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Impact of paternal education on epigenetic ageing in adolescence and mid-adulthood: a multi-cohort study in the USA and Mexico
Author(s) -
Brian T. Joyce,
Tao Gao,
Kalsea J. Koss,
Yinan Zheng,
Andrés Cárdenas,
Jonathan Heiss,
Allan C. Just,
Kai Zhang,
Linda Van Horn,
Norrina B. Allen,
Philip Greenland,
Sheldon Cohen,
Penny GordonLarsen,
Colter Mitchell,
Sara McLanahan,
Lisa Schneper,
Daniel A. Notterman,
Sheryl L. RifasShiman,
Emily Oken,
MarieFrance Hivert,
Robert O. Wright,
Andrea Baccarelli,
Donald M. LloydJones,
Lifang Hou
Publication year - 2021
Publication title -
international journal of epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.406
H-Index - 208
eISSN - 1464-3685
pISSN - 0300-5771
DOI - 10.1093/ije/dyab196
Subject(s) - gee , demography , neighbourhood (mathematics) , generalized estimating equation , cohort , medicine , epigenetics , gerontology , cohort study , obesity , developmental psychology , psychology , genetics , biology , mathematical analysis , statistics , mathematics , sociology , gene
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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