Key words
sudomotor function - insulin resistance
Introduction
Diabetes is a growing global concern that is affecting 285 million people today. By
2030, according to the IDF Diabetes Atlas, that number of people will have risen to
438 million [1], and double that if figures for metabolic syndrome or pre-diabetes are included.
These statistics also reveal a disturbing trend: the increase in number of children
and adolescents developing type 2 diabetes. Major prevention trials have shown that
diabetes can be delayed or prevented in individuals with impaired glucose tolerance
(IGT) using lifestyle intervention or medication [2]. However, incorporating the evidence from trials into clinical practice represents
one of the major challenges for public health and clinical care. Currently an evidence-based
guideline for the prevention of type 2 diabetes [3] and a practice tool kit for the implementation of programs for the prevention of
chronic metabolic diseases [4], especially diabetes mellitus, have been developed to promote improved patient compliance.
There is growing evidence that interventions to prevent diabetes from developing are
more efficient when applied earlier in subjects identified at risk for normoglycemic,
exhibiting insulin resistance but not hyperglycemic.
Diagnosing type 2 diabetes and screening for the different pre-diabetic stages of
risk require often resource-intensive measurements of a set of biomarkers, which are
yet to be universally standardized. Attempts to employ questionnaire-based risk scores,
for which there is some valid predictive efficacy, are unfortunately limited in the
practical sense. An added value to clinical practice would be to have a fast, non-invasive,
and reproducible diagnostic method to efficiently identify those with increased diabetes
risk.
Presently, several methods – including risk models based on HbA1c [5]
[6] and 1-h post-load glucose values [7] – are employed in diagnosing diabetes and pre-diabetes. However, debate continues
over which single tool is ideally suited for assessing diabetes risk or identifying
individuals with increased metabolic risk factors in a population-based public health
setting. In general, measurements that require blood sampling are time consuming,
have low sensitivity and cannot be used for screening of large population. On the
other hand, questionnaire-based methods used for early screening which require individual
data such as age, BMI, and blood pressure [8]
[9]
[10], have their own limitations. Clearly a simple-to-use and effective diagnostic or
screening tool is needed for preventive intervention programs where overall cost and
population receptivity are overriding factors.
Diabetic neuropathy is a known complication of diabetes mellitus and manifests as
impairment of nerve function due to hyperglycemia and insulin resistance [11]. To understand diabetic neuropathy development, several studies have explored sudomotor
dysfunction by using quantitative sudomotor axon reflex test (QSART) or sympathetic
skin response [12]
[13]. These studies have shown a direct link between impairment of sudomotor function
and insulin resistance and pre-diabetes [14]
[15]. Skin biopsies confirmed damage to the sympathetic C nerve fibers innervating sweat
glands in hyperglycemic and insulin resistance conditions; while, on the contrary,
the positive effects of lifestyle intervention led to the rejuvenation of those fibers
[16]. However, skin biopsies and traditional tests used to assess sudomotor function
are time-consuming and/or invasive and are not efficient for screening.
EZSCAN® (Acronym for “Easy Scan”) was recently developed to perform a noninvasive evaluation
of sweat gland function through measurement of electrochemical skin conductance, based
on an electrochemical reaction between sweat chloride and nickel electrodes in contact
with palms of the hands and soles of the feet [17]
[18]
[19]
[20]
[21]. A risk model based on hand and foot conductance values adjusted for age and BMI
was developed and evaluated in both controls and patients with diabetes, to predict
insulin resistance and current diabetes. This predictive model was tested and evaluated
in Indian and Chinese subjects at risk of diabetes according to ADA criteria [22]
[23], which approved the risk model in persons with normal glucose tolerance and diabetes
mellitus. Persons with IGT presented a unique picture and expressed repeatedly elevated
foot conductance. This was explained by the presence of an inflammatory process influencing
sweat gland innervation, previously explored through skin biopsies performed in patients
with pre-diabetes [24]. The risk model had to be adjusted using relative values of hand and foot conductances
and systolic blood pressure.
The aim of this study was to assess the ability of this new risk model previously
validated in India to detect pre-diabetes or diabetes in a German population at risk
for these conditions.
Methods
Participants and biochemical measurements
200 subjects from the city of Dresden and adjoining areas were recruited in the Division
for Prevention and Care of Diabetes mellitus as previously described [25]. Written informed consent was obtained from all subjects and all subjects were able
to give the consent themselves. Clinical investigations were conducted according to
the principles expressed in the Declaration of Helsinki. Ethical approval was obtained
by the University of Dresden (approval No.198062010). Most of the subjects came from
German families with a family history of type 2 diabetes, obesity, or dyslipoproteinaemia.
The average age of the subjects was 56±14 years; average BMI was 28.4±5.4 kg/m2. Exclusion criteria were: known diabetes mellitus, severe renal disease, disease
with a strong impact on life expectancy, and therapy with drugs known to influence
glucose tolerance (thiazide diuretics, beta blockers, steroids) or pregnancy of the
proband. Anthropometric data (body weight, body height, BMI, waist circumference),
HbA1C, lipids (triglycerides, total cholesterol, HDL-C, LDL-C) and inflammatory parameters
including hs-CRP were recorded. All individuals underwent a 75 g oral glucose tolerance
test following an overnight period of fasting (10 h minimum) with measurements of
plasma glucose, insulin, proinsulin, C-peptide and free fatty acids (NEFA) at fasting
and at 30, 60, 90 and 120 min after glucose challenge. The cohort was divided into
4 glucose tolerance groups according to the results of the baseline and follow-up
OGTT: normoglycemic (NGT), impaired glucose tolerance (IGT), those with impaired fasting
glucose (IFG), and type 2 diabetes mellitus based on the WHO criteria of 1997, which
was previously reported [25]
[26]
[27].
Insulin sensitivity, Matsuda index, HOMA-IR
The Minimal model was used to calculate insulin sensitivity (SI) index based on the following formula [28].
Where G is plasma glucose concentration; ΔG and ΔI are glucose and insulin concentrations above basal, respectively; AUC denotes the
area under the curve calculated from time 0 to t→∞, GE is glucose effectiveness (dl.kg−1min−1); DOGTT is the dose of ingested glucose per unit of body weight (mg/kg); and f is the fraction of ingested glucose that actually appears in the systemic circulation.
Calculation of S
I requires insertion of values for GE and f. Values proposed by Monti et al. were used: GE=0.024 dl.kg−1 ∙ min−1 and f=0.8 [29]. Homeostasis model assessment – insulin resistance (HOMA-IR) and Matsuda index were
also calculated.
Measurement of sweat function
EZSCAN, a patented device designed to perform a precise evaluation of sweat gland
function through reverse iontophoresis and chrono-amperometry, has been described
previously [17]
[20]. The apparatus consists of 2 sets of large area nickel electrodes for the hands
and the feet, which are connected to a computer for recording and data management
purposes; electrodes are used alternatively as an anode or a cathode and a direct
current (DC) incremental voltage ≤4 volts is applied on the anode. To conduct the
test, patients are required to place their hands and feet on the electrodes and then
to stand still for 2 min. Through reverse iontophoresis, the device generates a voltage
on the cathode and a current (intensity of about 0.2 mA) between the anode and the
cathode, proportional to electrochemical reaction between chlorides of the sweat and
nickel electrodes. At low voltages, less than 10 V, the stratum corneum is electrically
insulating and only sweat gland ducts are conductive [30]. The electrochemical sweat conductance (ESC), expressed in microSiemens (µS), is
the ratio between current generated and the constant DC stimulus applied on electrodes.
Patients were classified according to their risk score using a color classification
allowing easy patient understanding – green: no risk, yellow: moderate risk and orange/red:
high risk.
Statistical analyses
Results for quantitative variables are shown as mean values±standard deviations (SD).
Log transformation was performed for variables not normally distributed. Group means
were globally compared using student t-test. Pearson Chi-square or Fisher’s exact
tests were used for the comparisons of percentages. Analysis of variance (ANOVA) and
logistic regression, adjusted for age, have been performed to compare quantitative
variables and percentages. The statistical analysis was done using R 2.13.1 (The R-project
for statistical computing) [31]. As a rule, a p-value<0.05 was regarded as statistically significant.
Results
Of the 200 men and women included in the study initially, 134 (67%) were diagnosed
with NGT; 54 (27%) exhibited IGT/IFG; and 12 (6%) were newly diagnosed with type 2
diabetes according to WHO classification. Among the 54 subjects with IFG/IGT, 37 had
a moderate EZSCAN risk score and 15 a high risk according to risk model classification
based on sudomotor function assessment, whereas lower values mean lower risk and vice
versa. Among the 12 subjects with diabetes, 2 had a moderate risk and 10 a high risk
according to risk model classification. Considering subjects with no risk vs. subjects
with moderate or high risk, sensitivity to diagnose IFG/IGT or diabetes was 97% and
specificity 31% with a negative predictive value of 95%. Demography and main results
according to risk classification based on sudomotor dysfunction assessments are displayed
in [Table 1]. After adjustment for age a significant difference was observed between each risk
model group in AUC glucose (p=0.011), AUC C-peptide (p<0.001), HOMA-IR (p=0.009),
Matsuda (p=0.002), SI (p<0.001) and hs-CRP (p=0.025). No association was detected between the risk model
and the patient lipid profile. Blood concentrations during 2 h-OGTT for glucose, insulin,
pro-insulin, C-peptide and free fatty acids are displayed on [Fig. 1].
Fig. 1 AUC120 for Glucose a, insulin b, pro-insulin c, C-Peptide d and free fatty acid e according to risk classification defined by EZSCAN. Green: no risk, yellow: moderate
risk and red: high risk. Values are mean±SEM.
Table 1 Demography and main results according to EZSCAN risk model classification.
|
Overall
|
No risk
|
Moderate risk
|
High risk
|
P
|
P adj.**
|
|
|
(Green)
|
(Yellow)
|
(Orange/red)
|
|
|
|
n=200
|
n=43
|
n=86
|
n=71
|
|
|
|
age (yrs)
|
56±14
|
37±11
|
58±10
|
66±8
|
<0.001
|
–
|
|
gender (n men, %)
|
92 (46%)
|
22 (51%)
|
36 (42%)
|
34 (48%)
|
NS
|
–
|
|
BMI (kg/m2)
|
28.4±5.4
|
25.4±3.9
|
29.4±5.9
|
29.1±5.1
|
<0.001
|
<0.001
|
|
waist (cm)
|
97.0±13.3
|
88.8±10.8
|
99.2±14.1
|
99.3±11.9
|
<0.001
|
<0.001
|
|
SBP (mm Hg)
|
135±16
|
124±13
|
138±16
|
139±16
|
<0.001
|
0.019
|
|
HbA1C (%)
|
5.7±0.5
|
5.3±0.3
|
5.7±0.4
|
5.8±0.5
|
<0.001
|
0.098
|
|
FPG (mmol/L)
|
5.3 ±0.7
|
4.9±0.5
|
5.4±0.6
|
5.5±0.9
|
<0.001
|
0.085
|
|
OGTT-1h (mmol/L)
|
9.3±2.6
|
7.7±2.1
|
9.6±2.4
|
10.0±2.6
|
<0.001
|
0.004
|
|
OGTT-2h (mmol/L)
|
6.9±2.3
|
5.5±1.6
|
7.2±2.1
|
7.4±2.5
|
<0.001
|
0.080
|
|
fasting free fatty acid (mmol/L)
|
0.52±0.23
|
0.44±0.20
|
0.52±0.21
|
0.57±0.25
|
0.009
|
NS
|
|
total cholesterol (mmol/L)
|
5.4±1.0
|
5.1±1.1
|
5.4±0.9
|
5.6±0.9
|
0.008
|
NS
|
|
triglycerides (mmol/L)
|
1.4±0.9
|
1.3±0.9
|
1.4±1.0
|
1.4±0.8
|
NS
|
–
|
|
HDLC (mmol/L)
|
1.5±0.4
|
1.5±0.4
|
1.5±0.4
|
1.6±0.4
|
NS
|
–
|
|
LDLC (mmol/L)
|
3.3±0.9
|
3.1±1.0
|
3.3±0.8
|
3.5±0.8
|
0.054
|
NS
|
|
hs-CRP (mg/L)
|
2.2±3.1
|
1.6±2.3
|
2.0±2.2
|
2.7±4.1
|
0.015
|
0.025
|
|
AUC glucose 120 (mmol/Lmn)
|
983±221
|
836±174
|
1 009±202
|
1 041±233
|
<0.001
|
0.011
|
|
AUC insulin-120 (mmol/Lmn)
|
60 698±39 334
|
52 141±33 466
|
65 595±42 412
|
60 008±38 342
|
0.061
|
0.010
|
|
AUC pro-insulin-120 (mmol/Lmn)
|
3 245±2 560
|
3 185±2 679
|
3 394±2 810
|
3 102±2 164
|
NS
|
–
|
|
AUC C-peptide-120 (mmol/Lmn)
|
103±37
|
78±31
|
111±40
|
107±31
|
<0.001
|
<0.001
|
|
HOMA-IR
|
2.9±2.4
|
2.0±1.5
|
3.2±2.6
|
3.0±2.5
|
0.028
|
0.009
|
|
MATSUDA
|
4.4±2.9
|
5.9±3.8
|
3.9±2.5
|
4.0±2.4
|
0.001
|
0.002
|
|
SI
|
8.8±8.2
|
14.5±11.4
|
7.3±6.4
|
7.3±6.3
|
<0.001
|
<0.001
|
|
mean hands conductances (µS)
|
66±16
|
74±9
|
69±14
|
58±18
|
<0.001
|
<0.001
|
|
mean feet conductances (µS)
|
78±11
|
85±6
|
83±6
|
70±13
|
<0.001
|
<0.001
|
|
ratio (feet/hands)
|
1.26±0.33
|
1.17±0.16
|
1.26±0.32
|
1.30±0.40
|
0.100
|
NS
|
|
NGT (n, %)
|
134 (67%)
|
41 (95%)
|
47 (55%)
|
46 (65%)
|
<0.001
|
<0.001
|
|
IFG/IGT (n, %)
|
54 (27%)
|
2 (5%)
|
37 (43%)
|
15 (21%)
|
<0.001
|
<0.001
|
|
diabetes (n, %)
|
12 (6%)
|
0 (0%)
|
2 (2%)
|
10 (14%)
|
0.002
|
<0.001
|
Mean EZSCAN risk score values for NGT, IFG/IGT and diabetes groups were 48±26, 57±15
and 66±12, respectively.
No adverse event was reported during or after the study. More specifically, no patient
perceived the voltage applied or the current produced. No reddening of the palms or
soles was observed.
Discussion
The EZSCAN technology was evaluated in this study as a potential screening tool to
detect diabetes risk. This technology has several advantages: reproducibility, non-invasiveness,
and short test duration allowing numerous follow-up tests with very low supply costs.
This study, performed in a population of German subjects at risk for pre-diabetes
or diabetes, shows that non-invasive assessment of sudomotor function could present
an effective method to identify people with increased diabetes risk. The assessment
of sweat function based on an electrochemical reaction between sweat chloride and
nickel electrodes allows a valid detection of patients with insulin resistance or
type 2 diabetes, with a very low rate of false negative test results.
It is known that long diabetes duration and inadequately managed diabetes treatment
increase the risk of developing neuropathy. Neuropathy diagnosed in a clinical setting
is often in its late, irreparable stages, due to the diagnostic tools involved (biothesiometer
or turning fork measuring large fiber damages), and has already manifested as significant
impairment of nerve function [32]
[33]. From a clinical point of view there is only little known about very early stages
of autonomic nerve function impairment as it relates to the development of insulin
resistance. Recent studies have shown a link in patients with pre-diabetes between
more severe stages of insulin resistance and peripheral autonomic nerve fiber impairment
[15]
[34] and also and association in childhood obesity were increased BMI and elevated HOMA-IR
is associated with impaired autonomic function [35]. In addition a recent study evidenced that insulin resistance itself was independently
associated with peripheral and autonomic neuropathy [36]. Impairment of these autonomic sympathetic C nerve fibers innervating sweat glands
leads to sudomotor dysfunction. Applying electrodes with a current voltage inducing
current linked to an electrochemical reaction between sweat chloride and electrodes
allows a precise evaluation of the sweat dysfunction. The measured conductance (ratio
of the current and voltage) is directly related to the impairment of the C nerve fibers
innervating autonomic sweat glands. A proof of concept study has shown good correlation
between conductance and sweat chloride concentration as measured by a sweat test in
controls and patients with cystic fibrosis [17]. This is in conjunction with a current observational study showing that insulin
treatment improves the neuropathy measured by EZSCAN [33]. The advantage of this technology, EZSCAN, is that it is non-invasive, can be performed
quickly (in less than 3 min), and will allow qualitative assessment of pre-diabetic
stages based on the subject’s measured chloride-dependent conductance levels. Previous
studies have presented evidence of the method’s reproducibility [33]
[37]. A risk assessment model has been developed first in patients with diabetes, has
undergone validation in an Indian test population at risk of diabetes [22], and has now been confirmed in different stages of pre-diabetes risk in a German-Caucasian
population. Tests for insulin resistance showed a difference between subjects without
risk, according to the risk model, and subjects with moderate or high risk ([Table 1]). A significant difference could be seen in hs-CRP (a marker of inflammation) between
subjects with moderate risk and subjects with no risk, in accordance with the inflammation
process observed in sweat gland innervation of subjects with IGT [23]. This hypothesis has to be confirmed by assessment of other markers of inflammation.
These data help to further support the EZSCAN risk score as a technology that may
meet requirements for the screening of metabolic risk stages. EZSCAN could be developed
as an alternative to other biomarker-based medical risk assessment strategies or questionnaire-based
public health screening strategies.
Several limitations warrant interpretation of the study results. First, the patients
were not selected randomly, and were recruited based on their existing increased risk
of diabetes. This may generate a bias – potentially an underestimation of the EZSCAN
results. The second limitation of our study is that the association was only tested
against the EZSCAN-developed risk model and not vice versa. In particular, investigating
the differences between patients with IFG or IGT would be an interesting topic to
further evaluate the risk model against clinical patients’ profiles. Therefore, it
is necessary to validate the association between the risk model and insulin resistance
in a randomized study, and also in larger populations. In addition a follow-up study
seems necessary to assess the predictive power of the method.
The EZSCAN provides an interesting new technology for screening. The impairment of
autonomic sudomotor dysfunction can be used as indicator to identify people with high
risk for diabetes and maybe other metabolic and cardiovascular diseases. Recently,
this technology is applicable to adult population, but the concept of impaired autonomic
dysfunction is also visible in obese insulin resistant children. The validity of this
technology needs to be further investigated: it is part of the large European prospective
trial ePREDICE that just begins. Summarizing this, the EZSCAN provides an interesting
non-invasive technology for screening of chronic diseases in various settings.
