Predicting Diabetes and Reversing Insulin Resistance
By MedTech Outlook | Friday, April 19, 2019
Antibiotic drugs are the most commonly prescribed drugs that can be utilized in saving countless lives. However, the bacteria and other microbes designed to eradicate can develop ways of evading the drugs. This antibiotic resistance is increasing due to a variety of factors, can make it difficult—and sometimes impossible—to treat certain infections. Thiazolidinedione (TZD) drug targets receptor protein activity in patients with type 2 diabetes and thus reverses insulin resistance. On the other hand, the side effects such as weight gain, edema, and high cholesterol, limit their clinical use. Mitchell Lazar, director at the University of Pennsylvania's Perelman School of Medicine, demonstrated that individual genetic variation can be used to predict whether TZD rosiglitazone may produce unnecessary side effect like increased cholesterol levels in few people. The study is also published in the journal Cell Stem Cell.
PPAR (Peroxisome proliferator-activated receptor) is essential for maturation of fat cells which is TZDs target, reverses insulin resistance associated with type 2 diabetes which no other drugs can do. With the detailed study of the genome of fat cells derived from Penn Medicine patients, Lazar’s team discovered a genetic variation predicting whether rosiglitazone would increase the expression of an ABCA1 gene, which controls cholesterol levels. The variation is found in regions that code for molecules that regulate the expression level of ABCA1, but it doesn’t appear in the ABCA1 gene’s protein-coding region. By editing the variant from its inactive to the active form using CRISPR/Cas9, the team demonstrated the causal relationship between genetic variation and increased ABCA1 expression.
The active variant’s ability to predict whether rosiglitazone therapy will increase cholesterol was confirmed by studying 84 patients in Shanghai who were treated with the drug. While many genes clearly manage the overall effects of TZDs, the Penn study demonstrate that individual genetic variations can predict gene expression and metabolic physiology drug effects.
In addition to TZDs, these principles could be applied for other drug classes that are working at non-coding regions of the genome, as well as drugs that are targeting steroid hormone receptors. With this, there is a hope of predicting which patients are most likely to get benefitted over detrimental responses to drugs for individual drug therapy.