Apparatus and method for spectroscopic analysis of tissue to detect diabetes in an individual

• US 20030191378A1
• Filed: 04/04/2002
• Published: 10/09/2003
• Est. Priority Date: 04/04/2002
• Status: Active Grant

• Current Assignee: Veralight, Inc. (Britannia Life Sciences, Inc.)
• Original Assignee: Inlight Solutions, Inc.
• Inventors: Rowe, Robert K., Vanslyke, Stephen J., Davis, Herbert T. III
• Granted Patent: US 7,043,288 B2
• Time in Patent Office: Days
• US Class Current: 600/310
• CPC Class Codes: A61B 5/0059 using light, e.g. diagnosis by transillumination, diascopy, fluorescence A61B5/0093, A61B5/1455, A61B5/02416 and A61B5/0261 take precedence
• CPC Class Codes: A61B 5/0075 by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy A61B5/0071 takes precedence
• CPC Class Codes: A61B 5/6824 Arm or wrist
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Apparatus and methods for spectroscopic analysis of human tissue to classify an individual as diabetic or non-diabetic, or to determine the probability, progression or level of diabetes in an individual. Tissue optical information of an individual, including at least a measurement of at least one wavelength or group of wavelengths indicative of glycosylated collagen content in tissue, is analyzed using multivariate techniques. The multivariate techniques include an algorithm developed from optical information from individuals having a known disease state. At least one factor in the algorithm is dependent on or a function of the measurement of the at least one wavelength or group of wavelengths indicative of glycosylated collagen content in tissue from the optical information of individuals forming the database.

Claims

1. A method for indicating diabetes in an individual utilizing tissue optical information from the individual comprising the steps of:

obtaining tissue optical information from the individual, the tissue optical information including information from at least one wavelength indicative of glycosylation end product content in the tissue;

providing a multivariate algorithm developed from a database of optical information from individuals having a known disease state, the multivariate algorithm having at least one factor dependent on information from the at least one wavelength indicative of glycosylation end products in the tissue; and

applying the multivariate algorithm to the tissue optical information from the individual to indicate diabetes.

2. The method of claim 1, wherein the glycosylation end product includes glycosylated collagen.

3. The method of claim 1, wherein the at least one wavelength indicative of glycosylation end product content is selected from following bands:

2240 nm-2300 nm, 2145 nm-2200 nm, 2025 nm-2060 nm, and 1670 nm-1745 nm.

4. The method of claim 1, wherein the tissue optical information includes a second wavelength indicative of glycosylation end product content in the tissue and the multivariate algorithm includes a second factor dependent on information from the second wavelength indicative of glycosylation end product content.

5. The method of claim 4, wherein the second wavelength indicative of glycosylation end product content is selected from the following bands:

2240 nm-2300 nm, 2145 nm-2200 nm, 2025 nm-2060 nm, and 1670 nm-1745 nm.

23. The method of claim 1, wherein the at least one wavelength indicative of glycosylation end product content is greater than 500 nm and less than 3300 nm.

24. The method of claim 1, wherein the multivariate algorithm has at least one factor dependent on subject characteristics.

25. The method of claim 1, wherein the multivariate algorithm has at least one factor dependent on environmental risk factors.

6. A method for classifying an individual as non-diabetic, diabetic or indicating a probability of becoming diabetic utilizing a database of tissue optical information from other individuals having known disease states, the optical information including information from at least one wavelength indicative of glycosylation end product content in the tissue, the method comprising the steps of:

obtaining tissue optical information from the individual, the tissue optical information including information from the at least one wavelength indicative of glycosylation end product content in the tissue; and

using a multivariate algorithm to classify the individual as diabetic, non-diabetic or indicating a probability of becoming diabetic, the multivariate algorithm including at least one factor dependent on the information from the at least one wavelength indicative of glycosylation end product content in the tissue.

7. The method of claim 6, wherein the glycosylation end product includes glycosylated collagen.

8. The method of claim 6, wherein the at least one wavelength indicative of glycosylation end product content is selected from the following bands:

2240 nm-2300 nm, 2145 nm-2200 nm, 2025 nm-2060 nm, and 1670 nm-1745 nm.

9. The method of claim 6, wherein the tissue optical information includes information from a second wavelength indicative of glycosylation end product content in the tissue and the multivariate algorithm includes a second factor dependent on information from the second wavelength indicative of glycosylation end product content.

10. The method of claim 9, wherein the second wavelength indicative of glycosylation end product content is selected from the following bands:

2240 nm-2300 nm, 2145 nm-2200 nm, 2025 nm-2060 nm, and 1670 nm-1745 nm.

11. A method for non-invasively indicating diabetes using spectroscopic measurements of human tissue comprising the steps of:

providing an illumination source and irradiating tissue with light having at least one wavelength indicative of glycosylation end product content in the irradiated tissue;

collecting at least a portion of the light exiting the irradiated tissue;

measuring at least a portion of the spectrum of the light collected from the tissue; and

applying multivariate techniques to the measured spectra to predict the probability of a subject having diabetes, the multivariate techniques including as a factor the at least one wavelength indicative of glycosylation end product content in the irradiated tissue.

12. The method of claim 11, wherein the at least one wavelength indicative of glycosylation end product content is selected from the following bands:

2240 nm-2300 nm, 2145 nm-2200 nm, 2025 nm-2060 nm, and 1670 nm-1745 nm.

13. The method of claim 12, wherein the light irradiating the tissue includes a second wavelength indicative of glycosylated collagen content in the tissue and the multivariate techniques include as a second factor the second wavelength indicative of glycosylated collagen content.

14. The method of claim 13, wherein the second wavelength indicative of glycosylated collagen content is selected from the following bands:

2240 nm-2300 nm, 2145 nm-2200 nm, 2025 nm-2060 nm, and 1670 nm-1745 nm.

15. An apparatus for determining the probability, progression or level of diabetes, the apparatus comprising:

a light source that generates light, including light at at least one wavelength indicative of glycosylated end products in tissue;

a sampling means for coupling the light to tissue and collecting the light modified by the tissue;

a spectrometer coupled to the sampling means for measuring the optical information of the modified light collected from the tissue; and

means for processing the optical information to determine the probability, progression or level of diabetes, the means including an algorithm having as a factor the optical information from the at least one wavelength indicative of glycosylated end products in tissue.

16. The apparatus of claim 15, wherein the at least one wavelength indicative of glycosylation end products is selected from the following bands:

2240 nm-2300 nm, 2145 nm-2200 nm, 2025 nm-2060 nm, and 1670 nm-1745 nm.

17. The apparatus of claim 15, wherein the light source generates light including a second wavelength indicative of glycosylated collagen content in the tissue and the algorithm includes a second factor dependent on optical information at the second wavelength indicative of glycosylated collagen content.

18. The apparatus of claim 17, wherein the second wavelength indicative of glycosylated collagen content is selected from the following bands:

2240 nm-2300 nm, 2145 nm-2200 nm, 2025 nm-2060 nm, and 1670 nm-1745 nm.

19. An apparatus for non-invasively detecting the probability, progression or level of diabetes in human tissue by near-infrared spectroscopy comprising:

an illumination subsystem which generates near infrared light including at least one wavelength indicative of glycosylated collagen content in human tissue;

a tissue sampling subsystem optically coupled to the illumination subsystem which receives at least a portion of the infrared light, the tissue sampling subsystem including means for irradiating human tissue with at least a portion of the received infrared light and collecting at least a portion of the light diffusely reflected from the human tissue;

an FTIR spectrometer subsystem optically coupled to the tissue sampling subsystem to receive at least a portion of the light diffusely reflected from the tissue, the FTIR spectrometer subsystem including a spectrometer that creates an interferogram, the FTIR spectrometer subsystem further including a detector which receives the interferogram and converts the interferogram to an electrical representation;

a data acquisition subsystem which receives the electrical representation of the interferogram, the data acquisition subsystem including means for amplifying and filtering the electrical representation and converting a resulting electrical signal to its digital representation; and

a computing subsystem for receiving the digital representation and further including a multivariate algorithm for detecting the probability, progression or level of diabetes, wherein the algorithm includes at least one factor dependent on the measurement of the at least one wavelength indicative of glycosylated collagen content in human tissue.

20. The apparatus of claim 19, wherein the at least one wavelength indicative of glycosylated collagen content is selected from the following bands:

2240 nm-2300 nm, 2145 nm-2200 nm, 2025 nm-2060 nm, and 1670 nm-1745 nm.

21. The apparatus of claim 19, wherein the illumination subsystem generates light including a second wavelength indicative of glycosylated collagen content in the tissue and the multivariate algorithm includes a second factor dependent on the measurements at the second wavelength indicative of glycosylated collagen content.

22. The apparatus of claim 21, wherein the second wavelength indicative of glycosylated collagen content is selected from the following bands:

2240 nm-2300 nm, 2145 nm-2200 nm, 2025 nm-2060 nm, and 1670 nm-1745 nm.