By Jane Cook
August 12, 2021
One of the most common diseases in the world is type 2 diabetes, but despite its prevalence and extensive genetic heterogeneity, it is treated as a genetically homogenous disease.
This leaves a huge opportunity for the development of custom bioinformatic tools for patient stratification and improved patient outcomes, which is the focus of a recent article in the Journal of the American College of Cardiology (Kim et. al.).
The authors explore how the promise of precision medicine can be applied to type 2 diabetes using bioinformatics analysis rather than the traditional approach of grouping patients based solely on clinical observations, which can be clouded by environmental factors.
There are two main approaches based on genomic data: clustering and polygenic risk scores.
The principles behind clustering involve sorting significant type 2 diabetes-associated loci from a genome-wide association study (GWAS) into their functional categories. Two studies using this method defined six clusters, including groups associated with defective insulin processing and tissue-specific insulin sensitivity.
Polygenic Risk Scores
More significant is the “global” polygenic risk score (gPRS) developed by Khera et. al. in 2018. Using a bioinformatic machine-learning tool, those authors found that those in the top 5% for their type 2 diabetes gPRS had a 2.75x greater chance of developing type 2 diabetes. Data like this are as compelling as many single genetic biomarkers and thus could be implemented in the clinic for patient stratification, allowing for earlier interventions and treatments for high-risk patients.
This article highlights one of many gaps between clinical observations (significant heterogeneity in type 2 diabetes patients in this case) and the untapped power of genomic data analysis. Gaps like these will be filled in the coming years by bioinformatics service providers – the culmination of which will be clinical implementation and precision treatments not just for type 2 diabetes but for many complex diseases.
Jane Cook, Journalist & Content Writer, Bridge Informatics
Jane is a Content Writer at Bridge Informatics, a professional services firm that helps biotech customers implement advanced techniques in the management and analysis of genomic data. Bridge Informatics focuses on data mining, machine learning, and various bioinformatic techniques to discover biomarkers and companion diagnostics. If you’re interested in reaching out, please email [email protected] or [email protected].