bluuebunny/crossref_metadata_2025_split huggingface dataset:
Altered Disease Course after Initiation of Self-Monitoring of Blood Glucose in Noninsulin-Treated Type 2 Diabetes (ROSSO 3)
Authors: Kolb Hubert , Schneider Berthold , Heinemann Lutz , Lodwig Volker , Scherbaum Werner A. , Martin Stephan 
Published: January 2014

Background: Patients with noninsulin-treated type 2 diabetes were documented from diagnosis to determine whether patients taking up self-monitoring of blood glucose (SMBG) are distinct by baseline characteristics, exhibit a different natural disease course, and are treated differently. Methods: The German multicenter, retrospective cohort study (ROSSO) followed 3268 persons from diagnosis of type 2 diabetes for a mean of 6.5 years. During follow-up, 1912 persons received oral antidiabetic agents (OAD) for at least 1 year, but no insulin. Data were retrieved from patient files of randomly contacted primary care practices. Results: During follow-up, 742 patients (38.8%) began with SMBG prior to an end point. Initiation of SMBG was followed by improved glycemic control. Patients in the SMBG cohort were treated more often by an internist, younger by a mean of 3 years, and more often male (p < 0.001, each). A higher percentage of persons in the SMBG cohort were treated with metformin (74.7% vs 65.0%, p < 0.001) or changed OAD therapy (66.3% of patients vs 48.3% of patients, p < 0.001). SMBG was not accompanied by more comedication. In the SMBG cohort, 68 persons had a clinical end point (myocardial infarction, stroke, foot amputation, blindness, hemodialysis, or all-cause mortality) (9.2%) compared to 155 persons (13.2%) in the cohort without SMBG (p = 0.04 after multivariate adjustments). Conclusion: This first large documentation of OAD-treated persons from diagnosis for 6.5 years indicates that the use of SMBG is associated with younger age at diagnosis, a higher prescription rate of metformin, more frequent changes of oral therapy, and a lower risk of a clinical end point.
Endothelial Function and Arterial Stiffness in Uncomplicated Type 1 Diabetes and Healthy Controls and the Impact of Insulin on These Parameters during an Euglycemic Clamp
Authors: Wilhelm Birgit , Weber Matthias M. , Kreisselmeier Hans Peter , Kugler Matthias , Ries Claudius , Pfützner Andreas , Kann Peter H. , Forst Thomas 
Published: January 2014

Background: In addition to its role in glucose metabolism, insulin has shown to exert numerous vascular effects, and an impaired vascular function of insulin is assumed to be a major contributor in the development of vascular complications. Arterial augmentation (AP) and the augmentation index (Aix) are surrogate parameters of arterial stiffness and are commonly used as predictors for cardiovascular risk. The aim of this study is to investigate the effect of insulin on arterial stiffness and parameters of endothelial function in patients with type 1 diabetes and healthy control subjects. Methods: Fourteen patients with type 1 diabetes (six male, eight female) with a mean age of 36.6 ± 11.8 years and 14 healthy subjects (seven male, seven female) with a mean age of 27.3 ± 5.5 years were randomized to an euglygemic clamp with either a low (0.25 mU/kg/min) or a high (1.0 mU/kg/min) insulin dose on two different days. The mean HbA1c in the diabetic subjects was 7.3 ± 0.7%. In these subjects, arterial stiffness was measured by pulse wave analysis (SphygmoCor, AtCor Medical, Australia). AP was calculated as the difference between the second and the first systolic shoulders of the central pressure wave curve, and the Aix was expressed as the percentage of AP from total pulse pressure. As parameters of endothelial function, cyclic guanosine monophosphate, nitrotyrosine, and asymmetric dimethylarginine were determined at baseline and after 120 minutes. Results: Patients with type 1 diabetes showed increased values for AP with 3.5 ± 3.1 mm Hg and Aix with 12.5 ± 12.5% compared to healthy controls with −0.7 ± 2.6 mm Hg for AP and −4.2 ± 10.6% for Aix. This difference was statistically significant (p < 0.01). During the euglycemic clamp, insulin improved, but did not normalize the increased values for AP and Aix in patients with type 1 diabetes. Concerning parameters of endothelial function, patients with type 1 diabetes showed statistically significant increased values for nitrotyrosine compared to healthy controls at baseline [low insulin: Diabetes mellitus (DM) 1993.12 ± 1330.85 nmol/liter vs healthy controls 803.7 ± 726.91; high insulin DM: 2208.02 ± 1736.57 nmol/liter vs healthy controls: 750.83 ± 426.03 nmol/liter] (p < 0.05). Conclusion: Patients with type 1 diabetes mellitus revealed an increased arterial stiffness measured as augmentation and augmentation index and increased nitrotyrosine levels as a marker of oxidative stress compared to healthy control subjects at baseline. Application of insulin improves the arterial elastic properties, but was not able to normalize the vascular function in patients with type 1 diabetes.
Reconstruction of Glucose in Plasma from Interstitial Fluid Continuous Glucose Monitoring Data: Role of Sensor Calibration
Authors: Facchinetti Andrea , Sparacino Giovanni , Cobelli Claudio 
Published: January 2014

Background: Continuous glucose monitoring (CGM) sensors measure glucose concentration in the interstitial fluid (ISF). Equilibration between plasma and ISF glucose is not instantaneous. Therefore, ISF and plasma glucose concentrations exhibit different dynamic patterns, particularly during rapid changes. The purpose of this work was to investigate how well plasma glucose can be reconstructed from ISF CGM data. Methods: Six diabetic volunteers were monitored for 2 days using the TheraSense FreeStyle Navigator (Abbott Diabetes Care, Alameda, CA), a minimally invasive device that, on the basis of an initial calibration procedure (hereafter referred to as standard calibration), returns ISF glucose concentration. Simultaneously, plasma glucose concentration was also measured every 15 minutes. First we identified, in each subject, the linear time-invariant (LTI) two-compartment model of plasma-interstitium kinetics. Then, a nonparametric regularization deconvolution method was used to reconstruct plasma from ISF glucose. Results: Deconvoluted profiles were always closer to plasma glucose than ISF ones. However, the quality of the reconstruction is unsatisfactory. Some visible discrepancies between average plasma and ISF time series suggest problems either in the applicability of the LTI model of plasma-interstitium kinetics to normal life conditions or in the standard calibration with which ISF glucose is determined from the sensor internal readings. Assuming that the LTI model of plasma-interstitium kinetics is correct, we focused on the influence of calibration and we employed a recently proposed method to recalibrate ISF data. Conclusions: After the recalibration step, the relative error in reconstructing plasma glucose was reduced significantly. Results also demonstrate that further margins of improvement of plasma glucose reconstruction are possible by developing more sophisticated recalibration procedures.
Glucose Prediction Algorithms from Continuous Monitoring Data: Assessment of Accuracy via Continuous Glucose Error-Grid Analysis
Authors: Zanderigo Francesca , Sparacino Giovanni , Kovatchev Boris , Cobelli Claudio 
Published: January 2014

Aim: The aim of this article was to use continuous glucose error-grid analysis (CG-EGA) to assess the accuracy of two time-series modeling methodologies recently developed to predict glucose levels ahead of time using continuous glucose monitoring (CGM) data. Methods: We considered subcutaneous time series of glucose concentration monitored every 3 minutes for 48 hours by the minimally invasive CGM sensor Glucoday® (Menarini Diagnostics, Florence, Italy) in 28 type 1 diabetic volunteers. Two prediction algorithms, based on first-order polynomial and autoregressive (AR) models, respectively, were considered with prediction horizons of 30 and 45 minutes and forgetting factors (ff) of 0.2, 0.5, and 0.8. CG-EGA was used on the predicted profiles to assess their point and dynamic accuracies using original CGM profiles as reference. Results: Continuous glucose error-grid analysis showed that the accuracy of both prediction algorithms is overall very good and that their performance is similar from a clinical point of view. However, the AR model seems preferable for hypoglycemia prevention. CG-EGA also suggests that, irrespective of the time-series model, the use of ff = 0.8 yields the highest accurate readings in all glucose ranges. Conclusions: For the first time, CG-EGA is proposed as a tool to assess clinically relevant performance of a prediction method separately at hypoglycemia, euglycemia, and hyperglycemia. In particular, we have shown that CG-EGA can be helpful in comparing different prediction algorithms, as well as in optimizing their parameters.
Accuracy Requirements for a Hypoglycemia Detector: An Analytical Model to Evaluate the Effects of Bias, Precision, and Rate of Glucose Change
Authors: Noujaim Sharbel E. , Horwitz David , Sharma Manoj , Marhoul Joseph 
Published: January 2014

Background: There has been considerable debate on what constitutes a good hypoglycemia (Hypo) detector and what is the accuracy required from the continuous monitoring sensor to meet the requirements of such a detector. The performance of most continuous monitoring sensors today is characterized by the mean absolute relative difference (MARD), whereas Hypo detectors are characterized by the number of false positive and false negative alarms, which are more relevant to the performance of a Hypo detector. This article shows that the overall accuracy of the system and not just the sensor plays a key role. Methods: A mathematical model has been developed to investigate the relationship between the accuracy of the continuous monitoring system as described by the MARD, and the number of false negatives and false positives as a function of blood glucose rate change is established. A simulation method with N = 10,000 patients is used in developing the model and generating the results. Results: Based on simulation for different scenarios for rate of change (0.5, 1.0, and 5.0 mg/dl per minute), sampling rate (from 1, 2.5, 5, and 10 minutes), and MARD (5, 7.5, 10, 12.5, and 15%), the false positive and false negative ratios are computed. The following key results are from these computations. For a given glucose rate of change, there is an optimum sampling time. The optimum sampling time as defined in the critical sampling rate section gives the best combination of low false positives and low false negatives. There is a strong correlation between MARD and false positives and false negatives. For false positives of <10% and false negatives of <5%, a MARD of <7.5% is needed. Conclusions: Based on the model, assumptions in the model, and the simulation on N = 10,000 patients for different scenarios for rate of glucose change, sampling rate, and MARD, it is concluded that the false negative and false positive ratio will vary depending on the alarm Hypo threshold set by the patient and the MARD value. Also, to achieve a false negative ratio <5% and a false positive ratio <10% would require continuous glucose monitoring to have an MARD ≤7.5%.
Self-Measurement of Blood Glucose in Patients with Type 2 Diabetes: A Health Economic Assessment
Authors: Weber Christian , Neeser Kurt , Schneider Berthold , Lodwig Volker 
Published: January 2014

Background: The clinical role and the potential benefit of self-measurement of blood glucose (SMBG) for patients with type 2 diabetes are still under discussion. Even less information is available on the cost-effectiveness of performing SMBG by this patient group. The goal of this study was to establish cost-effectiveness ratios of performing SMBG by patients afflicted by this disease. Methods: We assessed the benefit and cost-effectiveness of SMBG in type 2 diabetes from a third-party payer perspective based on results of both a large epidemiologic cohort study reflecting the reality of care, and a Markov model calculation. Results: Analysis of cohort study data revealed that total costs cumulated over the observation period of 8 years were lower in the SMBG group than in the non-SMBG group according to savings of € 1'714 [oral antidiabetic drugs (OAD) only] and € 13'815 (OAD + insulin) per patient. Several scenarios were considered in the model-based calculation. The cost-effectiveness ratio varied from € 20'768/life year gained to domination of SMBG use compared to nonusers in OAD treated patients and from € 59'057/life year gained to domination of SMBG use compared to nonusers in OAD + insulin treated patients. Conclusion: Results indicate that SMBG in type 2 diabetes offers an excellent opportunity to get a high investment-outcome ratio in the treatment of this pandemic disease.
Numerical Simulation of the Effect of Rate of Change of Glucose on Measurement Error of Continuous Glucose Monitors
Authors: Taub Marc B. , Peyser Thomas A. , Rosenquist J. Erik 
Published: January 2014

Background: A 5-day in-patient study designed to assess the accuracy of the FreeStyle Navigator® Continuous Glucose Monitoring System revealed that the level of accuracy of the continuous sensor measurements was dependent on the rate of glucose change. When the absolute rate of change was less than 1 mg·dl−1·min−1 (75% of the time), the median absolute relative difference (ARD) was 8.5%, with 85% of all points falling within the A zone of the Clarke error grid. When the absolute rate of change was greater than 2 mg·dl−1·min−1 (8% of the time), the median ARD was 17.5%, with 59% of all points falling within the Clarke A zone. Method: Numerical simulations were performed to investigate effects of the rate of change of glucose on sensor measurement error. This approach enabled physiologically relevant distributions of glucose values to be reordered to explore the effect of different glucose rate-of-change distributions on apparent sensor accuracy. Results: The physiological lag between blood and interstitial fluid glucose levels is sufficient to account for the observed difference in sensor accuracy between periods of stable glucose and periods of rapidly changing glucose. Conclusions: The role of physiological lag on the apparent decrease in sensor accuracy at high glucose rates of change has implications for clinical study design, regulatory review of continuous glucose sensors, and development of performance standards for this new technology. This work demonstrates the difficulty in comparing accuracy measures between different clinical studies and highlights the need for studies to include both relevant glucose distributions and relevant glucose rate-of-change distributions.
Continuous Glucose Profiles in Healthy Subjects under Everyday Life Conditions and after Different Meals
Authors: Freckmann Guido , Hagenlocher Sven , Baumstark Annette , Jendrike Nina , Gillen Ralph C. , Rössner Katja , Haug Cornelia 
Published: January 2014

Background: This study investigated continuous glucose profiles in nondiabetic subjects. Methods: Continuous interstitial glucose measurement was performed under everyday life conditions (2 days) and after ingestion of four meals with standardized carbohydrate content (50 grams), but with different types of carbohydrates and variable protein and fat content. Twenty-four healthy volunteers (12 female, 12 male, age 27.1 ± 3.6 years) participated in the study. Each subject wore two microdialysis devices (SCGM1, Roche Diagnostics) simultaneously. Results: The mean 24-hour interstitial glucose concentration under everyday life conditions was 89.3 ± 6.2 mg/dl (mean ± SD, n = 21), and mean interstitial glucose concentrations at daytime and during the night were 93.0 ± 7.0 and 81.8 ± 6.3 mg/dl, respectively. The highest postprandial glucose concentrations were observed after breakfast: 132.3 ± 16.7 mg/dl (range 101–168 mg/dl); peak concentrations after lunch and dinner were 118.2 ± 13.4 and 123.0 ± 16.9 mg/dl, respectively. Mean time to peak glucose concentration was between 46 and 50 minutes. After ingestion of standardized meals with fast absorption characteristics, peak interstitial glucose concentrations were 133.2 ± 14.4 and 137.2 ± 21.1 mg/dl, respectively. Meals with a higher fiber, protein, and fat content induced a smaller increase and a slower decrease of postprandial glucose concentrations with peak values of 99.2 ± 10.5 and 122.1 ± 20.4 mg/dl, respectively. Conclusions: This study provided continuous glucose profiles in nondiabetic subjects and demonstrated that differences in meal composition are reflected in postprandial interstitial glucose concentrations. Regarding the increasing application of continuous glucose monitoring in diabetic patients, these data suggest that detailed information about the ingested meals is important for adequate interpretation of postprandial glucose profiles.
Going Mobile with a Multiaccess Service for the Management of Diabetic Patients
Authors: Lanzola Giordano , Capozzi Davide , D'Annunzio Giuseppe , Ferrari Pietro , Bellazzi Riccardo , Larizza Cristiana 
Published: January 2014

Background: Diabetes mellitus is one of the chronic diseases exploiting the largest number of telemedicine systems. Our research group has been involved since 1996 in two projects funded by the European Union proposing innovative architectures and services according to the best current medical practices and advances in the information technology area. Method: We propose an enhanced architecture for telemedicine giving rise to a multitier application. The lower tier is represented by a mobile phone hosting the patient unit able to acquire data and provide first-level advice to the patient. The patient unit also facilitates interaction with the health care center, representing the higher tier, by automatically uploading data and receiving back any therapeutic plan supplied by the physician. On the patient's side the mobile phone exploits Bluetooth technology and therefore acts as a hub for a wireless network, possibly including several devices in addition to the glucometer. Results: A new system architecture based on mobile technology is being used to implement several prototypes for assessing its functionality. A subsequent effort will be undertaken to exploit the new system within a pilot study for the follow-up of patients cared at a major hospital located in northern Italy. Conclusion: We expect that the new architecture will enhance the interaction between patient and caring physician, simplifying and improving metabolic control. In addition to sending glycemic data to the caring center, we also plan to automatically download the therapeutic protocols provided by the physician to the insulin pump and collect data from multiple sensors.
Linear Quadratic Gaussian-Based Closed-Loop Control of Type 1 Diabetes
Authors: Patek Stephen D. , Breton Marc D. , Chen Yuanda , Solomon Chad , Kovatchev Boris 
Published: January 2014

Background: We investigated the applicability of linear quadratic Gaussian (LQG) methodology to the subcutaneous blood glucose regulation problem. We designed an LQG-based feedback control algorithm using linearization of a previously published metabolic model of type 1 diabetes. A key feature of the controller is a Kalman filter used to estimate metabolic states of the patient based on continuous glucose monitoring. Insulin infusion is computed from linear quadratic regulator feedback gains applied to these estimates, generally seeking to minimize squared deviations from a target glucose concentration and basal insulin rate. We evaluated in silico subject-specific LQG control and compared it to preexisting proportional-integral-derivative control.
A Cause-and-Effect-Based Mathematical Curvilinear Model That Predicts the Effects of Self-Monitoring of Blood Glucose Frequency on Hemoglobin A1c and is Suitable for Statistical Correlations
Authors: Davidson Paul C. , Bode Bruce W. , Steed R. Dennis , Hebblewhite Harry R. 
Published: January 2014

Background: Previous studies have shown an association between the frequency of self-monitored blood glucose (SMBG) and hemoglobin A1c. Randomized controlled trials (RCTs) have shown this to be a causal correlation for insulin-using patients. Several studies have used linear regression, but a straight line will descend into negative hemoglobin A1c values (an impossibility). This study developed a cause-and-effect-based nonlinear model to predict the outcome of RCTs on this subject, tested this model with clinical data, and offered this model in place of linear regression, especially for the still-debated case of noninsulin-using patients. Method: The model was developed from cause-and-effect principles. The clinical study utilized retrospective data from patient histories of a large endocrine practice. Data sets were obtained for five treatment regimens: continuous subcutaneous insulin infusion (CSII), subcutaneous insulin (SC), no insulin (NI), oral medication (OM), and no medication (NM). OM and NM are subgroups of NI. The model was fitted to each group using nonlinear least-squares methods. Each group was ordered by SMBG tests per day (BGpd) and was divided in half; t tests were run between the AlC's of the two halves. Results: Self-monitored blood glucose readings from 1255 subjects were analyzed (CSII, N = 417; SC, N = 286; NI, N = 552; OM, N = 505; NM, N = 47). The CSII, SC, NI, and OM groups showed the expected declining statistically fitted curve and a significant association of BGpd with hemoglobin A1c (P < 0.004). The NM group showed insignificant results. Conclusions: The nonlinear model is based on cause-and-effect principles and mathematics. It yields a prediction that RCTs will be able to reveal that higher SMBG frequency causes lower hemoglobin A1c.
Continuous Subcutaneous Glucose Monitoring Shows a Close Correlation between Mean Glucose and Time Spent in Hyperglycemia and Hemoglobin A1c
Authors: Nielsen Jannik Kruse , Gravholt Claus Hojbjerg , Djurhuus Christian Born , Brandt Derek , Becker Joern , Heinemann Lutz , Christiansen Jens Sandahl 
Published: January 2014

Background: The Diabetes Control and Complications Trial and United Kingdom Prospective Diabetes Study highlighted hemoglobin A1c (HbAlc) as the main predictor of diabetic complications. Currently, diabetes is managed by frequent capillary spot glucose measurements, but continuous monitoring systems may have the capacity of improving diabetic control. The SCGM 1 system is microdialysis based and allows for monitoring of changes in interstitial fluid glucose levels every minute. The aim of this study was to evaluate the correlation between HbAlc and short-term glucose excursions in patients with type 1 diabetes. Material and Methods: We investigated 91 patients with type 1 diabetes (mean ± standard deviation (SD); age 34 ± 10 years, body mass index 24.2 ± 4.1 kg/m2) with a duration of diabetes of 17 ± 11 years for 4.8 ± 0.4 days. The average HbA1c was 7.9 ± 1.4%. From the monitoring profiles we determined individual mean glucose, the SD of glucose, and the relative time spent in hyperglycemia and hypoglycemia calculated as the area under the curve (AUC) with arbitrary cutoffs of 180 and 70 mg/dl, respectively. Results: Mean glucose, SD glucose, and hyperglycemic and hypoglycemic AUC all correlated with HbA1c, but with decreasing statistical power. In multiple linear regression analysis, mean glucose was the sole independent variable ( r = 0.626, p < 0.0001). A close correlation between HbA1c and various measures of short-term hyperglycemic values was observed. There was a close correlation between mean glucose and SD glucose, pointing toward increased variability with increasing mean glucose. Conclusion: Mean glucose generated after short-term continuous monitoring is the main predictor of HbA1c and reveals increased lability of glucose with increasing mean glucose and HbA1c.