Diabetes and its complications are due to complex interactions between modifiable and non-modifiable factors where age, disease duration and cardiometabolic control are major determinants. The familial clustering of diabetic complications, notably renal disease, supports genetic factors and shared environment as important predisposing factors. Researchers have used linkage and candidate gene approaches and discovered genomic loci and variants associated with diabetic kidney and heart disease. However, the use of genome-wide arrays have now become the preferred method for discovering these genetic variants. Despite the use of large case-control cohorts, the search for these variants have been elusive in both type 1 and type 2 diabetes, in part due to drug treatment which alter the clinical course even in genetically predisposed individuals. Often, the discovery of variants in one population are not reproducible due to differences in ethnicity, definitions and settings. Other researchers have discovered non-coding RNAs and inflammatory biomarkers associated with cardiovascular-renal complications. The integration of these multiomic data in well-characterized prospective cohorts may provide an opportunity to unravel these complexities. Given the evidence that multifactorial management can prevent diabetes and its complications, incorporation of these bio-genetic markers may improve the efficiency and effectiveness for implementing an integrated detection, prevention and management programme for diabetes and its complications.