The objective of this special issue is to highlight the absence of religion and spirituality in the human biological enterprise. We have assembled a set of articles covering physiology, psychology, cognition, and neurophenomenology. The influences on human biology are diverse, yet many have been historically overlooked, which we hope this special issue takes a large step in remedying. Here, we outline how this issue came together and introduce readers to the articles to follow.
Previously, we developed a mathematical model capable of predicting pubertal development (PD) through seven anthropometric variables, with an accuracy of 75%. We believe that it is possible to develop a similar model that uses fewer anthropometric measurements and provides greater precision.
Develop a mathematical model capable of predicting PD through anthropometric variables.
We evaluated the anthropometric profile and PD by medical analysis in 203 boys (Age = 12.6 ± 2.6). Subsequently, we divided the boys into groups: development (n = 121) and cross-validation (n = 82). Data from the development group were subjected to discriminant analysis to identify which anthropometric indicators would be potential predictors of PD. We subsequently developed an equation based on the indicated indicators and tested its validation using data from the cross-validation group.
Discriminant analyses showed that age and sitting-height were the variables with the greatest power to predict PD (p < 0.05). Consequently, the mathematical model was developed: Puberty-score = −17.357 + (0.603 × Age [years]) + (0.127 × Sitting-height [cm]). Based on the scores generated, we classified PD into stage-I (score ≤ −1.815), stage-II (score = −1.816 to −0.605), stage-III (score = −0.606 to 0.695), stage-IV (score = 0.696–3.410), and stage-V (score > 3.410). No differences were found between PD assessments performed by doctors and assessments using the mathematical model (p > 0.5). The prediction model showed high agreement (R 2 = 0.867; CCC = 0.899; ICC = 0.900; Kappa = 0.922; α-Krippendorff = 0.885; Bland–Altman LoAs = −2.0, 2.0; pure error = 0.0009) with accuracy of 82.8% and precision of 82%. Analyses in the cross-validation group confirmed the reliability of the prediction model.
The developed mathematical model presents high reliability, validity and accuracy and precision above 80% for determining PD in boys.
Experiencing illnesses or other stressors may disrupt processes of growth and development throughout the different stages of prepubertal development. Stunted stature for age is one potential outcome from such disruption, with both the nature and timing of stressors playing a role in the development of stunting. This study explores whether stress events in utero or during birth, and prepubertal development have an impact on stature, by examining associations between experience of prenatal or birth issues, postnatal stressor exposure by severity, and stature at time-of-death, with the impact of different stress experiences compared.
A coronial pediatric dataset of individuals aged 0–20.9 years (280 male, 195 female) who died in the state of New Mexico from years 2011 to 2019 was assessed for presence/absence of stunting associated with physiological stress exposure type whilst controlling for low socioeconomic status (as inferred by housing type) and sex using two multiple logistic regression models for ages < 12, and for ages 12+. Broad postnatal condition categories were then investigated, again controlling for socioeconomic status and sex for these two cohorts. A linear regression model was also used to assess relationships between stunting, physiological stress, low socioeconomic status housing, and age-at-death.
For both groups aged < 12 and 12+, experiencing moderate to severe illness prior to 12 years of age increased the odds of being stunted at death. Only experience of prenatal or birth issues was associated with younger age-at-death, but stunting itself was not, potentially reflecting the prolonged exposure to severe stressors necessary for stunted growth.
Childhood height is commonly used to measure children's health and nutritional status. It is used to define stunting, where a child is considered stunted if their height is shorter than two standard deviations below the Child Growth Standards median. Studies have shown that genetic and environmental factors contribute to childhood height, so understanding these contributions is important for stunting research. This systematic review and meta-analysis examined the contribution of these factors across study designs and ancestries.
A systematic search found 28 twin and 26 family studies spanning from birth to 17 years. We identified 162 293 twin, 380 195 parent-offspring, and 19 965 sibling pairs born between 1929 and 2004. These datasets were meta-analyzed using a random effects model. The review is registered in PROSPERO (ID CRD42023432550).
The estimated heritability (h 2) of height from twin design increased from birth to 17 years. For children under 5 years, the estimated heritability was 0.57 (95%CI, 0.52–0.61) for European, 0.48 (0.39–0.57) for Asian, and 0.46 (0.40–0.51) for multi ancestries. Heritability estimated from parent-offspring design was 0.46 (95%CI, 0.28–0.62) at birth and 0.76 (95%CI, 0.70–0.80) at 17 years. Meanwhile, the estimated heritability from sibling design remained constant across ages at 0.70 (95%CI, 0.52–0.83).
The heritability estimates of childhood height were slightly higher in European ancestries, but in general, they did not significantly differ across ancestry groups. While the shared environmental effects on height peak in early childhood, the estimated heritability increase in late teens. Unique environmental factors play a small, but significant role throughout childhood.
Whether or not an athlete plays with sports equipment in their hands may influence handgrip strength (HGS) changes during development, but longitudinal studies have not confirmed this. This study compared one-year HGS changes between two sports types (soccer vs. kendo) in children and adolescent athletes.
One hundred sixty-eight young athletes (86 kendo boys and 82 soccer boys) had two HGS measurements separated by 1 year. A 2 (sports) by 2 (timepoints 1 and 2) repeated measures ANOVA was used to determine whether HGS changed differently between sports.
There was no evidence for a sport × time interaction in HGS (p = 0.14); however, the mean difference and 95% CI were in the direction of favoring a greater change in kendo athletes [difference of 0.6 (95% CI: −0.2, 1.5) kg]. There was a main effect of time and sport. Kendo athletes had a 4.6 (95% CI: 1.8, 7.5) kg greater HGS than soccer athletes. There was no evidence that the change in HGS between sports depended on the initial age of the athlete (p = 0.205).
Using sports equipment during play may positively affect HGS.
The practice of anthropometry in schools at the beginning of the 20th century originated in Europe and Latin America in conjunction with the emergence of hygienism and epidemiological auxology. The aim of this work is to deepen the knowledge of these practices in Spain and Argentina and to compare the available growth data in order to identify possible differences between the populations.
The anthropometric data of 1693 boys and girls aged 7–15 years (877 Spanish; 816 Argentinean) from the period between 1903 and 1913 were analyzed. The data were taken from the Reports and Memoirs of the School Colonies of the National Pedagogical Museum (Spain) and the Archives of Pedagogy and Related Sciences of the Faculty of Law and Social Sciences of the National University of La Plata (UNLP) (Argentina).
The most pronounced differences in weight and height were observed between the ages of 11 and 12. The weight growth of Spanish schoolchildren was 22% (boys) and 24% (girls) lower than that of their Argentine counterparts, while linear growth was about 7% lower for both sexes. In addition, the Spanish had a lower body mass index up to the age of 12 years.
Argentine boys and girls of La Plata were taller and heavier than their Spanish counterparts of Madrid during the same period (between 1903 and 1913). These results can be attributed to the higher standard of living that characterized the Argentine population at that time.
There is evidence that alcohol consumption is influenced by prenatal sex steroids (as measured by digit ratio [2D:4D]). Here, we clarify the effect size of the relationship in a student (rather than a patient) population.
There were 258 (169 women) participants. Digit length was measured directly with calipers. Alcohol use was evaluated by the Polish version of Alcohol Use Disorders Identification Test (AUDIT) and operationalized as total AUDIT scores and grams of alcohol/week.
Digit ratios were sexually dimorphic (males < females). There were negative correlations between right 2D:4D and Dr-l (right 2D:4D minus left 2D:4D) and AUDIT scores and grams of alcohol/week in both sexes. Relationships varied from small (r = −0.29) to large (r = −0.69) and they were stronger in males in comparison to females and for right 2D:4D in comparison to Dr-l. In males only, there were small (r = 0.21) to moderate (r = 0.31) positive associations with body size (height, weight, and mean right digit length) and alcohol consumption. Multiple regression analyses showed relationships between digit ratios remained significant but those for body size did not.
Alcohol consumption was negatively related to 2D:4D, suggesting high prenatal testosterone and low prenatal estrogen are linked to its consumption. Correlations varied in strength from small to large with the strongest found for right 2D:4D and for males. Positive relationships between body size and alcohol were small to moderate, confined to males, and were not independent of digit ratios. Prenatal androgenization may influence alcohol drinking patterns in non-clinical individuals.
This study examined long-term changes in body composition and bone mineral characteristics among male long-distance runners from a high-profile university team, focusing on concerns about impaired musculoskeletal development due to extreme leanness and weight management practices in this population.
Trajectory analyses were performed using multilevel modeling of 608 dual-energy x-ray absorptiometry datasets from 109 runners (mean age, height, and weight of 18.0 years, 171.4 cm, and 56.8 kg at baseline, respectively) collected biannually over 4 years.
Linear increases in total and regional lean mass (LM) were observed on average, with the increase in leg LM being double that of arm LM (0.07 vs. 0.03 kg per occasion, respectively). Similarly, total bone mineral density (BMD) and content (BMC) exhibited linear growth on average, with BMD accrual being greater in the legs than in the arms (0.004 vs. 0.001 g/cm2 per occasion, respectively). However, rib BMD and BMC were predicted to decrease. Individually predicted growth rates in total LM were significantly associated with those in total BMD (r = 0.347, p < 0.001) and BMC (r = 0.424, p < 0.001).
These results indicate site-specific musculoskeletal adaptations to intensive long-distance running training. Moreover, a random slope model accurately captured the trajectories of most dependent variables, highlighting the heterogeneity of training responses. The predictive models developed in this study offer practical strategies for identifying runners at risk of suboptimal physical development, thereby facilitating the development of personalized conditioning programs.
Northern Japanese children tend to have larger physiques; however, the underlying cause remains unknown. Previous geographical correlation analyses revealed an unusual trend; effective day length was negatively correlated with height and positively correlated with weight (adjusted for height). This paradoxical relationship suggests a thyroid hormone-like effect and possible photoperiodic response. This study aimed to determine whether this phenomenon remains consistent over time and across different regions of Japan.
We used geographically and temporally weighted regression (GTWR) to examine whether the relationship between height and effective day length, which differs from the relationship between weight and effective day length, varies by location and time. GTWR models each observation point separately, allowing for spatial and temporal variations. The analysis included the average height and weight data of children and adolescents by prefecture from 1989 to 2019, along with effective day length considering illuminance above 5000 lx derived from the agrometeorological grid square data.
Height was used as the dependent variable, whereas weight and effective day length were used as independent variables. For height estimation, the coefficients of weight and effective day length were consistently positive and negative, respectively, although the regression coefficients showed minor geographical and temporal variations.
The opposite correlation between height and effective day length and that between weight and effective day length were consistent. This suggests that the phenomenon is more likely driven by environmental factors than by economic or genetic influences.
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