Early life conditions, reproductive and sexuality-related life history outcomes among human males: A systematic review and meta-analysis

Xu, Yin; Norton, Sam; Rahman, Qazi

doi:10.1016/j.evolhumbehav.2017.08.005

Cited by 54 publications

(50 citation statements)

References 114 publications

(163 reference statements)

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“…However, here are also studies that failed to find some of the theoretically expected associations: e.g. the absence of the relations between the age at first menarche and reproductive success (Hochberg, Gawlik, & Walker, 2011) or the lack of association between family socioeconomic status and pubertal timing (Xu, Norton, & Rahman, 2018). This is a consequence of the immense complexity of human populations and a great variability between the populations as well.…”

Section: Life History Theorymentioning

confidence: 81%

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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Section: Life History Theorymentioning

confidence: 81%

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

Međedović¹

2019

Preprint

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“…We conclude that the inconsistent relationship of family SES with pubertal timing probably reflected inherent differences in study populations such as ethnic and geographic variations, gender and genetic predisposition and changes in underlying mechanisms influenced by SES to activate puberty such as intrauterine conditions, health, nutrition, stress and environmental exposures (Parent et al 2003). Other methodological issues including differences in study designs and measurement of SES and pubertal timing indicators (Xu et al 2018) might have contributed to this inconsistency.…”

Section: Summary and Interpretation Of Resultsmentioning

confidence: 91%

“…In contrast, recent evidence revealed that low family SES markedly increased rates of early puberty in both boys and girls (Downing and Bellis 2009;Sun et al 2017), possibly through interactions with biological systems regulating pubertal timing (Sun et al 2017) or other risk factors such as having a higher body mass index (BMI) or being overweight (Downing and Bellis 2009;James-Todd et al 2010) and experiencing stressful life events (James-Todd et al 2010). However, a meta-analysis of studies among males found no significant association between family SES and pubertal timing (Xu et al 2018). Since there is limited research on determinants of pubertal onset among boys, the processes influencing male pubertal development were much less understood (Graber 2013).…”

Section: Summary and Interpretation Of Resultsmentioning

confidence: 99%

“…On one hand, high SES or 'privileged conditions' were shown to have shifted pubertal timing towards earlier ages (de Muinich Keizer and Mul 2001;Parent et al 2003), possibly due to improved childhood health status (de Muinich Keizer and Mul 2001;Bellis et al 2006) and nutrition (Parent et al 2003;Bellis et al 2006;Kyweluk et al 2018). On the other hand, low SES or childhood socioeconomic disadvantage was also found to accelerate pubertal onset (James-Todd et al 2010;Sun et al 2017) due to environmental stress, which hastens reproductive maturation (Obeidallah et al 2000;James-Todd et al 2010;Xu et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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Timing of puberty and reserve capacity in adolescence as pathways to educational level in adulthood—a longitudinal study

Acacio-Claro

Koivusilta

Doku

et al. 2019

Annals of Human Biology

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Background: Family socioeconomic status (SES) is related to a child's educational success. Intermediate pathways for this relationship, such as through pubertal timing and reserve capacity, occur in adolescence. Aim: To study whether family SES affects a child's adult education through a psychosocial and behavioural pathway (reserve capacity) and/or a biological pathway (pubertal timing) or only through school achievement in adolescence. Subjects and methods: Finnish adolescents sampled in five cross-sectional surveys from 1985 to 1995 (n ¼ 37,876) were followed through the Registry of Completed Education and Degrees until 2009, when they were 29-43 years old. Family SES data also came from this registry. Structural equation modelling adjusted for ages at baseline and follow-up was used. Results: Low family SES increased the probability of low adult education, delayed pubertal timing (in boys), weak reserve capacity and low school achievement. Reserve capacity and school achievement directly affected adult education and mediated the relationship of family SES with the outcome. Delayed pubertal timing predicted low adult education, except when school achievement was added to the model. Conclusions: The results show that family SES affects the child's adult education level through psychosocial and biobehavioural pathways, but the biological pathway is mediated by school achievement.

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“…Reported associations between developmental environment and adult traits are rarely controlled for genetic confounders (e.g. in twin-family designs) (McAdams et al 2014;Zietsch 2016;Barbaro et al 2017;Sherlock and Zietsch 2017b;Sherlock and Zietsch 2017a;Xu et al 2017), and when they are controlled the associations are often weaker or null (Mendle et al 2006;Mendle et al 2009;McAdams et al 2014; though see Tither and Ellis 2008). In light of these observations, a perspective focussed on adapted responses to early environmental conditions does not seem promising as a broad framework for explaining human trait covariation (Zietsch 2016).…”

Section: Genetic Coadaptation Vs Adapted Developmental Plasticitymentioning

confidence: 99%

A critique of life history approaches to human trait covariation

Zietsch¹,

Sidari²

2019

Preprint

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Covariation of life history traits across species may be organised on a ‘fast-slow’ continuum. A burgeoning literature in psychology and social science argues that trait covariation should be similarly organised across individuals within human populations. Here we describe why extrapolating from inter-species to inter-individual trait covariation is not generally appropriate. The process that genetically tailors species to their environments (i.e. Darwinian evolution) is fundamentally different from processes that tailor individuals to their environments (e.g. developmental plasticity), so their outcomes in terms of trait covariation need not be parallel or even related. We discuss why correlational selection, physical linkage, pleiotropy, and non-random mating do not substantively affect this claim in the context of complex human traits. We also discuss life history trade-offs and their relation to inter-individual trait covariation. We conclude that researchers should avoid hypotheses and explanations that assume trait covariation will correspond across and within species, unless they can mount a theoretically coherent argument to support this claim in the context of their research question.

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Early life conditions, reproductive and sexuality-related life history outcomes among human males: A systematic review and meta-analysis

Cited by 54 publications

References 114 publications

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

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

Timing of puberty and reserve capacity in adolescence as pathways to educational level in adulthood—a longitudinal study

A critique of life history approaches to human trait covariation

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