Lisa Chow always loved numbers, but not necessarily mathematics. “I’ve always liked being able to quantitate difficult problems. I joined a math club in high school,” Chow says, describing her childhood in St. Paul, MN. “But in college, math eventually doesn’t use numbers, and I decided that was too much for me,” she jokes. “When you hit the math class that doesn’t use numbers, that’s a really hard math class.” Still, Chow’s interest in numbers, in what is—or remains to be—quantifiable, eventually led her to medicine and, specifically, the data-rich field of endocrinology.
Chow got her undergraduate degree at Stanford University, where she majored in chemical engineering. She liked problem-solving, the combination of numbers of science, but there was a missing element for Chow.
“It’s one thing to use numbers to solve abstract problems, it’s another to use numbers to make a direct difference in the lives of others. I was interested in something more hands-on and personal,” she says.
Chow returned home after Stanford to enroll in the University of Minnesota (UMN) Medical School. She then went to the Mayo Clinic for her internal medicine residency and endocrinology fellowship before returning to UMN, where she is a now professor of medicine and division director for the Division of Diabetes, Endocrinology, and Metabolism as well as codirector of the National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases (NIH/NIDDK) Diabetes T32 DEM Research Training Program, codirector of the NIH/NIDDK R25 MN PRIMED program, and Pennock Family Land Grant Chair in Diabetes Research.
“There are a lot of numbers in endocrinology,” she says. “That’s what first attracted me when I was considering the different subspecialties.”
In her research, Chow focuses on using lifestyle intervention to treat insulin resistance, obesity, and diabetes. “I’m very interested in empowering people to make lifestyle changes to improve their lives,” she says, “whether that’s exercise, time-restricted eating, or caloric restriction.” She is currently the principal investigator of two active NIH R01 grants that investigate the effects of dietary changes on improving metabolic measures.
More recently, Chow has begun to explore the idea of personalized medicine and whether the outcomes of specific interventions can be predicted.
“Outcomes can vary, so there’s a real role for personalized treatment, predicting who will respond to a particular intervention, as well as what intervention will be most appropriate for a given person,” she says. “These can involve lifestyle changes as well as medications like the new class of GLP-1 or GIP/GLP-1 agonists.”
“It comes down to numbers,” she adds. “For example, continuous glucose monitors are extremely data rich. Wearable technology, mobile phone–based data, and plasma omics measurements are also becoming more common. The increasing availability of these robust data sets presents an exciting opportunity for integration through artificial intelligence and machine learning.”
Chow feels integrating big data to personalize outcomes is an important next step in research. “This is a very exciting time as we move forward,” she says.
