Apparatus and method for the analysis of the change of body composition and hydration status and for dynamic indirect individualized measurement of components of the human energy metabolism

• US 9,949,663 B1
• Filed: 11/13/2014
• Issued: 04/24/2018
• Est. Priority Date: 11/13/2014
• Status: Active Grant

• Current Assignee: Ori Diagnostic Instruments, LLC
• Original Assignee: Ori Diagnostic Instruments, LLC
• Inventors: Ori, Zsolt Peter
• Primary Examiner(s): Henson, Devin
• Assistant Examiner(s): MELHUS, BENJAMIN S
• Application Number:
  US14/541,033
• Time in Patent Office: 1,258 Days
• Field of Search: None
• CPC Class Codes: A61B 5/0537 Measuring body composition by impedance, e.g. tissue hydration or fat content
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/4872 Body fat
• CPC Class Codes: A61B 5/4875 Hydration status, fluid retention of the body
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A61B 5/7225 Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation A61B5/30, A61B5/316 take precedence; transformers for specific applications H01F38/00; amplifiers H03F
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Abstract

One embodiment of an apparatus for analysis of body composition and hydration status by detecting resistance of the human subject at zero and infinite frequency including a method for measuring indirectly extracellular water mass, intracellular water mass, lean body mass, and body fat mass; daily changes of extracellular water mass, intracellular water mass, lean body mass, and body fat mass; and acute changes of extracellular water mass and intracellular water mass; and for individualized calibration of these indirect measurements.

In addition, a method for fitting mathematical models to serial measurements of indirectly measured lean body mass and fat mass and for dynamic indirect individualized measurement using minimum variance estimation and prediction of daily changes of the body composition defined as change of glycogen store, change of fat store and change of protein store; daily utilized macronutrient energy intake defined as utilized carbohydrate, fat, and protein caloric intake; daily macronutrient oxidation rate defined as rate of carbohydrate oxidation, fat oxidation, and protein oxidation; daily resting metabolic rate; daily unknown forms of energy losses or gains; daily rate of endogenous lipolysis; daily nitrogen excretion; daily gluconeogenesis from protein; daily determination of extracellular water mass; daily determination of intracellular water mass; and acute change of extracellular water mass and intracellular water mass.

Claims

1. A method for dynamic indirect individualized measurement of a daily change of body composition vector, a daily utilized energy intake vector, a daily macronutrient oxidation vector, a daily resting metabolic rate, at least one of daily unknown energy losses and gains, a daily rate of endogenous lipolysis, a daily nitrogen excretion, and a daily gluconeogenesis from protein comprising:

at a computing device configured to measure and predict metrics associated with at least one of a hydration characteristic, a body composition characteristic, and an energy metabolism characteristic;

obtaining, by a sensor, daily serial measurements from a user, wherein the daily serial measurements comprise at least one of a macronutrient energy intake, a resting metabolic rate, a physical activity energy expenditure based on a user'"'"'s movements, and a weight amount;

deriving and solving a mathematical equation to calculate said daily utilized energy intake vector if ingested daily carbohydrate intake, ingested daily fat intake, and at least one of ingested daily protein intake are available and if ingested daily carbohydrate intake, ingested daily fat intake, and ingested daily protein intake are not available, deriving a mathematical model and using a minimum variance estimation and prediction method and a measured indirectly calculated change of body composition vector that can comprise use of at least one of a reference and a nominal trajectory method to estimate a daily utilized energy intake vector;

deriving and solving mathematical equations with said daily utilized energy intake vector to calculate said daily macronutrient oxidation vector, said daily resting metabolic rate, at least one of a daily estimation of unknown forms of energy losses and gains, said daily rate of endogenous lipolysis, said daily nitrogen excretion, said daily gluconeogenesis from protein, a daily estimation of a correction factor for gluconeogenesis from amino acids, a daily gluconeogenesis from glycerol, a daily estimation of a correction factor for de novo lipogenesis, a daily rate of de novo lipogenesis, a glycerol 3-phosphate synthesis, and an energy needed for fat synthesis;

deriving and solving a mathematical model and using said minimum variance estimation and prediction method and said measured indirectly calculated change of body composition vector that can comprise use of at least one of a reference and a nominal trajectory method to obtain an estimated indirectly calculated change of body composition vector;

performing a stochastic identification of an indirectly calculated correction factor for de novo lipogenesis, an indirectly calculated correction factor for gluconeogenesis from amino acids, and an indirectly calculated correction factor for at least one of unidentified energy losses and gains;

performing a state space model identification to estimate said daily change of body composition vector, said daily utilized energy intake vector, a daily macronutrient oxidation vector, said daily resting metabolic rate, at least one of daily unknown energy losses and gains, said daily rate of endogenous lipolysis, said daily nitrogen excretion, and said daily gluconeogenesis from protein;

in response to performing the stochastic and state space model identifications, updating a self correcting model of a utilized energy intake and a self adaptive model of the energy metabolism characteristic of a user;

generating an individualized metric and trend derived from the self correcting model and the self adaptive model regarding the at least one of the hydration characteristic, the body composition characteristic, and the energy metabolism characteristic of the user; and

presenting the individualized metric and trend to the user via the computing device, whereby the individualized metric and trend provides improved predictive health information to the user for use to manage at least one of a user'"'"'s health, fitness goals, body composition goals, hydration goals, and energy expenditure goals.

2. The method of claim 1, wherein performing the state space model identification comprises a state space model structure comprising:

a process model made up of matrix equation wherein said daily utilized energy intake vector is equal to the sum of a previous day'"'"'s dynamic transition matrix multiplied with a previous day'"'"'s utilized energy intake vector, plus a previous day'"'"'s dynamic coupling matrix multiplied with said daily indirectly calculated change of body composition vector, plus a previous day'"'"'s time varying bias vector; and

a measurement model made up of a matrix equation wherein said daily utilized energy intake vector is equal to a product of an inverse matrix of the time varying utilized energy intake coupling matrix in at least one of a Retained and Released Energy Model of the Human Energy Metabolism and an energy constant matrix of the at least one of Retained and Released Energy Model of the Human Energy Metabolism and said daily indirectly calculated change of body composition vector minus the product of said inverse matrix of the time varying utilized energy intake coupling matrix in the at least one of Retained and Released Energy Model of the Human Energy Metabolism and an indirectly calculated bias vector of the at least one of Retained and Released Energy Model of the Human Energy Metabolism.

3. The method of claim 1, wherein the mathematical model equations for calculating dynamic indirect individualized measurements further comprises a measurement model of the macronutrient oxidation vector by deriving and solving a system of three linear equations for a rate of carbohydrate oxidation, a rate of fat oxidation, and a rate of protein oxidation wherein the first equation sets the constraint that a total energy expenditure equates to the sum of said rate of carbohydrate oxidation, plus said rate of fat oxidation, plus said rate of protein oxidation, plus an estimation of at least one of unknown energy losses and gains;

the second equation sets the constraint that said total energy expenditure equals the sum of an energy production calculated with known heat equivalent of oxygen for carbohydrate, plus an energy production calculated with known heat equivalent of oxygen for fat, plus an energy production calculated with known heat equivalent of oxygen for protein, plus said estimation of the at least one of unknown energy losses and gains; and

the third equation sets the constraint that said energy production calculated with known heat equivalent of oxygen for protein equals six point twenty five times the energy density of protein times said daily nitrogen excretion minus said daily gluconeogenesis from protein.

4. The method of claim 1, wherein the mathematical model equations for calculating dynamic indirect individualized measurements further comprises modeling said daily resting metabolic rate by deriving and solving at least one of an equation for said daily resting metabolic rate with a filtering formula by adding up a time-varying constant energy expenditure plus a part of the resting metabolic rate which is independent of the body composition changes and the time-varying constant energy expenditure plus a synthesis cost of glycogen multiplied by a change of glycogen store on the previous day plus a synthesis cost of fat multiplied by a change of fat store on the previous day, plus a synthesis cost of protein multiplied by a change of protein store on the previous day and by deriving and solving an equation for said daily resting metabolic rate with a predictive formula by adding up said time-varying constant energy expenditure plus said part of the resting metabolic rate which is independent of the body composition changes and the time-varying constant energy expenditure plus the synthesis cost of glycogen multiplied by the change of glycogen store on a same day, plus the synthesis cost of fat multiplied by the change of fat store on the same day, plus the synthesis cost of proteins multiplied by the change of protein store on the same day.

5. The method of claim 4, wherein the modeling of the daily resting metabolic rate further comprises at least one of the filtering formula and the predictive formula, wherein said daily time-varying constant energy expenditure is estimated with said minimum variance estimation and prediction method and a directly measured resting metabolic rate from a calibration day.

6. The method of claim 1, wherein the mathematical model estimating said indirectly calculated change of body composition vector further comprises a state space model structure comprising:

a process model containing three linear equations wherein the first equation equates a daily energy of a glycogen store change with a sum of said daily utilized carbohydrate intake, plus said daily estimation of the correction factor for gluconeogenesis from amino acids multiplied by said daily gluconeogenesis from protein, plus said daily gluconeogenesis from glycerol, minus said daily estimation of the correction factor for de novo lipogenesis multiplied by said daily rate of de novo lipogenesis, minus said glycerol 3-phosphate synthesis, minus said energy needed for fat synthesis, minus said daily calculated rate of carbohydrate oxidation, minus said daily estimation of the at least one of unknown energy losses and gains, the second equation equates a daily energy of fat store change with a molecular weight ratio of free fatty acid to triglyceride multiplied with said daily utilized fat intake, plus said daily estimation of the correction factor for de novo lipogenesis multiplied by said daily rate of de novo lipogenesis, plus a molecular weight ratio glycerol to triglyceride multiplied with a daily change of the fat store, minus said daily calculated rate of fat oxidation, the third equation equates a daily energy of protein store change with said daily utilized protein intake, minus said daily estimation of the correction factor for gluconeogenesis from amino acids multiplied by said daily gluconeogenesis from protein, minus said daily calculated rate of protein oxidation;

a measurement model comprising a matrix equation wherein said daily indirectly calculated change of body composition vector equates with at least one of a matrix product of an inverse of a constant matrix of a Measurement Model of Body Composition Change from Lean-Fat-Protein and a change of an indirectly calculated Lean-Fat-Protein vector and a matrix equation wherein said daily indirectly calculated change of body composition vector equates with a matrix product of the inverse of a constant matrix of a Measurement Model of Body Composition Change from Lean-Fat-Resting-Metabolic-Rate and a daily change of the indirectly calculated Lean-Fat-Resting-Metabolic-Rate vector; and

a measurement model at least one of said change of indirectly calculated Lean-Fat-Protein vector, where said change of Lean-Fat-Protein vector is constructed from a daily change of indirectly measured lean body mass measured by a machine for detecting the resistance of a human subject at an extrapolated zero and infinite frequency, from a daily change of indirectly measured body fat mass measured by said machine, and from said daily energy of protein store change, and of said change of indirectly calculated Lean-Fat-Resting-Metabolic-Rate vector, where said Lean-Fat-Resting-Metabolic-Rate vector is constructed from said daily change of indirectly measured lean body mass measured by said machine, from said daily change of indirectly measured body fat mass measured by said machine, and from a daily resting metabolic rate with a filtering formula minus an indirectly calculated time-varying constant energy expenditure minus a part of the resting metabolic rate which is independent of a body composition changes and a time-varying constant energy expenditure.

7. The method of claim 6, further comprising using a daily indirectly calculated correction factor for de novo lipogenesis, a daily indirectly calculated correction factor for gluconeogenesis from amino acids, and a daily indirectly calculated correction factor for at least one of unknown energy losses and gains to estimate with said minimum variance estimation and prediction method said daily estimated correction factor for de novo lipogenesis, said daily estimated correction factor for gluconeogenesis from amino acids, and said daily estimated correction factor for the at least one of unknown energy losses and gains.

8. The method of claim 1, wherein the mathematical model equations for calculating dynamic indirect individualized measurements further comprises deriving and solving a linear invertible equation for said daily endogenous lipolysis by adding up a product of a daily fat store dependent coefficient for rate of endogenous lipolysis and a daily body fat mass, plus a product of a carbohydrate intake dependent coefficient for rate of endogenous lipolysis and a utilized carbohydrate intake, plus a daily bias for rate of endogenous lipolysis.

9. The method of claim 8, wherein the linear invertible equation for said daily endogenous lipolysis further comprises deriving and solving an equation for an estimated rate of endogenous lipolysis with calibration by multiplying said rate of endogenous lipolysis with said estimation of a correction factor for de novo lipogenesis and a new value for a baseline lipolysis rate after calibration and dividing the multiplication of said rate of endogenous lipolysis with said estimation of the correction factor for de novo lipogenesis and the new value for the baseline lipolysis rate after calibration by a baseline lipolysis rate before calibration.

10. The method of claim 1, wherein the mathematical model equations for calculating dynamic indirect individualized measurements further comprises deriving and solving an equation for said daily nitrogen excretion by first subtracting a product of the energy density of protein with a previous day'"'"'s change of protein store from said daily protein intake, then dividing a resulting value by six point twenty five times the energy density of protein.

11. The method of claim 1, wherein the mathematical model equations for calculating dynamic indirect individualized measurements further comprises deriving and solving a linear invertible equation for daily gluconeogenesis from protein by adding up a protein store dependent coefficient for gluconeogenesis from protein multiplied by a protein mass, plus a carbohydrate intake dependent coefficient for gluconeogenesis from protein multiplied by said utilized carbohydrate intake plus a protein intake dependent coefficient for gluconeogenesis from protein multiplied by said utilized protein intake, plus a bias for gluconeogenesis from protein.

12. The method of claim 11, wherein the modeling of the daily gluconeogenesis from protein further comprises an estimated gluconeogenesis from protein with calibration by deriving and solving an equation multiplying said gluconeogenesis from protein with an estimation of said correction factor for gluconeogenesis from amino acids and a new value for a basal gluconeogenesis rate after calibration and dividing the multiplication of said gluconeogenesis from protein with the estimation of said correction factor for gluconeogenesis from amino acids and the new value for the basal gluconeogenesis rate after calibration by the basal gluconeogenesis rate.

13. The method of claim 1, wherein the self correcting model of the utilized energy intake comprises of estimating a daily utilized energy of carbohydrate, a fat, and a protein caloric intake.

14. The method of claim 1, wherein the self adaptive model of the energy metabolism characteristic comprises of estimating a daily change of body composition, comprising a change of glycogen store, a fat store, and a protein store.