Physical growth is an indicator and predictor of both present and future health. Auxology, the science of physical growth, has historical roots in studies noting that poor social conditions harm children’s health and well-being as reflected by their size (Tanner 1981). Subsequent epidemiologic studies linking infant size to health risks emerging later in life identify that growth is a translational embodiment of health (Barker 2012). The full spectrum of biological processes by which size accrual translates life course health development remains to be clarified. Present challenges standing in the way of better understanding the nexus between growth and health include distinctions between public health information derived from population-level epidemiologic assessments and research evidence based on the study of growth biology of individuals; the current focus on attained size rather than the process of growth, or change in size, with a reliance on weight in lieu of length/height and body composition; and a tendency to interpret patterns derived from growth charts rather than understanding growth trajectories as they occur during individual biological processes.

Diet influences, and is influenced by, a wide range of socioeconomic, cultural, geographic, and genetic variables. Here we survey a matrix of such interactions as well as their connection to a variety of health outcomes, in a cohort of 689 diverse adults employed at Emory University and enrolled in the Center for Health Discovery and Well-Being (CHDWB) study. Principal component analysis (PCA) of the Block Food Frequency Questionnaire revealed seven PC cumulatively explaining 25.8% and each individually at least 2% of the proportional consumption of 110 food items. PC1 is strongly correlated with the Healthy Eating Index-2015 measure, and accordingly healthier scores associate with multiple measures of physical and mental health. It, as well as PC2 (likely a measure of food expense) and PC3 (carbohydrate versus protein consumption) show significant geographic structure across the Atlanta metropolitan area, correlating with race and ethnicity, income level, age and sex. Notably, a polygenic score for body mass index (BMI) consisting of 281 SNPs explains 2.8% of the variance in PC5, which is as strong as its association with BMI itself. PC5 appears to differentiate participants with respect to conscious eating behavior related to the choice of diet or comfort foods. Our analysis adds to the growing literature on factor analysis of socio-demographic influences on nutrition and health.

Hungry? Should you eat an apple or potato chips? Does it really matter? It turns out that what you eat as a child and adolescent affects your growth and development. It can also affect your health as an adult! We wanted to understand the link between nutrition and adolescent growth. We did a review of different scientific studies to see what is currently known about this. We found that not eating enough food, eating the wrong foods, and eating too much food all affect the body’s systems. But the effects are different in each case. We also learned that the negative effects of poor nutrition aren’t permanent if they’re corrected at the right time.

Objectives Human biologists have documented variability in reproductive maturation, fertility, and cancer risk related to developmental conditions. Yet no previous studies have directly examined the impact of pre- and post-natal energetic environments on sex steroids in infancy, a critical period for hypothalamic-pituitary-gonadal axis development. Thus, we examined the impact of maternal characteristics, birth size, and feeding practices on fecal sex steroid production in a longitudinal sample of 31 American infants followed from 2 weeks to 12 months of age. Methods Maternal characteristics and birth size were collected at study enrollment, infant diet was assessed through weekly 24-hr food diaries, and anthropometrics were measured weekly. Fecal estradiol and testosterone levels were assessed weekly using validated microassay RIA techniques. Mixed models were used to test for associations between maternal and birth characteristics, feeding practices, and sex steroids across the first year of life. Formal mediation analysis examined whether the relationship between infant feeding and hormone levels was mediated by infant size. Results Maternal and birth characteristics had persistent effects on fecal sex steroid levels, with taller maternal height and larger birth size associated with lower estradiol levels in girls and higher testosterone levels in boys. Infant diet was also associated with sex steroid levels independently of infant size. Formula feeding was associated with higher estradiol levels in boys and girls and with higher testosterone in girls. Conclusion These results suggest that markers of early energy availability influence sex hormone levels with potential long-term consequences for reproductive development and function.

The variability in fetal growth rates and gestation duration in humans is not well understood. Of interest are women presenting with an episode of preterm labor and subsequently delivering a term neonate, who is small relative to peers of similar gestational age. To further understand these relationships, fetal growth patterns predating an episode of preterm labor were investigated. Retrospective analysis of fetal biometry assessed by serial ultrasound in a prospectively studied sample of pregnancies in Santiago, Chile, tested the hypothesis that fetal growth patterns among uncomplicated pregnancies (n = 3,706) and those with an episode of preterm labor followed by term delivery (n = 184) were identical across the time intervals 16-22 weeks, 22-28 weeks, and 28-34 weeks in a multilevel mixed-effects regression. The hypothesis was not supported. Fetal weight growth rate was faster from 16 weeks among pregnancies with an episode of preterm labor (P < 0.05), declined across midgestation (22-28 weeks, P < 0.05), and rebounded between 28 and 34 weeks (P = 0.06). This was associated with perturbations in abdominal circumference growth and proportionately larger biparietal diameter from 22 gestational weeks (P = 0.03), greater femur (P = 0.01), biparietal diameter (P = 0.001) and head circumference (P = 0.02) dimensions relative to abdominal circumference across midgestation (22-28 weeks), followed by proportionately smaller femur diaphyseal length (P = 0.02) and biparietal diameter (P = 0.03) subsequently. A distinctive rapid growth phenotype characterized fetal growth preceding an episode of preterm labor among this sample of term-delivered neonates. Perturbations in abdominal circumference growth and patterns of proportionality suggest an altered growth strategy pre-dating the preterm labor episode.

Recent revolutionary advances at the intersection of medicine, omics, data sciences, computing, epidemiology, and related technologies inspire us to ponder their impact on health. Their potential impact is particularly germane to the biology of pregnancy and perinatal medicine, where limited improvement in health outcomes for women and children has remained a global challenge. We assembled a group of experts to establish a Pregnancy Think Tank to discuss a broad spectrum of major gestational disorders and adverse pregnancy outcomes that affect maternal-infant lifelong health and should serve as targets for leveraging the many recent advances. This report reflects avenues for future effects that hold great potential in 3 major areas: developmental genomics, including the application of methodologies designed to bridge genotypes, physiology, and diseases, addressing vexing questions in early human development; gestational physiology, from immune tolerance to growth and the timing of parturition; and personalized and population medicine, focusing on amalgamating health record data and deep phenotypes to create broad knowledge that can be integrated into healthcare systems and drive discovery to address pregnancy-related disease and promote general health. We propose a series of questions reflecting development, systems biology, diseases, clinical approaches and tools, and population health, and a call for scientific action. Clearly, transdisciplinary science must advance and accelerate to address adverse pregnancy outcomes. Disciplines not traditionally involved in the reproductive sciences, such as computer science, engineering, mathematics, and pharmacology, should be engaged at the study design phase to optimize the information gathered and to identify and further evaluate potentially actionable therapeutic targets. Information sources should include noninvasive personalized sensors and monitors, alongside instructive “liquid biopsies” for noninvasive pregnancy assessment. Future research should also address the diversity of human cohorts in terms of geography, racial and ethnic distributions, and social and health disparities. Modern technologies, for both data-gathering and data-analyzing, make this possible at a scale that was previously unachievable. Finally, the psychosocial and economic environment in which pregnancy takes place must be considered to promote the health and wellness of communities worldwide.

Objectives Lower birth weight within the normal range predicts adult chronic diseases, but the same birth weight in different ethnic groups may reflect different patterns of tissue development. Neonatal body composition was investigated among non-Hispanic Caucasians and African Americans, taking advantage of variability in gestational duration to understand growth during late gestation. Methods Air displacement plethysmography assessed fat and lean body mass among 220 non-Hispanic Caucasian and 93 non-Hispanic African American neonates. The two ethnic groups were compared using linear regression. Results At 36 weeks gestation, the average lean mass of Caucasian neonates was 2,515 g vs. that of 2,319 g of African American neonates (difference, P = 0.02). The corresponding figures for fat mass were 231 and 278 g, respectively (difference, P = 0.24). At 41 weeks, the Caucasians were 319 g heavier in lean body mass (P < 0.001) but were also 123 g heavier in fat mass (P = 0.001). The slopes for lean mass vs. gestational week were similar, but the slope of fat mass was 5.8 times greater (P = 0.009) for Caucasian (41.0 g/week) than for African American neonates (7.0 g/week). Conclusions By 36 weeks of gestation, the African American fetus developed similar fat mass and less lean mass compared with the Caucasian fetus. Thereafter, changes in lean mass among the African American fetus with increasing gestational age at birth were similar to the Caucasian fetus, but fat accumulated more slowly. We hypothesize that different ethnic fetal growth strategies involving body composition may contribute to ethnic health disparities in later life.

The development of the infant intestinal microbiome in response to dietary and other exposures may shape long-term metabolic and immune function. We examined differences in the community structure and function of the intestinal microbiome between four feeding groups, exclusively breastfed infants before introduction of solid foods (EBF), non-exclusively breastfed infants before introduction of solid foods (non-EBF), EBF infants after introduction of solid foods (EBF+S), and non-EBF infants after introduction of solid foods (non-EBF+S), and tested whether out-of-home daycare attendance was associated with differences in relative abundance of gut bacteria. Bacterial 16S rRNA amplicon sequencing was performed on 49 stool samples collected longitudinally from a cohort of 9 infants (5 male, 4 female). PICRUSt metabolic inference analysis was used to identify metabolic impacts of feeding practices on the infant gut microbiome. Sequencing data identified significant differences across groups defined by feeding and daycare attendance. Non-EBF and daycare-attending infants had higher diversity and species richness than EBF and non-daycare attending infants. The gut microbiome of EBF infants showed increased proportions of Bifidobacterium and lower abundance of Bacteroidetes and Clostridiales than non-EBF infants. PICRUSt analysis indicated that introduction of solid foods had a marginal impact on the microbiome of EBF infants (24 enzymes overrepresented in EBF+S infants). In contrast, over 200 bacterial gene categories were overrepresented in non-EBF+S compared to non-EBF infants including several bacterial methyl-accepting chemotaxis proteins (MCP) involved in signal transduction. The identified differences between EBF and non-EBF infants suggest that breast milk may provide the gut microbiome with a greater plasticity (despite having a lower phylogenetic diversity) that eases the transition into solid foods.

Study Objectives: The mechanisms underlying infant sleep irregularity are unknown. This study tests the hypothesis that sleep and episodic (saltatory) growth in infant length are temporally coupled processes. Study design: Daily parental diaries continuously recorded sleep onset and awakening for 23 infants (14 females) over 4-17 months (n = 5798 daily records). Multiple model-independent methods compared day-to-day sleep patterns and saltatory length growth. Measurements and Results: Approximate entropy (ApEn) quantified temporal irregularity in infant sleep patterns; breastfeeding and infant sex explained 44% of inter-individual variance (P = 0.001). Random effects mixed-model regression identified that saltatory length growth was associated with increased total daily sleep hours (P < 0.001) and number of sleep bouts (P = 0.001), with breastfeeding, infant sex, and age as covariates. Infant size and illness onset were non-contributory. CLUSTER analysis identified peaks in individual sleep of 4.5 more h and/or 3 more naps per day, compared to intervening intervals, that were non-randomly concordant with saltatory length growth for all individuals (P < 0.05), with a time lag of 0-4 days. Subject-specific probabilities of a growth saltation associated with sleep included a median odds ratio of 1.20 for each additional hour (n = 8, 95% CI 1.15 to 1.29) and 1.43 for each additional sleep bout (n = 12, 95% CI 1.21-2.03). Increased sleep bout duration predicted weight (P < 0.001) and abdominal skinfold accrual (P = 0.05) contingent on length growth, and truncal adiposity independent of growth (P < 0.001). Conclusions: Sleeping and length growth are temporally related biological processes, suggesting an integrated anabolic system. Infant behavioral state changes may reflect biological mechanisms underlying the timing and control of human growth. Citation: Lampl M; Johnson ML. Infant growth in length follows prolonged sleep and increased naps. SLEEP 2011;34(5):641-650.

Sex differences in fetal growth have been reported, but how this happens remains to be described. It is unknown if fetal growth rates, a reflection of genetic and environmental factors, express sexually dimorphic sensitivity to the mother herself. This analysis investigated homogeneity of male and female growth responses to maternal height and weight. The study sample included 3,495 uncomplicated singleton pregnancies followed longitudinally. Analytic models regressed fetal and neonatal weight on tertiles of maternal height and weight, and modification by sex was investigated (n=1,814 males, n=1,681 females) with birth gestational age, maternal parity, and smoking as covariates. Sex modified the effects of maternal height and weight on fetal growth rates and birth weight. Among boys, tallest maternal height influenced fetal weight growth before 18 gestational weeks of age (P = 0.006), and prepregnancy maternal weight and body mass index subsequently had influence (P <0.001); this was not found among girls. Additionally, interaction terms between sex, maternal height, and maternal weight identified that males were more sensitive to maternal weight among shorter mothers (P = 0.003) and more responsive to maternal height among lighter mothers (P - 0.03), compared to females. Likewise, neonatal birth weight dimorphism varied by maternal phenotype. A male advantage of 60 g occurred among neonates of the shortest and lightest mothers (P = 0.08), compared to 150 and 191 g among short and heavy mothers, and tall and light-weight mothers, respectively (P = 0.01). Sex differences in response to maternal size are under-appreciated sources of variation in fetal growth studies and may reflect differential growth strategies. Am. J. Hum. Biol. 22:431-443, 2010.