Use of Near-Infrared and Fluorescence Spectroscopy to
Detect Diabetes Based on Noninvasive Skin Measurements
1
Fleming ,
1
Davis ,
2
Ratner ,
1
Brown ,
Cliona M.
Herbert T.
Robert
Christopher D.
1
1
1
1
Marwood N. Ediger , Edward L. Hull , Rio Udell , and John D. Maynard
1InLight
2MedStar
Introduction
Research Institute
6495 New Hampshire Ave., Hyattsville, MD 20783
AM
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.
FPG
OGT
HbA1c
Day 1
NIR
FPG
OGT
Day 2
PM
NIR
NIR
Day 3
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.
No Visit
NIR
No Visit
Random
Glucose
No Visit
NIR
Fluor.
Fig 3. Study protocol for collection of training data.
Spectroscopy
Model Training Procedures
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.
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.
NHANES III OGTT and FPG Values
ROC Curves for FPG, NHANES III Database
126 mg/dl
1
600
500
0.9
0.8
Sensitivity
OGTT (mg/dl)
0.7
400
0.6
Sensitivity = 50%, Specificity = 96%
0.5
300
200 mg/dl
100
100
200
300
400
0.2
500
FPG (mg/dl)
Fig 2. JY SkinSkanTM
Fig 1. InLight NIR Spectrometer
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), 2hour 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).
Date
Population
Study Purpose
NIR Training
Fall 2002
Fluorescence
Training
Fall 2002
At Risk
At Risk
[supplemented with [supplemented with
22 known diabetics] 48 known diabetics]
Train multivariate
model; Evaluate atrisk population
NIR Validation 1
NIR Validation 2 /
NIR Validation 3
Summer 2002
Jan 2003 / Mar
2003
Case Control
[known diabetic
status]
Case Control
FPG, OGT, HbA1c
FPG, OGT, HbA1c
Self-reported DM
status
Self-reported DM
status
Number of
Subjects
140
171
154
73 / 75
Age Range
25-81
25-81
19-82
54-61
Diabetic
Prevalence
16%
28%
48%
47%
Table 1. Study Details
0.4
0.6
0.8
1
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
Evaluate caseTrain multivariate
Evaluate casecontrol
model; Evaluate at- control population;
population;
risk population
Wide age range
Narrow age range
160
140
120
100
Non-diabetic
Diabetic
80
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.
60
40
50
150
200
Known FPG value (mg/dl)
FPG (Training Studies)
NIR Training
NIR Validation 1
NIR Validation 2
NIR Validation 3
Fluorescence Training
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.2
0.4
0.6
0.8
1
False Positive Rate
Fig 7. FPG ROC curve and non-fasting
ROC curves for all spectroscopic studies.
FPG
(Training
Studies)
Equal Error
Rate
29%
25%
29%
24%
24%
23%
Sensitivity
(at 70%
Specificity)
73%
81%
74%
78%
78%
77%
Coefficient
of Variation
(Hoorn)
7.0%
N/A
7.9%
4.3%
4.2%
6.6%
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.
1
100
0.7
Fig 5. Corresponding ROC Curves
NIR Predictions vs FPG, Training Data
References
Collected
0.2
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.
NIR predicted value
Study Attribute
0
0
False Positive Rate
Fig 4. NHANES FPG and OGT values
0.8
NIR
NIR
NIR
Fluorescence
NIR
Validation
Validation
Training
Training
Validation 2
1
3
0.1
0
0
0.9
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.
ROC, Self-Declared Reference
ROC, OGT Reference
126 mg/dl Threshold
0.3
Non-Diabetic
Diabetic
1
Sensitivity = 46%, Specificity = 97%
0.4
200
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.
Receiver-Operator Characteristic Curves
Sensitivity
Solutions, Inc.
800 Bradbury SE, Albuquerque NM, 87106
250
Fig 6. NIR Diabetes Progression estimates
vs. known FPG value, Fall 2002 study.
P. Geladi et al., J. Near Infrared Spectrosc. 8, 217–227 (2000).
2 J. Nystrom et al, Med Biol Eng Comput. 4:324-9 (2003).
3 J.M. Mooy et al., Diabetologia 39:398-405 (1996).