Human reproduction comes at the expense of faster aging and a shorter life
Hukkanen, M.; Kankaanpaa, A.; Heikkinen, A.; Kaprio, J.; Cristofari, R.; Ollikainen, M.
Show abstract
Evolutionary theories suggest a trade-off between resources allocated to reproduction and those allocated to self-maintenance, and predict that higher reproductive output entails a shorter lifespan. This study investigates the impact of childbearing on aging and lifespan using data from contemporary Finnish twin women. We model the association between reproductive trajectories and survival in 17,080 women, and assess biological aging in a subset of participants (N=1,117) using the PCGrimAge clock, an algorithm trained to predict biological aging and mortality risk from DNA methylation. Our findings suggest that early childbearing, numerous pregnancies or nulliparity all contribute to accelerated aging and increased mortality risk. These results provide strong evidence for the existence of a trade-off between reproduction, aging and lifespan in modern humans, and provide novel insights into the genetic and lifestyle determinants of healthspan.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Deep Phenotyping and Lifetime Trajectories Reveal Limited Effects of Longevity Regulators on the Aging Process in C57BL/6J Mice 98%
- Regulation of the one carbon folate cycle as a shared metabolic signature of longevity 96%
- Accelerated cognitive decline in obese mouse model of Alzheimer's disease is linked to sialic acid-driven immune deregulation 96%
Similar papers in this journal
Similar papers in this journal
- Human genetic analyses of organelles highlight the nucleus in age-related trait heritability 96%
- Microglia aging in the hippocampus advances through intermediate states that drive activation and cognitive decline 95%
- The costs of competition: high social status males experience accelerated epigenetic aging in wild baboons 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.