The heterogeneity of diabetes has been revealed by the advances in technologies and the insight into large population-based datasets. In diabetes, pathological factors are heterogenous deriving from genetics, clinical characteristics and environmental components. In particular, by using single cell sequencing/imaging technologies, recent studies have also revealed that the beta cell is not a homogeneous cell population but rather a population with heterogeneous properties including different gene expression, cell surface biomarkers, and electrical activity. The recognition of the heterogeneity of the disease and islets would provide novel insight into the pathogenesis of diabetes and would be helpful in developing targeted therapeutic interventions.
It is critical for the heterogeneity of diabetes and islet to be recognized. This allows the identification of the regulation of genetic factors/metabolic pathways. These information could be eventually translated into new strategies for treating diabetes. We aim to discuss diabetes and islet heterogeneity from morphological and functional aspects. The scope of this research topic includes clinical and therapeutic aspects of diabetes/islet heterogeneity and related biomarkers (metabolites/genes/pathways). This research topic would provide a novel insight into developing therapeutic strategies for diabetes leading to personalized precision medicine.
We welcome submissions of reviews and original articles focusing on but not limited to the following topics:
1. Islet biology in health and diabetes.
2. Heterogeneity of diabetic phenotypes.
3. Identification of phenotypic and functional markers of islet cell subpopulations.
4. Islet heterogeneity and function (e.g., insulin secretion, maturation, proliferation, and aging).
5. Precision medicine in diabetes: novel regenerative and therapeutic strategies.
6. Islet metabolism and diabetes.
7. Islet micro-environment and islet cell interactions.
8. Engineering islet endocrine cells from stem cells.
9. Population-based omics data.
The heterogeneity of diabetes has been revealed by the advances in technologies and the insight into large population-based datasets. In diabetes, pathological factors are heterogenous deriving from genetics, clinical characteristics and environmental components. In particular, by using single cell sequencing/imaging technologies, recent studies have also revealed that the beta cell is not a homogeneous cell population but rather a population with heterogeneous properties including different gene expression, cell surface biomarkers, and electrical activity. The recognition of the heterogeneity of the disease and islets would provide novel insight into the pathogenesis of diabetes and would be helpful in developing targeted therapeutic interventions.
It is critical for the heterogeneity of diabetes and islet to be recognized. This allows the identification of the regulation of genetic factors/metabolic pathways. These information could be eventually translated into new strategies for treating diabetes. We aim to discuss diabetes and islet heterogeneity from morphological and functional aspects. The scope of this research topic includes clinical and therapeutic aspects of diabetes/islet heterogeneity and related biomarkers (metabolites/genes/pathways). This research topic would provide a novel insight into developing therapeutic strategies for diabetes leading to personalized precision medicine.
We welcome submissions of reviews and original articles focusing on but not limited to the following topics:
1. Islet biology in health and diabetes.
2. Heterogeneity of diabetic phenotypes.
3. Identification of phenotypic and functional markers of islet cell subpopulations.
4. Islet heterogeneity and function (e.g., insulin secretion, maturation, proliferation, and aging).
5. Precision medicine in diabetes: novel regenerative and therapeutic strategies.
6. Islet metabolism and diabetes.
7. Islet micro-environment and islet cell interactions.
8. Engineering islet endocrine cells from stem cells.
9. Population-based omics data.