Meta-analysis of diverse population improves understanding of type 2 diabetes genetic risks

Most studies of genetic risks for type 2 diabetes have focused solely on people of European ancestry.

A new international diabetes research study included a more diverse population, applying data from the 1000 Genomes Project, a detailed worldwide genetic data base. 

Their work appears in the June 2021 issue of Nature Genetics.

The meta-analysis aimed to provide more specific data on genetic traits associated with type 2 diabetes, for diagnosing and monitoring blood insulin and sugar levels with diverse ethnic populations. Genetic risks for disease are increasingly being used in medicine, and more precise data will improve diagnosis of metabolic health in different populations.

The study identified 99 new regions of the genome linked to type 2 diabetes, including 24 that "owe their discovery to the inclusion of data from participants of East Asian, Hispanic, African American, South Asian and sub-Saharan African ancestry," the authors write.

The database included 281,416 individuals without diabetes. Approximately 30% were of non-European ancestry, including 13% Asian, 7% Hispanic, 6% African American, 3% South Asian and 2% sub-Saharan African.

The researchers examined four glycemic traits: fasting glucose, glucose after a two-hour glucose challenge, glycated hemoglobin (that linked to a sugar), and fasting insulin. They were able to track these traits across ancestries and achieve fine-mapping resolution.

The international research group was led by Professor Inês Barroso, of the University of Exeter in England. 

Barroso said of the study: “Type 2 diabetes is an increasingly huge global health challenge--with most of the biggest increases occurring outside of Europe. While there are a lot of shared genetic factors between different countries and cultures, our research tells us that they do differ, in ways that we need to understand. It’s critical to ensuring we can deliver a precision diabetes medicine approach that optimizes treatment and care for everyone.”

The study concludes, "Combining genetic data from these traits with T2D [type 2 diabetes] data will further elucidate pathways that drive normal physiology and pathophysiology, and help to further develop useful predictive scores for disease classification and management."