Aaron I Vinik1, Xuan Kong, J Thomas Megerian, Shai N Gozani. 1. The Leonard Strelitz Diabetes Research Institutes, Departments of Internal Medicine and Pathology/Neurobiology, Eastern Virginia Medical School, Norfolk, Virginia, USA.
Abstract
BACKGROUND: Nerve conduction studies (NCS) are the most objective measure of nerve function, and their use is recommended in the clinical and epidemiological evaluation of diabetic polyneuropathy (DPN). The purpose of this study was to utilize automated NCS technology to characterize nerve conduction of patients with diabetes in primary care settings. METHODS: The Diabetes cohort was drawn from 28 community clinics. The Control cohort consisted of subjects without diabetes and without evidence of neuropathy. Bilateral peroneal NCS were performed with an automated NCS instrument (NC-stat, NeuroMetrix, Inc., Waltham, MA). Neuropathic symptoms were quantified using an abbreviated form of the NTSS-6 questionnaire. Risk factors for abnormal NCS were determined using multivariate regression modeling. RESULTS: Data were collected for 172 control subjects and 1,358 subjects with diabetes. Statistically significant differences in peroneal NCS were found. Of the Diabetes cohort, 75.1% had at least one NCS abnormality, and 53.2% had bilateral abnormalities. Of the asymptomatic patients, 45% had bilateral NCS abnormalities. By contrast, 40% of those with clinically significant symptoms lacked bilateral NCS abnormalities. Independent predictors for bilateral NCS abnormalities were age, height, weight, hemoglobin A1c (HbA1c), and duration of diabetes. Up to 16% of the variance in NCS measurements was explained by HbA1c, duration of diabetes, and several demographic variables. CONCLUSIONS: This study suggests that automated NCS can provide nerve conduction confirmation of DPN in primary care settings and has clinical utility. These findings have important implications for the clinical and epidemiological evaluation of DPN.
BACKGROUND: Nerve conduction studies (NCS) are the most objective measure of nerve function, and their use is recommended in the clinical and epidemiological evaluation of diabetic polyneuropathy (DPN). The purpose of this study was to utilize automated NCS technology to characterize nerve conduction of patients with diabetes in primary care settings. METHODS: The Diabetes cohort was drawn from 28 community clinics. The Control cohort consisted of subjects without diabetes and without evidence of neuropathy. Bilateral peroneal NCS were performed with an automated NCS instrument (NC-stat, NeuroMetrix, Inc., Waltham, MA). Neuropathic symptoms were quantified using an abbreviated form of the NTSS-6 questionnaire. Risk factors for abnormal NCS were determined using multivariate regression modeling. RESULTS: Data were collected for 172 control subjects and 1,358 subjects with diabetes. Statistically significant differences in peroneal NCS were found. Of the Diabetes cohort, 75.1% had at least one NCS abnormality, and 53.2% had bilateral abnormalities. Of the asymptomatic patients, 45% had bilateral NCS abnormalities. By contrast, 40% of those with clinically significant symptoms lacked bilateral NCS abnormalities. Independent predictors for bilateral NCS abnormalities were age, height, weight, hemoglobin A1c (HbA1c), and duration of diabetes. Up to 16% of the variance in NCS measurements was explained by HbA1c, duration of diabetes, and several demographic variables. CONCLUSIONS: This study suggests that automated NCS can provide nerve conduction confirmation of DPN in primary care settings and has clinical utility. These findings have important implications for the clinical and epidemiological evaluation of DPN.