Human reproduction and health: an evolutionary perspective

Jasieńska, Grażyna; Bribiescas, Richard G.; Furberg, Anne‐Sofie; Helle, Samuli; Mora, Alejandra Núñez-de la

doi:10.1016/s0140-6736(17)30573-1

Cited by 146 publications

(139 citation statements)

References 106 publications

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“…[83] Additionally, CMI would work together with other factors to dramatically decrease and ultimately block the probability of birth in older women. This might be advantageous because i) the woman may be too old to fully raise the child, [83] ii) a decreased investment in reproduction, especially in older women, will decrease the risk of diseases and increase longevity; [84] and/or iii) cessation of child bearing will make older women available to help with the raising of children of younger women, the so-called "grandmother effect." [85,86] Thus, CMI may be actively selected because of intrinsic evolutionary benefits, likely related to the prolonged period of postnatal child dependency in humans.…”

Section: Why Does CMI Exist?mentioning

confidence: 99%

Crossover Interference, Crossover Maturation, and Human Aneuploidy

et al. 2019

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A striking feature of human female sexual reproduction is the high level of gametes that exhibit an aberrant number of chromosomes (aneuploidy). A high baseline observed in women of prime reproductive age is followed by a dramatic increase in older women. Proper chromosome segregation requires one or more DNA crossovers (COs) between homologous maternal and paternal chromosomes, in combination with cohesion between sister chromatid arms. In human females, CO designations occur normally, according to the dictates of CO interference, giving early CO-fated intermediates. However, ≈25% of these intermediates fail to mature to final CO products. This effect explains the high baseline of aneuploidy and is predicted to synergize with age-dependent cohesion loss to explain the maternal age effect. Here, modern advances in the understanding of crossing over and CO interference are reviewed, the implications of human female CO maturation inefficiency are further discussed, and areas of interest for future studies are suggested.

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Section: Why Does CMI Exist?mentioning

confidence: 99%

Crossover Interference, Crossover Maturation, and Human Aneuploidy

et al. 2019

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“…However, this is not the case: fitness components constrain each other -investing in one component prevents organisms from investing in others. These constraints are called evolutionary trade-offs and they are empirically detected in humans as well: individuals with higher reproductive success tend to have a shorter lifespan (Jasienska, Bribiescas, Furberg, Helle, & Núñez-de la Mora, 2017), a higher number of offspring diminishes investment per individual child (Gillespie, Russell, & Lummaa, 2008), investing in parenting diminishes effort in finding new mates (Međedović, 2019), delaying reproduction enables somatic investment and resource acquisition but depletes overall fertility (Liu & Lummaa, 2011), etc. Pathways of fitness maximization are heavily dependent on local ecologies. The ecological theory of evolutionary trade-offs and fitness maximization is called life history theory.…”

Section: Life History Theorymentioning

confidence: 99%

Human life histories as dynamic networks: using Network Analysis to conceptualize and analyze life history data

Međedović¹

2019

Preprint

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Note: this preprint may undergo significant changes before publication. Please check for revisions and updates before citing.The author wishes to thank Milan Jordanov for his help in performing the Network analysis. He also expresses his gratitude to Marco Del Giudice and the anonymous reviewer for their useful comments on the previous version of the manuscript. Human life histories as dynamic networks: using Network Analysis to conceptualize and analyze life history dataThe examination of multiple life history indicators is essential to evolutionary sciences. However, the statistical analysis of life history parameters' covariation is not apparently clear, due to the statistical limitations of "classic" procedures like Factor Analysis and conceptual problems in interpreting covariation between life-history indicators as latent factors. Here we propose that Network Analysis represents a promising framework for the exploration of life history parameters. First, we briefly describe the basic metric of Network Analysis: nodes, edges, proximities, clustering, centrality indices and small-world estimations. Next, we show the implementation of Network Analysis using the empirical set of life history variables as an example (N=460). We collected information about 14 life history indicators that can be classified into five groups: environmental harshness, self-protection, mating, reproduction, and kin care. We showed that Network Analysis provided: 1) optimal level of non-trivial information (higher than factor analysis and lower than correlation analysis); 2) findings which are in accordance with the existing life-history data; 3) the estimation of age at first birth as a central node in the network; 4) dynamic view of life history events which can represent a solid basis for causal life history models. In sum, Network Analysis shows high potential both for conceptualizing life history pathways as dynamic networks and for statistical analysis of the covariation between the life history indicators.

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“…In the past decades, a growing body of experimental work demonstrated that organisms calibrate their behavioral strategies to the specific circumstances in which they live. In humans, higher local mortality appears to affect the way in which individuals deal with the reproductionmaintenance trade-off Jasienska et al, 2017;Nettle, 2010;Promislow & Harvey, 1990), such that, in industrial and post-industrial societies, harsher conditions lead to faster growth, earlier reproduction, increased number of offspring, and diminished investment in somatic maintenance. By contrast, more favorable circumstances are associated with longer growth, delayed reproduction, fewer offspring, and increased health efforts (Del Giudice et al, 2015;Ellis et al, 2009;Jasienska et al, 2017;Promislow & Harvey, 1990;Reznick & Endler, 1982).…”

Section: Introductionmentioning

confidence: 99%

Environmental harshness is associated with lower investment in collective actions

Lettinga

Jacquet

André

et al. 2019

Preprint

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Although humans cooperate universally, there is variability across individuals, times and cultures in the amount of resources people invest in cooperative activities. The origins of such variability are not known but recent work highlights that variations in environmental harshness may play a key role. A growing body of experimental work in evolutionary psychology suggests that humans adapt to their specific environment by calibrating their lifehistory strategy. In this paper, we apply structural equation models to test the association between current and childhood environmental harshness, life-history strategy and adult cooperation in two large-scale datasets (the World Values Survey and the European Values Study). The present study replicates existing research linking a harsher environment (both in adulthood and in childhood) with a modulated reproduction-maintenance trade-off and extends these findings to the domain of collective actions. Specifically, we find that a harsher environment (both in adulthood and in childhood) is associated with decreased involvement in collective action and that this association is mediated by individuals' life-history strategy.

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Human reproduction and health: an evolutionary perspective

Cited by 146 publications

References 106 publications

Crossover Interference, Crossover Maturation, and Human Aneuploidy

Crossover Interference, Crossover Maturation, and Human Aneuploidy

Human life histories as dynamic networks: using Network Analysis to conceptualize and analyze life history data

Environmental harshness is associated with lower investment in collective actions

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