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.
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