Prenatal and early life influences on epigenetic age in children: a study of mother-offspring pairs from two cohort studies.
Hum Mol Genet
; 25(1): 191-201, 2016 Jan 01.
Article
en En
| MEDLINE
| ID: mdl-26546615
DNA methylation-based biomarkers of aging are highly correlated with actual age. Departures of methylation-estimated age from actual age can be used to define epigenetic measures of child development or age acceleration (AA) in adults. Very little is known about genetic or environmental determinants of these epigenetic measures of aging. We obtained DNA methylation profiles using Infinium HumanMethylation450 BeadChips across five time-points in 1018 mother-child pairs from the Avon Longitudinal Study of Parents and Children. Using the Horvath age estimation method, we calculated epigenetic age for these samples. AA was defined as the residuals from regressing epigenetic age on actual age. AA was tested for associations with cross-sectional clinical variables in children. We identified associations between AA and sex, birth weight, birth by caesarean section and several maternal characteristics in pregnancy, namely smoking, weight, BMI, selenium and cholesterol level. Offspring of non-drinkers had higher AA on average but this difference appeared to resolve during childhood. The associations between sex, birth weight and AA found in ARIES were replicated in an independent cohort (GOYA). In children, epigenetic AA measures are associated with several clinically relevant variables, and early life exposures appear to be associated with changes in AA during adolescence. Further research into epigenetic aging, including the use of causal inference methods, is required to better our understanding of aging.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Envejecimiento
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Metilación de ADN
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Epigénesis Genética
Tipo de estudio:
Etiology_studies
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Incidence_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Child
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Humans
Idioma:
En
Revista:
Hum Mol Genet
Asunto de la revista:
BIOLOGIA MOLECULAR
/
GENETICA MEDICA
Año:
2016
Tipo del documento:
Article