BabyDiab Study
The BabyDiab study is one of the pioneering studies in the field of type 1 diabetes pathogenesis research.
It is the first birth cohort and has observed 1650 children of parents with type 1 diabetes from 1989 until today, i.e. for more than 30 years. BabyDiab aims to determine when islet autoantibodies first appear, which genetic and environmental factors influence their development, and what islet autoantibody characteristics were most associated with progression to type 1 diabetes. We examine study participants every three years using blood samples and questionnaires. The recruitment is complete. The BabyDiab study has provided fundamental insights into the development of type 1 diabetes.
The BabyDiab study is part of the T1DI (Type 1 Data Intelligence) project, an international consortium of type 1 diabetes progression monitoring studies from the United States, Sweden, Finland, and Germany and the Center for Computational Health of IBM Research and JDRF. T1DI aims to use machine learning methods to better understand the course of type 1 diabetes development and to identify factors that lead to the onset of type 1 diabetes in children or protect against it.
The BabyDiab study is one of the pioneering studies in the field of type 1 diabetes pathogenesis research.
It is the first birth cohort and has observed 1650 children of parents with type 1 diabetes from 1989 until today, i.e. for more than 30 years. BabyDiab aims to determine when islet autoantibodies first appear, which genetic and environmental factors influence their development, and what islet autoantibody characteristics were most associated with progression to type 1 diabetes. We examine study participants every three years using blood samples and questionnaires. The recruitment is complete. The BabyDiab study has provided fundamental insights into the development of type 1 diabetes.
The BabyDiab study is part of the T1DI (Type 1 Data Intelligence) project, an international consortium of type 1 diabetes progression monitoring studies from the United States, Sweden, Finland, and Germany and the Center for Computational Health of IBM Research and JDRF. T1DI aims to use machine learning methods to better understand the course of type 1 diabetes development and to identify factors that lead to the onset of type 1 diabetes in children or protect against it.
