Simplified electrophysiological approach combining a point-of-care nerve conduction device and an electrocardiogram produces an accurate diagnosis of diabetic polyneuropathy.

AIMS/INTRODUCTION: This study aimed to investigate the diagnostic potential of two simplified tests, a point-of-care nerve conduction device (DPNCheck™) and a coefficient of variation of R-R intervals (CVR-R), as an alternative to traditional nerve conduction studies for the diagnosis of diabetic polyneuropathy (DPN) in patients with diabetes. MATERIALS AND METHODS: Inpatients with type 1 or type 2 diabetes (n = 167) were enrolled. The study population consisted of 101 men, with a mean age of 60.8 ± 14.8 years. DPN severity was assessed using traditional nerve conduction studies, and differentiated based on Baba's classification (BC). To examine the explanatory potential of variables in DPNCheck™ and CVR-R regarding the severity of DPN according to BC, a multiple regression analysis was carried out, followed by a receiver operating characteristic analysis. RESULTS: Based on BC, 61 participants (36.5% of the total) were categorized as having DPN severity of stage 2 or more. The multiple regression analysis yielded a predictive formula with high predictive power for DPN diagnosis (estimated severity of DPN in BC = 2.258 - 0.026 × nerve conduction velocity [m/s] - 0.594 × ln[sensory nerve action potential amplitude (µV)] + 0.528In[age(years)] - 0.178 × ln[CVR-R], r = 0.657). The area under the curve in receiver operating characteristic analysis was 0.880. Using the optimal cutoff value for DPN with severer than stage 2, the predictive formula showed good diagnostic efficacy: sensitivity of 83.6%, specificity of 79.2%, positive predictive value of 51.7% and negative predictive value of 76.1%. CONCLUSIONS: These findings suggest that DPN diagnosis using DPNCheck™ and CVR-R could improve diagnostic efficiency and accessibility for DPN assessment in patients with diabetes.

The efficacy of switching basal-bolus insulin therapy to basal insulin-supported oral therapy with a glinide and an α-glucosidase inhibitor in patients with type 2 diabetes depends on insulin secretory capacity, but not on blood glucose profiles and insulin dosages prior to the switching.

Aims: We aimed to identify patients who would benefit from basal insulin-supported oral therapy (BOT) with a glinide and an α-glucosidase inhibitor (a fixed-dose combination tablet of mitiglinide 10 mg and voglibose 0.2 mg) in Japanese type 2 diabetic patients. Methods: Patients who were hospitalized to improve hyperglycemia received basal-bolus insulin therapy. After the reduction of glucose toxicity, a 75 g oral glucose tolerance test and a glucagon test were performed. Thereafter, the basal-bolus insulin therapy was switched to BOT with mitiglinide, followed by further addition of voglibose. Interstitial glucose levels were continuously monitored throughout the study period. Diurnal glucose profile was recorded and analyzed. Patients were divided into two groups according to whether their percentage of time in range (TIR, 70-180 mg/dL) under BOT with mitiglinide/voglibose was higher than 70% or not, and the differences in clinical characteristics between the groups were analyzed. Results: Twenty patients were enrolled, and 19 of them completed the study. BOT with mitiglinide/voglibose achieved ≥ 70% of TIR in thirteen patients. The area under the curve of serum C-peptide levels during the oral glucose tolerance test was significantly higher in the patients with ≥ 70% of TIR. The daily insulin dosages and blood glucose profiles were comparable between the two groups. Conclusions: The efficacy of BOT with mitiglinide/voglibose depended on residual insulin secretory abilities. This therapy would be a useful therapeutic option for patients with type 2 diabetes.

Neuroretinal dysfunction revealed by a flicker electroretinogram correlated with peripheral nerve dysfunction and parameters of atherosclerosis in patients with diabetes.

AIMS/INTRODUCTION: Diabetic polyneuropathy (DPN) develops in the early stage of diabetes. However, no common diagnostic protocol has yet been established. Here, to verify that the flicker electroretinogram using a hand-held device can detect the early dysfunction of the peripheral nervous system in patients with diabetes, we investigated the correlation between the progression of DPN and neuroretinal dysfunction. MATERIALS AND METHODS: In total, 184 participants with type 1 or 2 diabetes underwent a flicker electroretinogram (ERG) using a hand-held device RETeval™ and nerve conduction study. Participants were also evaluated for intima-media thickness, ankle-brachial index, toe brachial index and brachial-ankle pulse wave velocity. Parameters of the nerve conduction study were used to diagnose the severity according to Baba's classification. A multiple regression analysis was used to examine the associations of ERG parameters with the severity of DPN categorized by Baba's classification. Diagnostic properties of the device in DPN were evaluated using a receiver operating characteristic curve. RESULTS: A multiple regression model to predict the severity of DPN was generated using ERG. In the model, moderate-to-severe DPN was effectively diagnosed (area under the receiver operating characteristic curve 0.692, sensitivity 56.5%, specificity 78.3%, positive predictive value 70.6%, negative predictive value 66.1%, positive likelihood ratio 2.60, negative likelihood ratio 0.56). In the patients without diabetic retinopathy, the implicit time and amplitude in ERG significantly correlated with the parameters of the nerve conduction study, brachial-ankle pulse wave velocity and intima-media thickness. CONCLUSIONS: Electroretinogram parameters obtained by the hand-held device successfully predict the severity of DPN. The device might be useful to evaluate DPN.

Point-of-care nerve conduction device predicts the severity of diabetic polyneuropathy: A quantitative, but easy-to-use, prediction model.

AIMS/INTRODUCTION: A gold standard in the diagnosis of diabetic polyneuropathy (DPN) is a nerve conduction study. However, as a nerve conduction study requires expensive equipment and well-trained technicians, it is largely avoided when diagnosing DPN in clinical settings. Here, we validated a novel diagnostic method for DPN using a point-of-care nerve conduction device as an alternative way of diagnosis using a standard electromyography system. MATERIALS AND METHODS: We used a multiple regression analysis to examine associations of nerve conduction parameters obtained from the device, DPNCheck™, with the severity of DPN categorized by the Baba classification among 375 participants with type 2 diabetes. A nerve conduction study using a conventional electromyography system was implemented to differentiate the severity in the Baba classification. The diagnostic properties of the device were evaluated using a receiver operating characteristic curve. RESULTS: A multiple regression model to predict the severity of DPN was generated using sural nerve conduction data obtained from the device as follows: the severity of DPN = 2.046 + 0.509 × ln(age [years]) - 0.033 × (nerve conduction velocity [m/s]) - 0.622 × ln(amplitude of sensory nerve action potential [µV]), r = 0.649. Using a cut-off value of 1.3065 in the model, moderate-to-severe DPN was effectively diagnosed (area under the receiver operating characteristic curve 0.871, sensitivity 70.1%, specificity 87.7%, positive predictive value 83.0%, negative predictive value 77.3%, positive likelihood ratio 5.67, negative likelihood ratio 0.34). CONCLUSIONS: Nerve conduction parameters in the sural nerve acquired by the handheld device successfully predict the severity of DPN.

Alterations of retinal thickness measured by optical coherence tomography correlate with neurophysiological measures in diabetic polyneuropathy.

AIMS/INTRODUCTION: Diabetic polyneuropathy (DPN) and diabetic retinopathy (DR) are traditionally regarded as microvascular complications. However, these complications may share similar neurodegenerative pathologies. Here we evaluate the correlations in the severity of DPN and changes in the thickness of neuroretinal layers to elucidate whether these complications exist at similar stages of progression. MATERIALS AND METHODS: A total of 43 patients with type 2 diabetes underwent a nerve conduction study (NCS), a macular optical coherence tomography, and a carotid artery ultrasound scan. Diabetic polyneuropathy was classified according to Baba's classification using NCS. The retina was automatically segmented into four layers; ganglion cell complex (GCC), inner nuclear layer/outer plexiform layer (INL/OPL), outer nuclear layer/photoreceptor inner and outer segments, and retinal pigment epithelium (RPE). The thickness of each retinal layer was separately analyzed for the fovea and the parafovea. RESULTS: Fourteen patients were classified as having moderate to severe diabetic polyneuropathy. The thicknesses of the foveal and parafoveal INL/OPL increased in patients with diabetic polyneuropathy compared with patients without. The thickness of the parafoveal retinal pigment epithelium decreased in patients with diabetic polyneuropathy. The thinning of parafoveal ganglion cell complex and foveal and parafoveal retinal pigment epithelium were positively correlated with deterioration of nerve functions in the nerve conduction study, but the thickening of INL/OPL was positively correlated with the nerve function deterioration. The thinning of parafoveal ganglion cell complex and foveal retinal pigment epithelium were positively correlated with the thickening of the carotid intima-media. CONCLUSIONS: Depending on the progression of diabetic polyneuropathy, the ganglion cell complex and retinal pigment epithelium became thinner and the INL/OPL became thicker. These retinal changes might be noteworthy for pathological investigations and for the assessment of diabetic polyneuropathy and diabetic retinopathy.