Claus Bogh Juhl

One Week’s Treatment With the Long-Acting Glucagon-Like Peptide 1 Derivative Liraglutide (NN2211) Markedly Improves 24-h Glycemia and α- and β-Cell Function and Reduces Endogenous Glucose Release in Patients with Type 2 Diabetes

Glucagon-like peptide 1 (GLP-1) is potentially a very attractive agent for treating type 2 diabetes. We explored the effect of short-term (1 week) treatment with a GLP-1 derivative, liraglutide (NN2211), on 24-h dynamics in glycemia and circulating free fatty acids, islet cell hormone profiles, and gastric emptying during meals using acetaminophen. Furthermore, fasting endogenous glucose release and gluconeogenesis (3-(3)H-glucose infusion and (2)H(2)O ingestion, respectively) were determined, and aspects of pancreatic islet cell function were elucidated on the subsequent day using homeostasis model assessment and first- and second-phase insulin response during a hyperglycemic clamp (plasma glucose approximately 16 mmol/l), and, finally, on top of hyperglycemia, an arginine stimulation test was performed. For accomplishing this, 13 patients with type 2 diabetes were examined in a double-blind, placebo-controlled crossover design. Liraglutide (6 micro g/kg) was administered subcutaneously once daily. Liraglutide significantly reduced the 24-h area under the curve for glucose (P = 0.01) and glucagon (P = 0.04), whereas the area under the curve for circulating free fatty acids was unaltered. Twenty-four-hour insulin secretion rates as assessed by deconvolution of serum C-peptide concentrations were unchanged, indicating a relative increase. Gastric emptying was not influenced at the dose of liraglutide used. Fasting endogenous glucose release was decreased (P = 0.04) as a result of a reduced glycogenolysis (P = 0.01), whereas gluconeogenesis was unaltered. First-phase insulin response and the insulin response to an arginine stimulation test with the presence of hyperglycemia were markedly increased (P < 0.001), whereas the proinsulin/insulin ratio fell (P = 0.001). The disposition index (peak insulin concentration after intravenous bolus of glucose multiplied by insulin sensitivity as assessed by homeostasis model assessment) almost doubled during liraglutide treatment (P < 0.01). Both during hyperglycemia per se and after arginine exposure, the glucagon responses were reduced during liraglutide administration (P < 0.01 and P = 0.01). Thus, 1 weeks treatment with a single daily dose of the GLP-1 derivative liraglutide, operating through several different mechanisms including an ameliorated pancreatic islet cell function in individuals with type 2 diabetes, improves glycemic control throughout 24 h of daily living, i.e., prandial and nocturnal periods. This study further emphasizes GLP-1 and its derivatives as a promising novel concept for treatment of type 2 diabetes.

On high-frequency insulin oscillations

Insulin is released in a pulsatile manner, which results in oscillatory concentrations in blood. The oscillatory secretion improves release control and enhances the hormonal action. Insulin oscillates with a slow ultradian periodicity (approximately 140 min) and a high-frequency periodicity (approximately 6-10 min). Only the latter is reviewed in this article. At least 75% of the insulin secretion is released in a pulsatile manner. Individuals prone to developing diabetes or with overt type 2 diabetes are characterized by irregular oscillations of plasma insulin. Many factors have impact on insulin pulsatility such as age, insulin resistance and glycemic level. In addition, tiny glucose oscillations are capable of entraining insulin oscillations in healthy people in contrast to type 2 diabetic individuals emphasizing a profound disruption of the beta-cells in type 2 diabetes to sense or respond to physiological glucose excursions. A crucial question is how approximately 1,000,000 islets, each containing from a few to several thousand beta-cells, can be coordinated to secrete insulin in a pulsatile manner. This is blatantly a very complex operation to control involving an intra-pancreatic neural network, an intra-islet communication and metabolic oscillations in the beta-cell itself. Overnight beta-cell rest, e.g. during somatostatin administration, improves the disordered pulsatile insulin secretion in type 2 diabetes. Acute as well as long-term administration of sulphonylureas (SU) leads to substantial amplification (approximately 50%) of the pulsatile insulin secretion in type 2 diabetes. This is probably cardinal in terms of governing the hepatic glucose release in type 2 diabetes. Whether sulfonylureas also improve the ability of the beta-cells to sense glucose fluctuations remains to be explored. Thiazolidinediones reduce the pulsatile insulin secretion without affecting regularity, but appear to improve the ability of the beta-cell to be entrained by small glucose excursions. Finally, similar to SUs, the incretin hormone GLP-1 also results in an augmented pulsatile burst mass in both healthy and diabetic individuals, in the latter group, however, without influencing the disorderliness of pulses. This review will briefly describe the high-frequency insulin pulsatility during physiologic and pathophysiologic conditions as well as the influence of some hypoglycemic compounds on the insulin oscillations.

Detection of hypoglycemia associated EEG changes during sleep in type 1 diabetes mellitus

OBJECTIVE Nocturnal hypoglycemia is a feared complication to insulin treated diabetes. Impaired awareness of hypoglycemia (IAH) increases the risk of severe hypoglycemia. EEG changes are demonstrated during daytime hypoglycemia. In this explorative study, we test the hypothesis that specific hypoglycemia-associated EEG-changes occur during sleep and are detectable in time for the patient to take action. RESEARCH DESIGN AND METHODS Ten patients with type 1 diabetes (duration 23.7 years) with IAH were exposed to insulin-induced hypoglycemia during the daytime and during sleep. EEG was recorded and analyzed real-time by an automated multi-parameter algorithm. Participants received an auditory alarm when EEG changes met a predefined threshold, and were instructed to consume a meal. RESULTS Seven out of eight participants developed hypoglycemia-associated EEG changes during daytime. During sleep, nine out of ten developed EEG changes (mean BG 2.0 mmol/l). Eight were awakened by the alarm. Four corrected hypoglycemia (mean BG 2.2 mmol/l), while four (mean BG 1.9 mmol/l) received glucose infusion. Two had false alarms. EEG-changes occurred irrespective of sleep stage. Post hoc improvement indicates the possibility of earlier detection of hypoglycemia. CONCLUSIONS Continuous EEG monitoring and automated real-time analysis may constitute a novel technique for a hypoglycemia alarm in patients with IAH.

Hypoglycemia-associated changes in the electroencephalogram in patients with type 1 diabetes and normal hypoglycemia awareness or unawareness

Hypoglycemia is associated with increased activity in the low-frequency bands in the electroencephalogram (EEG). We investigated whether hypoglycemia awareness and unawareness are associated with different hypoglycemia-associated EEG changes in patients with type 1 diabetes. Twenty-four patients participated in the study: 10 with normal hypoglycemia awareness and 14 with hypoglycemia unawareness. The patients were studied at normoglycemia (5–6 mmol/L) and hypoglycemia (2.0–2.5 mmol/L), and during recovery (5–6 mmol/L) by hyperinsulinemic glucose clamp. During each 1-h period, EEG, cognitive function, and hypoglycemia symptom scores were recorded, and the counterregulatory hormonal response was measured. Quantitative EEG analysis showed that the absolute amplitude of the θ band and α-θ band up to doubled during hypoglycemia with no difference between the two groups. In the recovery period, the θ amplitude remained increased. Cognitive function declined equally during hypoglycemia in both groups and during recovery reaction time was still prolonged in a subset of tests. The aware group reported higher hypoglycemia symptom scores and had higher epinephrine and cortisol responses compared with the unaware group. In patients with type 1 diabetes, EEG changes and cognitive performance during hypoglycemia are not affected by awareness status during a single insulin-induced episode with hypoglycemia.

Hypoglycemia-Related Electroencephalogram Changes are Independent of Gender, Age, Duration of Diabetes, and Awareness Status in Type 1 Diabetes

Introduction: Neuroglycopenia in type 1 diabetes mellitus (T1DM) results in reduced cognition, unconsciousness, seizures, and possible death. Characteristic changes in the electroencephalogram (EEG) can be detected even in the initial stages. This may constitute a basis for a hypoglycemia alarm device. The aim of the present study was to explore the characteristics of the EEG differentiating normoglycemia and hypoglycemia and to elucidate potential group differences. Methods: We pooled data from experiments in T1DM where EEG was available during both normoglycemia and hypoglycemia for each subject. Temporal EEG was analyzed by quantitative electroencephalogram (qEEG) analysis with respect to absolute amplitude and centroid frequency of the delta, theta, alpha, and beta bands, and the peak frequency of the unified theta-alpha band. To elucidate possible group differences, data were subsequently stratified by age group (± 50 years), gender, duration of diabetes (± 20 years), and hypoglycemia awareness status (normal/impaired awareness of hypoglycemia). Results: An increase in the log amplitude of the delta, theta, and alpha band and a decrease in the alpha band centroid frequency and the peak frequency of the unified theta-alpha band constituted the most significant hypoglycemia indicators (all p < .0001). The size of these qEEG changes remained stable across all strata. Conclusions: Hypoglycemia-associated EEG changes remain stable across age group, gender, duration of diabetes, and hypoglycemia awareness status. This indicates that it may be possible to establish a general algorithm for hypoglycemia detection based on EEG measures.

Hypoglycemia-Related Electroencephalogram Changes Assessed by Multiscale Entropy

BACKGROUND Several clinical studies have shown that low blood glucose (BG) levels affect electroencephalogram (EEG) rhythms through the quantification of traditional indicators based on linear spectral analysis. Nonlinear measures used in the last decades to characterize the EEG in several physiopathological conditions have never been assessed in hypoglycemia. The present study investigates if properties of the EEG signal measured by nonlinear entropy-based algorithms are altered in a significant manner when a state of hypoglycemia is entered. SUBJECTS AND METHODS EEG was acquired from 19 patients with type 1 diabetes during a hyperinsulinemic-euglycemic-hypoglycemic clamp experiment. In parallel, BG was frequently monitored by the standard YSI glucose and lactate analyzer and used to identify two 1-h intervals corresponding to euglycemia and hypoglycemia, respectively. In each subject, the P3-C3 EEG derivation in the two glycemic intervals was assessed using the multiscale entropy (MSE) approach, obtaining measures of sample entropy (SampEn) at various temporal scales. The comparison of how signal irregularity measured by SampEn varies as the temporal scale increases in the two glycemic states provides information on how EEG complexity is affected by hypoglycemia. RESULTS For both glycemic states, the MSE analysis showed that SampEn increases at small time scales and then monotonically decreases as the time scale becomes larger. Comparing the two conditions, SampEn was higher in hypoglycemia only at medium time scales. CONCLUSIONS A decrease in the complexity of EEG occurs when a state of hypoglycemia is entered, because of a degradation of the EEG long-range temporal correlations. Thanks to its ability to assess nonlinear dynamics of the EEG signal, the MSE approach seems to be a useful tool to complement information brought by standard linear indicators and provide new insights on how hypoglycemia affects brain functioning.

Hypoglycemia-Associated Electroencephalogram and Electrocardiogram Changes Appear Simultaneously:

Background: Tight glycemic control in type 1 diabetes mellitus (T1DM) may be accomplished only if severe hypoglycemia can be prevented. Biosensor alarms based on the bodys reactions to hypoglycemia have been suggested. In the present study, we analyzed three lead electrocardiogram (ECG) and single-channel electroencephalogram (EEG) in T1DM patients during hypoglycemia. Materials and Methods: Electrocardiogram and EEG recordings during insulin-induced hypoglycemia in nine patients were used to assess the presence of ECG changes by heart rate, and estimates of QT interval (QTc) and time from top of T wave to end of T wave corrected for heartbeat interval and EEG changes by extraction of the power of the signal in the delta, theta, and alpha bands. These six features were assessed continuously to determine the time between changes and severe hypoglycemia. Results: QT interval changes and EEG theta power changes were detected in six and eight out of nine subjects, respectively. Rate of false positive calculations was one out of nine subjects for QTc and none for EEG theta power. Detection time medians (i.e., time from significant changes to termination of experiments) was 13 and 8 min for the EEG theta power and QTc feature, respectively, with no significant difference (p = .25). Conclusions: Severe hypoglycemia is preceded by changes in both ECG and EEG features in most cases. Electroencephalogram theta power may be superior with respect to timing, sensitivity, and specificity of severe hypoglycemia detection. A multiparameter algorithm that combines data from different biosensors might be considered.

EEG Signal Quality of a Subcutaneous Recording System Compared to Standard Surface Electrodes

Purpose. We provide a comprehensive verification of a new subcutaneous EEG recording device which promises robust and unobtrusive measurements over ultra-long time periods. The approach is evaluated against a state-of-the-art surface EEG electrode technology. Materials and Methods. An electrode powered by an inductive link was subcutaneously implanted on five subjects. Surface electrodes were placed at sites corresponding to the subcutaneous electrodes, and the EEG signals were evaluated with both quantitative (power spectral density and coherence analysis) and qualitative (blinded subjective scoring by neurophysiologists) analysis. Results. The power spectral density and coherence analysis were very similar during measurements of resting EEG. The scoring by neurophysiologists showed a higher EEG quality for the implanted system for different subject states (eyes open and eyes closed). This was most likely due to higher amplitude of the subcutaneous signals. During periods with artifacts, such as chewing, blinking, and eye movement, the two systems performed equally well. Conclusions. Subcutaneous measurements of EEG with the test device showed high quality as measured by both quantitative and more subjective qualitative methods. The signal might be superior to surface EEG in some aspects and provides a method of ultra-long term EEG recording in situations where this is required and where a small number of EEG electrodes are sufficient.

Hypoglycemia-Induced Decrease of EEG Coherence in Patients with Type 1 Diabetes

BACKGROUND Hypoglycemic events in patients with type 1 diabetes (T1D) are associated with measurable electroencephalography (EEG) changes. Previous studies have, however, evaluated these changes on a single EEG channel level, whereas multivariate analysis of several EEG channels has been scarcely investigated. The aim of the present work is to use a coherence approach to quantitatively assess how hypoglycemia affects mutual connectivity of different brain areas. MATERIALS AND METHODS EEG multichannel data were obtained from 19 patients with T1D (58% males; mean age, 55 ± 2.4 years; diabetes duration, 28.5 ± 2.6 years; glycated hemoglobin, 8.0 ± 0.2%) who underwent a hyperinsulinemic-hypoglycemic clamp study. The information partial directed coherence (iPDC) function was computed through multivariate autoregressive models during eu- and hypoglycemia in the theta and alpha bands. RESULTS In passing from eu- to hypoglycemia, absolute values of the iPDC function tend to decrease in both bands in all combinations of the considered channels. In particular, the scalar indicator [Formula: see text], which summarizes iPDC information, significantly decreased (P < 0.01) in 17 of 19 subjects: from T5-A1A2 to C3-A1A2 from O1-A1A2 to C4-A1A2 and from O2-A1A2 to Cz-A1A2 in the theta band and from O1-A1A2 to T4-A1A2 and from O1-A1A2 to C4-A1A2 in the alpha band. CONCLUSIONS The coherence decrease measured by iPDC in passing from eu- to hypoglycemia is likely related to the progressive loss of cognitive function and altered cerebral activity in hypoglycemia. This result encourages further quantitative investigation of EEG changes in hypoglycemia and of how EEG acquisition and real-time processing can support hypoglycemia alert systems.

Patient-reported Outcomes in Weight Loss and Body Contouring Surgery: A Cross-sectional Analysis Using the Body-q

Background: Health-related quality of life and satisfaction with appearance are important outcomes in bariatric and body contouring surgery. To investigate these outcomes, scientifically sound and clinically meaningful patient-reported outcome instruments are needed. The authors measured health-related quality of life and appearance in a cohort of Danish patients at different phases in the weight loss journey: before bariatric surgery, after bariatric surgery, before body contouring surgery, and after body contouring surgery. Methods: From June of 2015 to June of 2016, a cross-sectional sample of 493 bariatric and body contouring patients were recruited from four different hospital departments. Patients were asked to fill out the BODY-Q, a new patient-reported outcomes instrument designed specifically to measure health-related quality of life and appearance over the entire patient journey, from obesity to the post–body contouring surgery period. Data were collected using REDCap, and analyzed using SPSS software. Results: For all appearance and health-related quality-of-life scales, the mean score was significantly lower in the pre–bariatric surgery group compared with the post–body contouring group. Furthermore, the correlation between body mass index and mean scores was significant for all appearance and health-related quality-of-life scales, with higher scores associated with lower body mass index. The mean score for the group reporting no excess skin compared with the group reporting a lot of excess skin was significantly higher for five of seven appearance scales and four of five health-related quality-of-life scales. Conclusion: This study provides evidence to suggest that body contouring plays an important role in the weight loss patient’s journey and that patients need access to treatments.