Publications by authors named "Peter Diem"

55 Publications

Prevalence of diabetes mellitus in the greater Bern region (Bern-Mittelland) 2010-2014.

Swiss Med Wkly 2021 Mar 19;151:w20478. Epub 2021 Mar 19.

Endokrinologie Diabetologie Bern, Switzerland.

Objective: Concerning diabetes mellitus, one of the greatest burdens in public health in the 21st century, epidemiological data in Switzerland are scarce. To address this issue, this study intended to use a little-known but convenient way to quantify the prevalence of diabetes mellitus in the Swiss region of Bern-Mittelland.

Methods: In a population of approximately 330,000 people, the prevalence for the years 2010–2014 in adult persons was estimated using the capture-recapture method based on data collected routinely at the University Hospital in Bern (Inselspital) using outpatient lists and the registry of persons insured with Helsana Insurance Group.

Results: The estimated prevalence of diabetes mellitus was 3.97% (95% confidence interval [CI] 3.41–4.53%) in 2010, with a slight decrease to 3.65% (95% CI 3.24–4.06%) in 2014. An average of 3430 patients with diabetes or 26% of the total number appeared on at least one patient list. The remaining 74% were unknown patients identified by the capture-recapture method.

Conclusions: The estimated prevalence of diabetes mellitus was in a range comparable to national and international studies. Thus, administratively collected data in clinics and insurance companies constitute a convenient data source for epidemiological studies. In conjunction with the capture-recapture method an approach with comparatively low effort and costs for the surveillance of chronic disease can be provided.
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http://dx.doi.org/10.4414/smw.2021.20478DOI Listing
March 2021

Standardization process of continuous glucose monitoring: Traceability and performance.

Clin Chim Acta 2021 Apr 9;515:5-12. Epub 2021 Jan 9.

Isala Clinics, Zwolle, the Netherlands.

People with diabetes are required to regularly check their glucose to make therapy decisions. So far, systems for self-monitoring of blood glucose were used, but nowadays minimally invasive continuous glucose monitoring (CGM) systems are increasingly more often employed, sometimes to partially replace self-monitoring of blood glucose. Most CGM systems on the market measure glucose concentrations continuously in the interstitial fluid of the subcutaneous fatty tissue. However, CGM has a principle limitation. Collecting interstitial fluid frequently in sufficiently large volumes over short time periods is not easy. As a consequence, no internationally accepted reference measurement procedure is currently available for glucose in interstitial fluid which is a prerequisite to achieve an optimal metrological traceability. Recent studies indicate that the analytical performance of minimally invasive CGM systems differs not only between manufacturers but also between individual sensors of the same system, sometimes even in the same subject. Because manufacturers don't provide detailed information about the traceability chain and the measurement uncertainty of their systems glucose values obtained with CGM can currently not be adequately traced to higher-order standards or methods. Therefore, the Working Group on Continuous Glucose Monitoring aims at establishing a traceability chain for minimally invasive CGM systems, as well as procedures and metrics for the assessment of their analytical performance.
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http://dx.doi.org/10.1016/j.cca.2020.12.025DOI Listing
April 2021

[The Discovery of Insulin].

Authors:
Peter Diem

Ther Umsch 2020 Sep;77(7):289-296

Endokrinologie Diabetologie Bern.

The Discovery of Insulin The initiative for the work that led to the discovery of insulin in Toronto in 1921 came from Frederik G. Banting. He worked under the direction of John J. R. Macleod in the Institute of Physiology at the University of Toronto. In his experimental program he was assisted by the student Charles H. Best. On dogs with experimental diabetes they demonstrated the blood sugar-lowering effect of pancreatic extracts. Thanks to collaboration with Macleod and James B. Collip, a biochemist from the University of Alberta who was on sabbatical in Toronto, the work was quickly crowned with success and the first clinical applications of the extracts became possible in early 1922. As early as 1923, Banting and Macleod were awarded the Nobel Prize for Physiology or Medicine. Banting shared his half of the prize with Best, while Macleod shared his half with Collip. That their research was crowned with success is probably due in large part to Banting's abilities as a surgeon, Best's enthusiasm as a student, Collip's abilities as a biochemist and Macleod's prudence in bringing the group together and providing it with the necessary resources. In the 1950s, important advances were made in insulin research that were to spur further research in diabetology. These included the clarification of insulin structure and the possibility of measuring insulin in the blood. These two discoveries were awarded the Nobel Prize for Chemistry (see Kasten 1). In the 1960s-70s, insulin manufacturers developed ever better purification methods, which eventually led to preparations with very good tolerability and only very rare allergies. Later, in the 1980s, the possibility of biotechnological production of insulin led to an ever-increasing spread of human insulin. Based on the same technology, insulin analogues were produced in the 1990s and then in the new millennium, which, as "designer insulins" so to speak, enabled new clinically interesting active profiles. Today's variety of available insulins, modern forms of insulin application (insulin pens, insulin pumps) and blood glucose self-monitoring or continuous glucose monitoring form the basis of modern intensive insulin therapy.
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http://dx.doi.org/10.1024/0040-5930/a001194DOI Listing
September 2020

HbA-testing: Evaluation of two point-of-care analysers.

Prim Care Diabetes 2019 12 4;13(6):583-587. Epub 2019 Jun 4.

Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland. Electronic address:

Background: HbA is a critical parameter for the medical management of patients with diabetes mellitus. Interventions that reduce HbA levels lead to a diminution of microvascular complications. For two decades, point of care testing (POCT) methods have been regularly used to measure HbA. The results significantly impact on the management of patients with diabetes mellitus and the accuracy of the results is critical. It is important to know the performance of common methods of HbA measurements in daily life. We, therefore, aimed at evaluating the accuracy of two different analysers especially developed for POCT and compared them to a reference method.

Methods: We prospectively tested two widely used POCT methods to measure HbA, namely Afinion™ AS100 Analyzer (Axis-Shield, Oslo Norway) and DCA Vantage™ Analyzer (Siemens Healthcare Diagnostics, Tarrytown NY, US) in venous samples of 100 patients. As a reference method, we used the high-performance liquid chromatography method G8 HPLC used in the Biochemistry Laboratory of the Inselspital Bern. The National Glycohaemoglobin Standardization Program (NGSP) has certificated all methods used in this study. The comparability and degree of agreement was assessed using Bland-Altman plot.

Results: The HbA levels ranged from 33 to 116 mmol/mol (5.2-12.8%), 31-122 mmol/mol (5.0-13.3%) and 30-119 mmol/mol (4.9-13%) for Afinion™, DCA Vantage™ and G8 HPLC Analyzer, respectively. The 95% limits of agreement were between -0.84 and +0.30 for the Afinion™ and -0.71 and +0.29 for DCA Vantage™. The results of both POCT were significantly lower with a bias of -0.27% and -0.21% (p < 0.0001) for Afinion™ and DCA Vantage™ Analyzer, respectively.

Conclusions: The POCT methods tested in this study showed a good correlation with the laboratory reference method, however, with an overall negative bias.
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http://dx.doi.org/10.1016/j.pcd.2019.05.007DOI Listing
December 2019

A Dual Mode Adaptive Basal-Bolus Advisor Based on Reinforcement Learning.

IEEE J Biomed Health Inform 2019 11 17;23(6):2633-2641. Epub 2018 Dec 17.

Self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) are commonly used by type 1 diabetes (T1D) patients to measure glucose concentrations. The proposed adaptive basal-bolus algorithm (ABBA) supports inputs from either SMBG or CGM devices to provide personalised suggestions for the daily basal rate and prandial insulin doses on the basis of the patients' glucose level on the previous day. The ABBA is based on reinforcement learning, a type of artificial intelligence, and was validated in silico with an FDA-accepted population of 100 adults under different realistic scenarios lasting three simulated months. The scenarios involve three main meals and one bedtime snack per day, along with different variabilities and uncertainties for insulin sensitivity, mealtime, carbohydrate amount, and glucose measurement time. The results indicate that the proposed approach achieves comparable performance with CGM or SMBG as input signals, without influencing the total daily insulin dose. The results are a promising indication that AI algorithmic approaches can provide personalised adaptive insulin optimization and achieve glucose control-independent of the type of glucose monitoring technology.
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http://dx.doi.org/10.1109/JBHI.2018.2887067DOI Listing
November 2019

National public health system responses to diabetes and other important noncommunicable diseases : Background, goals, and results of an international workshop at the Robert Koch Institute.

Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2018 Oct;61(10):1300-1306

Department of Epidemiology and Health Monitoring, Robert Koch Institute (RKI), General-Pape-Straße, 12101, Berlin, Germany.

Diabetes mellitus and other noncommunicable diseases (NCDs) represent an emerging global public health challenge. In Germany, about 6.7 million adults are affected by diabetes according to national health surveys, including 1.3 million with undiagnosed diabetes. Complications of diabetes result in an increasing burden for individuals and society as well as enormous costs for the health care system. In response, the Federal Ministry of Health commissioned the Robert Koch Institute (RKI) to implement a diabetes surveillance system and the Federal Center for Health Education (BZgA) to develop a diabetes prevention strategy. In a two-day workshop jointly organized by the RKI and the BZgA, representatives from public health institutes in seven countries shared their expertise and knowledge on diabetes prevention and surveillance. Day one focused on NCD surveillance systems and emphasized both the strengthening of sustainable data sources and the timely and targeted dissemination of results using innovative formats. The second day focused on diabetes prevention strategies and highlighted the importance of involving all relevant stakeholders in the development process to facilitate its acceptance and implementation. Furthermore, the effective translation of prevention measures into real-world settings requires data from surveillance systems to identify high-risk groups and evaluate the effect of measures at the population level based on analyses of time trends in risk factors and disease outcomes. Overall, the workshop highlighted the close link between diabetes prevention strategies and surveillance systems. It was generally stated that only robust data enables effective prevention measures to encounter the increasing burden from diabetes and other NCDs.
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http://dx.doi.org/10.1007/s00103-018-2806-zDOI Listing
October 2018

Carbohydrate Estimation Supported by the GoCARB System in Individuals With Type 1 Diabetes: A Randomized Prospective Pilot Study.

Diabetes Care 2017 02 29;40(2):e6-e7. Epub 2016 Nov 29.

Department of Diabetes, Endocrinology, Clinical Nutrition and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland

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http://dx.doi.org/10.2337/dc16-2173DOI Listing
February 2017

Model-Free Machine Learning in Biomedicine: Feasibility Study in Type 1 Diabetes.

PLoS One 2016 21;11(7):e0158722. Epub 2016 Jul 21.

Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, 3008 Bern, Switzerland.

Although reinforcement learning (RL) is suitable for highly uncertain systems, the applicability of this class of algorithms to medical treatment may be limited by the patient variability which dictates individualised tuning for their usually multiple algorithmic parameters. This study explores the feasibility of RL in the framework of artificial pancreas development for type 1 diabetes (T1D). In this approach, an Actor-Critic (AC) learning algorithm is designed and developed for the optimisation of insulin infusion for personalised glucose regulation. AC optimises the daily basal insulin rate and insulin:carbohydrate ratio for each patient, on the basis of his/her measured glucose profile. Automatic, personalised tuning of AC is based on the estimation of information transfer (IT) from insulin to glucose signals. Insulin-to-glucose IT is linked to patient-specific characteristics related to total daily insulin needs and insulin sensitivity (SI). The AC algorithm is evaluated using an FDA-accepted T1D simulator on a large patient database under a complex meal protocol, meal uncertainty and diurnal SI variation. The results showed that 95.66% of time was spent in normoglycaemia in the presence of meal uncertainty and 93.02% when meal uncertainty and SI variation were simultaneously considered. The time spent in hypoglycaemia was 0.27% in both cases. The novel tuning method reduced the risk of severe hypoglycaemia, especially in patients with low SI.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0158722PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4956312PMC
July 2017

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study.

J Med Internet Res 2016 May 11;18(5):e101. Epub 2016 May 11.

ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference.

Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires.

Methods: The study was conducted at the Bern University Hospital, "Inselspital" (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital's restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user's experience with GoCARB.

Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use.

Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.
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http://dx.doi.org/10.2196/jmir.5567DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4880742PMC
May 2016

GoCARB in the Context of an Artificial Pancreas.

J Diabetes Sci Technol 2015 May 21;9(3):549-55. Epub 2015 Apr 21.

Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland Department of Endocrinology, Diabetes & Clinical Nutrition, Bern University Hospital "Inselspital," Bern, Switzerland

Background: In an artificial pancreas (AP), the meals are either manually announced or detected and their size estimated from the blood glucose level. Both methods have limitations, which result in suboptimal postprandial glucose control. The GoCARB system is designed to provide the carbohydrate content of meals and is presented within the AP framework.

Method: The combined use of GoCARB with a control algorithm is assessed in a series of 12 computer simulations. The simulations are defined according to the type of the control (open or closed loop), the use or not-use of GoCARB and the diabetics' skills in carbohydrate estimation.

Results: For bad estimators without GoCARB, the percentage of the time spent in target range (70-180 mg/dl) during the postprandial period is 22.5% and 66.2% for open and closed loop, respectively. When the GoCARB is used, the corresponding percentages are 99.7% and 99.8%. In case of open loop, the time spent in severe hypoglycemic events (<50 mg/dl) is 33.6% without the GoCARB and is reduced to 0.0% when the GoCARB is used. In case of closed loop, the corresponding percentage is 1.4% without the GoCARB and is reduced to 0.0% with the GoCARB.

Conclusion: The use of GoCARB improves the control of postprandial response and glucose profiles especially in the case of open loop. However, the most efficient regulation is achieved by the combined use of the control algorithm and the GoCARB.
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http://dx.doi.org/10.1177/1932296815583333DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4604547PMC
May 2015

Computer vision-based carbohydrate estimation for type 1 patients with diabetes using smartphones.

J Diabetes Sci Technol 2015 May 16;9(3):507-15. Epub 2015 Apr 16.

Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland Department of Endocrinology, Diabetes & Clinical Nutrition, Bern University Hospital "Inselspital," Bern, Switzerland

Background: Individuals with type 1 diabetes (T1D) have to count the carbohydrates (CHOs) of their meal to estimate the prandial insulin dose needed to compensate for the meal's effect on blood glucose levels. CHO counting is very challenging but also crucial, since an error of 20 grams can substantially impair postprandial control.

Method: The GoCARB system is a smartphone application designed to support T1D patients with CHO counting of nonpacked foods. In a typical scenario, the user places a reference card next to the dish and acquires 2 images with his/her smartphone. From these images, the plate is detected and the different food items on the plate are automatically segmented and recognized, while their 3D shape is reconstructed. Finally, the food volumes are calculated and the CHO content is estimated by combining the previous results and using the USDA nutritional database.

Results: To evaluate the proposed system, a set of 24 multi-food dishes was used. For each dish, 3 pairs of images were taken and for each pair, the system was applied 4 times. The mean absolute percentage error in CHO estimation was 10 ± 12%, which led to a mean absolute error of 6 ± 8 CHO grams for normal-sized dishes.

Conclusion: The laboratory experiments demonstrated the feasibility of the GoCARB prototype system since the error was below the initial goal of 20 grams. However, further improvements and evaluation are needed prior launching a system able to meet the inter- and intracultural eating habits.
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http://dx.doi.org/10.1177/1932296815580159DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4604531PMC
May 2015

Multi-model data fusion to improve an early warning system for hypo-/hyperglycemic events.

Annu Int Conf IEEE Eng Med Biol Soc 2014 ;2014:4843-6

Correct predictions of future blood glucose levels in individuals with Type 1 Diabetes (T1D) can be used to provide early warning of upcoming hypo-/hyperglycemic events and thus to improve the patient's safety. To increase prediction accuracy and efficiency, various approaches have been proposed which combine multiple predictors to produce superior results compared to single predictors. Three methods for model fusion are presented and comparatively assessed. Data from 23 T1D subjects under sensor-augmented pump (SAP) therapy were used in two adaptive data-driven models (an autoregressive model with output correction - cARX, and a recurrent neural network - RNN). Data fusion techniques based on i) Dempster-Shafer Evidential Theory (DST), ii) Genetic Algorithms (GA), and iii) Genetic Programming (GP) were used to merge the complimentary performances of the prediction models. The fused output is used in a warning algorithm to issue alarms of upcoming hypo-/hyperglycemic events. The fusion schemes showed improved performance with lower root mean square errors, lower time lags, and higher correlation. In the warning algorithm, median daily false alarms (DFA) of 0.25%, and 100% correct alarms (CA) were obtained for both event types. The detection times (DT) before occurrence of events were 13.0 and 12.1 min respectively for hypo-/hyperglycemic events. Compared to the cARX and RNN models, and a linear fusion of the two, the proposed fusion schemes represents a significant improvement.
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http://dx.doi.org/10.1109/EMBC.2014.6944708DOI Listing
September 2015

Estimating the prevalence of comorbid conditions and their effect on health care costs in patients with diabetes mellitus in Switzerland.

Diabetes Metab Syndr Obes 2014 1;7:455-65. Epub 2014 Oct 1.

Department of Health Sciences, Helsana Group, Zürich, Switzerland.

Background: Estimating the prevalence of comorbidities and their associated costs in patients with diabetes is fundamental to optimizing health care management. This study assesses the prevalence and health care costs of comorbid conditions among patients with diabetes compared with patients without diabetes. Distinguishing potentially diabetes- and nondiabetes-related comorbidities in patients with diabetes, we also determined the most frequent chronic conditions and estimated their effect on costs across different health care settings in Switzerland.

Methods: Using health care claims data from 2011, we calculated the prevalence and average health care costs of comorbidities among patients with and without diabetes in inpatient and outpatient settings. Patients with diabetes and comorbid conditions were identified using pharmacy-based cost groups. Generalized linear models with negative binomial distribution were used to analyze the effect of comorbidities on health care costs.

Results: A total of 932,612 persons, including 50,751 patients with diabetes, were enrolled. The most frequent potentially diabetes- and nondiabetes-related comorbidities in patients older than 64 years were cardiovascular diseases (91%), rheumatologic conditions (55%), and hyperlipidemia (53%). The mean total health care costs for diabetes patients varied substantially by comorbidity status (US$3,203-$14,223). Patients with diabetes and more than two comorbidities incurred US$10,584 higher total costs than patients without comorbidity. Costs were significantly higher in patients with diabetes and comorbid cardiovascular disease (US$4,788), hyperlipidemia (US$2,163), hyperacidity disorders (US$8,753), and pain (US$8,324) compared with in those without the given disease.

Conclusion: Comorbidities in patients with diabetes are highly prevalent and have substantial consequences for medical expenditures. Interestingly, hyperacidity disorders and pain were the most costly conditions. Our findings highlight the importance of developing strategies that meet the needs of patients with diabetes and comorbidities. Integrated diabetes care such as used in the Chronic Care Model may represent a useful strategy.
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http://dx.doi.org/10.2147/DMSO.S69520DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4199853PMC
October 2014

A food recognition system for diabetic patients based on an optimized bag-of-features model.

IEEE J Biomed Health Inform 2014 Jul;18(4):1261-71

Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the bag-of-features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.
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http://dx.doi.org/10.1109/JBHI.2014.2308928DOI Listing
July 2014

The role of self-monitoring of blood glucose in patients treated with SGLT-2 inhibitors: a European expert recommendation.

J Diabetes Sci Technol 2014 Jul 18;8(4):783-90. Epub 2014 May 18.

Hôpital du Bocage, Dijon, France.

The role for the novel treatment approach of sodium-glucose cotransporter-2 (SGLT-2) in type 2 diabetes is increasing. Structured self-monitoring of blood glucose (SMBG), based on a less intensive and a more intensive scheme, may contribute to an optimization of SGLT-2 inhibitor based treatment. The current expert recommendation suggests individualized approaches of SMBG, using simple and clinically applicable schemes. Potential benefits of SMBG in SGLT-2 inhibitor based treatment approaches are early assessment of treatment success or failure, timely modification of treatment, detection of hypoglycemic episodes, assessment of glucose excursions, and support of diabetes management and education. The length and frequency of SMBG should depend on the clinical setting and the quality of metabolic control.
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http://dx.doi.org/10.1177/1932296814534366DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4764233PMC
July 2014

The effect of a single 2 h bout of aerobic exercise on ectopic lipids in skeletal muscle, liver and the myocardium.

Diabetologia 2014 May 24;57(5):1001-5. Epub 2014 Feb 24.

Department of Endocrinology, Diabetology and Clinical Nutrition, Inselspital, University Hospital of Bern, Freiburgstrasse, 3010, Bern, Switzerland.

Aims/hypothesis: Ectopic lipids are fuel stores in non-adipose tissues (skeletal muscle [intramyocellular lipids; IMCL], liver [intrahepatocellular lipids; IHCL] and heart [intracardiomyocellular lipids; ICCL]). IMCL can be depleted by physical activity. Preliminary data suggest that aerobic exercise increases IHCL. Data on exercise-induced changes on ICCL is scarce. Increased IMCL and IHCL have been related to insulin resistance in skeletal muscles and liver, whereas this has not been documented in the heart. The aim of this study was to assess the acute effect of aerobic exercise on the flexibility of IMCL, IHCL and ICCL in insulin-sensitive participants in relation to fat availability, insulin sensitivity and exercise capacity.

Methods: Healthy physically active men were included. VO(2max) was assessed by spiroergometry and insulin sensitivity was calculated using the HOMA index. Visceral and subcutaneous fat were separately quantified by MRI. Following a standardised dietary fat load over 3 days, IMCL, IHCL and ICCL were measured using MR spectroscopy before and after a 2 h exercise session at 50-60% of VO(2max). Metabolites were measured during exercise.

Results: Ten men (age 28.9 ± 6.4 years, mean ± SD; VO(2max) 56.3 ± 6.4 ml kg(-1) min(-1); BMI 22.75 ± 1.4 kg/m(2)) were recruited. A 2 h exercise session resulted in a significant decrease in IMCL (-17 ± 22%, p = 0.008) and ICCL (-17 ± 14%, p = 0.002) and increase in IHCL (42 ± 29%, p = 0.004). No significant correlations were found between the relative changes in ectopic lipids, fat availability, insulin sensitivity, exercise capacity or changes of metabolites during exercise.

Conclusions/interpretation: In this group, physical exercise decreased ICCL and IMCL but increased IHCL. Fat availability, insulin sensitivity, exercise capacity and metabolites during exercise are not the only factors affecting ectopic lipids during exercise.
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http://dx.doi.org/10.1007/s00125-014-3193-0DOI Listing
May 2014

Role of diuretics, β blockers, and statins in increasing the risk of diabetes in patients with impaired glucose tolerance: reanalysis of data from the NAVIGATOR study.

BMJ 2013 Dec 9;347:f6745. Epub 2013 Dec 9.

Duke Clinical Research Institute, Durham, NC, USA.

Objective: To examine the degree to which use of β blockers, statins, and diuretics in patients with impaired glucose tolerance and other cardiovascular risk factors is associated with new onset diabetes.

Design: Reanalysis of data from the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research (NAVIGATOR) trial.

Setting: NAVIGATOR trial.

Participants: Patients who at baseline (enrolment) were treatment naïve to β blockers (n=5640), diuretics (n=6346), statins (n=6146), and calcium channel blockers (n=6294). Use of calcium channel blocker was used as a metabolically neutral control.

Main Outcome Measures: Development of new onset diabetes diagnosed by standard plasma glucose level in all participants and confirmed with glucose tolerance testing within 12 weeks after the increased glucose value was recorded. The relation between each treatment and new onset diabetes was evaluated using marginal structural models for causal inference, to account for time dependent confounding in treatment assignment.

Results: During the median five years of follow-up, β blockers were started in 915 (16.2%) patients, diuretics in 1316 (20.7%), statins in 1353 (22.0%), and calcium channel blockers in 1171 (18.6%). After adjusting for baseline characteristics and time varying confounders, diuretics and statins were both associated with an increased risk of new onset diabetes (hazard ratio 1.23, 95% confidence interval 1.06 to 1.44, and 1.32, 1.14 to 1.48, respectively), whereas β blockers and calcium channel blockers were not associated with new onset diabetes (1.10, 0.92 to 1.31, and 0.95, 0.79 to 1.13, respectively).

Conclusions: Among people with impaired glucose tolerance and other cardiovascular risk factors and with serial glucose measurements, diuretics and statins were associated with an increased risk of new onset diabetes, whereas the effect of β blockers was non-significant.

Trial Registration: ClinicalTrials.gov NCT00097786.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3898638PMC
http://dx.doi.org/10.1136/bmj.f6745DOI Listing
December 2013

Personalized tuning of a reinforcement learning control algorithm for glucose regulation.

Annu Int Conf IEEE Eng Med Biol Soc 2013 ;2013:3487-90

Artificial pancreas is in the forefront of research towards the automatic insulin infusion for patients with type 1 diabetes. Due to the high inter- and intra-variability of the diabetic population, the need for personalized approaches has been raised. This study presents an adaptive, patient-specific control strategy for glucose regulation based on reinforcement learning and more specifically on the Actor-Critic (AC) learning approach. The control algorithm provides daily updates of the basal rate and insulin-to-carbohydrate (IC) ratio in order to optimize glucose regulation. A method for the automatic and personalized initialization of the control algorithm is designed based on the estimation of the transfer entropy (TE) between insulin and glucose signals. The algorithm has been evaluated in silico in adults, adolescents and children for 10 days. Three scenarios of initialization to i) zero values, ii) random values and iii) TE-based values have been comparatively assessed. The results have shown that when the TE-based initialization is used, the algorithm achieves faster learning with 98%, 90% and 73% in the A+B zones of the Control Variability Grid Analysis for adults, adolescents and children respectively after five days compared to 95%, 78%, 41% for random initialization and 93%, 88%, 41% for zero initial values. Furthermore, in the case of children, the daily Low Blood Glucose Index reduces much faster when the TE-based tuning is applied. The results imply that automatic and personalized tuning based on TE reduces the learning period and improves the overall performance of the AC algorithm.
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http://dx.doi.org/10.1109/EMBC.2013.6610293DOI Listing
June 2015

The effect of aerobic exercise on intrahepatocellular and intramyocellular lipids in healthy subjects.

PLoS One 2013 14;8(8):e70865. Epub 2013 Aug 14.

Division of Endocrinology, Diabetology and Clinical Nutrition, University Hospital of Bern, Inselspital, Bern, Switzerland.

Background: Intrahepatocellular (IHCL) and intramyocellular (IMCL) lipids are ectopic lipid stores. Aerobic exercise results in IMCL utilization in subjects over a broad range of exercise capacity. IMCL and IHCL have been related to impaired insulin action at the skeletal muscle and hepatic level, respectively. The acute effect of aerobic exercise on IHCL is unknown. Possible regulatory factors include exercise capacity, insulin sensitivity and fat availability subcutaneous and visceral fat mass).

Aim: To concomitantly investigate the effect of aerobic exercise on IHCL and IMCL in healthy subjects, using Magnetic Resonance spectroscopy.

Methods: Normal weight, healthy subjects were included. Visit 1 consisted of a determination of VO2max on a treadmill. Visit 2 comprised the assessment of hepatic and peripheral insulin sensitivity by a two-step hyperinsulinaemic euglycaemic clamp. At Visit 3, subcutaneous and visceral fat mass were assessed by whole body MRI, IHCL and IMCL before and after a 2-hours aerobic exercise (50% of VO(2max)) using ¹H-MR-spectroscopy.

Results: Eighteen volunteers (12M, 6F) were enrolled in the study (age, 37.6±3.2 years, mean±SEM; VO(2max), 53.4±2.9 mL/kg/min). Two hours aerobic exercise resulted in a significant decrease in IMCL (-22.6±3.3, % from baseline) and increase in IHCL (+34.9±7.6, % from baseline). There was no significant correlation between the exercise-induced changes in IMCL and IHCL and exercise capacity, subcutaneous and visceral fat mass and hepatic or peripheral insulin sensitivity.

Conclusions: IMCL and IHCL are flexible ectopic lipid stores that are acutely influenced by physical exercise, albeit in different directions.

Trial Registration: ClinicalTrial.gov NCT00491582.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0070865PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3743875PMC
March 2014

An early warning system for hypoglycemic/hyperglycemic events based on fusion of adaptive prediction models.

J Diabetes Sci Technol 2013 May 1;7(3):689-98. Epub 2013 May 1.

Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Murtenstrasse 50, Bern, Switzerland.

Introduction: Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia/hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy.

Methods: The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models' outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS.

Results: The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms.

Conclusion: Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3869137PMC
http://dx.doi.org/10.1177/193229681300700314DOI Listing
May 2013

Self-monitoring of blood glucose in type 2 diabetes: recent studies.

J Diabetes Sci Technol 2013 Mar 1;7(2):478-88. Epub 2013 Mar 1.

Forschergruppe Diabetes e.V. at the Helmholtz Center Munich, Ingolstädter Landstrasse 1, 85764 Munich-Neuherberg, Germany.

The increasing role for structured and personalized self-monitoring of blood glucose (SMBG) in management of type 2 diabetes has been underlined by randomized and prospective clinical trials. These include Structured Testing Program (or STeP), St. Carlos, Role of Self-Monitoring of Blood Glucose and Intensive Education in Patients with Type 2 Diabetes Not Receiving Insulin, and Retrolective Study Self-Monitoring of Blood Glucose and Outcome in Patients with Type 2 Diabetes (or ROSSO)-in-praxi follow-up. The evidence for the benefit of SMBG both in insulin-treated and non-insulin-treated patients with diabetes is also supported by published reviews, meta-analyses, and guidelines. A Cochrane review reported an overall effect of SMBG on glycemic control up to 6 months after initiation, which was considered to subside after 12 months. Particularly, the 12-month analysis has been criticized for the inclusion of a small number of studies and the conclusions drawn. The aim of this article is to review key publications on SMBG and also to put them into perspective with regard to results of the Cochrane review and current aspects of diabetes management.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3737650PMC
http://dx.doi.org/10.1177/193229681300700225DOI Listing
March 2013

Reimbursement for continuous glucose monitoring: a European view.

J Diabetes Sci Technol 2012 Nov 1;6(6):1498-502. Epub 2012 Nov 1.

Science & Co., Düorf, Germany.

Different systems for continuous glucose monitoring (CGM) are available on the European market. There is no unlimited reimbursement for CGM use in any European country, but in some countries, reimbursement exists for certain clinical indications. The aim of this commentary is to describe the different reimbursement situations across Europe for this innovative but costly technology, as a prelude to establishing more uniform use. From the perspective of many scientists and clinicians, a number of randomized controlled trials have demonstrated the efficacy of real-time CGM versus self-monitoring of blood glucose, at least for hemoglobin A1c reduction. Nevertheless, according to many health care professionals and potential CGM users, national health services and health insurance organizations are reluctant to reimburse CGM. Imminent technological and manufacturing developments are expected to reduce the day-to-day costs of CGM.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3570892PMC
http://dx.doi.org/10.1177/193229681200600631DOI Listing
November 2012

Assessment of three frequently used blood glucose monitoring devices in clinical routine.

Swiss Med Wkly 2012 12;142:w13631. Epub 2012 Jul 12.

Inselspital, Bern University Hospital, and University of Bern, Switzerland.

Background: Self-monitoring of blood glucose plays an important role in the management of diabetes and has been shown to improve metabolic control. The use of blood glucose meters in clinical practice requires sufficient reliability to allow adequate treatment. Direct comparison of different blood glucose meters in clinical practice, independent of the manufactures is scarce. We, therefore, aimed to evaluate three frequently used blood glucose meters in daily clinical practice.

Methods: Capillary blood glucose was measured simultaneous using the following glucose meters: Contour® (Bayer Diabetes Care, Zürich, Switzerland), Accu-Chek® aviva (Roche Diagnostics, Rotkreuz, Switzerland), Free-Style® lite (Abbott Diabetes Care, Baar, Switzerland). The reference method consisted of the HemoCue® Glucose 201+ System (HemoCue® AB, Ängelholm, Sweden) with plasma conversion. The devices were assessed by comparison of the Mean Absolute Relative Differences (MARD), the Clarke Error Grid Analysis (EGA) and the compliance with the International Organization of Standardization criteria (ISO 15197:2003).

Results: Capillary blood samples were obtained from 150 patients. MARD was 10.1 ± 0.65%, 7.0 ± 0.62% and 7.8 ± 0.48% for Contour®, Accu-Chek® and Free-Style®, respectively. EGA showed 99.3% (Contour®), 98.7% (Accu-Chek®) and 100% (Free-Style®) of all measurements in zone A and B (clinically acceptable). The ISO criteria were fulfilled by Accu-Chek® (95.3%) and Free-Style® (96%), but not by Contour® (92%).

Conclusions: In the present study the three glucose meters provided good agreement with the reference and reliable results in daily clinical routine. Overall, the Free-Style® and Accu-Chek® device slightly outperformed the Contour® device.
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http://dx.doi.org/10.4414/smw.2012.13631DOI Listing
January 2013

The role of self-monitoring of blood glucose in glucagon-like peptide-1-based treatment approaches: a European expert recommendation.

J Diabetes Sci Technol 2012 May 1;6(3):665-73. Epub 2012 May 1.

Diabetes Research Group, Helmholtz Center, Munich, Germany.

The role of glucagon-like peptide (GLP)-1-based treatment approaches for type 2 diabetes mellitus (T2DM) is increasing. Although self-monitoring of blood glucose (SMBG) has been performed in numerous studies on GLP-1 analogs and dipeptidyl peptidase-4 inhibitors, the potential role of SMBG in GLP-1-based treatment strategies has not been elaborated. The expert recommendation suggests individualized SMBG strategies in GLP-1-based treatment approaches and suggests simple and clinically applicable SMBG schemes. Potential benefits of SMBG in GLP-1-based treatment approaches are early assessment of treatment success or failure, timely modification of treatment, detection of hypoglycemic episodes, assessment of glucose excursions, and support of diabetes management and diabetes education. Its length and frequency should depend on the clinical setting and the quality of metabolic control. It is considered to play an important role for the optimization of diabetes management in T2DM patients treated with GLP-1-based approaches.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3440044PMC
http://dx.doi.org/10.1177/193229681200600323DOI Listing
May 2012

Influence of time point of calibration on accuracy of continuous glucose monitoring in individuals with type 1 diabetes.

Diabetes Technol Ther 2012 Jul 18;14(7):583-8. Epub 2012 Apr 18.

Division of Endocrinology, Diabetes and Clinical Nutrition, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.

Background And Aims: Data on the influence of calibration on accuracy of continuous glucose monitoring (CGM) are scarce. The aim of the present study was to investigate whether the time point of calibration has an influence on sensor accuracy and whether this effect differs according to glycemic level.

Subjects And Methods: Two CGM sensors were inserted simultaneously in the abdomen on either side of 20 individuals with type 1 diabetes. One sensor was calibrated predominantly using preprandial glucose (calibration(PRE)). The other sensor was calibrated predominantly using postprandial glucose (calibration(POST)). At minimum three additional glucose values per day were obtained for analysis of accuracy. Sensor readings were divided into four categories according to the glycemic range of the reference values (low, ≤4 mmol/L; euglycemic, 4.1-7 mmol/L; hyperglycemic I, 7.1-14 mmol/L; and hyperglycemic II, >14 mmol/L).

Results: The overall mean±SEM absolute relative difference (MARD) between capillary reference values and sensor readings was 18.3±0.8% for calibration(PRE) and 21.9±1.2% for calibration(POST) (P<0.001). MARD according to glycemic range was 47.4±6.5% (low), 17.4±1.3% (euglycemic), 15.0±0.8% (hyperglycemic I), and 17.7±1.9% (hyperglycemic II) for calibration(PRE) and 67.5±9.5% (low), 24.2±1.8% (euglycemic), 15.5±0.9% (hyperglycemic I), and 15.3±1.9% (hyperglycemic II) for calibration(POST). In the low and euglycemic ranges MARD was significantly lower in calibration(PRE) compared with calibration(POST) (P=0.007 and P<0.001, respectively).

Conclusions: Sensor calibration predominantly based on preprandial glucose resulted in a significantly higher overall sensor accuracy compared with a predominantly postprandial calibration. The difference was most pronounced in the hypo- and euglycemic reference range, whereas both calibration patterns were comparable in the hyperglycemic range.
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http://dx.doi.org/10.1089/dia.2011.0271DOI Listing
July 2012

An Actor-Critic based controller for glucose regulation in type 1 diabetes.

Comput Methods Programs Biomed 2013 Feb 12;109(2):116-25. Epub 2012 Apr 12.

ARTORG Center for Biomedical Engineering Research, Diabetes Technology Research Group, University of Bern, Murtenstrasse 50, 3010 Bern, Switzerland.

A novel adaptive approach for glucose control in individuals with type 1 diabetes under sensor-augmented pump therapy is proposed. The controller, is based on Actor-Critic (AC) learning and is inspired by the principles of reinforcement learning and optimal control theory. The main characteristics of the proposed controller are (i) simultaneous adjustment of both the insulin basal rate and the bolus dose, (ii) initialization based on clinical procedures, and (iii) real-time personalization. The effectiveness of the proposed algorithm in terms of glycemic control has been investigated in silico in adults, adolescents and children under open-loop and closed-loop approaches, using announced meals with uncertainties in the order of ±25% in the estimation of carbohydrates. The results show that glucose regulation is efficient in all three groups of patients, even with uncertainties in the level of carbohydrates in the meal. The percentages in the A+B zones of the Control Variability Grid Analysis (CVGA) were 100% for adults, and 93% for both adolescents and children. The AC based controller seems to be a promising approach for the automatic adjustment of insulin infusion in order to improve glycemic control. After optimization of the algorithm, the controller will be tested in a clinical trial.
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http://dx.doi.org/10.1016/j.cmpb.2012.03.002DOI Listing
February 2013

Real-time adaptive models for the personalized prediction of glycemic profile in type 1 diabetes patients.

Diabetes Technol Ther 2012 Feb 12;14(2):168-74. Epub 2011 Oct 12.

Diabetes Technology Research Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.

Background: Prediction of glycemic profile is an important task for both early recognition of hypoglycemia and enhancement of the control algorithms for optimization of insulin infusion rate. Adaptive models for glucose prediction and recognition of hypoglycemia based on statistical and artificial intelligence techniques are presented.

Methods: We compared an autoregressive (AR) model using only glucose information, an AR model with external insulin input (ARX), and an artificial neural network (ANN) using both glucose and insulin information. Online adaptive models were used to account for the intra- and inter-subject variability of the population with diabetes. The evaluation of the predictive ability included prediction horizons (PHs) of 30 min and 45 min.

Results: The AR model presented root mean square error (RMSE) values of 14.0-21.6 mg/dL and correlation coefficients (CCs) of 0.92-0.95 for PH=30 min and 23.2-35.9 mg/dL and 0.79-0.87, respectively, for PH=45 min. The respective values for the ARX models were slightly better (PH=30 min, 13.3-18.8 mg/dL and 0.94-0.96; PH=45 min, 22.8-29.4 mg/dL and 0.83-0.88). For the ANN, the RMSE values ranged from 2.8 to 6.3 mg/dL, and the CC was 0.99 for all cases and PHs. The sensitivity of hypoglycemia prediction was 78% for AR, 81% for ARX, and 96% for ANN for PH=30 min and 65%, 67%, and 95%, respectively, for PH=45 min. The corresponding specificities were 96%, 96%, and 99% for PH=30 min and 93%, 93%, and 99% for PH=45 min.

Conclusions: The ANN appears to be more appropriate for the prediction of glucose profile based on glucose and insulin data.
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http://dx.doi.org/10.1089/dia.2011.0093DOI Listing
February 2012

Addressing schemes of self-monitoring of blood glucose in type 2 diabetes: a European perspective and expert recommendation.

Diabetes Technol Ther 2011 Sep 29;13(9):959-65. Epub 2011 Jun 29.

Diabetes Research Group, Helmholtz Center, Munich-Neuherberg, Germany.

Self-monitoring of blood glucose (SMBG) in type 2 diabetes has increasingly been shown to display beneficial effects on glycemic control. SMBG is not only associated with a reduction of hemoglobin A1c but has also been demonstrated to increase patients' awareness of the disease. SMBG has also the potential to visualize and predict hypoglycemic episodes. International guidelines by the International Diabetes Federation, the European Society of Cardiology, and the European Association for the Study of Diabetes and also the International Society for Pediatric and Adolescent Diabetes emphasize that SMBG is an integral part of self-management. More recently, two European consensus documents have been published to give recommendations for frequency and timing of SMBG also for various clinical scenarios. Recently, a European expert panel was held to further facilitate and enhance standardized approaches to SMBG. The aim was to present simple, clinically meaningful, and standardized SMBG strategies for type 2 diabetes. The panel recommended a less intensive and an intensive scheme for SMBG across the type 2 diabetes continuum. The length and frequency of SMBG performance depend on the clinical circumstances and the quality of glycemic control. The expert panel also recommended further evaluation of various schemes for SMBG in type 2 diabetes in clinical studies.
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http://dx.doi.org/10.1089/dia.2011.0028DOI Listing
September 2011

Mobile phone technologies and advanced data analysis towards the enhancement of diabetes self-management.

Int J Electron Healthc 2010 ;5(4):386-402

School of Electrical and Computer Engineering, National Technical University of Athens, 9, Heroon Polytechneiou Str., 15780 Zografou, Athens, Greece.

Advances in the area of mobile and wireless communication for healthcare (m-Health) along with the improvements in information science allow the design and development of new patient-centric models for the provision of personalised healthcare services, increase of patient independence and improvement of patient's self-control and self-management capabilities. This paper comprises a brief overview of the m-Health applications towards the self-management of individuals with diabetes mellitus and the enhancement of their quality of life. Furthermore, the design and development of a mobile phone application for Type 1 Diabetes Mellitus (T1DM) self-management is presented. The technical evaluation of the application, which permits the management of blood glucose measurements, blood pressure measurements, insulin dosage, food/drink intake and physical activity, has shown that the use of the mobile phone technologies along with data analysis methods might improve the self-management of T1DM.
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http://dx.doi.org/10.1504/IJEH.2010.036209DOI Listing
February 2011

Effect of valsartan on the incidence of diabetes and cardiovascular events.

N Engl J Med 2010 Apr 14;362(16):1477-90. Epub 2010 Mar 14.

Background: It is not known whether drugs that block the renin-angiotensin system reduce the risk of diabetes and cardiovascular events in patients with impaired glucose tolerance.

Methods: In this double-blind, randomized clinical trial with a 2-by-2 factorial design, we assigned 9306 patients with impaired glucose tolerance and established cardiovascular disease or cardiovascular risk factors to receive valsartan (up to 160 mg daily) or placebo (and nateglinide or placebo) in addition to lifestyle modification. We then followed the patients for a median of 5.0 years for the development of diabetes (6.5 years for vital status). We studied the effects of valsartan on the occurrence of three coprimary outcomes: the development of diabetes; an extended composite outcome of death from cardiovascular causes, nonfatal myocardial infarction, nonfatal stroke, hospitalization for heart failure, arterial revascularization, or hospitalization for unstable angina; and a core composite outcome that excluded unstable angina and revascularization.

Results: The cumulative incidence of diabetes was 33.1% in the valsartan group, as compared with 36.8% in the placebo group (hazard ratio in the valsartan group, 0.86; 95% confidence interval [CI], 0.80 to 0.92; P<0.001). Valsartan, as compared with placebo, did not significantly reduce the incidence of either the extended cardiovascular outcome (14.5% vs. 14.8%; hazard ratio, 0.96; 95% CI, 0.86 to 1.07; P=0.43) or the core cardiovascular outcome (8.1% vs. 8.1%; hazard ratio, 0.99; 95% CI, 0.86 to 1.14; P=0.85).

Conclusions: Among patients with impaired glucose tolerance and cardiovascular disease or risk factors, the use of valsartan for 5 years, along with lifestyle modification, led to a relative reduction of 14% in the incidence of diabetes but did not reduce the rate of cardiovascular events. (ClinicalTrials.gov number, NCT00097786.)
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http://dx.doi.org/10.1056/NEJMoa1001121DOI Listing
April 2010