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Project Title:
Genetic and Inflammatory Markers of Sepsis (GenIMS)
Funding Agency:
National Institute of General Medical Science
Total Project Period:
Apr 01, 2001 - Mar 31, 2006
Principal Investigator:
Derek Angus, MD
Co-Investigator(s):
Mitchell Fink; Kelly Wood; John Kellum, Jr; Russell Delude; Donald Yealy, Michael Martino, Mark Roberts, MD, Gregory Cooper, Robert Ferrell, David Finegold, Lisa Weissfeld, Vincent Arena, Eleanor Feingold
Project Summary:
Over 450,000 cases of sepsis occur per year in the US and frequently progress to organ dysfunction and death. Although experimental studies using cells and animals have greatly improved our understanding of the pathophysiology of sepsis, there remains a remarkable paucity of biochemical and genetic data regarding the natural history of this important public health problem.
Because pneumonia is the most common cause of sepsis, patients with this infection represent an excellent clinical model for studying sepsis in a relatively homogeneous population. We are studying a large cohort of patients (n=3,600) with community-acquired pneumonia (CAP). In addition to collecting detailed clinical data, we will perform careful genetic analyses, focusing on allelic variations in the coding or non-coding regions of genes whose products are important in the expression and/or regulation of the inflammatory response. We will also obtain measurements over time of the plasma concentrations or cell surface expression of several key inflammatory molecules.
We will determine the influence of specific polymorphisms on the development, course and outcome of pneumonia and sepsis. We will test whether genetic predisposition to an exuberant inflammatory response protects against infection yet also increases risk for adverse systemic effects and outcome. We will compare genetic data from patients with results obtained from a cohort of healthy controls (n=300). We will test several existing hypotheses regarding the association of circulating inflammatory molecules with outcome.
This study will generate: new and valuable information regarding existing lines of inquiry and laboratory investigation; new hypotheses arising from the use of time-dependent modeling, and new clinical decision tools that have immediate and practical value for designing clinical trials and improving patient care.
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