Genetics of diabetes mellitus and diabetes complications

Abstract

Diabetes is one of the fastest growing diseases worldwide, projected to affect 693 million adults by 2045. Devastating macrovascular complications (cardiovascular disease) and microvascular complications (such as diabetic kidney disease, diabetic retinopathy and neuropathy) lead to increased mortality, blindness, kidney failure and an overall decreased quality of life in individuals with diabetes. Clinical risk factors and glycaemic control alone cannot predict the development of vascular complications; numerous genetic studies have demonstrated a clear genetic component to both diabetes and its complications. Early research aimed at identifying genetic determinants of diabetes complications relied on familial linkage analysis suited to strong-effect loci, candidate gene studies prone to false positives, and underpowered genome-wide association studies limited by sample size. The explosion of new genomic datasets, both in terms of biobanks and aggregation of worldwide cohorts, has more than doubled the number of genetic discoveries for both diabetes and diabetes complications. We focus herein on genetic discoveries for diabetes and diabetes complications, empowered primarily through genome-wide association studies, and emphasize the gaps in research for taking genomic discovery to the next level.

Figure 1.. Phenotype complexity to diabetic kidney disease.

A schematic depicting the complexity to diagnosing diabetic kidney disease based on two primary markers, albuminuria and estimated glomerular filtration rate (eGFR), with increasing diabetes duration. Complications to diagnosis include early increases in eGFR (hyperfiltration), regression of microalbuminuria to normal levels, and independence of the markers such that not all individuals with DKD have both high levels of albuminuria and low eGFR.

Figure 2.. Expansion of DKD phenotypes for GWAS genetic discovery.

A stylistic representation of the DKD case control phenotype definitions used in both GENIE (all 10 comparisons) and SUMMIT consortiums (those comparisons marked with an asterisk). Phenotype names are taken directly from the DNCRI 2019 GWAS (Salem et al. JASN 2019). All phenotype definitions with a significant genome-wide finding are bolded to highlight the benefit of using multiple definitions.

Figure 3.. DNCRI DKD GWAS Manhattan Plot from Salem et al.

Manhattan plot from Salem et al JASN 2019 publication, highlighting the value in using multiple phenotype definitions of DKD for genetic discovery. Each locus reaching genome-wide significance is colored by its top phenotype. In addition, two distinct significance thresholds used in this study are highlighted.

Figure 4.. Visual representation of the COL4A3 gene and missense coding SNP rs55703767 associated with DKD.

COL4A3 missense coding variant rs55703767 (G → T; Aspartic acid to Tyrosine) in exon 17 in the collagenous domain of COL4A3 (between the triple-helical 7S domain and the non-collagenous NC1 domain).

Figure 5.. Association at COL4A3 SNP stratified by hyperglycemia status in the FinnDiane Study published in Salem et al.

Association of rs55703767 COL4A3 SNP with various DKD disease definitions from the DNCRI DKD GWAS stratified by HbA1c levels in the FinnDiane Study cohort. Though the confidence intervals overlap due to the small sample size, the effect of this SNP on DKD appears to be much stronger in a diabetic context.