Type 2 diabetes-related complications create some of the most significant health and economic burdens in developed and developing countries, costing over $450 billion per year to treat in the US alone. Complications associated with type 2 diabetes account for 80% of this spending due to expensive additional hospitalizations and treatments. In addition, they are directly responsible for poor quality of life for patients, with higher long-term social care costs and mortality rates.
We analyzed a dataset, comparing cases with a variety of type 2 diabetes-associated complications against gender-matched controls who had also been diagnosed with diabetes and had similar BMI measurements but had not (yet) developed any complications.
Our analysis revealed several single nucleotide polymorphisms (SNPs) and genes highly associated with developing specific diabetes-related complications. These associations would not have been found using standard Genome-Wide Association Studies (GWAS) analysis techniques. Having stratified this case population into distinct complication-specific subtypes, we identified individual genes and biological mechanisms associated with each.
These high-resolution disease insights enabled the development of combinatorial risk scores that predict the disease trajectory for individual patients, evaluate individual risk and enable personalized intervention recommendations.