Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Stem Cells. Mar 26, 2021; 13(3): 221-235
Published online Mar 26, 2021. doi: 10.4252/wjsc.v13.i3.221
Insulin resistance in diabetes: The promise of using induced pluripotent stem cell technology
Ahmed K Elsayed, Selvaraj Vimalraj, Manjula Nandakumar, Essam M Abdelalim
Ahmed K Elsayed, Manjula Nandakumar, Essam M Abdelalim, Diabetes Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha 34110, Qatar
Selvaraj Vimalraj, Centre for Biotechnology, Anna University, Chennai 600025, India
Essam M Abdelalim, College of Health and Life Sciences, Hamad Bin Khalifa University, Qatar Foundation, Doha 34110, Qatar
Author contributions: Elsayed AK, Vimalraj S and Nandakumar M wrote and reviewed the manuscript; Abdelalim EM conceptualized the idea, wrote and revised the manuscript; all authors approved the final version of the manuscript.
Supported by the Qatar National Research Fund, No. NPRP10-1221-160041.
Conflict-of-interest statement: The authors declare that they have no conflict of interest
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Essam M Abdelalim, DVM, MSc, PhD, Professor, Diabetes Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Al Rayyan, Doha 34110, Qatar.
Received: November 18, 2020
Peer-review started: November 18, 2020
First decision: January 25, 2021
Revised: February 7, 2021
Accepted: March 11, 2021
Article in press: March 11, 2021
Published online: March 26, 2021

Insulin resistance (IR) is associated with several metabolic disorders, including type 2 diabetes (T2D). The development of IR in insulin target tissues involves genetic and acquired factors. Persons at genetic risk for T2D tend to develop IR several years before glucose intolerance. Several rodent models for both IR and T2D are being used to study the disease pathogenesis; however, these models cannot recapitulate all the aspects of this complex disorder as seen in each individual. Human pluripotent stem cells (hPSCs) can overcome the hurdles faced with the classical mouse models for studying IR. Human induced pluripotent stem cells (hiPSCs) can be generated from the somatic cells of the patients without the need to destroy a human embryo. Therefore, patient-specific hiPSCs can generate cells genetically identical to IR individuals, which can help in distinguishing between genetic and acquired defects in insulin sensitivity. Combining the technologies of genome editing and hiPSCs may provide important information about the genetic factors underlying the development of different forms of IR. Further studies are required to fill the gaps in understanding the pathogenesis of IR and diabetes. In this review, we summarize the factors involved in the development of IR in the insulin-target tissues leading to diabetes. Also, we highlight the use of hPSCs to understand the mechanisms underlying the development of IR.

Keywords: Type 2 diabetes, Insulin target tissues, Human pluripotent stem cells, Induced pluripotent stem cells, Genetic factors, Disease modeling

Core Tip: The genetic factors involved in the development of insulin resistance (IR), associated with type 2 diabetes remains largely unknown due to the polygenic nature of IR and lack of the appropriate human model. In this review, we summarize and discuss the use of human pluripotent stem cell technology in studying the genetic defects underlying IR development as well as highlight the potential use of patient-derived pluripotent stem cell for in vitro IR modeling.