Wearable continuous glucose monitoring sensors: A revolution in diabetes treatment

Worldwide, the number of people affected by diabetes is rapidly increasing due to aging populations and sedentary lifestyles, with the prospect of exceeding 500 million cases in 2030, resulting in one of the most challenging socio-health emergencies of the third millennium. Daily management of diabetes by patients relies on the capability of correctly measuring glucose concentration levels in the blood by using suitable sensors. In recent years, glucose monitoring has been revolutionized by the development of Continuous Glucose Monitoring (CGM) sensors, wearable non/minimally-invasive devices that measure glucose concentration by exploiting different physical principles, e.g., glucose-oxidase, fluorescence, or skin dielectric properties, and provide real-time measurements every 1–5 min. CGM opened new challenges in different disciplines, e.g., medicine, physics, electronics, chemistry, ergonomics, data/signal processing, and software development to mention but a few. This paper first makes an overview of wearable CGM sensor technologies, covering both commercial devices and research prototypes. Then, the role of CGM in the actual evolution of decision support systems for diabetes therapy is discussed. Finally, the paper presents new possible horizons for wearable CGM sensor applications and perspectives in terms of big data analytics for personalized and proactive medicine

High Intensity Interval Training As A Novel Treatment For Impaired Awareness Of Hypoglycaemia In People With Type 1 Diabetes (Hit4hypos):a randomised parallel-group study

Aims/hypothesis: Impaired awareness of hypoglycaemia (IAH) in type 1 diabetes may develop through a process referred to as habituation. Consistent with this, a single bout of high intensity interval exercise as a novel stress stimulus improves counterregulatory responses (CRR) to next-day hypoglycaemia, referred to as dishabituation. This longitudinal pilot study investigated whether 4 weeks of high intensity interval training (HIIT) has sustained effects on counterregulatory and symptom responses to hypoglycaemia in adults with type 1 diabetes and IAH. Methods: HIT4HYPOS was a single-centre, randomised, parallel-group study. Participants were identified using the Scottish Diabetes Research Network (SDRN) and from diabetes outpatient clinics in NHS Tayside, UK. The study took place at the Clinical Research Centre, Ninewells Hospital and Medical School, Dundee, UK. Participants were aged 18–55 years with type 1 diabetes of at least 5 years’ duration and HbA 1c levels &lt;75 mmol/mol (&lt;9%). They had IAH confirmed by a Gold score ≥4, modified Clarke score ≥4 or Dose Adjustment For Normal Eating [DAFNE] hypoglycaemia awareness rating of 2 or 3, and/or evidence of recurrent hypoglycaemia on flash glucose monitoring. Participants were randomly allocated using a web-based system to either 4 weeks of real-time continuous glucose monitoring (RT-CGM) or RT-CGM+HIIT. Participants and investigators were not masked to group assignment. The HIIT programme was performed for 20 min on a stationary exercise bike three times a week. Hyperinsulinaemic–hypoglycaemic (2.5 mmol/l) clamp studies with assessment of symptoms, hormones and cognitive function were performed at baseline and after 4 weeks of the study intervention. The predefined primary outcome was the difference in hypoglycaemia-induced adrenaline (epinephrine) responses from baseline following RT-CGM or RT-CGM+HIIT. Results: Eighteen participants (nine men and nine women) with type 1 diabetes (median [IQR] duration 27 [18.75–32] years) and IAH were included, with nine participants randomised to each group. Data from all study participants were included in the analysis. During the 4 week intervention there were no significant mean (SEM) differences between RT-CGM and RT-CGM+HIIT in exposure to level 1 (28 [7] vs 22 [4] episodes, p=0.45) or level 2 (9 [3] vs 4 [1] episodes, p=0.29) hypoglycaemia. The CGM-derived mean glucose level, SD of glucose and glucose management indicator (GMI) did not differ between groups. During the hyperinsulinaemic–hypoglycaemic clamp studies, mean (SEM) change from baseline was greater for the noradrenergic responses (RT-CGM vs RT-CGM+HIIT: −988 [447] vs 514 [732] pmol/l, p=0.02) but not the adrenergic responses (–298 [687] vs 1130 [747] pmol/l, p=0.11) in those participants who had undergone RT-CGM+HIIT. There was a benefit of RT-CGM+HIIT for mean (SEM) change from baseline in the glucagon CRR to hypoglycaemia (RT-CGM vs RT-CGM+HIIT: 1 [4] vs 16 [6] ng/l, p=0.01). Consistent with the hormone response, the mean (SEM) symptomatic response to hypoglycaemia (adjusted for baseline) was greater following RT-CGM+HIIT (RT-CGM vs RT-CGM+HIIT: −4 [2] vs 0 [2], p&lt;0.05). Conclusions/interpretation: In this pilot clinical trial in people with type 1 diabetes and IAH, we found continuing benefits of HIIT for overall hormonal and symptomatic CRR to subsequent hypoglycaemia. Our findings also suggest that HIIT may improve the glucagon response to insulin-induced hypoglycaemia. Trial registration: ISRCTN15373978. Funding: Sir George Alberti Fellowship from Diabetes UK (CMF) and the Juvenile Diabetes Research Foundation. Graphical Abstract: [Figure not available: see fulltext.].</p

High Intensity Interval Training As A Novel Treatment For Impaired Awareness Of Hypoglycaemia In People With Type 1 Diabetes (Hit4hypos):a randomised parallel-group study

Aims/hypothesis: Impaired awareness of hypoglycaemia (IAH) in type 1 diabetes may develop through a process referred to as habituation. Consistent with this, a single bout of high intensity interval exercise as a novel stress stimulus improves counterregulatory responses (CRR) to next-day hypoglycaemia, referred to as dishabituation. This longitudinal pilot study investigated whether 4 weeks of high intensity interval training (HIIT) has sustained effects on counterregulatory and symptom responses to hypoglycaemia in adults with type 1 diabetes and IAH. Methods: HIT4HYPOS was a single-centre, randomised, parallel-group study. Participants were identified using the Scottish Diabetes Research Network (SDRN) and from diabetes outpatient clinics in NHS Tayside, UK. The study took place at the Clinical Research Centre, Ninewells Hospital and Medical School, Dundee, UK. Participants were aged 18–55 years with type 1 diabetes of at least 5 years’ duration and HbA 1c levels &lt;75 mmol/mol (&lt;9%). They had IAH confirmed by a Gold score ≥4, modified Clarke score ≥4 or Dose Adjustment For Normal Eating [DAFNE] hypoglycaemia awareness rating of 2 or 3, and/or evidence of recurrent hypoglycaemia on flash glucose monitoring. Participants were randomly allocated using a web-based system to either 4 weeks of real-time continuous glucose monitoring (RT-CGM) or RT-CGM+HIIT. Participants and investigators were not masked to group assignment. The HIIT programme was performed for 20 min on a stationary exercise bike three times a week. Hyperinsulinaemic–hypoglycaemic (2.5 mmol/l) clamp studies with assessment of symptoms, hormones and cognitive function were performed at baseline and after 4 weeks of the study intervention. The predefined primary outcome was the difference in hypoglycaemia-induced adrenaline (epinephrine) responses from baseline following RT-CGM or RT-CGM+HIIT. Results: Eighteen participants (nine men and nine women) with type 1 diabetes (median [IQR] duration 27 [18.75–32] years) and IAH were included, with nine participants randomised to each group. Data from all study participants were included in the analysis. During the 4 week intervention there were no significant mean (SEM) differences between RT-CGM and RT-CGM+HIIT in exposure to level 1 (28 [7] vs 22 [4] episodes, p=0.45) or level 2 (9 [3] vs 4 [1] episodes, p=0.29) hypoglycaemia. The CGM-derived mean glucose level, SD of glucose and glucose management indicator (GMI) did not differ between groups. During the hyperinsulinaemic–hypoglycaemic clamp studies, mean (SEM) change from baseline was greater for the noradrenergic responses (RT-CGM vs RT-CGM+HIIT: −988 [447] vs 514 [732] pmol/l, p=0.02) but not the adrenergic responses (–298 [687] vs 1130 [747] pmol/l, p=0.11) in those participants who had undergone RT-CGM+HIIT. There was a benefit of RT-CGM+HIIT for mean (SEM) change from baseline in the glucagon CRR to hypoglycaemia (RT-CGM vs RT-CGM+HIIT: 1 [4] vs 16 [6] ng/l, p=0.01). Consistent with the hormone response, the mean (SEM) symptomatic response to hypoglycaemia (adjusted for baseline) was greater following RT-CGM+HIIT (RT-CGM vs RT-CGM+HIIT: −4 [2] vs 0 [2], p&lt;0.05). Conclusions/interpretation: In this pilot clinical trial in people with type 1 diabetes and IAH, we found continuing benefits of HIIT for overall hormonal and symptomatic CRR to subsequent hypoglycaemia. Our findings also suggest that HIIT may improve the glucagon response to insulin-induced hypoglycaemia. Trial registration: ISRCTN15373978. Funding: Sir George Alberti Fellowship from Diabetes UK (CMF) and the Juvenile Diabetes Research Foundation. Graphical Abstract: [Figure not available: see fulltext.].</p

Umbilical cord mesenchymal stem cells modulate dextran sulphate sodium induced acute colitis in immunodeficient mice.

Inflammatory bowel diseases (IBD) are complex multi-factorial diseases with increasing incidence worldwide but their treatment is far from satisfactory. Unconventional strategies have consequently been investigated, proposing the use of stem cells as an effective alternative approach to IBD. In the present study we examined the protective potential of exogenously administered human umbilical cord derived mesenchymal stem cells (UCMSCs) against Dextran Sulphate Sodium (DSS) induced acute colitis in immunodeficient NOD.CB17-Prkdc scid/J mice with particular attention to endoplasmic reticulum (ER) stress. METHODS: UCMSCs were injected in NOD.CB17-Prkdc scid/J via the tail vein at day 1 and 4 after DSS administration. To verify attenuation of DSS induced damage by UCMSCs, Disease Activity Index (DAI) and body weight changes was monitored daily. Moreover, colon length, histological changes, myeloperoxidase and catalase activities, metalloproteinase (MMP) 2 and 9 expression and endoplasmic reticulum (ER) stress related proteins were evaluated on day 7. RESULTS: UCMSCs administration to immunodeficient NOD.CB17-Prkdc scid/J mice after DSS damage significantly reduced DAI (1.45\u2009\ub1\u20090.16 vs 2.08\u2009\ub1\u20090.18, p\u20093-fold), which were significantly reduced in mice receiving UCMSCs. Moreover, positive modulation in ER stress related proteins was observed after UCMSC administration. CONCLUSIONS: Our results demonstrated that UCMSCs are able to prevent DSS-induced colitis in immunodeficient mice. Using these mice we demonstrated that our UCMSCs have a direct preventive effect other than the T-cell immunomodulatory properties which are already known. Moreover we demonstrated a key function of MMPs and ER stress in the establishment of colitis suggesting them to be potential therapeutic targets in IBD treatment

Open-loop insulin dosing personalization in type 1 diabetes using continuous glucose monitoring data and patient characteristics

Patients with type 1 diabetes (T1D) require lifelong insulin therapy in order to maintain their blood glucose (BG) concentration within the euglycemic range preventing long-term complications associated with hyperglycemia and avoid- ing dangerous episodes of hypoglycemia. To achieve proper glycemic con- trol, people with T1D need to perform a constant learning process about how daily conditions (e.g. insulin administrations, meals schedule and composi- tion, physical activity, and illness) affect BG levels. More than 500,000 op- erations can be needed during the lifetime of a T1D patient to manage the therapy. For this reason, management of diabetes is burdensome for patients, and results in deteriorating their quality of life. One of the major issues in the daily management of T1D concerns with the amount of insulin that has to be administered, by a subcutaneous bolus injection, in order to compensate the increase of BG associated with meals. So far, a standard simple mathematical formula (SF), designed by clinical investigators on an empirical basis, is com- monly used by patients to calculate the size of insulin boluses. SF leverages on the current BG level obtained from self monitoring blood glucose (SMBG) samples, the estimated amount of carbohydrates (CHOs) present in the meal, and patient specific therapy parameters. While the SF is well-established in clinical practice, the insulin amount determined through its use could be sub- optimal due to several reasons, including the error patients make in estimating CHO, the intrinsical sparseness of SMBG, and the inability of accounting for many important factors such as patients’ intra-/interday variability. Margins of improvement over the SMBG-based SF emerged in the past decade, when diabetes management has been transformed by the introduction of min- imally invasive continuous glucose monitoring (CGM) sensors, which have been recently approved by regulatory agencies, such as the Food and Drug Ad- ministration (FDA), to be usable to make treatment decisions, such as insulin dosing. Of course, CGM provides an increased amount of available features on BG, such as the rate of change (ROC), that could be exploited to improve insulin standard therapy. As a matter of fact, several attempts have been pro- posed in the literature to account for CGM-derived information and adjust SF accordingly, but unfortunately, they fall short in personalizing such an adjust- ment patient-by-patient. In this thesis we propose new methodologies for determining a dose of in- sulin bolus able to effectively account for the "dynamic" information on BG provided by the ROC and patient characteristics, the final aim being to per- sonalize the standard insulin therapy and eventually improve the glycemic control. In particular, to identify the possible margins of improvement, in the first part of the thesis we assess and analyze the criticalities of three popular literature techniques that exploit the ROC magnitude and direction to adjust the insulin bolus amount computed through SF. To such a scope, we designed ad-hoc in silico clinical trials implemented using a popular powerful simula- tion tool, i.e. the UVa/Padova T1D Simulator. Then, in the second part, we propose two novel machine learning based algorithms that, being fed by in- formation on current patient status and characteristics, provide patients with new tools to adjust SF in a personalized manner. Finally, in the third part of the thesis, we abandon the idea of using the insulin bolus provided by SF as a sort of initial estimate to be simply adjusted, and we design a brand new formula for insulin bolus determination that naturally takes into account for CGM-derived information and current patient status and characteristics. This represents an innovation in the literature because no insulin bolus formulae specifically designed for use with CGM have been proposed yet

Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment

Worldwide, the number of people affected by diabetes is rapidly increasing due to aging populations and sedentary lifestyles, with the prospect of exceeding 500 million cases in 2030, resulting in one of the most challenging socio-health emergencies of the third millennium. Daily management of diabetes by patients relies on the capability of correctly measuring glucose concentration levels in the blood by using suitable sensors. In recent years, glucose monitoring has been revolutionized by the development of Continuous Glucose Monitoring (CGM) sensors, wearable non/minimally-invasive devices that measure glucose concentration by exploiting different physical principles, e.g., glucose-oxidase, fluorescence, or skin dielectric properties, and provide real-time measurements every 1–5 min. CGM opened new challenges in different disciplines, e.g., medicine, physics, electronics, chemistry, ergonomics, data/signal processing, and software development to mention but a few. This paper first makes an overview of wearable CGM sensor technologies, covering both commercial devices and research prototypes. Then, the role of CGM in the actual evolution of decision support systems for diabetes therapy is discussed. Finally, the paper presents new possible horizons for wearable CGM sensor applications and perspectives in terms of big data analytics for personalized and proactive medicine

Open-loop insulin dosing personalization in type 1 diabetes using continuous glucose monitoring data and patient characteristics

Patients with type 1 diabetes (T1D) require lifelong insulin therapy in order to maintain their blood glucose (BG) concentration within the euglycemic range preventing long-term complications associated with hyperglycemia and avoid- ing dangerous episodes of hypoglycemia. To achieve proper glycemic con- trol, people with T1D need to perform a constant learning process about how daily conditions (e.g. insulin administrations, meals schedule and composi- tion, physical activity, and illness) affect BG levels. More than 500,000 op- erations can be needed during the lifetime of a T1D patient to manage the therapy. For this reason, management of diabetes is burdensome for patients, and results in deteriorating their quality of life. One of the major issues in the daily management of T1D concerns with the amount of insulin that has to be administered, by a subcutaneous bolus injection, in order to compensate the increase of BG associated with meals. So far, a standard simple mathematical formula (SF), designed by clinical investigators on an empirical basis, is com- monly used by patients to calculate the size of insulin boluses. SF leverages on the current BG level obtained from self monitoring blood glucose (SMBG) samples, the estimated amount of carbohydrates (CHOs) present in the meal, and patient specific therapy parameters. While the SF is well-established in clinical practice, the insulin amount determined through its use could be sub- optimal due to several reasons, including the error patients make in estimating CHO, the intrinsical sparseness of SMBG, and the inability of accounting for many important factors such as patients’ intra-/interday variability. Margins of improvement over the SMBG-based SF emerged in the past decade, when diabetes management has been transformed by the introduction of min- imally invasive continuous glucose monitoring (CGM) sensors, which have been recently approved by regulatory agencies, such as the Food and Drug Ad- ministration (FDA), to be usable to make treatment decisions, such as insulin dosing. Of course, CGM provides an increased amount of available features on BG, such as the rate of change (ROC), that could be exploited to improve insulin standard therapy. As a matter of fact, several attempts have been pro- posed in the literature to account for CGM-derived information and adjust SF accordingly, but unfortunately, they fall short in personalizing such an adjust- ment patient-by-patient. In this thesis we propose new methodologies for determining a dose of in- sulin bolus able to effectively account for the "dynamic" information on BG provided by the ROC and patient characteristics, the final aim being to per- sonalize the standard insulin therapy and eventually improve the glycemic control. In particular, to identify the possible margins of improvement, in the first part of the thesis we assess and analyze the criticalities of three popular literature techniques that exploit the ROC magnitude and direction to adjust the insulin bolus amount computed through SF. To such a scope, we designed ad-hoc in silico clinical trials implemented using a popular powerful simula- tion tool, i.e. the UVa/Padova T1D Simulator. Then, in the second part, we propose two novel machine learning based algorithms that, being fed by in- formation on current patient status and characteristics, provide patients with new tools to adjust SF in a personalized manner. Finally, in the third part of the thesis, we abandon the idea of using the insulin bolus provided by SF as a sort of initial estimate to be simply adjusted, and we design a brand new formula for insulin bolus determination that naturally takes into account for CGM-derived information and current patient status and characteristics. This represents an innovation in the literature because no insulin bolus formulae specifically designed for use with CGM have been proposed yet

AGATA: A Toolbox for Automated Glucose Data Analysis

Background: Analyzing continuous glucose monitoring (CGM) data is a mandatory step for multiple purposes spanning from reporting clinical trial outcomes to developing new algorithms for diabetes management. This task is repetitive, and scientists struggle in computing literature glucose control metrics and waste time in reproducing possibly complex plots and reports. For this reason, to provide the diabetes technology community a unified tool, here we present Automated Glucose dATa Analysis (AGATA), an automated glucose data analysis toolbox developed in MATLAB/Octave. Methods: Automated Glucose dATa Analysis is an open-source software program to visualize and preprocess CGM data, compute glucose control metrics, detect adverse events, evaluate the effectiveness of users' prediction algorithms, and compare study arms. Automated Glucose dATa Analysis can be used as a standalone computer application accessible through a dedicated graphical user interface, particularly suitable for clinicians, or by integrating its functionalities in user-defined MATLAB/Octave scripts, which fits the need of researchers and developers. To demonstrate its features, we used AGATA to analyze CGM data of two subjects extracted from a publicly available data set of individuals with type one diabetes. Finally, AGATA's features are compared against those of 12 noncommercial software programs for CGM data analysis. Results: Using AGATA, we easily preprocessed, analyzed, and visualized CGM data in a handy way, in compliance with the requirements and the standards defined in the literature. Compared to the other considered software programs, AGATA offers more functionalities and capabilities. Conclusion: Automated Glucose dATa Analysis is easy to use and reduces the burden of CGM data analysis. It is freely available in GitHub at https://github.com/gcappon/agata

A new integrated platform for gathering and managing multivariable and multisensor data in diabetes clinical studies

Gathering and managing biomedical and sensor data has become, in recent days, crucial in many research fields. In particular, medical data is one of the most complex data categories because of the multiple origins and format, but also because of the security concerns it has. Diabetes is one example of disease for which large quantity of data, having different origins, is needed to be collected and stored every day. In addition, part of the medical data related to diabetes therapy is still collected on personal paper diaries, making the procedure of patient’s data evaluation by the diabetologist even more complex. Integrating all such data in digital format and in a single integrated framework will allow more efficient analysis by clinicians and faster development and porting of new algorithms. The aim of this work is to present a new prototype of platform that has been developed by our research group for such a purpose. The platform is composed by a complete database, a mobile application and a web interface, following the standard telemedicine structure, and has important key features such as sensors and other health applications integration, with attention to privacy and usability requirements for both the patient and the clinician

Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications

By providing blood glucose (BG) concentration measurements in an almost continuous-time fashion for several consecutive days, wearable minimally-invasive continuous glucose monitoring (CGM) sensors are revolutionizing diabetes management, and are becoming an increasingly adopted technology especially for diabetic individuals requiring insulin administrations. Indeed, by providing glucose real-time insights of BG dynamics and trend, and being equipped with visual and acoustic alarms for hypo- and hyperglycemia, CGM devices have been proved to improve safety and effectiveness of diabetes therapy, reduce hypoglycemia incidence and duration, and decrease glycemic variability. Furthermore, the real-time availability of BG values has been stimulating the realization of new tools to provide patients with decision support to improve insulin dosage tuning and infusion. The aim of this paper is to offer an overview of current literature and future possible developments regarding CGM technologies and applications. In particular, first, we outline the technological evolution of CGM devices through the last 20 years. Then, we discuss about the current use of CGM sensors from patients affected by diabetes, and, we report some works proving the beneficial impact provided by the adoption of CGM. Finally, we review some recent advanced applications for diabetes treatment based on CGM sensors