Gregory Tasian, MD, MSc, MSCE

The long-term goal of Dr. Tasian's clinical and research programs is to improve the lives of children with nephrolithiasis and congenital urologic disease. His research group uses a combination of large data analytics, behavioral economics, nutritional profiling, and human biospecimen analysis to discover determinants of kidney stone disease with the goal of translating this knowledge into new treatments for kidney stone prevention.

Active projects include examination of metabolic pathways in the gut-kidney axis that could be targets for novel therapeutics to prevent kidney stones. Specifically, Dr. Tasian is investigating the impact of diet and antibiotic exposure on the gut microbiome and metabolites in the intestinal and urinary tract and their contribution to calcium kidney stones in children and adults. He is also performing prospective cohort studies to compare stone clearance and patients’ experiences after alternative surgical interventions for kidney stones. He also applies machine learning to improve understanding of the pathophysiology, diagnosis, risk stratification, and prediction of treatment responses of benign urological disease among children.

Dr. Tasian and his team have made the following discoveries:

  • Characterized the rapid increase in kidney stone disease among children, adolescents, and young adult women.
  • Defined the precise relationship between daily temperatures and kidney stone presentation, which has important implications for the impact of climate change on human health.
  • Recently published the first evidence that oral antibiotics are associated with an increased risk of kidney stones. The risk was greatest for those exposed at younger ages, which could help explain the rapid increase in the incidence of nephrolithiasis among children.
  • Found that lower renal parenchymal area was associated with an increased risk of end-stage renal disease in boys with posterior urethral valves. Collaborations with Yong Fan at the University of Pennsylvania determined that machine learning of ultrasound images accurately identified children with congenital urologic disease.
Associate Director for Clinical Trials


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