SGLT2 inhibitors (SGLT2i) are the most recently approved class of oral antidiabetic drugs. Our long-term objective is to elucidate the genetic factors that regulate the response to SGLT2 inhibitors. Prior to conducting a GWAS, we propose a Pilot & Feasibility study to inquire whether homozygous loss-of-function mutations in candidate genes alter pharmacodynamic responses to SGLT2i. We have identified premature chain termination mutations in two SGLT-family members in the Old Order Amish population: E139X in SGLT3 (SLC5A4 gene) and E593X in SGLT4 (SLC5A9 gene) with allele frequencies of 0.08 and 0.15, respectively. Furthermore, we propose to study the V253I mutation in GLUT9 (SLC2A9 gene; allele frequency = 0.16), which has been reported to decrease serum uric acid levels in the Amish population. Because of cross-talk among these transporters in the renal proximal tubule, we hypothesize that mutations in these 3 candidate genes will affect the response to SGLT2i. Our proposed study has two specific aims: 1. To test the hypotheses that homozygosity for a nonsense mutation in SGLT3 (E139X) will decrease, while homozygosity for a nonsense mutation in SGLT4 (E593X) will increase SGLT2i-induced glycosuria. 2. To test the hypothesis that homozygosity for the V253I mutation in GLUT9 will decrease the uricosuric response to an SGLT2i. This project takes an innovative genotype-to-phenotype approach. An exome chip was used to identify individuals with mutations in candidate genes in the Amish population, thereby enabling studies to characterize the phenotype associated with that genotype. The proposed Pilot & Feasibility studies will set the stage for a future GWAS of SGLT2i pharmacogenomics. Such a study has potential to identify biomarkers to predict which diabetic patients will derive the greatest net clinical benefit from SGLT2-inhibitors. Moreover, ou studies have potential to provide important scientific insights into the function of SGLT3 and SGLT4, two SGLT-family members about which little is known, but may themselves represent novel drug targets for treatment of diabetic kidney disease, hypertension, and non-alcoholic steatohepatitis.
Twelve classes of antidiabetic drugs are approved in the US, but there is a paucity of data to predict the best drug for an individual patient. This pilot study represents a first step toward identifying genes to predict the response to SGLT2 inhibitors, the most recently approved class of antidiabetic drugs. If physicians are able to predict a patient's response to a drug, this has potential to improve clinical care for patients with diabetes.
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