The British Journal of Diabetes & Vascular Disease http://dvd.sagepub.com/ EZSCAN a new technology to detect diabetes risk
Peter EH Schwarz, Philippe Brunswick and Jean-Henri Calvet British Journal of Diabetes & Vascular Disease 2011 11: 204 DOI: 10.1177/1474651411402629 The online version of this article can be found at: http://dvd.sagepub.com/content/11/4/204
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EZSCAN™ a new technology to detect diabetes risk PETER EH SCHWARZ1, PHILIPPE BRUNSWICK2, JEAN-HENRI CALVET2 Abstract
K
ey to putting prevention of diabetes into practice is finding the people with increased risk. Several tools are currently in use: oral glucose tolerance test, fasting glucose measurement and a number of questionnaires to identify those with increased risk. Each has its own advantages and disadvantages. One new tool that can identify those with increased diabetes risk is the EZSCAN™. This new diagnostic device developed by Impeto Medical uses the sweat gland function to detect risk for insulin resistance and diabetes. The basic pathophysiology behind this technology is supported by the growing number of clinical studies worldwide which show a strong association between small nerve neuropathies to insulin resistance and diabetes risk. Because the EZSCAN™ test takes only three minutes to run, is noninvasive and easy to operate, it is an ideal diagnostic tool for both the medical and paramedical setting. Several applications are possible: the EZSCAN™ can be used to monitor insulin resistance-based treatment, to diagnose increased diabetes risk and to aid proposing diabetes prevention programmes. EZSCAN™ has the potential to become a very useful tool in diabetes risk diagnostics. Br J Diabetes Vasc Dis 2011;11:204-209 Keywords: diabetes screening, EZSCAN™, non-invasive sweat function
Introduction Worldwide, many countries are confronted with growing healthcare costs associated with caring for the needs of a rapidly growing number of persons with common chronic illnesses, especially diabetes mellitus. Type 2 diabetes can be delayed or prevented among people who have IGT with lifestyle interventions or medication as shown by major clinical trials of diabetes prevention, but it is a completely different challenge to carry 1Carl
Gustav Carus Medical Faculty, Technical University of Dresden, Dresden, Germany. 2Impeto Medical, Paris, France. Corresponding author: Prof. Dr. Peter EH Schwarz, Department for Prevention and Care of Diabetes, Medical Clinic III, University Clinic Carl Gustav Carus at the Technical University Dresden, Fetscherstraße 74, 01307 Dresden, Germany. Tel: +49 (0)351 458 2715; Fax: +49 (0)351 458 7319 E-mail:
[email protected]
Abbreviations and acronyms AHA BMI DC ESC FINDRISC FPG HbA1c IGT MS NCEP NGT OGTT QSART WHO
American Heart Association body mass index direct current electrochemical sweat conductance FINnish Diabetes Risk SCore fasting plasma glucose glycated haemoglobin A1c impaired glucose tolerance metabolic syndrome National Cholesterol Education Program normal glucose tolerance oral glucose tolerance test quantitative sudomotor axon reflex test World Health Organization
that evidence into clinical practice.1 These studies had a strong focus on increased physical activity and dietary modification as well as weight reduction among overweight participants. The key issue seems to be a comprehensive approach to correct several risk factors simultaneously.2 Furthermore, long-term follow-up studies of lifestyle interventions, even over relatively short durations, seem to have a long-lasting carry-over effect on risk factors and diabetes incidence. The growing challenge to the successful implementation of programmes for the primary prevention of type 2 diabetes is to find simple non-invasive methods to identify those with a disease risk. These solutions or technologies need to be reproducible and validated by good science. There have been numerous attempts over the last 30 years to develop non-invasive methods to measure glucose or to use scores based on anthropometric or other parameters for diabetes risk assessment. Most successful approaches have been risk scores based on anthropometric parameters through self-assessments.3 However, self assessments are limited in the ability to motivate people to lifestyle change. Currently, screening tests for type 2 diabetes include risk assessment questionnaires, biochemical tests and combinations of both. The biochemical tests currently available are blood glucose or urine glucose measurements, blood HbA1c or fructosamine measurements.4 Several questionnaires have also been developed to screen for undiagnosed diabetes. However, since the main purpose of screening is to detect asymptomatic undiagnosed diabetes, questionnaires which are based on the symptoms of diabetes are not adequate. The usefulness of urinary glucose as a screening test for undiagnosed
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DOI: 10.1177/1474651411402629 204
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diabetes is limited because of the low sensitivity (21–64% with specificity > 98%) in studies which included performing an OGTT in the entire study population or a random sample of negative screens.4 The FPG test is recommended for initial screening of non-pregnant adults. However, it is an invasive test which has a low sensitivity in many populations.5 Hence, a noninvasive, cost-effective tool which is easy to administer with high sensitivity and specificity would be of great advantage and benefit for diabetes screening. The value of such a tool would increase if it could be operated by non-clinical personnel.
verified with a subsequent second test. Nevertheless, HbA1c as a diagnostic tool is not without controversy as there are limitations in sensitivity and specificity, especially among certain ethnic populations. In summary, there are several tools used to stratify diabetes risk, each with varying relevance aspects of practicability, validity, specificity and diagnostic value. The perfect diagnostic test is one which would be easy to handle, transparent, not cost and time consuming and which would give a robust diagnostic stratification of diabetes risk.
EZSCAN™ Diagnosis of diabetes risk Early detection of diabetes risk and, in particular, the ability to assess a patient’s specific level of risk in developing type 2 diabetes, is challenging.3 Currently, there are several well-known indicators, for example, anthropometric, laboratory-based and other lifestyle-dependent markers. Some of the challenges are to define their relevance and stratify them in the constellation of other risk factors. Furthermore, risk factors, such as being overweight, have completely different relevance based on different genetic backgrounds leading to a small proportion of obese patients with a low metabolic risk and others with a small BMI, but increased visceral fat, being at higher risk. Having a clear and reproducible diabetes risk diagnosis helps to provide a further reason for the at-risk patient to follow lifestyle changes. The need is clear to develop diagnostic tools for the detection of metabolic and diabetes-specific risk.
Stratification of diabetes risk Diagnostic tools for diabetes risk which can also be used quickly in daily clinical practice have even higher utility.6 Several risk scores have been developed in the past with varying degrees of success. Out of the more than 20 existing scores, the most prominent is the FINDRISC score.3 Some scores are based on the evaluation of anthropometric parameters and patient selfassessment factors; others are based on clinical and laboratory risk factors. Based on epidemiological studies, these risk factors vary in relevance to predict diabetes risk. Similar scores exist for the metabolic syndrome and also for cardiovascular risk factors. The advantage of these scores is primarily their availability to the widest possible segment of the public.7 The disadvantage is that they often lack the ability to convince patients to change their lifestyle. The gold standard in diabetes diagnostic is the performance of the OGTT. A postprandial glucose elevation and also a high fasting glucose value are often good predictive factors for diabetes mellitus. Postprandial glucose elevation is an early indicator for insulin resistance as well as the beta-cell failure. Latest research also cites the 1-hour OGTT as having a high predictive value for diabetes.8 All this is only relevant if the OGTT is performed in a standardised manner. Lack of standardisation and non-compliance to a formal method in performing OGTT has led some to question the diagnostic outputs. As a result, HbA1c has become the defacto diagnostic tool for diabetes mellitus. Not surprisingly, the easier a diagnostic procedure, the better it is able to reach more patients in a first step, which can be
THE BRITISH JOURNAL OF DIABETES AND VASCULAR DISEASE
In this vein, Impeto Medical has developed a non-invasive diagnostic tool to identify those with increased diabetes risk. The person to be tested does not need to be fasting and the test can be performed in different settings and need only take 2 to 3 minutes for a diagnostic output. The EZSCAN™ device is designed to perform a precise evaluation of the sweat gland function through reverse iontophoresis and chronoamperometry, allowing the measurement of electrochemical skin conductance based on sweat chloride concentrations.9 EZSCAN™ allows early detection of small autonomic neuropathies. A risk model for diabetes risk and early disease detection has been developed depending on these parameters. Assessment of sudomotor dysfunction is known from the QSART to be an established method to test the function of small autonomic nerves.10,11 Impeto has applied this technology to metabolic diseases and extrapolated the function of autonomic sweat glands to a metabolic risk. It is known that metabolic diseases alter the function of small autonomic nerve fibres. Those alterations are directly connected to insulin resistance and, with this, to increased diabetes risk or already present type 2 diabetes. Such diagnostic technology would be the ideal tool for diabetes screening and early risk detection in the medical and paramedical setting.
Principle and description Measurements are performed where sweat glands are most numerous, i.e. on the palms of the hands, on the soles of the feet and on the forehead.12 Large area nickel electrodes are used alternatively as an anode or a cathode and a DC incremental voltage ≤ 4 volts is applied on the anode. This DC generates a voltage on the cathode through reverse iontophoresis and a current (intensity of about 0.2 mA) between the anode and the cathode, proportional to chloride concentration as measured by chronoamperometry.
Principle Nickel (Ni) was chosen for the electrodes due to its high sensitivity to electrochemical reactions. The influence of pH, sodium, chloride (Cl−), urea and lactate concentrations on the Ni electrodes behaviour were tested using sweat mimicking solutions. On the anodic voltammograms a large current is observed at high potentials due to the reaction between Cl− and Ni (figure 1). The increase in Cl− concentration shifts the breakdown potential towards lower potentials. pH was also shown
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Figure 1. Cyclic voltammograms of Nickel (Ni) electrode in aerated carbonate buffer solution (36 mM; pH = 6.4) in presence of increasing NaCl concentrations (curve 1: 0 mM; curve 2 : 30 mM; curve 3 = 60 mM; curve 4 = 90 mM; curve 5 = 120 mM). E: potential in Volts (V)
to have identical influence while sodium, lactate and urea concentrations have very little effect on the electrochemical behaviour of Ni electrodes. At low voltages, less than 10 V, the stratum corneum is electrically insulating and only sweat gland ducts are conductive. Chizmadzhev et al. proposed a model with ‘one dimensional’ cylindrical tube and two layers of electroporous epithelial cells.9 A new model including the most abundant ions in the sweat, Cl−, Na+, H+, their concentrations and their velocities was developed to allow simulations of very low sweat rates. Sodium and chloride conductance values used for calculations were taken from Quinton et al.13 When a 1 V potential is applied, the chloride concentration is high close to the anode surface, increasing proportionally with the applied potential and will govern the electrodes reaction while a lower variation is observed for the hydrogen current at the cathode.
Description of the device and expression of results The apparatus consists of two sets of electrodes for the hands and the feet and a headband for the forehead, all of which are connected to a computer for recording and data management purposes. To conduct the test the patients are required to place their hands and feet on the electrodes, and place the headband electrodes on their foreheads. The patients are then required to stand still for 2 minutes. During the test six combinations of 15 different low DC voltages are applied. In figure 2 a copy of the screen with the presentation of the results for three subjects is displayed. According to the conductance values expressed in micro Siemens (µS), measured on hands and feet, a score is calculated and results are displayed according to this score with a colour index. Green indicates (< 50%) no sweat dysfunction (figure 2a), yellow (50–65%) median sweat dysfunction (figure 2b) and orange-red (> 65%) high sweat dysfunction (figure 2c).
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Conductances on anode (purple), cathode (blue) and total (green) are displayed on the upper part of each screenshot. In each heptagon, small circles in the lower part are for the feet ESC, the small circles in middle part are for the hands and the small circles in the upper part are for the forehead. In figure 2b (yellow case) a decrease in feet and hands ESC is observed, with an increase in forehead ESC when compared to figure 2a (green case). In figure 2c (orange-red case) an important decrease in feet ESC and an important increase in forehead ESC are observed when compared to figures 2a or 2b.
Main results Main characteristics of measurements Symmetry: With diabetic neuropathy being mostly symmetric in nature, it was important to ensure that ESC measurements between the right and left sides were comparable. In this way, ESC in hands and feet were compared between the right and left sides using a Bland and Altman plot. Coefficient of variation calculated in 1365 of the subjects involved in the studies or surveys performed was 3% for hands and 2% for feet between the right and left sides. Gender effect: No significant difference was observed in ESC measured in hands and feet between female and male subjects involved in the studies or surveys performed. Reproducibility: To ensure reproducibility, measurements were assessed twice on the same day in patients with at least one cardiovascular risk and in patients with diabetes. Results were compared using a Bland and Altman plot; this is based on the difference between two measurements against their mean. The coefficient of variation was 7% in hands and 5% in feet in patients with cardiovascular risk and 15% in hands and 7% in feet in patients with diabetes in which the coefficient of variation for glycaemia between the two measurements was 32%. VOLUME 11 ISSUE 4 . JULY/AUGUST 2011
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Figure 2. Screenshots of EZSCAN™ measurements in subjects with normal sudomotor function (a, green case), with moderate sudomotor dysfunction (b, yellow case) and important sudomotor dysfunction (c, orange-red case). Conductances in anode (purple), cathode (blue) and total (green) are displayed on the upper part of each screen shot. In each heptagon, small circles in lower part are for feet ESC (electrochemical sweat conductance), small circles in middle part are for hands and small circles in upper part are for forehead
Effects of glycaemia: As this technology has to be used in patients with pre-diabetes or diabetes, with potential high variations in glycaemia, it was important to ensure that measurements were not influenced directly by glycaemia. Measurements were taken in 12 patients with increased glycaemia higher than 18 mmol/L and compared with measurements performed in the same patients when glycaemia was at normal levels. The coefficient of variation from a Bland and Altman plot with or without hyperglycaemia was 10% in feet. Effects of exercise: As exercise could influence sweat function, measurements were taken before and after an exercise test using an ergonomic bicycle at a level of 87% of their maximum heart rate. The before and after measurements produced a variation coefficient of 4% in feet and 8% in hands respectively. Effects of the device: As EZSCAN™ is to be used as a screening tool it was important to ensure the integrity of the set of electrodes used. In this way three measurements were performed in 21 patients with three different devices. There was no significant difference between the three measurements and the paired Spearman test evidenced a coefficient of correlation higher than 0.96 for each comparison (p
