Prenatal interventions aimed at the well-being of children of parents with depression should consider targeting the nutritional environment.

Preterm birth is a universal health problem that is one of the largest unmet medical needs contributing to the global burden of disease. Adding to its complexity is that there are no means to predict who is at risk when pregnancy begins or when women will actually deliver. Until these problems are addressed, there will be no interventions to reduce the risk because those who should be treated will not be known. Considerable evidence now exists that chronic life, generational or accumulated stress is a risk factor for preterm delivery in animal models and in women. This wear and tear on the body and mind is called allostatic load. This review explores the evidence that chronic stress contributes to preterm birth and other adverse pregnancy outcomes in animal and human studies. It explores how allostatic load can be used to, firstly, model stress and preterm birth in animal models and, secondly, how it can be used to develop a predictive model to assess relative risk among women in early pregnancy. Once care providers know who is in the highest risk group, interventions can be developed and applied to mitigate their risk.

Maternal smoking during pregnancy is associated with low birth weight. Common variation at rs1051730 is robustly associated with smoking quantity and was recently shown to influence smoking cessation during pregnancy, but its influence on birth weight is not clear. We aimed to investigate the association between this variant and birth weight of term, singleton offspring in a well-powered meta-analysis. We stratified 26 241 European origin study participants by smoking status (women who smoked during pregnancy versus women who did not smoke during pregnancy) and, in each stratum, analysed the association between maternal rs1051730 genotype and offspring birth weight. There was evidence of interaction between genotype and smoking (P = 0.007). In women who smoked during pregnancy, each additional smoking-related T-allele was associated with a 20 g [95% confidence interval (95% CI): 4–36 g] lower birth weight (P = 0.014). However, in women who did not smoke during pregnancy, the effect size estimate was 5 g per T-allele (95% CI: −4 to 14 g; P = 0.268). To conclude, smoking status during pregnancy modifies the association between maternal rs1051730 genotype and offspring birth weight. This strengthens the evidence that smoking during pregnancy is causally related to lower offspring birth weight and suggests that population interventions that effectively reduce smoking in pregnant women would result in a reduced prevalence of low birth weight.

Longitudinal cohort studies are ideal for investigating how epigenetic patterns change over time and relate to changing exposure patterns and the development of disease. We highlight the challenges and opportunities in this approach.

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Longitudinal cohort studies are ideal for investigating how epigenetic patterns change over time and relate to changing exposure patterns and the development of disease. We highlight the challenges and opportunities in this approach.

In a monochorionic twin cohort, fetal growth restriction results in lower neurocognitive scores in early childhood, and there remain significant differences in size. Longer term follow-up will be required to determine whether growth or cognitive differences persist in later child or adulthood, and whether there are any associated longer term metabolic sequelae.

Stress is one of the most powerful experiences to influence health and disease. Through epigenetic mechanisms, stress may generate a footprint that propagates to subsequent generations. Programming by prenatal stress or adverse experience in parents, grandparents, or earlier generations may thus be a critical determinant of lifetime health trajectories. Changes in regulation of microRNAs (miRNAs) by stress may enhance the vulnerability to certain pathogenic factors. This review explores the hypothesis that miRNAs represent stress-responsive elements in epigenetic regulation that are potentially heritable. Recent findings suggest that miRNAs are key players linking adverse early environments or ancestral stress with disease risk, thus they represent useful predictive disease biomarkers. Since miRNA signatures of disease are potentially heritable, big data management platforms will be vital to harness multi-generational information and capture succinct yet potent biomarkers capable of directing preventative treatments. This feature would offer a unique window of opportunity to advance personalized medicine.