معرفی
Ayodeji Adegunsoye is an Assistant Professor in the Department of Medicine-Pulmonary and Critical Care at the University of Chicago. As a pulmonary & critical care physician-scientist, his work focuses on improving outcomes for patients with pulmonary fibrosis through innovative approaches integrating genomic data, statistical modeling, and clinical risk prediction.
Dr. Adegunsoye's research centers on the intersection between clinical risk-prediction models and genomic markers to improve pulmonary fibrosis outcomes. His work addresses critical limitations in current genomic advances, which have identified factors influencing disease heterogeneity but are limited by healthcare disparities and the exclusion of major racial groups. He leverages advanced statistical techniques to improve care delivery for patients with fibrotic lung diseases, with particular emphasis on utilizing genetic data from diverse populations to enhance clinical decision-making for all patients. His key research areas include interstitial lung disease, pulmonary fibrosis genomics, healthcare disparities, statistical modeling, and telomere biology in pulmonary disease.
His extensive publication record demonstrates significant contributions to understanding genetic and genomic factors in pulmonary fibrosis, with particular attention to racial and ethnic disparities. His work spans genomic analyses, statistical modeling, radiological assessment, and clinical trials, addressing how reference equations impact lung function interpretation, the role of environmental exposures in disease progression, and proteomic biomarkers of survival. Dr. Adegunsoye frequently collaborates with leading researchers in pulmonary medicine, contributing to high-impact clinical guidelines and consensus statements.
Dr. Adegunsoye has been awarded an NIH K23 Career Development Award (K23HL146942, 2020-2025) as Principal Investigator for his project 'Improving Pulmonary Fibrosis Classification with Genomics Informed Phenotypic Clusters,' supporting his innovative work in developing better classification systems for pulmonary fibrosis using genomic data from diverse populations.
