Recently, the use of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) has been associated with prevention of kidney function decline and reducing the risk of chronic kidney disease (CKD). However, it is not clear whether GLP-1 receptor agonism has a direct causal effect on renal function. We aimed to investigate the causal association between GLP-1 receptor agonism, kidney function, and the risk of CKD using the Mendelian randomization approach. We constructed genetic instruments for GLP-1 receptor agonism using expression quantitative trait loci (eQTL), focusing on those with significant associations between genetic variants and gene expression in pancreatic tissue from the GTEx database. The inverse variance weighted model was utilized to estimate causal associations, and summary genetic association estimates for the estimated glomerular filtration rate (eGFR)(N=33,152) and CKD (N=117,165) were obtained from a mixed-ancestry genome-wide association study (GWAS) from the CKD Gen Consortium. We identified 7 genetic variants, or eQTL, that were significantly associated with GLP1 receptor mRNA expression in pancreatic tissue. Using these as instrumental variables we observed that genetically predicted GLP1 receptor agonism was significantly associated with improved kidney function as estimated by eGFR (beta 1.97×10-3, 95% CI 6.08×10-4-3.33×10-3, P=5.38×10-3). However, we were not able to find any causal association between genetically predicted GLP1 receptor agonism and reduction in risk of CKD (OR=0.99, 95% CI 0.95-1.03, P=0.65). There was no evidence of horizontal pleiotropy, as indicated by MR-Egger intercept and no obvious heterogeneity (all P>0.05) was observed in each analysis.
In conclusion, genetic evidence suggests that GLP-1 receptor agonism is likely to improve kidney function in the general population. Further studies are warranted to determine the impact of GLP-1 receptor agonism on reducing the risk of CKD especially in people with diabetes.
J. Choi: None. H. Lee: None. J. Lee: None. J. Park: None. Y. Jo: None. J. Sung: None. S. Lee: None. S. Kwak: Employee; SNUH Venture. M. Kho: None.
National Research Foundation of Korea grant funded by the Korean Ministry of Science and ICT (RS-2023-00262002)