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

• US 20070083335A1
• Filed: 09/28/2006
• Published: 04/12/2007
• Est. Priority Date: 04/01/2003
• Status: Active Grant

• Current Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories)
• Original Assignee: Abbott Diabetes Care Incorporated (Abbott Laboratories)
• Inventors: Moerman, Piet
• Granted Patent: US 8,718,943 B2
• Time in Patent Office: 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. The dual analyte model may calculate a discrepancy between an actual insulin activity level and a theoretical insulin activity level. The dual analyte model of the present invention may be used to identify individuals at risk for metabolic syndrome, insulin resistance and non-insulin dependent diabetes, and allows monitoring of the progression of those disease states, as well as progress made by therapeutic interventions.

Claims

1. A 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 first analyte in a biological fluid sample reflecting body fat metabolism and an amount of a second analyte in the biological fluid sample reflecting glucose metabolism,

assessing a real insulin activity level based on the amount of the first analyte in the biological fluid sample,

comparing the real insulin activity with a theoretical amount of insulin to calculate the insulin activity error; and

assessing a glucose level based on the amount of the second analyte in the biological fluid and the calculated insulin activity error

2. The method of claim 1, wherein the first analyte comprises one of ketones, glycerol, Free Fatty Acids and a fatty acid indicative of Free Fatty Acid levels.

3. The method of claim 1, wherein the second analyte comprises one 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 assessing a glucose level comprises predicting the likelihood of the user developing one of hypoglycaemia and hyperglycaemia.

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

7. The method of claim 1, wherein the assessment of a glucose level takes the form of an effect on the expected glucose levels for a future period.

8. The method of claim 1 wherein the step of assessing comprises recommending a timing for a future assessment of analyte levels in the user.

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

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

assessing a real insulin activity level in the user based on the amount of the first analyte in the biological fluid sample;

comparing the real insulin activity level with a theoretical amount of insulin to calculate an insulin activity error;

utilizing an additional parameter comprising at least one of;

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

utilizing a relevant algorithm corrected for the insulin activity error to model the glucose levels for a future period.

10. The method of claim 9, further comprising the step of calculating the likelihood of the user developing one of hypoglycaemia and hyperglycaemia.

11. The method of claim 9, further comprises calculating the likelihood of one of a past hypoglycaemia incident and a past hyperglycaemia incident.

12. The method of claim 9, further comprises the step of formulating test frequency advice to the user.

13. The method of claim 9, further comprising the step of formulating medication dosing advice to the user.

14. The method of claim 9, further comprising the step of automatically instructing the right insulin dose to an insulin delivery device.

15. A health-monitoring device comprising:

a test element for measuring an amount of a first analyte in a biological fluid sample reflecting body fat metabolism and an amount of a second analyte in the biological fluid sample reflecting glucose metabolism, and

a processor for assessing a real insulin activity level of the user based on the amount of the first analyte in the biological fluid sample, comparing the calculated real insulin activity with a theoretical amount of insulin to calculate an insulin activity error, and assessing a glucose level based on the amount of the second analyte in the biological fluid and the calculated insulin activity error.

16. The method of claim 15, where the test element comprises one or more of a photometric, reflectometric, electrochemical and fluorescense based system for analyzing the biological fluid sample.

17. The device of claim 15, where a single test element measures both an amount of a first analyte in the biological fluid sample reflecting body fat metabolism and an amount of a second analyte in the biological fluid sample reflecting glucose metabolism.

18. The device of claim 15, wherein the device automatically or continuously measures the second analyte.

19. The device of claim 15, wherein the device automatically or continuously measures the first analyte.

20. The device of claim 15, further comprising a memory element for tracking the evolution of one of the real insulin activity and the insulin activity error for the user.