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The British Journal of Diabetes & Vascular

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 DOI: 10.1177/1474651411402629

2011 11: 204British Journal of Diabetes & Vascular DiseasePeter EH Schwarz, Philippe Brunswick and Jean-Henri Calvet

a new technology to detect diabetes riskEZSCAN  

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Abstract

Key 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 ques-tionnaires 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 patho-physiology behind this technology is supported by the growing number of clinical studies worldwide which show a strong association between small nerve neuropa-thies to insulin resistance and diabetes risk. Because the EZSCAN™ test takes only three minutes to run, is non-invasive 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

IntroductionWorldwide, many countries are confronted with growing healthcare costs associated with caring for the needs of a rap-idly 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 interven-tions or medication as shown by major clinical trials of diabetes prevention, but it is a completely different challenge to carry

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 dis-ease risk. These solutions or technologies need to be reproduc-ible and validated by good science. There have been numerous attempts over the last 30 years to develop non-invasive meth-ods to measure glucose or to use scores based on anthropo-metric or other parameters for diabetes risk assessment. Most successful approaches have been risk scores based on anthro-pometric 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 use-fulness of urinary glucose as a screening test for undiagnosed

EZSCAN™ a new technology to detect diabetes riskPETER EH SCHWARZ1, PHILIPPE BRUNSWICK2, JEAN-HENRI CALVET2

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: peter.schwarz@uniklinikum-dresden.de

402629 DVDXXX10.1177/1474651411402629Schwarz et al.British Journal of Diabetes & Vascular Disease

Abbreviations and acronyms

AHA American Heart Association

BMI body mass index

DC direct current

ESC electrochemical sweat conductance

FINDRISC FINnish Diabetes Risk SCore

FPG fasting plasma glucose

HbA1c glycated haemoglobin A1c

IGT impaired glucose tolerance

MS metabolic syndrome

NCEP National Cholesterol Education Program

NGT normal glucose tolerance

OGTT oral glucose tolerance test

QSART quantitative sudomotor axon reflex test

WHO World Health Organization

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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 non-invasive, 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.

Diagnosis of diabetes riskEarly detection of diabetes risk and, in particular, the ability to assess a patient’s specific level of risk in developing type 2 dia-betes, 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 dif-ferent 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 riskDiagnostic 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 self-assessment 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 perfor-mance 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 indi-cator 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 per-formed 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

verified with a subsequent second test. Nevertheless, HbA1c as a diagnostic tool is not without controversy as there are limita-tions in sensitivity and specificity, especially among certain eth-nic 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 per-fect diagnostic test is one which would be easy to handle, trans-parent, not cost and time consuming and which would give a robust diagnostic stratification of diabetes risk.

EZSCAN™In this vein, Impeto Medical has developed a non-invasive diag-nostic 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 chronoamperome-try, allowing the measurement of electrochemical skin conduct-ance 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 diabe-tes. Such diagnostic technology would be the ideal tool for diabetes screening and early risk detection in the medical and paramedical setting.

Principle and descriptionMeasurements 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 incre-mental voltage ≤ 4 volts is applied on the anode. This DC gen-erates 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.

PrincipleNickel (Ni) was chosen for the electrodes due to its high sensi-tivity to electrochemical reactions. The influence of pH, sodium, chloride (Cl−), urea and lactate concentrations on the Ni elec-trodes 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 break-down potential towards lower potentials. pH was also shown

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to have identical influence while sodium, lactate and urea con-centrations have very little effect on the electrochemical behav-iour of Ni electrodes.

At low voltages, less than 10 V, the stratum corneum is electrically insulating and only sweat gland ducts are conduc-tive. Chizmadzhev et al. proposed a model with ‘one dimen-sional’ 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 resultsThe 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).

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 impor-tant decrease in feet ESC and an important increase in forehead ESC are observed when compared to figures 2a or 2b.

Main resultsMain characteristics of measurementsSymmetry: 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 varia-tion 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 coeffi-cient of variation for glycaemia between the two measure-ments was 32%.

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)

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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 meas-urements were not influenced directly by glycaemia. Measurements were taken in 12 patients with increased glycae-mia 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 func-tion, 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 pro-duced 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 per-formed 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<0.0001).

Results with EZSCAN™ used as a screening tool for diabetes or pre-diabetesA preliminary study with the EZSCAN™ device was undertaken on 90 diabetic subjects and compared with a control popula-tion of 142 non-diabetic subjects. It showed a significant reduc-tion of the electrochemical conductance in the diabetes population compared to the control population (56±1.4 versus 78±0.7 µS, p<0.001). As the preliminary data showed that this device could be used as a screening tool for detecting diabetes, a cross-sectional study was initiated at three research centres in Chennai, India, in one research centre in China and in one research centre in Germany.

The ability of this technology to detect IGT and type 2 dia-betes was assessed in a population of Indian subjects at risk for diabetes. An OGTT, HbA1C, serum lipids tests and ESC measure-ment were performed on 212 eligible subjects (mean age 43±9 years, BMI 28±5). Diabetes and IGT were defined according to WHO definitions and metabolic syndrome was defined accord-ing to the AHA/NCEP criteria modified to suit the Asian popula-tion.14 According to clinical and biological measurements, 24 subjects were diagnosed with diabetes, 30 with IGT, 57 subjects had NGT with metabolic syndrome and 101 had NGT without metabolic syndrome. FPG at a cut-off level of 7.0 mmol/L had 29% sensitivity to detect diabetes and 3% for IGT. EZSCAN™ at a cut-off value of 50% on its scale had 75% sensitivity to detect diabetes, 70% for IGT and 84% for NGT with metabolic syndrome.14

To complete this cross-sectional study, a longitudinal study has been performed in 69 subjects with NGT at the start of the study. Eight months later, 23% of these subjects had IGT or diabetes. The odds ratio for a subject having glucose intolerance

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

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was 6.19 for subjects classified initially at high risk (EZSCAN™ score > 65%) and 3.0 for subjects with median risk with EZSCAN™ (score between 50 and 65%) when compared with subjects without risk EZSCAN™ score < 50%.

A parallel study was performed in a population of 212 Chinese subjects at risk of diabetes (mean age 59±7 years, BMI 25±3). According to the same criteria, 32 subjects were diag-nosed with diabetes, 50 with IGT, 51 subjects had NGT with metabolic syndrome and 79 had NGT without metabolic syn-drome. FPG at a cut-off level of 7.0 mmol/L had 48% sensitivity to detect diabetes and 4% for IGT. EZSCAN™ at a cut-off value of 50% on its scale had 88% sensitivity to detect diabetes, 78% for IGT and 82% for NGT with metabolic syndrome (unpublished data).

The first large-scale study in Europe was performed in Germany on 193 healthy subjects at risk of developing diabetes (mean age 59±14 years, BMI 28±5, HbA1c 5.6±0.4). The aim of the study was to test the screening ability of EZSCAN™ in patients at risk of diabetes. Of these six subjects were newly diagnosed with diabetes and were correctly identified with the EZSCAN™, while 30 of 31 subjects with IGT were correctly detected with the EZSCAN™. A longitudinal study is ongoing to complete this cross-sectional study.

EZSCAN™ presents a repeatedly tested good discrimination between normal and high glucose, c-peptide and proinsulin values enabling the differentiation between different metabolic phenotypes in subjects with normal glucose tolerance. The evolution of glucose, c-peptide and pro-insulin values (figure 3)

during the OGTT in relation to the EZSCAN™ classification is given in the three curves.

General safety and acceptability by the subjectFor all these studies no safety issues arose, particularly no adverse events during and after measurement were reported. Surveys performed in India and Germany showed excellent acceptability with 59% and 32% considering the overall impression as good or excellent respectively, with less than 1% of patients experiencing discomfort during the test and with 48% feeling that the test took a moderate amount of time and 22% hardly any time.

DiscussionAssessment of sudomotor dysfunction based on sweat chloride concentrations through reverse iontophoresis and chrono-amperometry is a sensitive method for the detection of insulin resistance, IGT or diabetes. Assessment of EZSCAN™ showed reproducible results in various conditions with low influence of usual physiological variations due to its focus on chloride con-centrations, which are less dependent on sweat rates than cur-rent methods used for assessment of sweat function.11,15,16 Results observed in subjects with IGT and diabetes are in accordance with the decrease in sweat production evidenced using QSART,17 the decrease in intraepidermal nerve density using skin biopsies18 and neural dysfunction using Neurometer for current perception threshold.19 All these results are in accordance with the high prevalence of IGT or diabetes in

Figure 3. The three EZSCAN™ classes (green (normal), yellow (IGT), orange/red (diabetes)) are characterised by a different profile for glucose, c-peptide and proinsulin, parallel to the classification seen during an oral glucose tolerance test (normal, impaired glucose tolerance, diabetic)

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THE BRITISH JOURNAL OF DIABETES AND VASCULAR DISEASE 209

patients with sensory peripheral neuropathy, with a rate of up to 42% in cases initially thought to be idiopathic compared with 14% in the general population. In the same way, a recent study of individuals with idiopathic neuropathy, but normal glucose tolerance, found that the majority had features of the metabolic syndrome.20

No safety concern was reported, probably due to much lower current intensities than those used in iontophoresis for the sweat tests or other tests used for assessment of sudomo-tor function. The coherence between the Indian, Chinese and German data were very impressive with excellent discriminatory function between insulin sensitive and insulin resistant. A potential explanation can be derived from the autonomic nerv-ous system being a very early developmental system and, there-fore, very stable in function. If this keeps true, the assessment of sudomotor dysfunction through the measurement of sweat chloride concentrations, based on reverse iontophoresis and chronoamperometry, can become a unique screening test for diabetes risk with global standards.

The development of a valid, safe, fast, cost-effective diagnos-tic procedure to detect diabetes risk is imperative. The diagnostic gold standard is the OGTT, which has limitations in performance. Self-assessment risk scores to describe the dia betes risk often do not reach the necessary awareness and self-management neces-sary. Compared to other methods, EZSCAN™ is a rapid, non-invasive and reproducible test and appears to be a suitable method for large-scale screening of metabolic dysfunction pre-ceding the use of more specific but costly methods. EZSCAN™ has an enormous potential because it is a non-invasive 3-minute test and may be associated with the differentiation of metabolic risk in subjects with normal glucose tolerance. In conclusion, the EZSCAN™ is a new device which can detect diabetes and IGT in a sensitive, fast and non-invasive way.

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advancing towards the mature stage in Europe. Horm Metab Res 2010;42(Suppl. 1):1-2.

2. Reimann M, Bonifacio E, Solimena M et al. An update on preven-tive and regenerative therapies in diabetes mellitus. Pharmacol Ther 2009;121:317-31.

3. Schwarz PE, Li J, Lindstrom J, Tuomilehto J. Tools for predicting the risk of type 2 diabetes in daily practice. Horm Metab Res 2009;41:86-97.

4. IDF. Diabetes Atlas, fourth edition, Vol 4. Brussels: International Diabetes Federation, 2009.

5. Engelgau MM, Narayan KM, Herman WH. Screening for type 2 diabe-tes. Diabetes Care 2000;23:1563-80.

6. Pajunen P, Landgraf R, Muylle F et al. Quality indicators for the pre-vention of type 2 diabetes in Europe–IMAGE. Horm Metab Res 2010;42(Suppl 1):S56-63.

7. Schwarz PE, Muylle F, Valensi P, Hall M. The European perspective of diabetes prevention. Horm Metab Res 2008;40:511-4.

8. Abdul-Ghani MA, Lyssenko V, Tuomi T et al. Fasting versus postload plasma glucose concentration and the risk for future type 2 diabetes: results from the Botnia Study. Diabetes Care 2009;32:281-6.

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10. Ogawa K, Sasaki H, Yamasaki H et al. Peripheral nerve functions may deteriorate parallel to the progression of microangiopathy in diabetic patients. Nutr Metab Cardiovasc Dis 2006;16:313-21.

11. Riedel A, Braune S, Kerum G et al. Quantitative sudomotor axon reflex test (QSART): a new approach for testing distal sites. Muscle Nerve 1999;22:1257-64.

12. Sato K, Kang WH, Saga K, Sato KT. Biology of sweat glands and their disorders. I. Normal sweat gland function. J Am Acad Dermatol 1989;20:537-63.

13. Quinton PM. Cystic fibrosis: a disease in electrolyte transport. Faseb J 1990;4:2709-17.

14. Ramachandran A, Moses A, Shetty S et al. A new non-invasive tech-nology to screen for dysglycaemia including diabetes. Diabetes Res Clin Pract 2010;88:302-6.

15. Shimada H, Kihara M, Kosaka S et al. Comparison of SSR and QSART in early diabetic neuropathy–the value of length-dependent pattern in QSART. Auton Neurosci 2001;92:72-5.

16. Low VA, Sandroni P, Fealey RD, Low PA. Detection of small-fiber neu-ropathy by sudomotor testing. Muscle Nerve 2006;34:57-61.

17. Grandinetti A, Chow DC, Sletten DM et al. Impaired glucose tolerance is associated with postganglionic sudomotor impairment. Clin Auton Res 2007;17:231-3.

18. Smith AG, Russell J, Feldman EL et al. Lifestyle intervention for pre-diabetic neuropathy. Diabetes Care 2006;29:1294-9.

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Key messages

The EZSCAN™ test:●● is a valid, fast and cost-effective procedure for

detection of diabetes risk●● uses electrochemical skin conductance to detect risk

for insulin resistance and diabetes