Type 2 Diabetes Complications Disease Study

Combinatorial risk scores to predict and prevent disease

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20

genes identified to be closely associated with risk of type-II diabetes complications

40%

cost savings possible through early intervention of type 2 diabetes complications

combinatorial risk scores

predict which type of complications a type-II diabetes patient is likely to experience

Overview

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.

Type 2 diabetes is among the most common chronic diseases globally, affecting around 5–10% of populations in both developed and developing nations worldwide. It leads to long-term reduction in quality of life and decreases life expectancy by up to 10 years. Due to their severity and long-term nature, diabetes-related complications impose one of the greatest economic and health burdens associated with any disease.

Diabetes-related complications can broadly be split into several categories, including micro- and macrovascular complications, as well as peripheral neuropathies and renal, ophthalmic, and acute conditions such as ketoacidosis and coma. Such complications are one of the main causes of reduced quality of life, long-term care needs, and mortality in diabetic patients. They also predispose patients to long-term debilitating conditions such as cardiovascular disease and various forms of dementia. Both the primary disease itself and, particularly, its complications are preventable with the appropriate management of blood glucose levels, lifestyle, and educational and therapeutic intervention.

To identify at-risk patients early and intervene effectively, we need to predict an individual’s predisposition to a specific disease outcome at the point of initial diagnosis.

Type 2 Diabetes Disease Architecture

severe disease risk loci (SNPs)

risk-associated genes mapped to disease associated mechanisms

druggable targets

120

drug repurposing compounds

Having stratified patients into distinct complication-specific subtypes, we identified individual genes and biological mechanisms associated with each. These high-resolution disease insights enable novel drug discovery, the development of combinatorial risk scores and biomarkers to distinguish the patient subgroups and predict individual risk.

We found significant genetic differences between the case and control populations, indicating that there is a subset of diabetic patients with genetic features that predispose them to severe diabetes and related complications, independent of lifestyle and environmental factors.

Mapping the SNPs to genes revealed 20 protein-coding genes that are highly associated with the risk of developing type 2 diabetes-related complications. Several of these genes are implicated in diabetes-related pathological mechanisms such as insulin resistance and angiogenesis, while others have already been associated with diabetes-specific complications such as diabetic retinopathy and end-stage renal disease. This provides validation for the combinatorial analytics approach and indicates that there are significant genetic differences between individuals who develop diabetes-related complications and those who do not.

Our analysis revealed several SNPs and genes that were highly associated with the development of specific diabetes related complications. The diabetes complication subgroups for which genetic signatures were identified included:

general (five genes associated with an increase in all complications)
acute (ketoacidosis/coma – five genes)
neurological (six genes)
peripheral (circulatory/angiopathy issues – eight genes)
renal (four genes)
ophthalmic (seven genes)
type 1 diabetes (four genes associated with a mis-diagnosed cohort)

We then built a diabetes complications combinatorial risk score (CRS) model that predicts the disease trajectory for individual patients across the 5 main types of complications. The personalized intervention recommendations suited to each patient subgroup are now determined and delivered to their clinician for consideration.