Comparator Data Characteristics and Testing Procedures for the Clinical Performance Evaluation of Continuous Glucose Monitoring Systems.

Comparing the performance of different continuous glucose monitoring (CGM) systems is challenging due to the lack of comprehensive guidelines for clinical study design. In particular, the absence of concise requirements for the distribution of comparator (reference) blood glucose (BG) concentrations and their rate of change (RoC) that are used to evaluate CGM performance, impairs comparability. For this article, several experts in the field of CGM performance testing have collaborated to propose characteristics of the distribution of comparator measurements that should be collected during CGM performance testing. Specifically, it is proposed that at least 7.5% of comparator BG concentrations are <70 mg/dL (3.9 mmol/L) and >300 mg/dL (16.7 mmol/L), respectively, and that at least 7.5% of BG-RoC combinations indicate fast BG changes with impending hypo- or hyperglycemia, respectively. These proposed characteristics of the comparator data can facilitate the harmonization of testing conditions across different studies and CGM systems and ensure that the most relevant scenarios representing real-life situations are established during performance testing. In addition, a study protocol and testing procedure for the manipulation of glucose levels are suggested that enable the collection of comparator data with these characteristics. This work is an important step toward establishing a future standard for the performance evaluation of CGM systems.

Combining Glucose Monitoring and Insulin Infusion in an Integrated Device: A Narrative Review of Challenges and Proposed Solutions.

The introduction of automated insulin delivery (AID) systems has enabled increasing numbers of individuals with type 1 diabetes (T1D) to improve their glycemic control largely. However, use of AID systems is limited due to their complexity and costs associated. The user must wear both a continuously monitoring glucose system and an insulin infusion pump. The glucose sensor and the insulin catheter must be inserted at two different body sites using different insertion devices. In addition, the user must pair and manage the different systems. These communicate with the AID software implemented on the pump or on a third device such as a dedicated display device or smart phone application. These components might be developed and commercialized by different manufacturers, which in turn can cause difficulties for patients seeking technical support. A possible solution to these challenges would be to integrate the glucose sensor and insulin catheter into a single device. This would allow the glucose sensor and insulin catheter to be inserted simultaneously, eliminating the need for pairing, and simplifying system management. In recent years, different technologies have been developed and evaluated in clinical investigations that combine the glucose sensor and the insulin catheter in one platform. The consistent finding of all these studies is that integration has no adverse effect on insulin infusion and glucose measurements provided that certain conditions are met. In this review, we discuss the perceived challenges of such an approach and discuss possible solutions that have been proposed.

Clinical Performance Evaluation of Continuous Glucose Monitoring Systems: A Scoping Review and Recommendations for Reporting.

The use of different approaches for design and results presentation of studies for the clinical performance evaluation of continuous glucose monitoring (CGM) systems has long been recognized as a major challenge in comparing their results. However, a comprehensive characterization of the variability in study designs is currently unavailable. This article presents a scoping review of clinical CGM performance evaluations published between 2002 and 2022. Specifically, this review quantifies the prevalence of numerous options associated with various aspects of study design, including subject population, comparator (reference) method selection, testing procedures, and statistical accuracy evaluation. We found that there is a large variability in nearly all of those aspects and, in particular, in the characteristics of the comparator measurements. Furthermore, these characteristics as well as other crucial aspects of study design are often not reported in sufficient detail to allow an informed interpretation of study results. We therefore provide recommendations for reporting the general study design, CGM system use, comparator measurement approach, testing procedures, and data analysis/statistical performance evaluation. Additionally, this review aims to serve as a foundation for the development of a standardized CGM performance evaluation procedure, thereby supporting the goals and objectives of the Working Group on CGM established by the Scientific Division of the International Federation of Clinical Chemistry and Laboratory Medicine.

Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA): A Novel Approach for the Statistical Accuracy Assessment of Continuous Glucose Monitoring Systems.

BACKGROUND: The accuracy of continuous glucose monitoring (CGM) systems is crucial for the management of glucose levels in individuals with diabetes mellitus. However, the discussion of CGM accuracy is challenged by an abundance of parameters and assessment methods. The aim of this article is to introduce the Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA), a new approach for a comprehensive characterization of CGM point accuracy which is based on the U.S. Food and Drug Administration requirements for "integrated" CGM systems. METHODS: The statistical concept of tolerance intervals and data from two approved CGM systems was used to illustrate the CG-DIVA. RESULTS: The CG-DIVA characterizes the expected range of deviations of the CGM system from a comparison method in different glucose concentration ranges and the variability of accuracy within and between sensors. The results of the CG-DIVA are visualized in an intuitive and straightforward graphical presentation. Compared with conventional accuracy characterizations, the CG-DIVA infers the expected accuracy of a CGM system and highlights important differences between CGM systems. Furthermore, it provides information on the incidence of large errors which are of particular clinical relevance. A software implementation of the CG-DIVA is freely available (https://github.com/IfDTUlm/CGM_Performance_Assessment). CONCLUSIONS: We argue that the CG-DIVA can simplify the discussion and comparison of CGM accuracy and could replace the high number of conventional approaches. Future adaptations of the approach could thus become a putative standard for the accuracy characterization of CGM systems and serve as the basis for the definition of future CGM performance requirements.

Evaluation of type 2 diabetes care management in nine primary care practices before and after implementation of the Criteria of Good Disease Management of Diabetes established by the Swiss Society of Endocrinology and Diabetology.

AIMS OF THE STUDY: Little is known about the quality of diabetes management of patients with type 2 diabetes mellitus (T2DM) in Swiss primary care. Based on the recommendations of the National Council Quality Assurance Programme, an interprofessional working group of the Swiss Society of Endocrinology and Diabetology (SSED) established population-based national criteria for good disease management of T2DM in primary health care (the diabetes score). The objective of this study was to assess whether the implementation of these criteria improve diabetes management in primary care. METHODS: The diabetes score comprises eight criteria including three biometric measurements, two lifestyle-specific items and screening of three diabetes-associated complications. Practices can evaluate adherence to the criteria based on a point system, with the recommended aim to achieve ≥70/100 points. Group practices and single practices were included in this study and started implementing the SSED criteria in January 2018. The resulting score was compared with data retrospectively obtained for 2017. The primary endpoint was the overall change in Diabetes Score between 2017 and 2018 at each practice, further stratified by practice type. The absolute effect on individual diabetes score criteria was assessed by pooling all patient-level data. RESULTS: Nine practices (six single and three group) participated in the study. In 2017 and 2018, the primary care practices treated 727 and 704 patients with T2DM, respectively, of whom 676 were treated both years. Around half of the patients were cared for in group practices and half in single practices. Between 2017 and 2018 the median (interquartile range) diabetes score improved from 40 (35, 65) to 55 (45, 70; p = 0.078). One practice (single) obtained a score ≥70 in 2017, three practices (all single) achieved this target in 2018. Pooling patient-level data, we observed a significant absolute improvement in the following criteria: number of regular diabetes check ups, body mass index, glycated haemoglobin, blood pressure, low density lipoprotein cholesterol and screenings for diabetes-associated complications (all p <0.05). However, the extent of the improvements were often insufficient to reach the prefixed targets of the diabetes score criteria on the practice level. CONCLUSION: Overall, the implementation of the SSED criteria in the current setting led to a modest, nonsignificant improvement of the diabetes score. Only three (all single practices) out of the nine practices reached the recommended 70-point target, indicating that further strategies are needed to improve diabetes care in primary care practice. Trial registration: ClinicalTrials.gov (ID NCT04216875).

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

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.

Standardization process of continuous glucose monitoring: Traceability and performance.

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.

[The Discovery of Insulin]. / Die Entdeckung des Insulins.

The Discovery of Insulin Abstract. 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.

HbA1c-testing: Evaluation of two point-of-care analysers.

BACKGROUND: HbA1c is a critical parameter for the medical management of patients with diabetes mellitus. Interventions that reduce HbA1c levels lead to a diminution of microvascular complications. For two decades, point of care testing (POCT) methods have been regularly used to measure HbA1c. 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 HbA1c 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 HbA1c, 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 HbA1c 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.

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

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.

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.

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.

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

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.

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

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.

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

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.

GoCARB in the Context of an Artificial Pancreas.

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.

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

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

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.

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

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.

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

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.