Luke C Pilling1, Roby Joehanes2, David Melzer1, Lorna W Harries3, William Henley4, Josée Dupuis5, Honghuang Lin6, Marcus Mitchell3, Dena Hernandez7, Sai-Xia Ying2, Kathryn L Lunetta5, Emelia J Benjamin8, Andrew Singleton7, Daniel Levy9, Peter Munson2, Joanne M Murabito10, Luigi Ferrucci11. 1. Epidemiology and Public Health, Medical School, University of Exeter, RILD, Exeter EX2 5DW, UK. 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. 3. Institute of Biomedical and Clinical Sciences, Medical School, University of Exeter, RILD, Exeter EX2 5DW, UK. 4. Institute for Health Services Research, University of Exeter Medical School, Exeter EX1 2LU, UK. 5. 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. 6. National Heart, Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, USA; Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA. 7. Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA. 8. 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. 9. National Heart, Lung and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, USA; The Population Sciences Branch, National Heart, Lung, and Blood Institute, Bethesda, 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. 11. Clinical Research Branch, National Institute on Aging, Baltimore, MD, USA. Electronic address: ferruccilu@grc.nia.nih.gov.
Abstract
INTRODUCTION: Chronically elevated circulating inflammatory markers are common in older persons but mechanisms are unclear. Many blood transcripts (>800 genes) are associated with interleukin-6 protein levels (IL6) independent of age. We aimed to identify gene transcripts statistically mediating, as drivers or responders, the increasing levels of IL6 protein in blood at older ages. METHODS: Blood derived in-vivo RNA from the Framingham Heart Study (FHS, n=2422, ages 40-92 yrs) and InCHIANTI study (n=694, ages 30-104 yrs), with Affymetrix and Illumina expression arrays respectively (>17,000 genes tested), were tested for statistical mediation of the age-IL6 association using resampling techniques, adjusted for confounders and multiple testing. RESULTS: In FHS, IL6 expression was not associated with IL6 protein levels in blood. 102 genes (0.6% of 17,324 expressed) statistically mediated the age-IL6 association of which 25 replicated in InCHIANTI (including 5 of the 10 largest effect genes). The largest effect gene (SLC4A10, coding for NCBE, a sodium bicarbonate transporter) mediated 19% (adjusted CI 8.9 to 34.1%) and replicated by PCR in InCHIANTI (n=194, 35.6% mediated, p=0.01). Other replicated mediators included PRF1 (perforin, a cytolytic protein in cytotoxic T lymphocytes and NK cells) and IL1B (Interleukin 1 beta): few other cytokines were significant mediators. CONCLUSIONS: This transcriptome-wide study on human blood identified a small distinct set of genes that statistically mediate the age-IL6 association. Findings are robust across two cohorts and different expression technologies. Raised IL6 levels may not derive from circulating white cells in age related inflammation. Published by Elsevier Inc.
INTRODUCTION: Chronically elevated circulating inflammatory markers are common in older persons but mechanisms are unclear. Many blood transcripts (>800 genes) are associated with interleukin-6 protein levels (IL6) independent of age. We aimed to identify gene transcripts statistically mediating, as drivers or responders, the increasing levels of IL6 protein in blood at older ages. METHODS: Blood derived in-vivo RNA from the Framingham Heart Study (FHS, n=2422, ages 40-92 yrs) and InCHIANTI study (n=694, ages 30-104 yrs), with Affymetrix and Illumina expression arrays respectively (>17,000 genes tested), were tested for statistical mediation of the age-IL6 association using resampling techniques, adjusted for confounders and multiple testing. RESULTS: In FHS, IL6 expression was not associated with IL6 protein levels in blood. 102 genes (0.6% of 17,324 expressed) statistically mediated the age-IL6 association of which 25 replicated in InCHIANTI (including 5 of the 10 largest effect genes). The largest effect gene (SLC4A10, coding for NCBE, a sodium bicarbonate transporter) mediated 19% (adjusted CI 8.9 to 34.1%) and replicated by PCR in InCHIANTI (n=194, 35.6% mediated, p=0.01). Other replicated mediators included PRF1 (perforin, a cytolytic protein in cytotoxic T lymphocytes and NK cells) and IL1B (Interleukin 1 beta): few other cytokines were significant mediators. CONCLUSIONS: This transcriptome-wide study on human blood identified a small distinct set of genes that statistically mediate the age-IL6 association. Findings are robust across two cohorts and different expression technologies. Raised IL6 levels may not derive from circulating white cells in age related inflammation. Published by Elsevier Inc.
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