When people ask me why I, an applied mathematician, study diabetes, I tell them that I am motivated for both scientific and human reasons.
Type 2 diabetes runs in my family. My grandfather died of complications related to the condition. My mother was diagnosed with the disease when I was 10 years old, and my Aunt Zacharoula suffered from it. I myself am pre-diabetic.
As a teen, I remember being struck by the fact that my mother and her sister received different treatments from their respective doctors. My mother never took insulin, a hormone that regulates blood sugar levels; instead, she ate a limited diet and took other oral drugs. Aunt Zacharoula, on the other hand, took several injections of insulin each day.
Though they had the same heritage, the same parental DNA and the same disease, their medical trajectories diverged. My mother died in 2009 at the age of 75 and my aunt died the same year at the age of 78, but over the course of her life dealt with many more serious side effects.
When they were diagnosed back in the 1970s, there were no data to show which medicine was most effective for a specific patient population.
Today, 29 million Americans are living with diabetes. And now, in an emerging era of precision medicine, things are different.
Increased access to troves of genomic information and the rising use of electronic medical records, combined with new methods of machine learning, allow researchers to process large amounts data. This is accelerating efforts to understand genetic differences within diseases – including diabetes – and to develop treatments for them. The scientist in me feels a powerful desire to take part.
Using big data to optimize treatment
My students and I…