Globally, more than one in 10 adults now live with diabetes. In India alone, the number of people with diabetes is projected to reach 125 million by 2045 .
If you have South Asian roots, that risk is even higher, and it hits earlier than it does in many other populations.
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. Take polygenic risk scores, which combine the effects of many genetic variants associated with a disease to estimate a person’s overall genetic risk. “Most predictions about how variants affect gene expression or cell function are inferred from European datasets, and we don’t know which of those predictions hold in South Asians. For most South Asian countries, genomic research can be difficult to prioritise given more immediate and pressing public health needs such as infectious diseases, maternal and child health and non-communicable diseases.
A 2023 study found that polygenic risk scores for multiple sclerosis were less accurate when applied to South Asian populations.
“These single-cell atlases are becoming the reference maps for biology and medicine, and they are increasingly used to train the AI models that will shape future research and care,” said Kuan-lin Huang, senior author of the study, who is an associate professor of genetics and genomic sciences, and AI in human health, at the Icahn School of Medicine, United States. This limits our ability to understand disease mechanisms and identify drug targets relevant to South Asian populations,” said Shweta Ramdas, a geneticist based in Bengaluru.
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.
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.
