Honghuang Lin1, Roby Joehanes2, Luke C Pilling3, Josée Dupuis4, Kathryn L Lunetta4, Sai-Xia Ying5, Emelia J Benjamin6, Dena Hernandez7, Andrew Singleton7, David Melzer3, Peter J Munson2, Daniel Levy8, Luigi Ferrucci9, Joanne M Murabito10. 1. Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA; National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, USA. Electronic address: hhlin@bu.edu. 2. National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, USA; Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institute of Health, Bethesda, MD, USA; Population Sciences Branch, National Heart, Lung, and Blood Institute, Bethesda, MD, USA. 3. Epidemiology and Public Health, Medical School, University of Exeter, Exeter EX1 2 LU, UK. 4. National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, USA; Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA. 5. Mathematical and Statistical Computing Laboratory, Center for Information Technology, National Institute of Health, Bethesda, MD, USA. 6. National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, USA; Section of Cardiovascular Medicine and Preventive Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA; Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA. 7. Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA. 8. National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, USA; Population Sciences Branch, National Heart, Lung, and Blood Institute, Bethesda, MD, USA. 9. Clinical Research Branch, National Institute on Aging, Baltimore, MD, USA. 10. National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, USA; Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA. Electronic address: murabito@bu.edu.
Abstract
BACKGROUND: Circulating interleukin-6 levels increase with advancing age and are a risk factor for various diseases and mortality. The characterization of gene expression profiles associated with interleukin-6 levels might suggest important molecular events underlying its regulation. METHODS AND RESULTS: We studied the association of transcriptional profiles with interleukin-6 levels in 2422 participants from the Framingham Heart Study Offspring Cohort using Affymetrix Human Exon 1.0 ST Array. We identified 4139 genes that were significantly associated with interleukin-6 levels (FDR<0.05) after adjusting for age, sex and blood cell components. We then replicated 807 genes in the InCHIANTI study with 694 participants. Many of the top genes are involved in inflammation-related pathways or erythrocyte function, including JAK/Stat signaling pathway and interleukin-10 signaling pathway. CONCLUSION: We identified and replicated 807 genes that were associated with circulating interleukin-6 levels. Future characterization of interleukin-6 regulation networks may facilitate the identification of additional potential targets for treating inflammation-related diseases.
BACKGROUND: Circulating interleukin-6 levels increase with advancing age and are a risk factor for various diseases and mortality. The characterization of gene expression profiles associated with interleukin-6 levels might suggest important molecular events underlying its regulation. METHODS AND RESULTS: We studied the association of transcriptional profiles with interleukin-6 levels in 2422 participants from the Framingham Heart Study Offspring Cohort using Affymetrix Human Exon 1.0 ST Array. We identified 4139 genes that were significantly associated with interleukin-6 levels (FDR<0.05) after adjusting for age, sex and blood cell components. We then replicated 807 genes in the InCHIANTI study with 694 participants. Many of the top genes are involved in inflammation-related pathways or erythrocyte function, including JAK/Stat signaling pathway and interleukin-10 signaling pathway. CONCLUSION: We identified and replicated 807 genes that were associated with circulating interleukin-6 levels. Future characterization of interleukin-6 regulation networks may facilitate the identification of additional potential targets for treating inflammation-related diseases.
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