Non-invasive Screening of Diabetes Risk by Assessing Abnormalities of Sudomotor Function

Abstract

Background:

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: S_I 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), S_I (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.

Conclusions:

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.

Key words

sudomotor function - insulin resistance

• References

• 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