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. 2018 Jul 23;8(1):11100.
doi: 10.1038/s41598-018-29486-4.

Reproduction predicts shorter telomeres and epigenetic age acceleration among young adult women

Calen P Ryan  1 , M Geoffrey Hayes  2   3 , Nanette R Lee  4   5 , Thomas W McDade  6   7   8 , Meaghan J Jones  9 , Michael S Kobor  8   9 , Christopher W Kuzawa  6   7 , Dan T A Eisenberg  10   11
Affiliations

Reproduction predicts shorter telomeres and epigenetic age acceleration among young adult women

Calen P Ryan et al. Sci Rep. .

Abstract

Evolutionary theory predicts that reproduction entails costs that detract from somatic maintenance, accelerating biological aging. Despite support from studies in human and non-human animals, mechanisms linking 'costs of reproduction' (CoR) to aging are poorly understood. Human pregnancy is characterized by major alterations in metabolic regulation, oxidative stress, and immune cell proliferation. We hypothesized that these adaptations could accelerate blood-derived cellular aging. To test this hypothesis, we examined gravidity in relation to telomere length (TL, n = 821) and DNA-methylation age (DNAmAge, n = 397) in a cohort of young (20-22 year-old) Filipino women. Age-corrected TL and accelerated DNAmAge both predict age-related morbidity and mortality, and provide markers of mitotic and non-mitotic cellular aging, respectively. Consistent with theoretical predictions, TL decreased (p = 0.031) and DNAmAge increased (p = 0.007) with gravidity, a relationship that was not contingent upon resource availability. Neither biomarker was associated with subsequent fertility (both p > 0.3), broadly consistent with a causal effect of gravidity on cellular aging. Our findings provide evidence that reproduction in women carries costs in the form of accelerated aging through two independent cellular pathways.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Relationship between mitotic (TL) and non-mitotic (DNAmAge acceleration) measures of cellular aging and reproductive history (number of pregnancies) in young women. (a) Residualized TL for all variables in Table 2, Model 3, and statistics from same model. (b) Residualized DNAmAge for all variables in Table 2, Model 7, and statistics from same model. Graphs are labeled and dots are colored by relative aging for each marker (blue, low; red, high) and best fit lines are drawn with 95% CI of beta value.
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