scholarly journals Classification of Postprandial Glycemic Status with Application to Insulin Dosing in Type 1 Diabetes—An In Silico Proof-of-Concept

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3168 ◽  
Author(s):  
Cappon ◽  
Facchinetti. ◽  
Sparacino. ◽  
Georgiou ◽  
Herrero
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Real Time ◽  
In Silico ◽  
Blood Glucose Concentration ◽  
Insulin Dose ◽  
Target Range ◽  
Glycemic Status ◽  
Meal Time

In the daily management of type 1 diabetes (T1D), determining the correct insulin dose to be injected at meal-time is fundamental to achieve optimal glycemic control. Wearable sensors, such as continuous glucose monitoring (CGM) devices, are instrumental to achieve this purpose. In this paper, we show how CGM data, together with commonly recorded inputs (carbohydrate intake and bolus insulin), can be used to develop an algorithm that allows classifying, at meal-time, the post-prandial glycemic status (i.e., blood glucose concentration being too low, too high, or within target range). Such an outcome can then be used to improve the efficacy of insulin therapy by reducing or increasing the corresponding meal bolus dose. A state-of-the-art T1D simulation environment, including intraday variability and a behavioral model, was used to generate a rich in silico dataset corresponding to 100 subjects over a two-month scenario. Then, an extreme gradient-boosted tree (XGB) algorithm was employed to classify the post-prandial glycemic status. Finally, we demonstrate how the XGB algorithm outcome can be exploited to improve glycemic control in T1D through real-time adjustment of the meal insulin bolus. The proposed XGB algorithm obtained good accuracy at classifying post-prandial glycemic status (AUROC = 0.84 [0.78, 0.87]). Consequently, when used to adjust, in real-time, meal insulin boluses obtained with a bolus calculator, the proposed approach improves glycemic control when compared to the baseline bolus calculator. In particular, percentage time in target [70, 180] mg/dL was improved from 61.98 (± 13.89) to 67.00 (± 11.54; p < 0.01) without increasing hypoglycemia.

scholarly journals A Modular Safety System for an Insulin Dose Recommender: A Feasibility Study

2019 ◽  
Vol 14 (1) ◽  
pp. 87-96 ◽  
Author(s):  
Chengyuan Liu ◽  
Parizad Avari ◽  
Yenny Leal ◽  
Marzena Wos ◽  
Kumuthine Sivasithamparam ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Insulin Dose ◽  
Safety System ◽  
Target Range ◽  
Percentage Time ◽  
Viable Solution ◽  
System Functioning ◽  
Safety Constraint

Background: Delivering insulin in type 1 diabetes is a challenging, and potentially risky, activity; hence the importance of including safety measures as part of any insulin dosing or recommender system. This work presents and clinically evaluates a modular safety system that is part of an intelligent insulin dose recommender platform developed within the EU-funded PEPPER project. Methods: The proposed safety system is composed of four modules which use a novel glucose forecasting algorithm. These modules are predictive glucose alerts and alarms; a predictive low-glucose basal insulin suspension module; an advanced rescue carbohydrate recommender for resolving hypoglycemia; and a personalized safety constraint applied to insulin recommendations. The technical feasibility of the proposed safety system was evaluated in a pilot study including eight adult subjects with type 1 diabetes on multiple daily injections over a duration of six weeks. Glycemic control and safety system functioning were compared between the two-weeks run-in period and the end point at eight weeks. A standard insulin bolus calculator was employed to recommend insulin doses. Results: Overall, glycemic control improved over the evaluated period. In particular, percentage time in the hypoglycemia range (<3.0 mmol/l) significantly decreased from 0.82% (0.05-4.79) at run-in to 0.33% (0.00-0.93) at endpoint ( P = .02). This was associated with a significant increase in percentage time in target range (3.9-10.0 mmol/l) from 52.8% (38.3-61.5) to 61.3% (47.5-71.7) ( P = .03). There was also a reduction in number of carbohydrate recommendations. Conclusion: A safety system for an insulin dose recommender has been proven to be a viable solution to reduce the number of adverse events associated to glucose control in type 1 diabetes.

scholarly journals Impact of Carbohydrate Counting Error on Glycemic Control in Open-Loop Management of Type 1 Diabetes: Quantitative Assessment Through an in silico Trial

2021 ◽  
pp. 193229682110123
Author(s):  
Chiara Roversi ◽  
Martina Vettoretti ◽  
Simone Del Favero ◽  
Andrea Facchinetti ◽  
Pratik Choudhary ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
In Silico ◽  
Open Loop ◽  
Random Errors ◽  
Linear Regression Models ◽  
Reference Case ◽  
Counting Error ◽  
The Impact

Background: In the management of type 1 diabetes (T1D), systematic and random errors in carb-counting can have an adverse effect on glycemic control. In this study, we performed an in silico trial aiming at quantifying the impact of different levels of carb-counting error on glycemic control. Methods: The T1D patient decision simulator was used to simulate 7-day glycemic profiles of 100 adults using open-loop therapy. The simulation was repeated for different values of systematic and random carb-counting errors, generated with Gaussian distribution varying the error mean from -10% to +10% and standard deviation (SD) from 0% to 50%. The effect of the error was evaluated by computing the difference of time inside (∆TIR), above (∆TAR) and below (∆TBR) the target glycemic range (70-180mg/dl) compared to the reference case, that is, absence of error. Finally, 3 linear regression models were developed to mathematically describe how error mean and SD variations result in ∆TIR, ∆TAR, and ∆TBR changes. Results: Random errors globally deteriorate the glycemic control; systematic underestimations lead to, on average, up to 5.2% more TAR than the reference case, while systematic overestimation results in up to 0.8% more TBR. The different time in range metrics were linearly related with error mean and SD ( R2>0.95), with slopes of [Formula: see text], [Formula: see text] for ∆TIR, [Formula: see text], [Formula: see text] for ∆TAR, and [Formula: see text], [Formula: see text] for ∆TBR. Conclusions: The quantification of carb-counting error impact performed in this work may be useful understanding causes of glycemic variability and the impact of possible therapy adjustments or behavior changes in different glucose metrics.

scholarly journals Feasibility study of real-time online text-based CBT to support self-management for people with type 1 diabetes: the Diabetes On-line Therapy (DOT) Study

2021 ◽  
Vol 9 (1) ◽  
pp. e001934
Author(s):  
Anne M Doherty ◽  
Anne Herrmann-Werner ◽  
Arann Rowe ◽  
Jennie Brown ◽  
Scott Weich ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Real Time ◽  
Instant Messaging ◽  
Controlled Trial ◽  
Self Management ◽  
Diabetes Distress ◽  
The Mean ◽  
Distress Scale

IntroductionThis study examines the feasibility of conducting diabetes-focused cognitive–behavioral therapy (CBT) via a secure online real-time instant messaging system intervention to support self-management and improve glycemic control in people with type 1 diabetes.Research design and methodsWe used a pre–post uncontrolled intervention design over 12 months. We recruited adults with type 1 diabetes and suboptimal glycemic control (HbA1c ≥69 mmol/mol (DCCT 8.5%) for 12 months) across four hospitals in London. The intervention comprised 10 sessions of diabetes-focused CBT delivered by diabetes specialist nurses. The primary outcomes were number of eligible patients, rates of recruitment and follow-up, number of sessions completed and SD of the main outcome measure, change in HbA1c over 12 months. We measured the feasibility of collecting secondary outcomes, that is, depression measured using Patient Health Questionnaire-9 (PHQ-9), anxiety measured Generalised Anxiety Disorder (GAD) and the Diabetes Distress Scale (DDS).ResultsWe screened 3177 patients, of whom 638 were potentially eligible, from whom 71 (11.1%) were recruited. The mean age was 28.1 (13.1) years, and the mean HbA1c was 84.6 mmol/mol (17.8), DCCT 9.9%. Forty-six (65%) patients had at least 1 session and 29 (41%) completed all sessions. There was a significant reduction in HbA1c over 12 months (mean difference −6.2 (2.3) mmol/mol, DCCT 0.6%, p=0.038). The change scores in PHQ-9, GAD and DDS also improved.ConclusionsIt would be feasible to conduct a full-scale text-based synchronized real-time diabetes-focused CBT as an efficacy randomized controlled trial.

Additional Insulin for Coping with Fat- and Protein-Rich Meals in Adolescents with Type 1 Diabetes: The Protein Unit

2020 ◽  
Author(s):  
Roland Schweizer ◽  
Susann Herrlich ◽  
Martina Lösch-Binder ◽  
Michaela Glökler ◽  
Magdalena Heimgärtner ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
G Protein ◽  
Insulin Dose ◽  
Dietary Fats ◽  
Target Range ◽  
Pump Therapy ◽  
Glucose Levels ◽  
Protein Rich ◽  
Carbohydrate Unit

Abstract Objective Dietary proteins raise blood glucose levels; dietary fats delay this rise. We sought to assess the insulin amount required to normalize glucose levels after a fat- and protein-rich meal (FPRM). Methods Sixteen adolescents (5 female) with type 1 diabetes (median age: 18.2 years; range: 15.2–24.0; duration: 7.1 years; 2.3–14.3; HbA1c: 7.2%; 6.2–8.3%) were included. FPRM (carbohydrates 57 g; protein 92 g; fat 39 g; fibers 7 g; calories 975 Kcal) was served in the evening, with 20 or 40% extra insulin compared to a standard meal (SM) (carbohydrates 70 g; protein 28 g; fat 19 g; fibers 10 g; calories 579 Kcal) or carbohydrates only. Insulin was administered for patients on intensified insulin therapy or as a 4-hour-delayed bolus for those on pump therapy. The 12-hour post-meal glucose levels were compared between FPRM and SM, with the extra insulin amount calculated based on 100 g proteins as a multiple of the carbohydrate unit. Results Glucose levels (median, mg/dL) 12-hour post-meal with 20% extra insulin vs. 40% vs. insulin dose for SM were 116 vs. 113 vs. 91. Glucose-AUC over 12-hour post-meal with 20% extra insulin vs. 40% vs. insulin dose for SM was 1603 mg/dL/12 h vs. 1527 vs. 1400 (no significance). Glucose levels in the target range with 20% extra insulin vs. 40% were 60% vs. 69% (p=0.1). Glucose levels <60 mg/dL did not increase with 40% extra insulin. This corresponds to the 2.15-fold carbohydrate unit for 100 g protein. Conclusions We recommend administering the same insulin dose given for 1 carbohydrate unit (10 g carbs) to cover 50 g protein.

Impact of Real-Time CGM Data Sharing on Quality of Life in the Caregivers of Adults and Children with Type 1 Diabetes

2020 ◽  
pp. 193229682097842
Author(s):  
William H. Polonsky ◽  
Addie L. Fortmann
Keyword(s):  
Quality Of Life ◽  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Health Outcomes ◽  
Real Time ◽  
Data Sharing ◽  
Diabetes Management ◽  
Health Benefits

Background: To examine caregivers’ experiences with real-time continuous glucose monitoring (RT-CGM) data sharing and its impact on quality of life (QoL) and health outcomes. Methods: Parents of children with type 1 diabetes (T1D) ( N = 303) and spouses/partners of T1D adults ( N = 212) using the Dexcom G5 Mobile or G6 RT-CGM system and who were actively following their T1Ds’ RT-CGM data completed a survey examining their perceived value of data sharing, the impact of sharing on their own QoL and their child/partner’s health, and how they used RT-CGM data to support their T1Ds’ diabetes management. Regression analyses examined whether their actions were linked to reported changes in QoL and health outcomes. Results: Respondents were predominantly non-Hispanic White (91.1% parents; 88.7% partners), female (78.2% parents; 54.7% partners), and college-educated (65.3% parents; 61.8% partners). The majority reported that data sharing had enhanced hypoglycemic confidence (97.7% parents; 98.1% partners), overall well-being (60.4% parents; 63.2% partners), and sleep quality (78.0% parents; 61.3% partners). Of note, three positive caregiver actions were broadly consistent and significant predictors of QoL and health benefits for both parents and partners: celebrating success related to glycemic control, providing encouragement when glycemic control is challenging, and teamwork discussions about how the caregiver should respond to out-of-range values. Conclusions: RT-CGM data sharing was associated with a range of QoL and health benefits for caregivers. Degree of benefits was influenced by the collaborative actions taken by caregivers to support their child’s or partner’s diabetes management. To determine the most effective strategies for collaborative data sharing, longitudinal trials are needed.

scholarly journals Continuous Glucose Monitors and Activity Trackers to Inform Insulin Dosing in Type 1 Diabetes: The University of Virginia Contribution

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5386 ◽  
Author(s):  
Chiara Fabris ◽  
Basak Ozaslan ◽  
Marc D. Breton
Keyword(s):  
Physical Activity ◽  
Type 1 Diabetes ◽  
Glycemic Control ◽  
In Silico ◽  
University Of Virginia ◽  
Percent Time ◽  
Activity Trackers ◽  
Continuous Glucose Monitors ◽  
The University

Objective: Suboptimal insulin dosing in type 1 diabetes (T1D) is frequently associated with time-varying insulin requirements driven by various psycho-behavioral and physiological factors influencing insulin sensitivity (IS). Among these, physical activity has been widely recognized as a trigger of altered IS both during and following the exercise effort, but limited indication is available for the management of structured and (even more) unstructured activity in T1D. In this work, we present two methods to inform insulin dosing with biosignals from wearable sensors to improve glycemic control in individuals with T1D. Research Design and Methods: Continuous glucose monitors (CGM) and activity trackers are leveraged by the methods. The first method uses CGM records to estimate IS in real time and adjust the insulin dose according to a person’s insulin needs; the second method uses step count data to inform the bolus calculation with the residual glucose-lowering effects of recently performed (structured or unstructured) physical activity. The methods were tested in silico within the University of Virginia/Padova T1D Simulator. A standard bolus calculator and the proposed “smart” systems were deployed in the control of one meal in presence of increased/decreased IS (Study 1) and following a 1-hour exercise bout (Study 2). Postprandial glycemic control was assessed in terms of time spent in different glycemic ranges and low/high blood glucose indices (LBGI/HBGI), and compared between the dosing strategies. Results: In Study 1, the CGM-informed system allowed to reduce exposure to hypoglycemia in presence of increased IS (percent time < 70 mg/dL: 6.1% versus 9.9%; LBGI: 1.9 versus 3.2) and exposure to hyperglycemia in presence of decreased IS (percent time > 180 mg/dL: 14.6% versus 18.3%; HBGI: 3.0 versus 3.9), tending toward optimal control. In Study 2, the step count-informed system allowed to reduce hypoglycemia (percent time < 70 mg/dL: 3.9% versus 13.4%; LBGI: 1.7 versus 3.2) at the cost of a minor increase in exposure to hyperglycemia (percent time > 180 mg/dL: 11.9% versus 7.5%; HBGI: 2.4 versus 1.5). Conclusions: We presented and validated in silico two methods for the smart dosing of prandial insulin in T1D. If seen within an ensemble, the two algorithms provide alternatives to individuals with T1D for improving insulin dosing accommodating a large variety of treatment options. Future work will be devoted to test the safety and efficacy of the methods in free-living conditions.

Serum leptin concentrations in children with type 1 diabetes mellitus: Relationship to body mass index, insulin dose, and glycemic control

Metabolism ◽  
2002 ◽  
Vol 51 (3) ◽  
pp. 292-296 ◽  
Author(s):  
Ashraf T. Soliman ◽  
Magdi Omar ◽  
Hala M. Assem ◽  
Ibrahim S. Nasr ◽  
Mohamed M. Rizk ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Body Mass Index ◽  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Type 1 Diabetes Mellitus ◽  
Body Mass ◽  
Insulin Dose ◽  
Serum Leptin

scholarly journals Liraglutide as additional treatment for type 1 diabetes

2011 ◽  
Vol 165 (1) ◽  
pp. 77-84 ◽  
Author(s):  
Ajay Varanasi ◽  
Natalie Bellini ◽  
Deepti Rawal ◽  
Mehul Vora ◽  
Antoine Makdissi ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Insulin Dose ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Additional Treatment ◽  
The Body ◽  
The Mean ◽  
Bolus Insulin

ObjectiveTo determine whether the addition of liraglutide to insulin to treat patients with type 1 diabetes leads to an improvement in glycemic control and diminish glycemic variability.Subjects and methodsIn this study, 14 patients with well-controlled type 1 diabetes on continuous glucose monitoring and intensive insulin therapy were treated with liraglutide for 1 week. Of the 14 patients, eight continued therapy for 24 weeks.ResultsIn all the 14 patients, mean fasting and mean weekly glucose concentrations significantly decreased after 1 week from 130±10 to 110±8 mg/dl (P<0.01) and from 137.5±20 to 115±12 mg/dl (P<0.01) respectively. Glycemic excursions significantly improved at 1 week. The mean s.d. of glucose concentrations decreased from 56±10 to 26±6 mg/dl (P<0.01) and the coefficient of variation decreased from 39.6±10 to 22.6±7 (P<0.01). There was a concomitant fall in the basal insulin from 24.5±6 to 16.5±6 units (P<0.01) and bolus insulin from 22.5±4 to 15.5±4 units (P<0.01).In patients who continued therapy with liraglutide for 24 weeks, mean fasting, mean weekly glucose concentrations, glycemic excursions, and basal and bolus insulin dose also significantly decreased (P<0.01). HbA1c decreased significantly at 24 weeks from 6.5 to 6.1% (P=0.02), as did the body weight by 4.5±1.5 kg (P=0.02).ConclusionLiraglutide treatment provides an additional strategy for improving glycemic control in type 1 diabetes. It also leads to weight loss.

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