The Type 1 Diabetes Self-Care Manual: A Complete Guide to Type 1 Diabetes Across the Lifespan for People with Diabetes, Parents, and Caregivers offers practical, evidence-based and common sense help for people with type 1 diabetes and their caregivers. For the close to 1.5 million people with type 1 diabetes in the United States alone and their family and friends, this book will help them understand the effects of type 1 diabetes, not just when diagnosed, but throughout their lifespan. Dr. Jamie Wood and Dr. Anne Peters, two of the most respected and sought-after endocrinologists, provide an easy-to-follow narrative on all aspects of the disease. The Type 1 Diabetes Self-Care Manual will be the go-to reference for everyone touched by type 1 diabetes.

The American Diabetes Association/JDRF Type 1 Diabetes Sourcebook serves as both an evidence-based reference work and consensus report outlining the most critical components of care for individuals with type 1 diabetes throughout their lifespan. The volume serves not only as a comprehensive guide for clinicians, but also reviews the evidence supporting these components of care and provides a perspective on the critical areas of research that are needed to improve our understanding of type 1 diabetes diagnosis and treatment. The volume focuses specifically on the needs of patients with type 1 diabetes and provides clear and detailed guidance on the current standards for the optimal treatment of type 1 diabetes from early childhood to later life. To accomplish the book’s editorial goals, Editors-in-Chief, Drs. Anne Peters and Lori Laffel, assembled an editorial steering committee of prominent research physicians, clinicians, and educators to develop the topical coverage. In addition, a Managing Editor was brought on to help the authors write and focus their chapters.

Valuable tips, tricks, and advice from a veteran young adult with Type 1 diabetes. Type 1 diabetes (T1D) can be a daunting diagnosis, especially for a young kid or a teen. Patrick McAllister knows. Diagnosed with T1D at age twelve, McAllister’s life changed forever, and he faced an uncertain future of insulin shots, diet regulations, and high school. If only I had a roadmap, he thought. So, years after he learned things the hard way, he decided to write one. Whether it is managing mood swings, hormones, or blood sugar levels, Highs & Lows of Type 1 Diabetes is the ultimate teenager’s and young adult’s handbook for surviving, thriving, and flourishing with T1D during one of the most terrifying, yet exciting, phases of your life. Many think of T1D as a scary disease that is sporadic and uncontrollable, but after eight years of dealing with the literal and figurative highs and lows of T1D, McAllister has learned that it is more a lifestyle change. These pages detail a framework for every situation you could possibly imagine involving T1D, from coming home from the hospital after your diagnosis to preparing to leave your nest for freshman year at college. Learn how to: Count carbohydrates, pump insulin like a pro, and correct irregular blood sugar levels Tell your friends, get good grades, and survive school Play sports with the right game-plan Navigate sex, drugs, and rock ‘n’ roll And more! Type 1 diabetes stinks, but you don’t have to go through it blind and alone! Some have learned it the hard way, but Highs & Lows of Type 1 Diabetes will ensure that you will take control of your T1D diagnosis, conquer your adolescent years, and live a healthy and fulfilling life.

Type 1 diabetes is a chronic auto-immune disease. Type 1 diabetes was previously called insulin-dependent diabetes mellitus (IDDM) or juvenile-onset diabetes. Type 1 diabetes develops when the body's immune system destroys pancreatic beta cells, the only cells in the body that make the hormone insulin that regulates blood glucose. To survive, people with type 1 diabetes must have insulin delivered by injection or a pump. Topics discussed in this compilation include genetic and epigenetic studies of type 1 diabetes; blood pressure and diabetes during pregnancy; comparison of diseases-specific quality of life, metabolic control and cardiorespiratory fitness in physically active and inactive youths with type 1 diabetes; stem cells for the treatment of patients with type 1 diabetes; and the causes and potential complications of type 1 diabetes.

Personalized Predictive Modeling in Diabetes features state-of-the-art methodologies and algorithmic approaches which have been applied to predictive modeling of glucose concentration, ranging from simple autoregressive models of the CGM time series to multivariate nonlinear regression techniques of machine learning. Developments in the field have been analyzed with respect to: (i) feature set (univariate or multivariate), (ii) regression technique (linear or non-linear), (iii) learning mechanism (batch or sequential), (iv) development and testing procedure and (v) scaling properties. In addition, simulation models of meal-derived glucose absorption and insulin dynamics and kinetics are covered, as an integral part of glucose predictive models. This book will help engineers and clinicians to: select a regression technique which can capture both linear and non-linear dynamics in glucose metabolism in diabetes, and which exhibits good generalization performance under stationary and non-stationary conditions; ensure the scalability of the optimization algorithm (learning mechanism) with respect to the size of the dataset, provided that multiple days of patient monitoring are needed to obtain a reliable predictive model; select a features set which efficiently represents both spatial and temporal dependencies between the input variables and the glucose concentration; select simulation models of subcutaneous insulin absorption and meal absorption; identify an appropriate validation procedure, and identify realistic performance measures. Describes fundamentals of modeling techniques as applied to glucose control Covers model selection process and model validation Offers computer code on a companion website to show implementation of models and algorithms Features the latest developments in the field of diabetes predictive modeling