Method and device for utilizing analyte levels to assist in the treatment of diabetes

• US 8,718,943 B2
• Filed: 09/28/2006
• Issued: 05/06/2014
• Est. Priority Date: 04/01/2003
• Status: Active Grant

• Current Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories Incorporated)
• Original Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories Incorporated)
• Inventors: Moerman, Piet
• Primary Examiner(s): Riggs, II, Larry D
• Application Number:
  US11/528,997
• Publication Number:
  US 20070083335A1
• Time in Patent Office: 2,777 Days
• US Class Current: 702/19
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61P 3/10 for hyperglycaemia, e.g. antidiabetics
• CPC Class Codes: G01N 21/8483 Investigating reagent band test-element handling not specific to a test method G01N33/4875; analytical elements specific to chemical analysis of biological material G01N33/52; autometer with reagent band G01N35/04
• CPC Class Codes: G16H 40/63 for local operation

Abstract

A health-monitoring device assesses the health of a user based on levels of two analytes in a biological fluid. A first analyte that is utilized to assess a user'"'"'s health is a fat metabolism analyte, such as ketones, free fatty acids and glycerol, which is indicative of fat metabolism. A second analyte that is utilized is a glucose metabolism analyte, such as glucose. The levels of the two analytes may be used to assess insulin sensitivity, to detect both recent hypoglycemia and the cause of high glucose levels, and/or to guide therapeutic intervention.

Claims

1. A computer-implemented method of assessing an insulin activity error and its effect on glucose levels, the method comprising the computer-implemented steps of:

measuring an amount of a fat metabolism analyte in a biological fluid sample and an amount of a glucose metabolism analyte in the biological fluid sample,

determining, using a computer, a real insulin activity level, wherein the real insulin activity level comprises a reciprocal conversion of the amount of the fat metabolism analyte in the biological fluid sample,

comparing, using the computer, the real insulin activity level with a theoretical insulin activity level to calculate the insulin activity error; and

predicting, using the computer, a prospective glucose level using the amount of the glucose metabolism analyte in the biological fluid and the calculated insulin activity error,

wherein a positive insulin activity error is predictive of a glucose level which is lower than that predicted by the theoretical insulin activity level, and

wherein a negative insulin activity error is predictive of a glucose level which is higher than that predicted by the theoretical insulin activity level.

2. The method of claim 1, wherein the fat metabolism analyte is selected from the group consisting of ketones, glycerol, free fatty acids and a fatty acid indicative of free fatty acid levels.

3. The method of claim 1, wherein the glucose metabolism analyte is selected from the group consisting of glucose, pyruvate, glucose-6-phosphate and lactate.

4. The method of claim 1, wherein the sample comprises one of blood, a derivate of blood, interstitial fluid, urine and saliva.

5. The method of claim 1, wherein the step of predicting a prospective glucose level comprises predicting a likelihood of a user developing one of hypoglycaemia and hyperglycaemia.

6. The method of claim 1, wherein the method comprises a retrospective diagnosis of one of hypoglycaemia and hyperglycaemia.

7. The method of claim 1 wherein the method comprises recommending a timing for a future determination of analyte levels in a user.

20. The method of claim 1, wherein the fat metabolism analyte is a ketone and the glucose metabolism analyte is glucose.

21. The method of claim 1, wherein the biological fluid sample is an extracted biological fluid sample.

8. A computer-implemented method of predicting a user'"'"'s glucose levels in the future, the method comprising the computer-implemented steps of:

measuring an amount of a fat metabolism analyte in a biological fluid sample and an amount of a glucose metabolism analyte in the biological fluid sample,

determining, using a computer, a real insulin activity level in the user, wherein the real insulin activity level comprises a reciprocal conversion of the amount of the fat metabolism analyte in the biological fluid sample;

comparing, using the computer, the real insulin activity level with a theoretical insulin activity level to calculate an insulin activity error; and

running, using the computer, a glucose prediction algorithm corrected for the insulin activity error, wherein the correction comprises decreasing a glucose level predicted by the theoretical insulin activity when the insulin activity error is positive and increasing a glucose level predicted by the theoretical insulin activity when the insulin activity error is negative, the algorithm taking into consideration an additional parameter comprising at least one of;

body mass index, gender, meal intake, medication, exercise duration and intensity, alcohol consumption and weight.

9. The method of claim 8, further comprising the step of:

calculating, using the computer, a likelihood of the user developing one of hypoglycaemia and hyperglycaemia.

10. The method of claim 8, further comprising the step of:

calculating, using the computer, a likelihood of one of a past hypoglycaemia incident and a past hyperglycaemia incident.

11. The method of claim 8, further comprising the step of:

formulating test frequency advice to the user.

12. The method of claim 8, further comprising the step of:

formulating medication dosing advice to the user.

13. The method of claim 8, further comprising the step of:

automatically communicating insulin dose information to an insulin delivery device.

28. The method of claim 8, wherein the measuring is performed with a test element configured to contact the biological fluid sample.

29. The method of claim 28, wherein the test element comprises a test strip.

30. The method of claim 29, wherein the test strip is an electrochemical-based test strip.

31. The method of claim 30, wherein the test strip is configured to measure both the amount of the fat metabolism analyte and the amount of the glucose metabolism analyte.

32. The method of claim 28, wherein the test element comprises a skin-inserted device.

33. The method of claim 28, wherein the test element comprises a non-invasive measurement device.

14. A health-monitoring device comprising:

a test element configured to receive a biological fluid sample and measure;

an amount of a fat metabolism analyte in the biological fluid sample, and

an amount of a glucose metabolism analyte in the biological fluid sample;

a processor programmed to;

assess a real insulin activity level of a user, wherein the real insulin activity level comprises a reciprocal conversion of the amount of the fat metabolism analyte in the biological fluid sample,

compare the real insulin activity level with a theoretical insulin activity level to calculate an insulin activity error, and

predict a prospective glucose level using the amount of the glucose metabolism analyte in the biological fluid and the calculated insulin activity error,

wherein a positive insulin activity error is predictive of a glucose level which is lower than that predicted by the theoretical insulin activity level, and

wherein a negative insulin activity error is predictive of a glucose level which is higher than that predicted by the theoretical insulin activity level; and

a display device for displaying the real insulin activity level, the insulin activity error, or the prospective glucose level.

15. The device of claim 14, wherein the test element comprises one or more of a photometric, reflectometric, electrochemical and fluorescence based system for analyzing the biological fluid sample.

16. The device of claim 14, wherein a single test element measures both an amount of a fat metabolism analyte in the biological fluid sample and an amount of a glucose metabolism analyte in the biological fluid sample.

17. The device of claim 14, wherein the device automatically or continuously measures the glucose metabolism analyte.

18. The device of claim 14, wherein the device automatically or continuously measures the fat metabolism analyte.

19. The device of claim 14, further comprising:

a memory element for tracking the evolution of one of the real insulin activity level and the insulin activity error for the user.

22. The device of claim 14, wherein the test element comprises an electrochemical based system for analyzing the biological fluid sample.

23. The device of claim 22, wherein the test element comprises a test strip.

24. The device of claim 14, wherein the test element comprises a test strip.

25. The device of claim 16, wherein the test element comprises a test strip.

26. The device of claim 14, wherein the test element comprises a skin-inserted device.

27. The device of claim 14, wherein the test element comprises a non-invasive measurement device.