Parents of early‐maturing girls die younger

Abstract

According to the life‐history theory, rates of sexual maturation have coevolved with mortality rates so that individuals who mature faster tend to die younger. We used two data sets, providing different markers for the speed of pubertal development to test whether rates of sexual maturation of women predict the age at death of their parents. In the data set of Estonian schoolgirls born between 1936 and 1961, the rate of breast development predicted lifespan of both mothers and fathers (irrespectively of their socio‐economic position), so that parents of rapidly maturing girls died at younger age. This finding supports the view that fast maturation rates in humans have coevolved with short lifespans and that such trade‐offs can be detected as intergenerational phenotypic correlations in modern populations. Menarcheal age of participants of Estonian Biobank (born between 1925 and 1996) did not predict the age of death of their mothers; however, it did predict survival of their fathers, but only in environment where the genetic variation is exposed (families where at least one parent had tertiary education). In such families (where girls also matured 0.2–0.4 years earlier than in poorly educated families), 1‐year delay in daughter's menarche corresponded to 9% lower hazard of father's death. Heritability of menarcheal age was also highest in well‐educated families. The latter findings are consistent with the idea that genetic differences in the rate of pubertal maturation may be expressed most clearly in well‐off families because in such families, the contribution of environmental variance to total phenotypic variance in menarcheal age is smallest. Our findings suggest that with global improvement and equalization of growth conditions, reductions of environmental variation in the rate of maturation increasingly expose the genetic differences in menarcheal age to selection. Under such conditions, selection on menarcheal age has a potential to affect the evolution of lifespan.

Morphometric traits predict educational attainment independently of socioeconomic background

“…Current study is the second largest one after that of Nave and co-authors [27], showing that cranial dimensions and height predict the educational attainment, controlling for the biosocial background. Its strengths are that due to historical reasons, the participation was not voluntary, which considerably reduces the recruitment selection and that all the participants were younger than 20, which almost entirely eliminates the mortality selection in the sample see [81]. For instance, the sample of Nave et al [27] consisted almost entirely of adults and elderly that overrepresented individuals of higher SEP.…”

Why and how does early adversity influence development? Toward an integrated model of dimensions of environmental experience

“…For example, in a comparison of 22 small-scale human societies (hunter-gatherers and subsistence-based horticulturalists), poor energetic conditions were associated with later ages of menarche and first birth, whereas higher childhood mortality rates were associated with earlier ages of menarche and first birth (Walker et al, 2006). Likewise, in cohort studies in Estonia, the Philippines, and Brazil, complex adversity exposures involving energetic deprivation together with other forms of harshness or unpredictability (e.g., poverty, parental instability, sibling death) predicted both later pubertal development and earlier ages at first reproduction (Gettler et al, 2015;Hõrak et al, 2019;Valge et al, 2021;Wells et al, 2019). In the Brazilian cohort, other shifts toward faster life history-related traits were also observed, including greater risky behavior indicative of future discounting (i.e., smoking, criminal behavior) (Wells et al, 2019).…”

Sexually antagonistic selection on educational attainment and body size in Estonian children

“…6, 13, and 84). Genetic influences on the expression of morphometric and behavioral traits in humans are high 85 and increasing in postindustrial populations 39,82,86 . Such traits (including LRS) are also highly polygenic 87 and show high cross‐sex genetic correlations, meaning that largely the same genes and regulatory regions affect the trait expression in males and females 65,88–91 .…”

“…Children were measured once but ca 60% of them could be later identified in the Population Registry (data obtained in 2018), enabling assessment of their education and life‐history parameters (Figure S2 in ESM1, and Refs. 7, 34, and 39). The data on LRS of women in the Population Registry are more precise than those of men because the birth data are always linked to the identity of mothers, while identities of fathers are linked only in case if these are reported by mothers.…”