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South Asians face higher rates of type 2 diabetes, cardiovascular disease and asthma than people of European ancestry, which means the tools built on European-heavy data are less accurate for the population that needs them most. Image used for representational purposes only
| Photo Credit: Getty Images/iStockphoto
Globally, more than one in 10 adults now live with diabetes. If you have South Asian roots, that risk is even higher, and it hits earlier than it does in many other populations. In India alone, the number of people with diabetes is projected to reach 125 million by 2045.Â
Yet, when scientists try to understand why diseases such as diabetes and cardiovascular disease affect South Asians differently, they often have to rely on genetic data drawn from European populations.
Advances in artificial intelligence and machine learning are allowing scientists to mine vast amounts of genomic and health data to detect disease earlier, predict risk, monitor patients and tailor treatments to individuals. But at the heart of this changing landscape lies an old, constant problem – the data used to build these tools lack diversity.
Integrated biobanks such as the U.K. Biobank, which combine participants’ genomic information with electronic health records, environmental exposures and lifestyle data, have transformed biomedical research. These repositories have accelerated drug development, informed clinical guidelines and helped shape public health policy across the world.
