Scientists who study physiology and in other biomedical research fields—including anatomy, biochemistry, pathology and pharmacology—network, collaborate and communicate about the latest research at the American Physiological Society (APS) annual meeting at Experimental Biology (EB). This week’s post explores how a virtual model may help prevent kidney damage in Black adults in the U.S.
Thousands of physiologists from around the world gathered in person—for the first time since COVID-19 erupted—at the APS annual meeting at EB 2022 in Philadelphia. The April meeting was all about networking and sharing the latest research findings in some of medical science’s biggest challenges.
John S. Clemmer, PhD, from the University of Mississippi Medical Center in Jackson, was one of the esteemed researchers who attended EB. He and his team presented their research on a virtual model that predicted kidney damage in Black adults in the U.S. The model used data from a previous long-term trial to see how calcium channel blockers accurately predicted the extent of kidney damage. Clemmer and his team added medication to a virtual patient online (an angiotensin inhibitor) and reduced salt intake to simulate stopping kidney damage that had already occurred. The simulated treatments also improved the virtual patients’ heart sizes.
Clemmer said he hopes this technique could be used to predict potential risks to certain treatment in groups that have a higher risk of kidney disease.
Black people are three times as likely as their white counterparts to have kidney failure. In addition, Black people have “much higher rates of high blood pressure, diabetes, obesity and heart disease, all of which increase the risk for kidney disease,” according to the National Kidney Foundation, which makes it that much more important to continue research in this area.
For those of us who are Black, this discovery is welcome news. Many of us can point to our own relatives and friends who have kidney disease. Any new treatments or techniques to reduce these disproportionate statistics would be a step in the right direction.