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Canonical Representation of the Human Energy Metabolism of Lean Mass, Fat Mass, and Insulin Resistance*
Zsolt Ori, MD, MS, FACPOri Diagnostic Instruments, LLC
Durham, NC, USA [email protected]
Ilona Ori, JDOri Diagnostic Instruments, LLC
Durham, NC, [email protected]
*Research supported by Ori Diagnostic Instruments, L.L.C; U.S. patent pending: application numbers 14541033 and 62372363
The prevalence of obesity and economical burden of prediabetes and diabetes in the US:
• More than one-third (35.7 percent) of adults are considered to be obese
• Diabetes 13 percent• Prediabetes 37.5%• half of the U.S. adult population has either diabetes or prediabetes• economic burden of diabetes is exceeding more than $322 billion/yr• the cost of prediabetes increased 74 percent over the past 5 years (to
$44 billion)
From Cybernetics to Self-directed Cyber-therapy in the Era of Mobile Communication and Computing
- The Age of “Apps”-Cybernetics Norbert Wiener (1894 – 1964)
• Communication & computingJohn von Neuman (1903 – 1957)
• Feedback & statistical estimationKálmán Rudolf Emil (1930 – 2016)
• Control Norbert Wiener (1894 – 1964) & Andrey Kolmogorov (1903–1987)
Cyber-therapy - the Metabolic Health Monitor App• Mobile communication and computer
devices• Feedback- Objective metrics with use of
personalized mathematical modeling• Self-Control - Adaptive predictive control
and individualized dynamic behavior change models
• Coaching by remote healthy lifestyle team and telemedicine
• Social media networking
Ori, 2015: Self-adaptive model of the human energy metabolism (SAM-HEM)
Ori, 2016: Self-adjusting input output model (SIO-HEM) of the human energy metabolism
Ori, 2016: Self-adjusting R-ratio input output model of the human energy metabolism (RIO-HEM) in canonical form
(1b)
Three proposed metabolic metrics and trajectories that uniquely identify the individual’s metabolic
trendThe daily energy density of the fat mass change :
The daily energy density of the lean mass change :
The daily ratio of lean body mass change velocity and fat mass change velocity or R ratio :
Daily change in fat mass
Daily energy imbalance
ϱ𝐿∗𝑘
ϱ𝐹∗𝑘
𝑅𝑘∗
Daily change in lean mass
Daily change in fat mass
Daily change in fat mass
Daily energy imbalance
The R-Ratio, a Novel Surrogate Market of the Insulin Resistance
Energy balance experiments and clinical experience demonstrate also that the ability to gain or lose weight depends not only on the energy balance but also on the state of insulin sensitivity/ resistance and along with it on the body’s fat mass itself.
Estimation of the Daily Fat Oxidation and theMetabolic Health Monitor App
Advantages of the current modeling of the metabolism
• individualized model through self-adjusting mathematical features• calibration possible• inverse calculation from body composition change to model input• intra- and inter-individual comparison• metric for value based purchasing• metric for medical research of insulin resistance• metric for clinical use and services• metric for behavior model development• allows for sharing metrics • provides confidence intervals for the metabolic calculations• choosing the best working diet and exercise regime for the user
Proof of Feasibility Through Simulation Studies
• Minnesota starvation and overfeeding experiment [26]:– SAM-HEM: the average daily prediction errors of
• fat mass change estimates: 0.44±1.16 g/day • fat free mass: -2.6±64.98 g/day
– SIO-HEM/RIO/HEM: the average daily prediction errors of • fat mass change estimates: 3.72±6.63 g/day• fat free mass: -86.54±383.3 g/day
Proof of Feasibility through Simulation Studies
• Effects of brief perturbations in energy balance on indices of glucose homeostasis in healthy lean men [23]:– The calculated correlation coefficient between HOMA-IR and R ratio change
estimation across all experimental phases was -0.78898 with a P value of 5.41105 .∙
• Dietary Weight Loss and Exercise Effects on Insulin Resistance in Postmenopausal Women [29]:– The calculated correlation coefficient between HOMA-IR and R ratio change
estimation across all experimental phases was -0.8383 with a P value of 0.0093.
Motivation improvement by MHM
• MHM helps to dole out the individual's metabolic health goal into small achievable daily sub-goals and allows for a minimal contact approach (weekly text messages)
• Repeated positive experiences from achieving the small sub-goals successfully with no frustration will motivate the user
𝑃𝐴𝐸 𝐸𝐼 “controller”
Perception
MHM
HEM 𝐸𝐼𝐾
𝑃𝐴𝐸𝐾
Measurement
𝑅𝑀𝑅𝐾 ∆𝐵𝐶𝐾
∆𝐵𝐶𝑖∗ 𝑅𝑀𝑅𝑖∗ Objective feedback
HLT SE+SNFP
Subjective observation
Unknown inner loop
Human
Subjective feedback
HLT: healthy lifestyle team
SE: social environment
SNFP: social network, family, and peers
Cyber Therapy Through Predictive Adaptive Control Using Control, Communication and Feedback
Conclusion• A canonical representation of the metabolism was created • Metrics were created for dynamic changes, trend prediction for fat mass, lean
body mass, and insulin resistance • The foundation for cyber therapy of obesity and insulin resistance has been
developed with communication technology, self-directed control and web based feedback
• Proof of concept for clinical observability and controllability has been provided with simulation studies using clinical trial data
• For the first time the otherwise difficult to measure parameters of daily utilized macronutrient energy intake, macronutrient oxidation rate, daily changes of fat weight, lean body mass, and insulin resistance have been estimated with minimum error variance supported by the Kalman filter
OPEN INVITATION FOR COLLABORATION ON FURTHER DEVELOPMENTS
• for clinical use to treat obesity, pre-diabetes and insulin resistance using coaching and the minimal contact approach
• For academia to study insulin resistance related research and nutrition• for dynamic behavioral model development regarding lifestyle change• for insurance companies to create “Value Based Incentive Programs”• government policy making regarding prevention of obesity, pre-diabetes, diabetes and
education• future product development
– For measurement of body composition, intra and extracellular water mass– Camera tools for food recognition and analysis– Body scan for body volume measurements– Home portable RMR measurement– Well calibrated PAE measurement
Metabolic Health Monitor App and Web site development
• www.FinelyFit.com• Joint product development with TC2 Labs
