Type 2 diabetes mellitus (T2DM) is highly prevalent, and treatment can be challenging. The thiazolidinediones (including rosiglitazone, which targets PPARγ to reverse insulin resistance) can be effective therapies for T2DM. However, some patients experience severe adverse effects with these drugs and up to 30% of patients do not respond to thiazolidinediones. New research has revealed details of genetic variations that could be used to predict how a patient will respond to thiazolidinediones.
The researchers isolated human adipose stem cells (hASCs) from the subcutaneous adipose tissue of five patients with obesity. After differentiation and culturing, the hASC-derived adipocytes were treated with rosiglitazone for 48 h. Transcriptome analysis revealed that, as expected, rosiglitazone treatment upregulated genes with protein products involved in lipid metabolic processes and PPAR signalling. However, the adipocytes derived from different patients varied widely in their pattern of gene responsiveness to rosiglitazone, with one patient having 87 genes that were unresponsive to rosiglitazone, but responsive in other patients.
Further analysis revealed a key SNP near the ABCA1 gene (ABCA1 is involved in cholesterol metabolism). Patient-derived adipocytes with the inactive rs4743771 allele had reduced PPARγ binding in this genome region, and rosiglitazone did not induce ABCA1 gene expression. Next, using CRISPR–Cas9 editing, the researchers replaced the inactive rs4743771 with an active SNP, which rescued PPARγ binding and responsiveness to rosiglitazone. These findings were translated to a group of 84 patients with T2DM who were treated with rosiglitazone. Patients who were homozygous for the inactive A allele did not experience increased levels of cholesterol (an adverse effect of rosiglitazone), yet this SNP did not affect the beneficial effects of rosiglitazone on HbA1c and fasting levels of glucose.
The researchers conclude that genetic variation influences how a patient responds to antidiabetic therapies, and suggest that these findings could be used in the development of personalized therapies for T2DM.