317-OR: Identification of Genetic Regulators of Circulating Biomarkers of Kidney Decline in Diabetes

Background: Over the past decade, major progress has been made in identifying predictors of kidney disease progression in patients with diabetes mellitus (DM). Despite this progress, however, a key feature that has yet to be considered in current prognostic models, and one that is critical toward implementing Precision Medicine and Precision Diagnostics in diabetic kidney disease (DKD), is the contribution of genetic factors on the levels of these biomarkers. To advance this area, we set out to define the genetic drivers of circulating biomarkers of kidney disease progression in individuals with diabetes.

Methods: Leveraging genetic and proteomic data from 3,267 participants with diabetes from the UK Biobank Pharma Proteomics Project (UKB-PPP), we performed genome-wide association study (GWAS) analyses using REGENIE, adjusting for age, gender, BMI, and principal components, and biomarker data for 21 Joslin Kidney Panel (JKP) biomarkers measured on the OLINK Explore 3072 platform.

Results: We identified genome-wide significant (p-value < 5x10^-8) cis-protein quantitative trait loci (pQTLs) at 12 of the 21 JKP biomarkers (57%). In total, 1,855 SNPs achieved genome-wide significance across these 12 cis-pQTLs. The strongest association was observed at rs13278062 (p-value = 1.25x10^-54) with TNFRSF10A protein levels. In addition to cis-pQTLs, we also identified genome-wide significant trans-pQTLs for 4 JKP biomarkers. Colocalization of transcriptomic and epigenomic data nominate 237 putative regulatory SNPs across the 12 cis-pQTLs.

Conclusion: Our results indicate that genetic variation (both cis and trans) is associated with circulating levels of the majority of JKP biomarkers (14/21, 67%) and suggest that incorporating information on genetic regulators (i.e., genetic-biomarkers) of protein-biomarkers of kidney disease progression in prognostic models may further improve risk stratification in DKD.