In the US, diabetes occurs more often in Black, Hispanic, and Native American people than in White people, according to the US Centers for Disease Control and Prevention. For Judith Simcox, a biochemist at the University of Wisconsin–Madison, this is more than a statistic: the data points represent her friends and family on the Crow Indian Reservation in Montana, where she grew up.
Simcox has long wondered why this difference occurred. Until 2019, her research had focused on the mind-boggling diversity of lipids in mice and used mass spectrometry to identify various fats and their functions. She switched to studying lipids in humans shortly after she moved to Madison that year to set up her new lab. There, colleagues working on a study of aging known as Midlife in the United States (MIDUS) needed help validating lipidomic data they had just received on some human serum samples. But just as Simcox and her team were to begin their analyses, the pandemic shut their lab down.
Without access to the lab bench, they delved into the data available. They built a machine learning algorithm to spot which lipids in the MIDUS data set were correlated with disease. And they began to probe biomarkers of metabolic syndrome, a condition that often precedes a person developing heart disease or diabetes.
Continue reading this story by C&EN freelance science journalist Jyoti Madhusoodanan in ACS Central Science.