2024 Jeffrey M. Hoeg Arteriosclerosis, Thrombosis, and Vascular Biology Award for Basic Science and Clinical Research Lecturer - Chiara Giannarelli, MD, PhD, FAHA

Chiara Giannarelli, MD, PhD, is an associate professor of medicine and pathology in the Department of Medicine, Division of Cardiology, and Cardiovascular Research Center at NYU Langone NYU Grossman School of Medicine in New York.

Dr. Giannarelli completed her medical degree and subsequent residency and clinical fellowship before pursuing a PhD in pharmacological sciences and pathophysiological mechanisms of cardiovascular disease at the University of Pisa, Italy.

In 2008, she relocated to the U.S., where she conducted a research fellowship at the Icahn School of Medicine at Mount Sinai, eventually being appointed assistant professor in 2014.

Joining NYU in 2021, Dr. Giannarelli’s research integrates systems pharmacology and systems immunology with molecular biology and imaging to uncover novel mechanisms underlying cardiovascular disease. A groundbreaking accomplishment of her laboratory was the pioneering use of single-cell proteomic and transcriptomic analyses (CyTOF, CITE-seq, scRNA-seq) of human vascular samples — recognized as a significant milestone in inflammation research in atherosclerosis (Nature Medicine, 2019).

Dr. Giannarelli has leveraged systems immunology and drug repositioning approaches to identify new compounds targeting the innate immune system in atherosclerosis, mitigating plaque inflammation (Nature Cardiovascular Research, June 2023). Her work has also explored how SARS-CoV-2 infection triggers proatherogenic immune responses in the human vasculature (Nature Cardiovascular Research, October 2023).

Continuously funded by the NIH/NHLBI, NIH/NCATS and prestigious programs such as the American Heart Association Strategically Focused Research Networks, Chan-Zuckerberg Foundation and PolyBio Foundation, Dr. Giannarelli’s expertise in translational systems immunology and drug repurposing approaches has made her a vital contributor to large multiple-PI programs.