A Variable State Dimension Approach to Meal Detection and Meal Size Estimation: In Silico Evaluation Through Basal-Bolus Insulin Therapy for Type 1 Diabetes

2017 ◽  
Vol 64 (6) ◽  
pp. 1249-1260 ◽  
Author(s):  
Jinyu Xie ◽  
Qian Wang
Keyword(s):  
Type 1 Diabetes ◽  
Insulin Therapy ◽  
In Silico ◽  
Meal Size ◽  
Size Estimation ◽  
Bolus Insulin ◽  
Basal Bolus

scholarly journals OPTIMAL REGIMENS OF THE BASAL-BOLUS INSULIN THERAPY IN ADOLESCENTS WITH TYPE 1 DIABETES MELLITUS

2015 ◽  
Vol 14 (5) ◽  
pp. 15-21
Author(s):  
G. A. Galkina ◽  
A. A. Voropay ◽  
M. A. Levkovich ◽  
S. V. Vorobiov ◽  
M. V. Komkova ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Insulin Therapy ◽  
Type 1 Diabetes Mellitus ◽  
Bolus Insulin ◽  
Basal Bolus

Meal Detection and Meal Size Estimation for Type 1 Diabetes Treatment: A Variable State Dimension Approach

2015 ◽  
Author(s):  
Jinyu Xie ◽  
Qian Wang
Keyword(s):  
Type 1 Diabetes ◽  
Goodness Of Fit ◽  
Estimation Error ◽  
Artificial Pancreas ◽  
Young Patients ◽  
Estimation Algorithm ◽  
Glucose Variability ◽  
Meal Size ◽  
Size Estimation

To compensate the glucose variability caused by meals is essential in developing Artificial Pancreas for type 1 diabetes. Most existing algorithms rely on meal announcements and determine the insulin doses based on an Insulin-to-Carbohydrate ratio (I:C ratio). However, patients, especially young patients, often forget to provide meal information under natural living conditions. A Variable State Dimension (VSD) based algorithm is developed to detect meals which are unknown to the controller (unannounced meals). The algorithm is evaluated using an FDA-approved UVa/Padova simulator and has demonstrated to achieve 95% success rate in meal detection with less than 17% false alarm rate. In addition, the average meal size estimation error is no more than 13%. We then integrate the VSD-based meal detection and estimation algorithm with our previous published glucose dynamics model consisting of both insulin and carbohydrate inputs. The goodness of fit for 30min-ahead glucose predictions using meal information provided by the VSD-based algorithm has increased by 86% in average compared to the prediction using a model without meal input based on plasma blood glucose (BG) data. Simulation results also show that compared to several meal detection/estimation algorithms in the literature, the VSD-based algorithm has comparable or shorter detection time.

scholarly journals MON-671 Embarrassed to Eat: Two Cases of Gustatory Hyperhidrosis

2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Omer H Tarar ◽  
Andres J Munoz
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Medical History ◽  
Early Identification ◽  
Hemoglobin A1c ◽  
Past Medical History ◽  
History Of ◽  
Bolus Insulin ◽  
Basal Bolus

Abstract Introduction Diabetic Gustatory Hyperhidrosis is characterized by profuse sweating with eating and may be a manifestation of Diabetic autonomic dysfunction. Most patients have evidence of other microvascular complications including nephropathy, retinopathy, peripheral neuropathy and other signs of autonomic neuropathy. We present 2 cases of gustatory hyperhidrosis associated with longstanding poorly controlled type 1 diabetes. Case 1: 49 year old Male with past medical history of longstanding type 1 diabetes with poor control, complicated with diabetic retinopathy, polyneuropathy, albuminuria presented to endocrine clinic for management of diabetes. His hemoglobin A1c was 10.8%. He was on basal-bolus Insulin at home. However, he admitted to missing most doses of prandial Insulin. On further questioning, he mentioned having episodes of profuse head and neck sweating while eating any type of food. He attributed these episodes to “low blood sugars” without checking and therefore tried to avoid Insulin. However, he continued having these episodes. He was diagnosed with Diabetic gustatory hyperhidrosis and started on topical Aluminum hexahydrate. Case 2: 34 year old Female with past medical history of long-standing DM type 1 complicated with poly- neuropathy, autonomic dysfunction, nephropathy, Retinopathy, chronic kidney disease stage III presented for follow up of her diabetes. Her hemoglobin A1c was 9.8%. She was on basal-bolus Insulin at home and reported good compliance. Given her extensive polyneuropathy, she was questioned about hyperhidrosis. She reported having profuse facial and neck sweating with eating all types of food which led to increased embarrassment while eating in public. She was diagnosed with diabetic gustatory hyperhidrosis and started on topical aluminum hexahydrate, with plans for Botox if symptoms persisted. Discussion Diabetic Gustatory Hyperhidrosis is an under- recognized condition and may be misdiagnosed as hypoglycemia, anxiety, gastroparesis or other conditions. This gustatory sweating is a source of severe distress and embarrassment for patients and can have serious emotional, social and professional implications. Associated symptoms may also be mistaken for hypoglycemia and in turn lead to nonadherence with Insulin and other diabetic medications causing suboptimal glycemic control. Topical anti-perspirants like Aluminum Chloride hexahydrate are often used as first line therapy. Second line treatment options include glycopyrrolate, Oxybutynin and Botulinum toxin. Conclusion Most patients are reluctant to mention these symptoms to health care providers and diligent history taking with specific questions in high risk patients may help in early identification and management of this condition. Early identification and management can also help promote overall confidence, quality of life and better glycemic control.

scholarly journals The Role of Protein and Fat Intake on Insulin Therapy in Glycaemic Control of Paediatric Type 1 Diabetes: A Systematic Review and Research Gaps

Nutrients ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 3558
Author(s):  
Dieter Furthner ◽  
Andreas Lukas ◽  
Anna Maria Schneider ◽  
Katharina Mörwald ◽  
Katharina Maruszczak ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Insulin Therapy ◽  
Dietary Fat ◽  
Health Care Professionals ◽  
Scientific Evidence ◽  
Observation Time ◽  
Bolus Insulin ◽  
The Impact

Carbohydrate counting (CHC) is the established form of calculating bolus insulin for meals in children with type 1 diabetes (T1DM). With the widespread use of continuous glucose monitoring (CGM) observation time has become gapless. Recently, the impact of fat, protein and not only carbohydrates on prolonged postprandial hyperglycaemia have become more evident to patients and health-care professionals alike. However, there is no unified recommendation on how to calculate and best administer additional bolus insulin for these two macronutrients. The aim of this review is to investigate: the scientific evidence of how dietary fat and protein influence postprandial glucose levels; current recommendations on the adjustment of bolus insulin; and algorithms for insulin application in children with T1DM. A PubMed search for all articles addressing the role of fat and protein in paediatric (sub-)populations (<18 years old) and a mixed age population (paediatric and adult) with T1DM published in the last 10 years was performed. Conclusion: Only a small number of studies with a very low number of participants and high degree of heterogeneity was identified. While all studies concluded that additional bolus insulin for (high) fat and (high) protein is necessary, no consensus on when dietary fat and/or protein should be taken into calculation and no unified algorithm for insulin therapy in this context exists. A prolonged postprandial observation time is necessary to improve individual metabolic control. Further studies focusing on a stratified paediatric population to create a safe and effective algorithm, taking fat and protein into account, are necessary.

Sign in / Sign up

Export Citation Format

Share Document