Diabetic Cardiomyopathy Modelling Using Induced Pluripotent Stem Cell Derived Cardiomyocytes: Recent Advances and Emerging ModelsShow others and affiliations
2019 (English)In: Stem Cell Reviews, ISSN 1550-8943, E-ISSN 1558-6804, Vol. 15, no 1, p. 13-22Article in journal (Refereed) Published
Abstract [en]
The global burden of diabetes has drastically increased over the past decades and in 2017 approximately 4 million deaths were caused by diabetes and cardiovascular complications. Diabetic cardiomyopathy is a common complication of diabetes with early manifestations of diastolic dysfunction and left ventricular hypertrophy with subsequent progression to systolic dysfunction and ultimately heart failure. An in vitro model accurately recapitulating key processes of diabetic cardiomyopathy would provide a useful tool for investigations of underlying disease mechanisms to further our understanding of the disease and thereby potentially advance treatment strategies for patients. With their proliferative capacity and differentiation potential, human induced pluripotent stem cells (iPSCs) represent an appealing cell source for such a model system and cardiomyocytes derived from induced pluripotent stem cells have been used to establish other cardiovascular related disease models. Here we review recently made advances and discuss challenges still to be overcome with regard to diabetic cardiomyopathy models, with a special focus on iPSC-based systems. Recent publications as well as preliminary data presented here demonstrate the feasibility of generating cardiomyocytes with a diabetic phenotype, displaying insulin resistance, impaired calcium handling and hypertrophy. However, capturing the full metabolic- and functional phenotype of the diabetic cardiomyocyte remains to be accomplished.
Place, publisher, year, edition, pages
Springer, 2019. Vol. 15, no 1, p. 13-22
Keywords [en]
Cardiomyocytes, Diabetic cardiomyopathy, Disease modeling, Induced pluripotent stem cells, Insulin resistance
National Category
Cell Biology
Research subject
Bioinformatics; INF502 Biomarkers
Identifiers
URN: urn:nbn:se:his:diva-16413DOI: 10.1007/s12015-018-9858-1ISI: 000457386100003PubMedID: 30343468Scopus ID: 2-s2.0-85055676513OAI: oai:DiVA.org:his-16413DiVA, id: diva2:1264644
Note
CC BY 4.0
2018-11-202018-11-202023-09-21Bibliographically approved