Luseogliflozin improves liver fat deposition compared to metformin in type 2 diabetes patients with non-alcoholic fatty liver disease: A prospective randomized controlled pilot study.

This study aimed to assess the effect of luseogliflozin on liver fat deposition and compare luseogliflozin to metformin in type 2 diabetes (T2D) patients with non-alcoholic fatty liver disease (NAFLD). Thirty-two T2D patients with NAFLD diagnosed by computed tomography or abdominal sonography were recruited. Participants were randomly assigned to receive either luseogliflozin (2.5 mg, newly administered) or metformin (1500 mg, newly or additionally administrated). Data on the liver-to-spleen attenuation ratio (L/S), visceral fat area, body mass index, glycated hemoglobin (HbA1c), alanine aminotransferase (ALT), fasting plasma glucose, C-peptide immunoreactivity (CPR), and CPR index were collected at baseline and after 6 months. The change in L/S was significantly greater in the luseogliflozin group than in the metformin group. Similarly, the changes in the visceral fat area, HbA1c, and body mass index were significantly greater in the luseogliflozin group than in the metformin group. The changes in ALT, fasting glucose, CPR, and CPR index were not significant in both groups. In conclusion, luseogliflozin significantly reduced liver fat deposition as compared to metformin, which may indicate clinical relevant benefits for NAFLD.

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.

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.

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.

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.

Investigating the Relationship between Morning Glycemic Variability and Patient Characteristics Using Continuous Glucose Monitoring Data in Patients with Type 2 Diabetes.

Objective To investigate the relationship between patient characteristics and morning glycemic variability. Methods We retrospectively evaluated 106 patients with type 2 diabetes who underwent continuous glucose monitoring during admission. The highest postprandial glucose level (within 3 hours after breakfast; 'highest level'), the time from the start of breakfast to the highest postprandial glucose level ('highest time'), the difference between the pre-breakfast and highest postprandial breakfast glucose level ('increase'), the area under the curve (AUC; ≥180 mg/dL) for the glycemic variability within 3 hours after breakfast ('morning AUC'), and the post-breakfast glucose gradient ('gradient') were calculated. We analyzed the associations between these factors and nocturnal hypoglycemia and the patients' characteristics by using a regression analysis. Results After stepwise multivariate adjustment, significant independent associations were found between 'highest level' and high age, low BMI, and high HbA1c; 'highest time' and high HbA1c, low C-peptide immunoreactivity (CPR), and low fasting plasma glucose (FPG); the 'increase' and high age, low BMI, high HbA1c, low FPG and hypoglycemia; 'morning AUC' and high age, high HbA1c and hypoglycemia; and 'gradient' and long duration of diabetes and low BMI. Conclusion Higher age and lower BMI are associated with higher 'highest' and 'increase' levels. Higher HbA1c levels were linked to a longer 'highest time', and longer durations of the diabetes, while lower BMI values were related to a higher 'gradient'.

Major Increases between Pre- and Post-breakfast Glucose Levels May Predict Nocturnal Hypoglycemia in Type 2 Diabetes.

Objective The aim of this study was to determine whether nocturnal hypoglycemia may be predicted according to morning glucose levels. Methods We retrospectively evaluated 106 patients with type 2 diabetes who underwent continuous glucose monitoring during admission. The pre-breakfast glucose level (Pre-breakfast level), highest postprandial glucose level within 3 hours after breakfast (Highest level), time from the start of breakfast to the highest postprandial glucose level (Highest time), difference between the pre-breakfast and highest postprandial breakfast glucose levels (Increase), area under the glucose curve (≥180 mg/dL) within 3 hours after breakfast (Morning AUC), post-breakfast glucose gradient (Gradient), and the increase-to-pre-breakfast ratio (Increase/Pre-breakfast) were calculated. The subjects were divided into hypoglycemic and non-hypoglycemic patients and compared for the above parameters using the t-test. A receiver operating characteristic analysis was used to determine the optimal cut-off values to predict nocturnal hypoglycemia (Hypoglycemia). Results Twenty-eight patients (26.4%) had hypoglycemia. The Pre-breakfast levels were significantly lower in patients with hypoglycemia than those without (p=0.03). The Increases were significantly higher in patients with hypoglycemia than those without (p=0.047). The Increase/Pre-breakfast ratio were significantly larger in patients with hypoglycemia than those without (p=0.0002). Their cut-off values were as follows (level, sensitivity, specificity, and area under the curve): 123 mg/dL, 0.89, 0.55, and 0.78 (p<0.0001); 90.5 mg/dL, 0.75, 0.64, and 0.76 (p<0.0001); and 90.2%, 0.75, 0.76, and 0.78 (p<0.0001), respectively. Conclusion Major increases between the pre- and post-breakfast glucose levels may predict nocturnal hypoglycemia in patients with type 2 diabetes.