System and method for physiological health simulation
Systems and methods for health and body simulations in order to predict numerous physiological parameters in a subject or a population of subjects based on the input of limited physiological data.
1. A method for predicting a time to diabetes onset in a subject, comprising:
a. determining, for the subject, subject parameter values comprising height, weight, age, gender, fasting blood glucose concentration, and serum HbA1c concentration at a first time;
b. calculating initial parameter set values comprising basal metabolic rate, excess caloric intake, EC50(FFA), and beta cell apoptosis rate based on the subject parameter values by
(i) setting an age estimate to an age younger than the age determined in step (a), setting a weight estimate to a weight that is different from the weight determined in step (a), setting a fasting blood glucose concentration estimate to a value different from the fasting blood glucose concentration determined in step (a), and setting a serum HbA1c estimate to a value different from the serum HbA1c concentration determined in step (a),
(ii) providing an initial estimate for each value in the initial parameter set values at the age estimate,
(iii) iteratively calculating the initial parameter set values, the weight estimate, the fasting blood glucose concentration estimate, and the serum HbA1c estimate, in a time-dependent manner in which the age estimate is increased by a first time step in each iteration from the age estimate set in step (b)(i) until the age estimate is substantially equal to the age, and
(iv) iteratively performing steps (ii)-(iii) until the weight estimate is substantially equal to the weight, the fasting blood glucose concentration estimate is substantially equal to the fasting blood glucose concentration, and the serum HbA1c estimate is substantially equal to the serum HbA1c concentration when the age estimate is substantially equal to the age;
c. iteratively calculating a future time and a projected fasting blood glucose concentration using the initial parameter set calculated in step (b)(iii) and the subject parameter values, in a time-dependent manner in which the first time is increased by a second time step in each iteration until the projected fasting blood glucose concentration at the future time is calculated to be greater than 125 mg/dL;
d. identifying the future time iteratively calculated in step (c) as the time to diabetes onset; and
e. displaying the future time identified in step (d) as the predicted time for diabetes onset.
2. The method of claim 1 , wherein the age estimate set in step (b)(i) is at least one year less than the age.
3. The method of claim 1 , wherein the weight estimate set in step (b)(i) is less than the weight.
4. The method of claim 1 , wherein the serum HbA1c estimate set in step (b)(i) is less than 3%.
5. The method of claim 1 , wherein the fasting blood glucose concentration estimate set in step (b)(i) is less than 100 mg/dL.
6. The method of claim 1 , wherein the first time step is 5-365 days.
7. The method of claim 6 , wherein the first time step is constant for all iterations.
8. The method of claim 1 , wherein the second time step is 5-365 days.
9. The method of claim 8 , wherein the second time step is constant for all iterations.
10. The method of claim 1 , wherein receiving the subject parameter values comprises:
receiving raw health data from one or more of patient specific electronic medical records, published clinical studies, claims data, prescription data, patient biomarkers, and wearable user devices;
receiving health data about the subject; and
determining the subject parameter values of the subject based on the received raw health data and the received subject health data.
11. The method of claim 1 , wherein performing step (iv) comprises:
adding a perturbation factor to one or more of the initial estimates for the initial parameter set values, wherein the perturbation factor is configured to be adjustable in each increase to increase an accuracy of calculating the initial parameter set values.
12. The method of claim 1 , wherein performing step (c) of iteratively calculating the future time and the projected fasting blood glucose concentration comprises:
receiving time-varying information comprising diet information, physical activity information, or both diet information and physical activity information; and
applying the time-varying information during each of the iteratively calculated future time and projected fasting blood glucose concentration.
13. The method of claim 12 , wherein the time-varying information comprises the diet information, and wherein the diet information comprises consumption rates or amounts for a plurality of macronutrients.
14. The method of claim 13 , wherein the plurality of macronutrients comprises carbohydrates, fats, and proteins.
15. A system, comprising a processor and a non-transitory memory computer readable medium storing programming instructions executable by the processor, wherein the programming instructions comprise:
a. determining, for the subject, subject parameter values comprising height, weight, age, gender, fasting blood glucose concentration, and serum HbA1c concentration at a first time;
b. calculating initial parameter set values comprising basal metabolic rate, excess caloric intake, EC50(FFA), and beta cell apoptosis rate based on the subject parameter values by
(i) setting an age estimate to an age younger than the age determined in step (a), setting a weight estimate to a weight that is different from the weight determined in step (a), setting a fasting blood glucose concentration estimate to a value different from the fasting blood glucose concentration determined in step (a), and setting a serum HbA1c estimate to a value different from the serum HbA1c concentration determined in step (a),
(ii) providing an initial estimate for each value in the initial parameter set values at the age estimate,
(iii) iteratively calculating the initial parameter set values, the weight estimate, the fasting blood glucose concentration estimate, and the serum HbA1c estimate, in a time-dependent manner in which the age estimate is increased by a first time step in each iteration from the age estimate set in step (b)(i) until the age estimate is substantially equal to the age, and
(iv) iteratively performing steps (ii)-(iii) until the weight estimate is substantially equal to the weight, the fasting blood glucose concentration estimate is substantially equal to the fasting blood glucose concentration, and the serum HbA1c estimate is substantially equal to the serum HbA1c concentration when the age estimate is substantially equal to the age;
c. iteratively calculating a future time and a projected fasting blood glucose concentration using the initial parameter set calculated in step (b)(iii) and the subject parameter values, in a time-dependent manner in which the first time is increased by a second time step in each iteration until the projected fasting blood glucose concentration at the future time is calculated to be greater than 125 mg/dL;
d. identifying the future time iteratively calculated in step (c) as the time to diabetes onset; and
e. displaying the future time identified in step (d) as the predicted time for diabetes onset.
16. The system of claim 15 , wherein the age estimate set in step (b)(i) is at least one year less than the age.
17. The system of claim 15 , wherein the weight estimate set in step (b)(i) is less than the weight.
18. The system of claim 15 , wherein the serum HbA1c estimate set in step (b)(i) is less than 3%.
19. The system of claim 15 , wherein the fasting blood glucose concentration estimate set in step (b)(i) is less than 100 mg/dL.
20. The system of claim 15 , wherein the first time step is 5-365 days.
21. The system of claim 20 , wherein the first time step is constant for all iterations.
22. The system of claim 15 , wherein the second time step is 5-365 days.
23. The system of claim 22 , wherein the second time step is constant for all iterations.
24. The system of claim 15 , wherein receiving the subject parameter values comprises:
receiving raw health data from one or more of patient specific electronic medical records, published clinical studies, claims data, prescription data, patient biomarkers, and wearable user devices;
receiving health data about the subject; and
determining the subject parameter values of the subject based on the received raw health data and the received subject health data.
25. The system of claim 15 , wherein performing step (iv) comprises:
adding a perturbation factor to one or more of the initial estimates for the initial parameter set values, wherein the perturbation factor is configured to be adjustable in each increase to increase an accuracy of calculating the initial parameter set values.
26. The system of claim 15 , wherein performing step (c) of iteratively calculating the future time and the projected fasting blood glucose concentration comprises:
receiving time-varying information comprising diet information, physical activity information, or both diet information and physical activity information; and
applying the time-varying information during each of the iteratively calculated future time and projected fasting blood glucose concentration.
27. The system of claim 26 , wherein the time-varying information comprises the diet information, and wherein the diet information comprises consumption rates or amounts for a plurality of macronutrients.
28. The system of claim 27 , wherein the plurality of macronutrients comprises carbohydrates, fats, and proteins.