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Non-invasive Screening of Diabetes Risk by Assessing Abnormalities of Sudomotor Function
received 03 July 2013
first decision 03 September 2013
accepted 10 September 2013
05 May 2014 (online)
The early detection of diabetes, and subsequent lifestyle intervention, may reduce the burden of diabetes and its complications. Several studies have identified a link between sudomotor dysfunction, insulin resistance, and pre-diabetes. The aim of this study was to evaluate the ability of a new non-invasive device EZSCAN evaluating sudomotor function to detect pre-diabetes in a German population at risk for diabetes.
Methods and findings:
200 German subjects at risk for diabetes (mean age 56±14 years, BMI 28.4±5.4 kg/m2) were measured for anthropometric data on inflammatory parameters, including high sensitivity C reactive protein (hs-CRP). The subjects also underwent an oral glucose tolerance test with measurements of plasma glucose, insulin, proinsulin, C-peptide and free fatty acids during 2 h following glucose challenge. Indexes for sensitivity to insulin were calculated: SI using minimal model, HOMA-IR and Matsuda index. Based on the measurement of electrochemical sweat conductance, subjects were classified as no risk, moderate risk or high risk. According to this risk model classification, a significant difference was observed between OGTT-1 h (p=0.004), 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) after adjustment for age. Among the 54 subjects with impaired fasting glucose or impaired glucose tolerance according to WHO classification, 37 had a moderate risk and 15 a high risk according to the EZSCAN risk model classification. Among the 12 subjects with newly diagnosed diabetes, 2 had a moderate risk and 10 a high risk according to the risk model classification. No adverse event was reported during or after the study.
These results, in accordance with a previous study performed in India, show that EZSCAN could be developed as a screening tool for diabetes risk, and could help to improve diabetes screening strategies. Results obtained from an at-risk population would have to be confirmed in a larger population.
- 1 Idf . Diabetes Atlas. Fourth Edition. Brussels: International Diabetes Federation; 2009
- 2 Schwarz PE, Greaves CJ, Lindstrom J et al. Nonpharmacological interventions for the prevention of type 2 diabetes mellitus. Nature reviews Endocrinology 2012; 8: 363-373 DOI: 10.1038/nrendo.2011.232.
- 3 Paulweber B, Valensi P, Lindström J et al. A European evidence-based guideline for the prevention of type 2 diabetes. Horm Metab Res 2010; 42: S3-S36
- 4 Lindstrom J, Neumann A, Sheppard KE et al. Take action to prevent diabetes – the IMAGE toolkit for the prevention of type 2 diabetes in Europe. Hormone and metabolic research=Hormon- und Stoffwechselforschung=Hormones et metabolisme 2010; 42 (Suppl. 01) S37-S55 DOI: 10.1055/s-0029-1240975.
- 5 Ziemer DC, Kolm P, Weintraub WS et al. Glucose-independent, black-white differences in hemoglobin A1c levels: a cross-sectional analysis of 2 studies. Ann Intern Med 152: 770-777
- 6 Bergman M. Inadequacies of absolute threshold levels for diagnosing prediabetes. Diabetes Metab Res Rev 26: 3-6
- 7 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: 281-286
- 8 Lindstrom J, Tuomilehto J. The Diabetes Risk Score: A practical tool to predict type 2 diabetes risk. Diabetes care 2003; 26: 725-731
- 9 Schwarz PE, Li J, Lindstrom J et al. Tools for predicting the risk of type 2 diabetes in daily practice. Hormone and metabolic research=Hormon- und Stoffwechselforschung=Hormones et metabolisme 2009; 41: 86-97
- 10 Schwarz PE, Li J, Reimann M et al. The Finnish Diabetes Risk Score Is Associated with Insulin Resistance and Progression towards Type 2 Diabetes. The Journal of clinical endocrinology and metabolism 2009; 94: 920-926
- 11 Kim B, McLean LL, Philip SS et al. Hyperinsulinemia induces insulin resistance in dorsal root ganglion neurons. Endocrinology 2011; 152: 3638-3647
- 12 Low PA. Evaluation of sudomotor function. Clin Neurophysiol 2004; 115: 1506-1513
- 13 Provitera V, Nolano M, Caporaso G et al. Evaluation of sudomotor function in diabetes using the dynamic sweat test. Neurology 2010; 74: 50-56
- 14 Gibbons CH, Illigens BM, Wang N et al. Quantification of sudomotor innervation: a comparison of three methods. Muscle & nerve 2010; 42: 112-119
- 15 Grandinetti A, Chow DC, Sletten DM et al. Impaired glucose tolerance is associated with postganglionic sudomotor impairment. Clinical autonomic research: official journal of the Clinical Autonomic Research Society 2007; 17: 231-233
- 16 Smith AG, Russell J, Feldman EL et al. Lifestyle intervention for pre-diabetic neuropathy. Diabetes care 2006; 29: 1294-1299
- 17 Hubert D, Brunswick P, Calvet JH et al. Abnormal electrochemical skin conductance in cystic fibrosis. Journal of cystic fibrosis: official journal of the European Cystic Fibrosis Society 2011; 10: 15-20 DOI: 10.1016/j.jcf.2010.09.002.
- 18 Ayoub H, Griveau S, Lair V et al. Electrochemical characterization of nickel electrodes in phosphate and carbonate electrolytes in view of assessing a medical diagnostic device for the detection of early diabetes. Electroanalysis 2010; 22: 2483-2490
- 19 Brunswick P, Mayaudon H, Albin V et al. Use of Ni electrodes chronoamperometry for improved diagnostics of diabetes and cardiac diseases. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society Conference 2007; 2007: 4544-4547 DOI: 10.1109/iembs.2007.4353350.
- 20 Mayaudon H, Miloche PO, Bauduceau B. A new simple method for assessing sudomotor function: relevance in type 2 diabetes. Diabetes & metabolism 2010; 36: 450-454
- 21 Ayoub H, Griveau S, Lair V et al. Electrochemical characterization of nickel electrodes in phosphate and carbonate electrolytes in view of assessing a medical diagnostic device for the detection of early diabetes. Electroanalysis 2012; 24: 386-391
- 22 Ramachandran A, Moses A, Shetty S et al. A new non-invasive technology to screen for dysglycaemia including diabetes. Diabetes research and clinical practice 2010; 88: 302-306 DOI: 10.1016/j.diabres.2010.01.023.
- 23 Sheng CS, Zeng WF, Huang QF et al. Accuracy of a Novel Non-Invasive technology based EZSCAN system for the diagnosis of diabetes mellitus in Chinese. Diabetol Metab Syndr 2011; 3: 36 DOI: 10.1186/1758-5996-3-36.
- 24 Lacomis D. Small-fiber neuropathy. Muscle & nerve 2002; 26: 173-188
- 25 Schwarz PE, Towers GW, Fischer S et al. Hypoadiponectinemia is associated with progression toward type 2 diabetes and genetic variation in the ADIPOQ gene promoter. Diabetes care 2006; 29: 1645-1650 DOI: 10.2337/dc05-2123.
- 26 Paulweber B, Valensi P, Lindstrom J et al. A European evidence-based guideline for the prevention of type 2 diabetes. Hormone and metabolic research=Hormon- und Stoffwechselforschung=Hormones et metabolisme 2010; 42 (Suppl. 01) S3-S36 DOI: 10.1055/s-0029-1240928.
- 27 Abdul-Ghani MA, Abdul-Ghani T, Muller G et al. Role of glycated hemoglobin in the prediction of future risk of T2DM. The Journal of clinical endocrinology and metabolism 2011; 96: 2596-2600 DOI: 10.1210/jc.2010-1698.
- 28 Breda E, Cavaghan MK, Toffolo G et al. Oral glucose tolerance test minimal model indexes of beta-cell function and insulin sensitivity. Diabetes 2001; 50: 150-158
- 29 Monti LD, Poma R, Caumo A et al. Intravenous infusion of diarginylinsulin, an insulin analogue: effects on glucose turnover and lipid levels in insulin-treated type II diabetic patients. Metabolism: clinical and experimental 1992; 41: 540-544
- 30 Chizmadzhev YA, Indenbom AV, Kuzmin PI et al. Electrical properties of skin at moderate voltages: contribution of appendageal macropores. Biophys J 1998; 74: 843-856
- 31 R. Development. Core. Team . R: A Language and Environment for Statistical Computing. In: http://www.R-project.org (ed.). Vienna, Austria: Foundation for Statistical Computing; 2011
- 32 Tesfaye S, Boulton AJ, Dyck PJ et al. Diabetic neuropathies: update on definitions, diagnostic criteria, estimation of severity, and treatments. Diabetes care 2010; 33: 2285-2293 DOI: 10.2337/dc10-1303.
- 33 Calvet JH, Dupin J, Winiecki H et al. Assessment of Small Fiber Neuropathy through a Quick, Simple and Non Invasive Method in a German Diabetes Outpatient Clinic. Experimental and clinical endocrinology & diabetes: official journal, German Society of Endocrinology [and] German Diabetes Association 2013; 121: 80-83 DOI: 10.1055/s-0032-1323777.
- 34 Sumner CJ, Sheth S, Griffin JW et al. The spectrum of neuropathy in diabetes and impaired glucose tolerance. Neurology 2003; 60: 108-111
- 35 Baum P, Petroff D, Classen J et al. Dysfunction of autonomic nervous system in childhood obesity: a cross-sectional study. PloS one 2013; 8: e54546 DOI: 10.1371/journal.pone.0054546.
- 36 Lee KO, Nam JS, Ahn CW et al. Insulin resistance is independently associated with peripheral and autonomic neuropathy in Korean type 2 diabetic patients. Acta diabetologica 2012; 49: 97-103 DOI: 10.1007/s00592-010-0176-6.
- 37 Schwarz PE, Brunswick P, Calvet JH. EZSCAN a new tool to detect diabetes risk. Br J Diabetes Vasc Dis 2011; 11: 204-209