Identifying Clinical Phenotypes of Type 1 Diabetes for the Co-Optimization of Weight and Glycemic Control

Obesity is an increasing concern in the clinical care of youth with type 1 diabetes (T1D). Standard approaches to co-optimize weight and glycemic control are challenged by profound population-level heterogeneity. Therefore, the goal of the dissertation was to apply novel analytic methods to understand heterogeneity in the co-occurrence of weight, glycemia, and underlying patterns of minute-to-minute dysglycemia among youth with T1D. Data from the SEARCH for Diabetes in Youth study were used to characterize subgroups of youth with T1D showing similar weight status and level of glycemic control as distinct ‘weight-glycemia phenotypes’ of T1D. Cross-sectional weight-glycemia phenotypes were identified at the 5+ year follow-up visit (n=1,817) using hierarchical clustering on five measures summarizing the joint distribution of body mass index z-score (BMIz) and hemoglobin A1c (HbA1c), generated by reinforcement learning tree predictions. Longitudinal weight-glycemia phenotypes spanning eight years were identified with longitudinal k-means clustering using baseline and follow-up BMIz and HbA1c measures (n=570). Logistic regression modeling tested for differences in the emergence of early/subclinical diabetes complications across subgroups. Seven-day blinded continuous glucose monitoring (CGM) data from baseline of the Flexible Lifestyles Empowering Change randomized trial (n=234, 13-16 years, HbA1c 8-13%) was clustered with a neural network approach to identify subgroups of adolescents with T1D and elevated HbA1c sharing patterns in their CGM data as ‘dysglycemia phenotypes.’ We identified six cross-sectional weight-glycemia phenotypes, including four normal-weight, one overweight, and one subgroup with obesity. Subgroups showed striking differences in other sociodemographic and clinical characteristics suggesting underlying health inequity. We identified four longitudinal weight-glycemia phenotypes associated with different patterns of early/subclinical complications, providing evidence that exposure to co-occurring obesity and worsening glycemic control may accelerate the development and increase the burden of co-morbid complications. We identified three dysglycemia phenotypes with significantly different patterns in hypoglycemia, hyperglycemia, glycemic variability, and 18-month changes in HbA1c. Patient-level drivers of the dysglycemia phenotypes appear to be different from risk factors for poor glycemic control as measured by HbA1c. These studies provide pragmatic, clinically-relevant examples of how novel statistics may be applied to data from T1D to derive patient subgroups for tailored interventions to improve weight alongside glycemic control.Doctor of Philosoph

A Simple Modeling Framework For Prediction In The Human Glucose-Insulin System

In this paper, we build a new, simple, and interpretable mathematical model to describe the human glucose-insulin system. Our ultimate goal is the robust control of the blood glucose (BG) level of individuals to a desired healthy range, by means of adjusting the amount of nutrition and/or external insulin appropriately. By constructing a simple yet flexible model class, with interpretable parameters, this general model can be specialized to work in different settings, such as type 2 diabetes mellitus (T2DM) and intensive care unit (ICU); different choices of appropriate model functions describing uptake of nutrition and removal of glucose differentiate between the models. In both cases, the available data is sparse and collected in clinical settings, major factors that have constrained our model choice to the simple form adopted. The model has the form of a linear stochastic differential equation (SDE) to describe the evolution of the BG level. The model includes a term quantifying glucose removal from the bloodstream through the regulation system of the human body, and another two terms representing the effect of nutrition and externally delivered insulin. The parameters entering the equation must be learned in a patient-specific fashion, leading to personalized models. We present numerical results on patient-specific parameter estimation and future BG level forecasting in T2DM and ICU settings. The resulting model leads to the prediction of the BG level as an expected value accompanied by a band around this value which accounts for uncertainties in the prediction. Such predictions, then, have the potential for use as part of control systems which are robust to model imperfections and noisy data. Finally, a comparison of the predictive capability of the model with two different models specifically built for T2DM and ICU contexts is also performed.Comment: 47 pages, 9 figures, 7 table

The few touch digital diabetes diary : user-involved design of mobile self-help tools for peoplewith diabetes

Paper number 2, 4, 5 and 7 are not available in Munin, due to publishers' restrictions: 2. Årsand E, and Demiris G.: "User-Centered Methods for Designing Patient-Centric Self-Help Tools", Informatics for Health and Social Care, 2008 Vol. 33, No. 3, Pages 158-169 (Informa Healthcare). Available at http://dx.doi.org/10.1080/17538150802457562 4. Årsand E, Olsen OA, Varmedal R, Mortensen W, and Hartvigsen G.: "A System for Monitoring Physical Activity Data Among People with Type 2 Diabetes", pages 173-178 in S.K. Andersen, et.al. (eds.) "eHealth Beyond the Horizon - Get IT There", Proceedings of MIE2008, Studies in Health Technology and Informatics, Volume 136, May 2008, ISBN: 978-1-58603-864-9 5. Årsand E, Tufano JT, Ralston J, and Hjortdahl P.: "Designing Mobile Dietary Management Support Technologies for People with Diabetes", Journal of Telemedicine and Telecare, 2008 Volume 14, Number 7, Pp. 329-332 (Royal Society of Medicine Press). Available at http://dx.doi.org/10.1258/jtt.2008.007001 7. Årsand E, Walseth OA, Andersson N, Fernando R, Granberg O, Bellika JG, and Hartvigsen G.: "Using Blood Glucose Data as an Indicator for Epidemic Disease Outbreaks", pages 199-204 in R. Engelbrecht et.al. (eds.): "Connecting Medical Informatics and Bio-Informatics", Proceedings of MIE2005, Studies in Health Technology and Informatics, Volume 116, August 2005, ISBN: 978-1-58603-549-5. Check availabilityParadoxically, the technological revolution that has created a vast health problem due to a drastic change in lifestyle also holds great potential for individuals to take better care of their own health. The first consequence is not addressed in this dissertation, but the second represents the focus of the work presented, namely utilizing ICT to support self-management of individual health challenges. As long as only 35% of the patients in Norway achieve the International Diabetes Federation‟s goal for blood glucose (HbA1c), actions and activities to improve blood glucose control and related factors are needed. The presented work focuses on the development and integration of alternative sensor systems for blood glucose and physical activity, and a fast and effortless method for recording food habits. Various user-interface concepts running on a mobile terminal constitute a digital diabetes diary, and the total concept is referred to as the “Few Touch application”. The overall aim of this PhD project is to generate knowledge about how a mobile tool can be designed for supporting lifestyle changes among people with diabetes. Applying technologies and methods from the informatics field has contributed to improved insight into this issue. Conversely, addressing the concrete use cases for people with diabetes has resulted in the achievement of ICT designs that have been appreciated by the cohorts involved. Cooperation with three different groups of patients with diabetes over several years and various methods and theories founded in computer science, medical informatics, and telemedicine have been combined in design and research on patient-oriented aids. The blood glucose Bluetooth adapter, the step counter, and the nutrition habit registration system that have been developed were all novel and to my knowledge unique designs at the time they were first tested, and this still applies to the latter two. Whether it can be claimed that the total concept presented, the Few Touch application, will increase quality of life, is up to future research and large-scale tests of the system to answer. However, results from the Type 2 diabetes half-year study showed that several of the participants did adjust their medication, food habits and/or physical activity due to use of the application

The Prediabetes Detection and Physical Activity Intervention Delivery (PRE-PAID) Project

Prediabetes is a prevalent condition which is a precursor to type 2 diabetes (T2D) and physical activity is known to counter T2D. Given the potential for alleviating health care expenditures through the prevention or delay of T2D, targeting individuals with prediabetes using physical activity intervention is a critical research question. The aim of this project was to; i) identify persons with prediabetes and thus at high risk for developing T2D, ii) engage individuals with prediabetes in community-based, culturally-preferred physical activity classes led by culturally-matched instructors with the goal of improving glycemic control and iii) investigate how two modes of laboratory-based aerobic exercise intervention (high intensity intervals versus continuous moderate intensity) impact glycemic control in persons with prediabetes. Participants were recruited in various ethnic communities known to have high prevalence rates of T2D. Critical blood biomarkers and measures of physical and physiological fitness were assessed at different time points to ascertain the effectiveness of both community-based physical activity classes and two modes of laboratory-based exercise. The results of this project show that the PRE-PAID risk questionnaire coupled with point-of-care testing of glycated hemoglobin (A1C) are an effective tool for identifying persons with prediabetes who are at high risk for T2D. Individuals, who participated in community-based culturally matched physical activity classes, experienced improved glycemic control evidenced by reductions in A1C after 3 and 6 months plus improvements in resting blood pressure, combined hand grip strength and aerobic fitness after 6 months. There were no differences between the laboratory-based aerobic exercise interventions of high intensity intervals vs. continuous moderate intensity for any of the measured outcomes. However, the participants who underwent both laboratory exercise modes experienced significant improvements in glycemic control, beta cell function, waist circumference and aerobic fitness following 3 months of supervised exercise. This research provides evidence for early detection of persons with prediabetes and strategies for improving glycemic control and physical plus physiological fitness in this population. The observed improvements could potentially help prevent or delay the onset of T2D