Biomarkers and Machine Learning Applications in Obesity
Next-generation sequencing, multi-omics, high-resolution medical imaging technologies, and a wide range of (smart) sensors have led the biosciences into the big data era. To this end, machine learning approaches are more than ever indispensable to transfo
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ity and Diabetes Scientific Advances and Best Practice Second Edition
Obesity and Diabetes
Joel Faintuch • Salomão Faintuch Editors
Obesity and Diabetes Scientific Advances and Best Practice Second Edition
Editors Joel Faintuch Hospital das Clinicas Sao Paulo University Medical School Sao Paulo, Brazil
Salomão Faintuch Harvard Medical School Beth Israel Deaconess Medical Center Boston, MA, USA
ISBN 978-3-030-53369-4 ISBN 978-3-030-53370-0 https://doi.org/10.1007/978-3-030-53370-0
(eBook)
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Foreword
Obesity and diabetes mellitus are two interrelated, chronic, debilitating, incurable diseases that have increased in incidence worldwide. In the USA, over 60% of adult Americans are overweight or obese. Most countries around the world are also seeing dramatically increasing rates of obesity. Although not all people with type 2 diabetes are overweight or obese, the majority are. In addition, while most individuals who suffer from obesity are not diabetic, the incidence of T2DM increases as adiposity increases. So intertwined are these seemingly independent metabolic disorders that some refer to them together as “diabesity.” Despite spending billions of dollars on research and the development of a multitude of different treatments, we have failed to control either disease and find ourselves heading off the cliff. While there has been an explosion of new medications, surgical procedures, and the introduction of metabolic/bariatric devices, the incidence of both diseases continues to rise. It has been estimated that within a few short years, the cost to treat the various related medical conditions of these two
