Fighting Weight Problems and Insulin Resistance with the Metabolic Health Monitor App for Patients in the Setting of Limited Access to Health Care in Rural America*

Zsolt Ori, MD, MS, FACPOri Diagnostic Instruments, LLC

Durham, NC, USA zsolt.ori56@gmail.com

Ilona Ori, JDOri Diagnostic Instruments, LLC

Durham, NC, USAilona@uchicago.edu

*Research supported by Ori Diagnostic Instruments, L.L.C; U.S. patent pending: application numbers 14541033 and 62372363

Rural America has:• Higher rate of obesity than average• Higher rate of poverty• Higher rate of sedentariness• Less access to health care provider• Fewer doctors• Higher rate of prediabetes prevalence• Less academic research

“Non intellecti nulla est curatio morbi...”“Without proper understanding, there will be no cure for disease...”--Cornelius Gallus (i.e. 70-26) Roman poet, orator and politician

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 :

Change in lean mass

Change in fat mass

Change in energy balance

ϱ𝐿∗𝑘

ϱ𝐿∗𝑘

𝑅𝑘∗

The R-Ratio, a Novel Surrogate Market of the Insulin Resistance

Insulin resistance, and the compensatory hyperinsulinemia that follows, appear to be caused primarily by excess body fat, particularly around the abdomen and organs, which leads to inflammation and elevated blood glucose levels.

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.

(ϱ𝐿∗𝑘·𝑅𝑘∗+ϱ𝐹

∗𝑘 ) ·∆𝐹 𝑘+1=𝐶𝐼𝑘+𝐹𝐼𝑘+𝑃𝐼𝑘−𝑇𝐸𝐸𝑘

My Fitness Pal Metabolic Health Monitor appTracks macronutrient calorie intake(carbohydrate, fat, protein)6 million food itemsDaily entries needed (5-20 min a day)Calculates the arhythmical sum of ingested calories, does not deal with missing data

Tracks macronutrient calorie intake(carbohydrate, fat, protein)www.ars.usda.govBiweekly one-day calorie counting onlyEstimates past utilized macronutrient intakesPredicts future utilized macronutrient intakes

Tracks Physical ActivityContinuous measurement is needed

Tracks Physical Activity Continuous measurement is needed

Tracks WeightFrequent measurements encouraged

Tracks WeightBiweekly body composition measurementEstimates past and predicts future changes of Fat mass Lean mass Protein mass 

Calculates the arhythmical sum of ingested calories minus calorie expenditure

Estimation and Prediction of the daily Total Energy Calorie Balance Fat balance Carbohydrate balance Protein balance 

Additional functioning in MHMbut non-existing in My Fitness Pal

Macronutrient Oxidation• Estimates past and predicts future changes of• Fat oxidation• Carbohydrate oxidation• Protein oxidationUtilized macronutrient energy intake• Estimates past and predicts future changes of• Utilized fat energy• Utilized carbohydrate energy• Utilized protein energyRelative measurement of insulin resistance • R = Lean mass change/ Fat mass change

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 interindividual 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.

The psychological processes of self-directed weight management and the loops of information flow, emphasizing control by feedback

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

Conclusion• MHM offers tools for objective observation of the actual state and trends and

trajectories of an individual’s metabolic processes. The loop of feedback of information gained by the proposed MHM app allows for non-judgmental observation and self-organization of the complex biological system of the human energy metabolism.

• The MHM supported framework for improving fat mass and insulin resistance through improved diet and exercise appears to be quite applicable in the resource limited setting that exists in rural America. Our proposal would facilitate to gain control and self-manage weigh and insulin resistance.

• We offer the MHM app for free for volunteers in rural America for self-treatment and for result data gathering to help forge a battle against obesity and insulin resistance in rural America.

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