Brian T Joyce1,2, Tao Gao1,2, Kalsea Koss3, Yinan Zheng1,2, Andres Cardenas4, Jonathan Heiss5, Allan Just5, Kai Zhang6, Linda van Horn1, Norrina Bai Allen1, Philip Greenland1, Sheldon Cohen7, Penny Gordon-Larsen8, Colter Mitchell9, Sara McLanahan10, Lisa Schneper11, Daniel Notterman11, Sheryl L Rifas-Shiman12, Emily Oken12, Marie-France Hivert12,13, Robert Wright5, Andrea Baccarelli14, Donald Lloyd-Jones1, Lifang Hou1,2. 1. Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. 2. Center for Global Oncology, Institute for Global Health, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. 3. Department of Human Development and Family Science, University of Georgia, Athens, GA, USA. 4. Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA, USA. 5. Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mt. Sinai, New York, NY, USA. 6. Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA. 7. Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA. 8. Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. 9. Center for Human Growth and Development, University of Michigan, Ann Arbor, MI, USA. 10. Department of Sociology, Princeton University, Princeton, NJ, USA. 11. Department of Molecular Biology, Princeton University, Princeton, NJ, USA. 12. Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA. 13. Department of Endocrinology, Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA. 14. Department of Environmental Health Sciences, Columbia University, New York, NY, USA.
Abstract
BACKGROUND: Both parental and neighbourhood socio-economic status (SES) are linked to poorer health independently of personal SES measures, but the biological mechanisms are unclear. Our objective was to examine these influences via epigenetic age acceleration (EAA)-the discrepancy between chronological and epigenetic ages. METHODS: We examined three USA-based [Coronary Artery Risk Disease in Adults (CARDIA) study, Fragile Families and Child Wellbeing Study (FFCWS) and Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS)] and one Mexico-based (Project Viva) cohort. DNA methylation was measured using Illumina arrays, personal/parental SES by questionnaire and neighbourhood disadvantage from geocoded address. In CARDIA, we examined the most strongly associated personal, parental and neighbourhood SES measures with EAA (Hannum's method) at study years 15 and 20 separately and combined using a generalized estimating equation (GEE) and compared with other EAA measures (Horvath's EAA, PhenoAge and GrimAge calculators, and DunedinPoAm). RESULTS: EAA was associated with paternal education in CARDIA [GEEs: βsome college = -1.01 years (-1.91, -0.11) and β<high school = 1.05 (0.09, 2.01) vs college graduates] and FFCWS [GEEs: β<high school = 0.62 (0.00, 1.24)]. We found stronger associations for some paternal education categories among White adults (for GEE, βsome college = -1.39 (-2.41, -0.38)], men (βsome college = -1.76 (-3.16, -0.35)] and women [β<high school = 1.77 (0.42, 3.11)]. CONCLUSIONS: These findings suggest that EAA captures epigenetic impacts of paternal education independently of personal SES later in life. Longitudinal studies should explore these associations at different life stages and link them to health outcomes. EAA could be a useful biomarker of SES-associated health and provide important insight into the pathogenesis and prevention of chronic disease.
BACKGROUND: Both parental and neighbourhood socio-economic status (SES) are linked to poorer health independently of personal SES measures, but the biological mechanisms are unclear. Our objective was to examine these influences via epigenetic age acceleration (EAA)-the discrepancy between chronological and epigenetic ages. METHODS: We examined three USA-based [Coronary Artery Risk Disease in Adults (CARDIA) study, Fragile Families and Child Wellbeing Study (FFCWS) and Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS)] and one Mexico-based (Project Viva) cohort. DNA methylation was measured using Illumina arrays, personal/parental SES by questionnaire and neighbourhood disadvantage from geocoded address. In CARDIA, we examined the most strongly associated personal, parental and neighbourhood SES measures with EAA (Hannum's method) at study years 15 and 20 separately and combined using a generalized estimating equation (GEE) and compared with other EAA measures (Horvath's EAA, PhenoAge and GrimAge calculators, and DunedinPoAm). RESULTS: EAA was associated with paternal education in CARDIA [GEEs: βsome college = -1.01 years (-1.91, -0.11) and β<high school = 1.05 (0.09, 2.01) vs college graduates] and FFCWS [GEEs: β<high school = 0.62 (0.00, 1.24)]. We found stronger associations for some paternal education categories among White adults (for GEE, βsome college = -1.39 (-2.41, -0.38)], men (βsome college = -1.76 (-3.16, -0.35)] and women [β<high school = 1.77 (0.42, 3.11)]. CONCLUSIONS: These findings suggest that EAA captures epigenetic impacts of paternal education independently of personal SES later in life. Longitudinal studies should explore these associations at different life stages and link them to health outcomes. EAA could be a useful biomarker of SES-associated health and provide important insight into the pathogenesis and prevention of chronic disease.
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