The Biostatistics faculty include: Chiang-Ching (Spencer) Huang, and Cheng Zheng. The Biostatistics program includes the Laboratory for Public Health Informatics and Genomics. Learn more about featured research projects by the Biostatistics faculty:
Chiang-Ching Huang, Taura Bar, Reyna VanGilder
Atherosclerosis is the main cause of cardiovascular disease (CVD), the number one cause of death in the world. Increasing evidence shows that both innate and adaptive immune systems tightly regulate atherogenesis. Several immune molecules have been suggested to play a critical role in the inflammatory process of atherosclerosis. However, the fundamental knowledge of dynamic immune regulation in atherosclerosis is far from complete. This project addresses this gap in knowledge by investigating the transcriptional network structure of two major innate and adaptive immune pathways, toll-like receptor and T-cell receptor signaling in atherosclerosis, myocardial infarction (MI), and ischemic stroke (IS). A parallel comparison of transcriptional patterns across these physiopathological conditions will shed light on how these two immune systems interact to influence disease progression and identify patients at a higher risk for developing MI or IS.
Chiang-Ching Huang, Mary McDermott, Kiang Liu, Jane Tseng
Compared to individuals without peripheral arterial disease (PAD), those with PAD have a nearly two-fold increased risk of all-cause mortality and two- to three-fold increased rate of acute coronary syndrome (ACS), even after adjusting for cardiovascular disease (CVD) risk factors and comorbidities. To date, there is no robust classification system to discriminate high-risk (e.g., PAD) patients who are more likely to suffer near-term mortality or ACS events from those who are less likely. Since established risk factors discriminate near-term risk poorly, identifying novel pathways that may signal near-term ACS events is expected to improve our discrimination ability and understanding of the pathogenesis of ACS events. The objective of this project is to develop a multi- metabolite classification system for near-term ACS events in patients with PAD. This study will use high sensitive metabolomics/lipidomic techniques to systematically identify metabolic pathways and metabolites associated with near-term ACS events.