Honghuang Lin1,2, Kathryn L Lunetta1,3, Qiang Zhao3, Pooja R Mandaviya4, Jian Rong1,3, Emelia J Benjamin1,5,6, Roby Joehanes7, Daniel Levy1,8, Joyce B J van Meurs4, Martin G Larson1,3, Joanne M Murabito1,9. 1. National Heart Lung and Blood Institute's and Boston University's Framingham Heart Study, Massachusetts. 2. Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Massachusetts. 3. Department of Biostatistics, Boston University School of Public Health, Massachusetts. 4. Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands. 5. Section of Cardiovascular Medicine and Preventive Medicine, Department of Medicine, Boston University School of Medicine, Massachusetts. 6. Department of Epidemiology, Boston University School of Public Health, Massachusetts. 7. Hebrew SeniorLife, Harvard Medical School, Boston, Massachusetts. 8. Population Sciences Branch, National Heart, Lung, and Blood Institute, Bethesda, Maryland. 9. Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Massachusetts.
Abstract
Background: Biologic age may better reflect an individual's rate of aging than chronologic age. Methods: We conducted a transcriptome-wide association study with biologic age estimated with clinical biomarkers, which included: systolic blood pressure, forced expiratory volume at 1 second (FEV1), total cholesterol, fasting glucose, C-reactive protein, and serum creatinine. We assessed the association between the difference between biologic age and chronologic age (∆age) and gene expression in whole blood measured using the Affymetrix Human Exon 1.0st Array. Results: Our discovery sample included 2,163 participants from the Framingham Offspring cohort (mean age 67 ± 9 years, 55% women). A total of 481 genes were significantly associated with ∆age (p < 2.8 × 10-6). Among them, 415 genes were validated (p < .05/481 = 1.0 × 10-4) in 2,946 participants from the Framingham Third Generation cohort (mean age 46 ± 9 years, 53% women). Many of the significant genes were involved in the ubiquitin-mediated proteolysis pathway. The replication in 414 Rotterdam Study participants (mean age 59 ± 8, 52% women) found 104 of 415 validated genes reached nominal significance (p < .05). Conclusion: We identified and validated 415 genes associated with ∆age in a community-based cohort. Future functional characterization of the biologic age-related gene network may identify targets to test for interventions to delay aging in older adults.
Background: Biologic age may better reflect an individual's rate of aging than chronologic age. Methods: We conducted a transcriptome-wide association study with biologic age estimated with clinical biomarkers, which included: systolic blood pressure, forced expiratory volume at 1 second (FEV1), total cholesterol, fasting glucose, C-reactive protein, and serum creatinine. We assessed the association between the difference between biologic age and chronologic age (∆age) and gene expression in whole blood measured using the Affymetrix Human Exon 1.0st Array. Results: Our discovery sample included 2,163 participants from the Framingham Offspring cohort (mean age 67 ± 9 years, 55% women). A total of 481 genes were significantly associated with ∆age (p < 2.8 × 10-6). Among them, 415 genes were validated (p < .05/481 = 1.0 × 10-4) in 2,946 participants from the Framingham Third Generation cohort (mean age 46 ± 9 years, 53% women). Many of the significant genes were involved in the ubiquitin-mediated proteolysis pathway. The replication in 414 Rotterdam Study participants (mean age 59 ± 8, 52% women) found 104 of 415 validated genes reached nominal significance (p < .05). Conclusion: We identified and validated 415 genes associated with ∆age in a community-based cohort. Future functional characterization of the biologic age-related gene network may identify targets to test for interventions to delay aging in older adults.
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