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Introduction

Chronic hyperglycemia associated with diabetes mellitus results in numerous chemical and morphological changes in human skin. Both near-infrared (NIR) reflectance spectroscopy and fluorescence spectroscopy are sensitive to skin chemistry and morphology, and reports suggest that diabetes-related skin modifications may be detected optically.1,2 InLight Solutions has conducted several clinical studies to assess the utility of NIR and fluorescence spectroscopies for noninvasive diabetes screening.

Spectroscopy

NIR spectroscopy was accomplished with a proprietary FTIR spectrometer specifically designed for noninvasive skin reflectance measurements (Fig 1). Subjects were measured for ~3 minutes on the volar forearm with no skin pretreatment. Spectral data in the 4200-7200 cm-1 (1.4-2.4 mm) range were used for multivariate models. Fluorescence spectroscopy was conducted in a similar manner using the SkinSkanTM skin fluorescence spectrometer (Jobin-Yvon, Edison, NJ, Fig 2). Wavelength ranges and data processing are described in a companion poster.

Fig 1. InLight NIR Spectrometer Fig 2. JY SkinSkanTM

Clinical Studies

NIR and fluorescence training data were collected on individuals at risk for type 2 diabetes and self-reported type 2 diabetics. In addition to spectroscopic data, Fasting Plasma Glucose (FPG), 2-hour Oral Glucose Tolerance (OGT), and HbA1c reference values were also collected. On study days in which only spectroscopic data were collected, no fasting requirement was imposed (Fig. 3). NIR validation data were acquired in each of three additional studies (Table 1).

600

500

FPG (Training Studies)

400

NIR Training

NIR Validation 1

300

NIR Validation 2

NIR Validation 3

Fluorescence Training

200

100

0

0

100

200

300

400

500

Receiver-Operator Characteristic Curves

1

0.9

0.8

0.7

0.6

Sensitivity

0.5

0.4

0.3

0.2

0.1

0

0

0.2

0.4

0.6

0.8

1

False Positive Rate

ROC Curves for FPG, NHANES III Database

NHANES III OGTT and FPG Values

1

0.9

126 mg/dl

0.8

0.7

0.6

Sensitivity = 50%, Specificity = 96%

OGTT (mg/dl)

Sensitivity

0.5

Sensitivity = 46%, Specificity = 97%

0.4

200 mg/dl

ROC, Self-Declared Reference

0.3

ROC, OGT Reference

Non-Diabetic

0.2

126 mg/dl Threshold

Diabetic

0.1

0

0

0.2

0.4

0.6

0.8

1

FPG (mg/dl)

False Positive Rate

NIR Predictions vs FPG, Training Data

160

140

120

NIR predicted value

100

Non-diabetic

Diabetic

80

60

40

50

100

150

200

250

Known FPG value (mg/dl)

Use of Near-Infrared and Fluorescence Spectroscopy to Detect Diabetes Based on Noninvasive Skin MeasurementsCliona M. Fleming1, Herbert T. Davis1, Robert Ratner2, Christopher D. Brown1,Marwood N. Ediger1, Edward L. Hull1, Rio Udell1, and John D. Maynard11InLight Solutions, Inc. 2MedStar Research Institute800 Bradbury SE,Albuquerque NM, 87106 6495 New Hampshire Ave., Hyattsville, MD 20783

Table 2 summarizes several metrics that may be used to evaluate the performance of these diagnostic tests, and, where applicable, compares them to the FPG on the same subjects. The point on the ROC curve at which the False Positive and False Negative rates are identical defines the Equal Error Rate (EER). The reproducibility of the tests is assessed by the coefficient of variation (CV). In Table 2, CVs have been calculated according to the formula used in the Hoorn study3. In addition, the sensitivity of each test at a specificity of 70% (i.e., a false positive rate of 30%) is given.

Table 2. Summary of study results

Conclusions

Multiple studies have demonstrated that the diagnostic performance of noninvasive NIR and fluorescence spectroscopy is comparable to that of the Fasting Plasma Glucose test. The lack of a fasting requirement or other pre-test preparations, coupled with test convenience and potential broad availability, make noninvasive spectroscopy an attractive candidate for diabetes screening. Additional investigations into the source of the optical diabetes signal are warranted; these experiments are the subject of a companion poster.

Acknowledgements

This research was funded by LifeScan, Inc.

1P. Geladi et al., J. Near Infrared Spectrosc.8, 217–227 (2000).

2J. Nystrom et al, Med Biol Eng Comput. 4:324-9 (2003).

3 J.M. Mooy et al., Diabetologia39:398-405 (1996).

Fig 3. Study protocol for collection of training data.

Model Training Procedures

Classification models map response variables to disease status via measurements and associated reference values obtained during the training period. Consider, for example, the relationship between the FPG and OGT tests in NHANES III, depicted in Figure 4. Current diagnostic thresholds for both tests are shown in Figure 4, and associated Receiver-Operator Characteristic (ROC) curves are shown in Figure 5.

Fig 4. NHANES FPG and OGT values Fig 5. Corresponding ROC Curves

Although the relationship between FPG measurements and OGT reference values is not linear, the FPG serves as a useful diagnostic for diabetes, with sensitivities and false positive rates (FPR = 1-specificity) shown in Fig 5.

We use the Partial Least-Squares (PLS) algorithm to create a multivariate model that quantifies the degree of diabetes progression evident in a noninvasive spectrum. When training the PLS model, FPG test data are used as reference values for diabetes progression. While FPG values are by no means perfect markers of diabetes disease state, they contain sufficient information for model training.

Results

The ROC curve associated with the data in Figure 6 is presented in Figure 7, along with curves for the NIR validation and fluorescence training studies. The ROC curve for FPG tests conducted during the training studies is also shown.

All curves use self-declared diabetic status as truth. ROC curves for noninvasive spectroscopy were constructed from spectroscopic measurements for which subjects were not required to fast.

Fig 7. FPG ROC curve and non-fasting ROC curves for all spectroscopic studies.

Cross-validated estimates of diabetes progression from the NIR training study are plotted against their known FPG reference values in Figure 6. A relationship similar to that depicted in Figure 4 is noted.

Fig 6. NIR Diabetes Progression estimates vs. known FPG value, Fall 2002 study.

Table 1. Study Details


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