3 results match your criteria: "National Cancer Institute Leidos Biomedical Research[Affiliation]"
J Am Soc Nephrol
October 2019
Section on Nephrology, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina.
Background: Two coding variants in the apo L1 gene () are strongly associated with kidney disease in blacks. Kidney disease itself increases the risk of cardiovascular disease, but whether these variants have an independent direct effect on the risk of cardiovascular disease is unclear. Previous studies have had inconsistent results.
View Article and Find Full Text PDFJAMA Cardiol
August 2018
Molecular Genetic Epidemiology Section, Basic Research Laboratory, Basic Science Program, National Cancer Institute Leidos Biomedical Research, Frederick National Laboratory, Frederick, Maryland.
Importance: APOL1 genotypes are associated with kidney diseases in African American individuals and may influence cardiovascular disease and mortality risk, but findings have been inconsistent.
Objective: To discern whether high-risk APOL1 genotypes are associated with cardiovascular disease and stroke in postmenopausal African American women, who are at high risk for these outcomes.
Design, Setting, And Participants: The Women's Health Initiative is a prospective cohort that enrolled 161 838 postmenopausal women into clinical trials and an observational study between 1993 and 1998.
Gut
November 2015
College of Veterinary Medicine, Oregon State University, Corvallis, Oregon, USA Ghost Lab, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA.
Objective: Despite widespread use of antibiotics for the treatment of life-threatening infections and for research on the role of commensal microbiota, our understanding of their effects on the host is still very limited.
Design: Using a popular mouse model of microbiota depletion by a cocktail of antibiotics, we analysed the effects of antibiotics by combining intestinal transcriptome together with metagenomic analysis of the gut microbiota. In order to identify specific microbes and microbial genes that influence the host phenotype in antibiotic-treated mice, we developed and applied analysis of the transkingdom network.