Genetic Insights into Kidney Function and Disease Risk in Type 1 Diabetes

Type 1 diabetes is an autoimmune disease with a strong genetic component, characterized by the presence of pancreatic islet-specific autoantibodies leading to β-cell injury and destruction. Genome-wide association studies (GWAS) have identified HLA and non-HLA variants for type 1 diabetes. These variants, combined into genetic risk scores, have shown a high discrimination for type 1 diabetes versus type 2 diabetes or monogenic forms of diabetes, supporting differences in genetic causes that could be leveraged for diabetes clinical classification.¹ Kidney disease occurs in one-third of type 1 diabetes patients and is highly heritable.² However, GWAS has been less successful in identifying genetic variants accounting for the susceptibility to diabetic kidney disease, including for type 1 diabetes. The strongest finding was provided by Salem et al.,³ which reported associations for a common missense variant (protein-altering variant, rs55703767) in the collagen type 4 α 3 chain gene, which encodes a structural component of the glomerular basement membrane. This variant was associated with a protective effect on diabetic nephropathy and albuminuria among individuals with type 1 diabetes. Mutations in this gene and other collagen type 4 genes cause Alport disease.

The limited gene discovery for diabetic nephropathy is intriguing and may be related to small GWAS sample size and low power to detect variant associations (given expected small effects for common variants), heterogeneity in kidney disease causes including uncertainty if CKD is due to diabetes (given kidney biopsies are usually not performed), potential differences in genetic susceptibility for disease severity or progression,⁴ and the complexities in defining appropriate control groups.³ These challenges are not unique to GWAS research.

GWAS discovery of type 1 diabetic nephropathy has relied on finding risk variants enriched in individuals with type 1 diabetes.³ The study by Limonte et al. used a different approach.⁵ By applying polygenic scores (PGS) of eGFR and urine albumin to creatinine ratio (ACR), obtained from a large GWAS meta-analysis, they tested whether the genetic susceptibility to kidney disease in type 1 diabetes can be accounted by genetic variants associated with eGFR or ACR. The study applied previously published PGS of eGFR and ACR to 1304 type 1 diabetes participants from the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications study (EPIC). DCCT demonstrated the benefits in risk reduction of diabetes microvascular complications for intensive glycemic therapy versus conventional therapy, and EPICis an observational follow-up study of the DCCT cohort. Limone et al. identified associations of the PGS of eGFR (eGFR increasing alleles) with increased eGFR at baseline and a lower risk of CKD defined by a reduced eGFR at follow-up, and associations of the ACR PGS (ACR increasing alleles) with sustained microalbuminuria. The study also tested the collagen type 4 α 3 chain rs55703767 variant, which was associated with a lower risk of macroalbuminuria.

PGS aggregates the effects of many genetic variants identified through GWAS to summarize an individual's genetic risk. PGSs of eGFR and ACR represent the polygenic contributions to variation in these kidney traits. Several approaches exist for deriving PGSs, and algorithms differ in the number and type of variants selected and in how variant weighs are assigned. PGS performance varies on the basis of population characteristics, environmental contexts and genetic backgrounds. Concerns about performance and reproductivity have led to the development of best practices for deriving, validating, and reporting PGSs,⁶ although several challenges remain.

Limonte et al. minimized issues related to poor transferability of PGSs across populations (because of differences in genetic background) by testing DCCT/EPIC participants of European ancestry and selecting PGSs derived from GWAS that predominantly included individuals of European ancestry. The eGFR PGS, which includes near 1.5 million variants, is available at the PGS Catalog—a repository of published scores that provides details on how PGSs are derived and if they were validated, to support reproducibility.⁶ In the original study, this eGFR PRS had a low area under the receiver operating characteristic of 0.68 for incident CKD. The ACR PGS, by contrast, consists of only 46 independently associated variants, and it is not included in the PGS Catalog. Evaluating the performance of these PGSs in DCCT/EPIC participants would be informative. Relevant metrics include the proportion of variance of eGFR and urine albumin excretion rate explained by the respective PGSs and the area under the receiver operating characteristic to address risk discrimination for diabetic nephropathy outcomes. A query into the PGS Catalog identified more than 90 PGSs for eGFR and four for ACR that could have been tested.⁵ This highlights opportunities to compare the performance of different PGSs for kidney outcomes, which may help inform the selection of specific scores.

There has been substantial interest in using PGS in clinical care or to test new hypothesis as demonstrated in the study by Limonte et al. Although the study identified significant associations with diabetic nephropathy outcomes, the conclusion of shared genetic risk factors between type 1 diabetes and the general population is premature. The GWAS meta-analysis used to derive the eGFR PRS included cohorts with both type 1 and type 2 diabetes in addition to studies ascertained for various diseases, and shared genetic effects were not tested. Still, the work by Limonte et al. contributes to the growing body of research evaluating PGS for kidney disease risk prediction and raises important discussions about the challenges and potential utility of PGS in kidney research.

Supplementary Material

cjasn-21-1496-s001.pdf ^{(1.4MB, pdf)}

Disclosures

Disclosure forms, as provided by each author, are available with the online version of the article at https://links.lww.com/CJN/C831.

Author Contributions

Conceptualization: Nora Franceschini.

Funding acquisition: Nora Franceschini.

Writing – original draft: Nora Franceschini.

Writing – review & editing: Nora Franceschini.

Funding

N. Franceschini: National Lung, Heart, and Blood Institute (R01HL175681), National Institute of Diabetes and Digestive and Kidney Diseases (K26DK138425).