, and weight based on a formula obtained by multiple regression analysis, making it possible to estimate body composition based on measurable values ​​without invasiveness. Furthermore, since there is no need to use a body composition analyzer, body composition can be estimated even for users with implanted pacemakers.

[0050] Furthermore, by using estimated lean body mass and muscle mass, it is possible to determine sarcopenia eligibility from FFMI based on non-invasive, measurable values, evaluate survival rates, and calculate insurance eligibility criteria. While skeletal muscle percentage, the proportion of skeletal muscle in the body, is sometimes used as an indicator, it tends to be higher in individuals with less body fat. Therefore, using FFMI, which can be evaluated regardless of body fat, improves the reliability of the calculated values.

[0051] [Other embodiments] It should be noted that the present invention is not limited to the embodiments described above, and various modifications are possible without departing from the spirit of the invention.

[0052] In the above embodiment, the functions of each means 100 to 105 of the control unit 10 were implemented by program, but all or part of each means may be implemented by hardware such as an ASIC. Furthermore, the program used in the above embodiment can be stored and provided on a recording medium such as a CD-ROM. Also, the steps described in the above embodiment can be rearranged, deleted, or added without altering the essence of the present invention. [Explanation of Symbols]

[0053] 1a, 1b: Terminals 2:Measuring instrument 3, 4: User 10: Control Unit 11: Storage section 12: Communications Department 13: Display section 14:Operation section 100: Means for acquiring measurement values 101: Means for estimating lean mass 102: Muscle mass estimation method 103: Sarcopenia Diagnosis Method 104: Survival Rate Evaluation Methods 105: Standard index calculation means 110: Body composition estimation program 111: Measurement Information 112: Estimated Value Information 113: Judgment information 114: Survival rate information 115: Standard index information

Claims

1. Computers, A body composition estimation program designed to function as an estimation tool for estimating lean body mass based on the user's age, gender, height, and weight.

2. The body composition estimation program according to claim 1, wherein the estimation means estimates muscle mass based on the user's age, gender, height, and weight.

3. The body composition estimation program according to claim 1, further configured to function as a determination means for determining the likelihood of sarcopenia based on the lean body mass.

4. The body composition estimation program according to claim 2, further functioning as a determination means for determining the likelihood of sarcopenia based on at least one of the lean body mass and the muscle mass.

5. The body composition estimation program according to claim 1, further configured to function as an evaluation means for evaluating survival rate based on the lean body mass.

6. The body composition estimation program according to claim 2, further functioning as an evaluation means for evaluating survival rate by comparing at least one of the lean body mass and muscle mass with a predetermined threshold.

7. The body composition estimation program according to claim 1, further functioning as a calculation means for calculating a standard index for insurance coverage based on the lean body mass.

8. The estimation means estimates the lean body mass based on the following formula: Fat removal amount = α １ ×year+β １ ×Weight +γ １ × Height ２ +δ １ ×gender + ε １ The coefficient in the formula is α １ = -0.036, β １ = 0.261, γ １ = 10.361, δ １ = 5.845, ε １ = 1.135, and the gender is male = 1, female = 0. The body composition estimation program according to claim 1

9. The estimation means estimates the muscle mass based on the following formula: Muscle mass = α ２ ×year+β ２ ×Weight +γ ２ × Height ２ +δ ２ ×gender + ε ２ The coefficient in the formula is α ２ = -0.033, β ２ = 0.245, γ ２ = 9.600, δ ２ = 5.708, ε ２ The body composition estimation program according to claim 2, wherein the ratio is 1.548, and the gender is 1 for male and 0 for female.

10. An information processing device having an estimation means for estimating lean body mass based on the user's age, gender, height, and weight.

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