Systems, apparatus, and methods related to modeling, monitoring, and/or managing metabolism

Abstract

Systems, apparatus, and methods related to modeling, monitoring, and/or managing metabolism of a subject include measuring a respiratory quotient (RQ) level in a subject and/or optimizing and executing a nonlinear feedback model to model energy substrate utilization in the subject based on at least one of a macronutrient composition and caloric value of food consumed by the subject, an intensity and duration of activity by the subject, a rate and maximum capacity of glycogen storage in the subject, a rate and maximum capacity of de novo lipogenesis in the subject, a quality and duration of sleep by the subject, and/or an RQ level in the subject.

1 - 11 . (canceled) 
     
     
         12 . A computer-facilitated method for modeling metabolism in a subject and/or managing body weight of the subject, the method comprising:
 determining from data related to the subject obtained via at least one input device:
 a macronutrient composition and caloric value of food consumed by the subject; 
 an intensity and duration of activity by the subject; 
 a rate and maximum capacity of glycogen storage in the subject; and 
 a rate and maximum capacity of de novo lipogenesis in the subject; and 
   optimizing, via at least one processor, a nonlinear feedback model to model energy substrate utilization in the subject based on the macronutrient composition and caloric value of food consumed by the subject, the intensity and duration of activity by the subject, the rate and maximum capacity of glycogen storage in the subject, and the rate and maximum capacity of de novo lipogenesis in the subject.   
     
     
         13 . The computer-facilitated method of  claim 12 , further comprising:
 obtaining metabolic data for the energy substrate utilization in the subject, the metabolic data including respiratory quotient (RQ) data acquired from the subject.   
     
     
         14 . The computer-facilitated method of  claim 13 , further comprising:
 controlling, via the at least one processor, operation of the optimized nonlinear feedback model based on the metabolic data to determine a target value of one or more energy substrate utilization variables that at least one of maintains and increases energy substrate utilization in the subject.   
     
     
         15 . The computer-facilitated method of  claim 14 , wherein the one or more energy substrate utilization variables comprise at least one of:
 the macronutrient composition and caloric value of food consumed by the subject; and   the intensity and duration of activity by the subject.   
     
     
         16 . The computer-facilitated method of  claim 12 , further comprising:
 determining from data related to the subject obtained via the at least one input device a quality and duration of sleep by the subject, and   wherein optimizing, via the at least one processor, the nonlinear feedback model is based further on the quality and duration of sleep by the subject.   
     
     
         17 . The computer-facilitated method of  claim 12 , further comprising:
 obtaining at least one initial physiological parameter associated with the subject, the at least one initial physiological parameter including an initial body weight of the subject; and         
     
     
         18 . The computer-facilitated method of  claim 17 , further comprising:
 controlling, via the at least one processor, operation of the optimized nonlinear feedback model based on the at least one initial physiological parameter to determine a target value of one or more energy substrate utilization variables that at least one of maintains and alters the body weight of the subject   
     
     
         19 . The computer-facilitated method of  claim 18 , wherein the one or more energy substrate utilization variables comprise at least one of:
 the macronutrient composition and caloric value of food consumed by the subject; and   the intensity and duration of activity by the subject.   
     
     
         20 . The computer-facilitated method of  claim 17 , wherein the at least one initial physiological parameter further comprises at least one of height, age, gender, body mass index (BMI), body fat percentage, waist circumference, hip circumference, and chest circumference. 
     
     
         21 . A system for optimizing a nonlinear feedback model of energy substrate utilization in a subject, the system comprising:
 at least one input device for obtaining data related to the subject;   at least one memory device for storing the data related to the subject and processor-executable instructions; and   at least one processor in communication with the at least one input device and the at least one memory device, wherein upon execution of the processor-executable instructions, the at least one processor:
 determines from the data related to the subject:
 a macronutrient composition and caloric value of food consumed by the subject; 
 an intensity and duration of activity by the subject; 
 a rate and maximum capacity of glycogen storage in the subject; and 
 a rate and maximum capacity of de novo lipogenesis in the subject; and 
 
 optimizes the nonlinear feedback model to model energy substrate utilization in the subject based on the macronutrient composition and caloric value of food consumed by the subject, the intensity and duration of activity by the subject, the rate and maximum capacity of glycogen storage in the subject, and the rate and maximum capacity of de novo lipogenesis in the subject. 
   
     
     
         22 . The system of  claim 21 , wherein the at least one processor, upon execution of the processor-executable instructions, optimizes the nonlinear feedback model to model energy substrate utilization in the subject based on a quality and duration of sleep by the subject, and
 the one or more energy substrate utilization variables comprise at least one of the macronutrient composition and caloric value of food consumed by the subject or the intensity and duration of activity by the subject.   
     
     
         23 . A system for managing body weight of a subject, the system comprising:
 at least one input device for obtaining data related to the subject;   at least one memory device for storing the data related to the subject and processor-executable instructions; and   at least one processor in communication with the at least one input device and the at least one memory device, wherein upon execution of the processor-executable instructions, the at least one processor:
 determines from the data related to the subject at least one initial physiological parameter associated with the subject, the at least one initial physiological parameter including an initial body weight of the subject; and 
 controls operation of a nonlinear feedback model to determine, based on the at least one initial physiological parameter, a target value of one or more energy substrate utilization variables that at least one of maintains and alters the body weight of the subject, wherein:
 the nonlinear feedback model is optimized to model energy substrate utilization in the subject based on at least one of:
 a macronutrient composition and caloric value of food consumed by the subject; 
 an intensity and duration of activity by the subject; 
 a rate and maximum capacity of glycogen storage in the subject; 
 a rate and maximum capacity of de novo lipogenesis in the subject; and 
 a quality and duration of sleep by the subject; and 
 
 the one or more energy substrate utilization variables comprise at least one of:
 the macronutrient composition and caloric value of food consumed by the subject; and 
 the intensity and duration of activity by the subject. 
 
 
   
     
     
         24 . The system of  claim 23 , wherein the at least one initial physiological parameter further comprises at least one of height, age, gender, body mass index (BMI), body fat percentage, waist circumference, hip circumference, and chest circumference. 
     
     
         25 . The system of  claim 23 , wherein the nonlinear feedback model is optimized further based on a quality and duration of sleep by the subject.