Search tips
Search criteria 


Logo of envhperEnvironmental Health PerspectivesBrowse ArticlesAbout EHPGeneral InformationAuthorsMediaProgramsPartnerships
Environ Health Perspect. 2012 October; 120(10): 1353–1361.
Published online 2012 June 6. doi:  10.1289/ehp.1204934
PMCID: PMC3491941

Predicting Later-Life Outcomes of Early-Life Exposures


Background: In utero exposure of the fetus to a stressor can lead to disease in later life. Epigenetic mechanisms are likely mediators of later-life expression of early-life events.

Objectives: We examined the current state of understanding of later-life diseases resulting from early-life exposures in order to identify in utero and postnatal indicators of later-life diseases, develop an agenda for future research, and consider the risk assessment implications of this emerging knowledge.

Methods: This review was developed based on our participation in a National Research Council workshop titled “Use of in Utero and Postnatal Indicators to Predict Health Outcomes Later in Life: State of the Science and Research Recommendations.” We used a case study approach to highlight the later-life consequences of early-life malnutrition and arsenic exposure.

Discussion: The environmental sensitivity of the epigenome is viewed as an adaptive mechanism by which the developing organism adjusts its metabolic and homeostatic systems to suit the anticipated extrauterine environment. Inappropriate adaptation may produce a mismatch resulting in subsequent increased susceptibility to disease. A nutritional mismatch between the prenatal and postnatal environments, or early-life obesogen exposure, may explain at least some of the recent rapid increases in the rates of obesity, type 2 diabetes, and cardiovascular diseases. Early-life arsenic exposure is also associated with later-life diseases, including cardiovascular disease and cancer.

Conclusions: With mounting evidence connecting early-life exposures and later-life disease, new strategies are needed to incorporate this emerging knowledge into health protective practices.

Keywords: arsenic, development, epigenetics, exposure, fetal, malnutrition, obesogen, PPAR

There are now well-described instances of human in utero exposures that have produced significant increases in later-life susceptibility to disease. Best known are the studies of the Dutch famine (Painter et al. 2005). During the winter of 1944–1945, toward the end of World War II, the population in German-occupied western Holland had only very limited food available, with an average daily intake of < 1,000 calories for several months. Children born to women who were pregnant during this famine were small for gestational age (SGA). Later in life, this in utero–deprived cohort developed an increased incidence of various adult-onset diseases, including obesity, diabetes, cardiovascular disease, and renal dysfunction. In addition, the children born to members of this in utero–deprived cohort were also SGA, indicating a passage of this predilection through generations (Painter et al. 2008). Another example of the later-life consequences of an early-life chemical exposure is in utero and early childhood exposure to arsenic-contaminated drinking water in Chile. Beginning in 1958, with the development of a new water supply, the population of a large town in the Antofagasta region of northern Chile was exposed to very high levels of arsenic [~ 800 ppb in drinking water; the U.S. Environmental Protection Agency (EPA) maximum contaminant level is 10 ppb], an exposure that abruptly ended with the institution of water filtration in 1970 (Dauphine et al. 2011; Smith et al. 2006; Yuan et al. 2007). The cohort of individuals exposed to arsenic early in life was later found to have significant deficiencies in lung function and increases in cardiovascular mortality compared with a nonexposed control group (Dauphine et al. 2011; Smith et al. 2006; Yuan et al. 2007).

The potential scope of the problem is illustrated by an example from the recent economics literature, where evidence of effects of the 1918 Spanish flu pandemic was seen in the economic performance and achievements of its victims. Men born to U.S. mothers who contracted the flu while pregnant had reduced educational attainment, increased rates of physical disabilities, lower socioeconomic status, 5–9% overall lower income, and approximately 30% greater welfare payments (Almond 2006). In a Brazilian cohort born during and soon after this same flu pandemic, children of flu-exposed mothers were less likely to be literate, to have graduated college, to be employed, or to ever have had formal employment (Nelson 2010). Although not every association seen in the U.S. cohort was observed in the Brazilian counterpart—and despite the fact that no aggregate economic impact number has been estimated—the results between the two studies were impressively concordant. The end result of this body of work is the powerful indication that early-life exposures have a strong, significant, and long-lasting effect on later-life function and disease in this circumstance.

Here we discuss examples of human exposures to adverse intrauterine environments that underscore the dramatic biological consequences of interference with normal development. Human data provide strong biological plausibility connecting early-life exposures to later-life disease and raise the important question of the underlying molecular mechanisms responsible for these long-lasting effects. Because alterations in DNA sequence per se do not explain the later-life effects of these exposures, epigenetic mechanisms have been invoked. To study these epigenetic mechanisms in detail has required the development of appropriate animal models such as the agouti mouse (Dolinoy et al. 2007). To date, this emerging knowledge has not been incorporated into risk assessment processes or regulatory practice. Indeed, significant scientific and conceptual barriers must still be overcome as health protective measures are developed for early-life exposures that induce molecular effects resulting in later-life disease. In this review, we explore the scientific basis for these latent effects and discuss the risk assessment context, forming the basis for the development of a research agenda designed to generate the knowledge base necessary for understanding and appropriately regulating early-life exposures.


Early-life exposures, later-life effects, and epigenetic mechanisms. In humans, early insults are associated with later-life liabilities, including prematurity, low birth weight, maternal infection during pregnancy, toxic exposures, and malnutrition. Premature birth (< 37 completed weeks gestation) is an important early-life event of increasing incidence (Martin 2011). Decreasing age at birth has been associated with increased odds of high systolic blood pressure in a population-based cohort study of young adult men (Johansson et al. 2005), and premature birth before 35 weeks gestational age predicted the development of diabetes in both adult men and women (Kajantie et al. 2010; Pilgaard et al. 2010).

Maternal infection during pregnancy has been associated with neuropsychiatric disorders such as autism and schizophrenia (Brown and Patterson 2011; Meyer et al. 2011; Patterson 2011). Both human and animal studies support this association, with suggested mechanisms ranging from altered hippocampal neurotransmitter signaling to persistent chronic inflammation (Baharnoori et al. 2010; Buehler 2011; Meyer et al. 2008; Moreno et al. 2011).

Numerous early-life toxic exposures have been linked to later-life health effects, with particularly strong evidence regarding maternal smoking during pregnancy being predictive of impaired fertility, obesity, hypertension, and neurobehavioral deficits (Bruin et al. 2010; Gustafsson and Kallen 2011; Heinonen et al. 2011; Simonetti et al. 2011; Thiering et al. 2011). Animal studies suggest that nicotine alone may be enough to elicit the long-term consequences of maternal smoking on progeny (Bruin et al. 2010) and that prenatal and perinatal toxicant exposures can result in latent effects, including elevated blood pressure, insulin resistance, and obesity (Fraites et al. 2009; Lassiter et al. 2008; Leasure et al. 2008).

Malnutrition is an example of an environmental stressor that invokes a predictive adaptive response in the developing organism (Hanson et al. 2011). The fetus appears to use the in utero environment to predict and prepare for the postnatal environment. That is, an organism alters its developmental path to produce a phenotype that gives it a survival or reproductive advantage in postnatal life (Figure 1).

Figure 1
The combination of maternal nutrition (i.e., in utero nutrition) and postnatal nutrition can be adaptive or maladaptive, leading to increased or decreased disease risk later in life. During development, an organism responds to an environmental ...

Fetal malnutrition can be defined as either overnutrition or undernutrition, or as a deficiency of specific nutrients. Studies of the Dutch famine of 1944–1945 have implicated maternal undernutrition in the pathogenesis of multiple diseases, including coronary heart disease, hypertension, obesity, insulin resistance, and schizophrenia (de Rooij et al. 2006; Painter et al. 2005; Ravelli et al. 1976, 1998; Roseboom et al. 2000; Susser et al. 1996). Fetal nutrition is also affected by placental dysfunction and uteroplacental insufficiency. In developed countries, the diseases associated with placental insufficiency, such as maternal smoking and pregnancy-induced hypertension (e.g., preeclampsia), are the most common causes of fetal undernutrition (Bergmann et al. 2008; Henriksen and Clausen 2002). Preeclampsia, for example, affects up to 3 million people in the United Kingdom and 15 million people in the United States (Davis et al. 2012). Moreover, the incidence of diseases such as preeclampsia is increasing in developing countries (Lopez-Jaramillo et al. 2005). Fetal undernutrition leads to intrauterine growth restriction (IUGR; the failure of the fetus to reach its genetic growth potential due to a pathological event). IUGR in humans predicts adult disease, including hypertension, diabetes, obesity, cardiovascular disease, respiratory dysfunction, and neurocognitive disease (Joss-Moore and Lane 2009; Lahti et al. 2006; Varvarigou 2010). The relationship between IUGR and adult disease is often studied using measures such as the ponderal index (birth weight × 100/crown–heel length3). The ponderal index reflects fetal undernutrition because, when presented with limited nutrition, the fetus will maintain body length but not body weight (asymmetric growth restriction or brain sparing) (Bartha et al. 1998; Fok et al. 2009; Patterson and Pouliot 1987). A reduced ponderal index has been associated with multiple diseases, including insulin resistance, obesity, behavioral symptoms of attention deficit hyperactivity disorder, coronary heart disease, and hypertension (Fan et al. 2010; Jarvelin et al. 2004; Lahti et al. 2006; Lithell et al. 1996; Loaiza et al. 2011; Walther 1988). Other measures of fetal malnutrition in the newborn that have been predictive of adult disease include placental efficiency (fetal weight/placental weight), placental morphometry, and combinations of maternal and placental size (Eriksson et al. 2011; Hemachandra et al. 2006).

IUGR in animals similarly predicts adult morbidities such as hypertension, obesity, and insulin resistance (Baserga et al. 2007; Desai et al. 2005, 2007; Joss-Moore et al. 2010b; Simmons et al. 2001; Tsirka et al. 2001). Common animal models of fetal malnutrition include placental insufficiency, food restriction, protein restriction, micronutrient deficiency, and glucocorticoid exposure. Although the molecular pathogenesis may differ, these models give rise to a similar profile of adult diseases. Both offspring sex and the timing of exposure influence the later-life consequences of exposure (Fu et al. 2009; Ke et al. 2006).

Timing and intergenerational effects of exposures. The Dutch famine cohort illustrates the critical effect of timing of exposures in humans (Figure 2). Individuals who were in utero early in gestation during the famine suffer from an increased incidence of coronary heart disease, hypertension, dyslipidemia, and obesity, whereas those who were in utero midgestation suffer from an increased incidence of obstructive airway disease and impaired glucose tolerance (Roseboom et al. 2001, 2006).

Figure 2
Studies of the Dutch famine birth cohort underscore the importance of timing in developmental processes. The timing of in utero nutritional deprivation is associated with different later-life disease outcomes (Roseboom et al. 2001, 2006 ...

In an examination of the impact of exposures across generations using the Dutch famine cohort, Painter et al. (2008) found evidence that progeny (F2) of women (F1) born during the famine suffered from increased neonatal adiposity and poor adult health, including neurological disorders, and respiratory and autoimmune conditions. Animal studies also provide support for the occurrence of intergenerational consequences of exposure. F1 female rats, hypertensive as a result of prenatal malnutrition, transmit the predisposition toward hypertension and endothelial dysfunction to their F2 progeny (Torrens et al. 2008). F1 male rats exposed to the endocrine disruptor vinclozolin in utero may also pass on a number of disease states involving prostate, kidney, immune system, and metabolism (hypercholesterolemia) to the F4 generation (Anway et al. 2006).

Epigenetic mechanisms. The underlying mechanism responsible for these myriad effects of exposures is, at least in part, epigenetic (Figure 3). Epigenetics forms the basis of how eukaryotes regulate gene expression. Epigenetic modifications direct access of the transcriptional machinery and cofactors to regulatory regions of the gene, modulating transcriptional initiation, elongation, and termination. Studies in multiple model systems demonstrate that epigenetic modifications are important along the entire gene, including untranslated regions (Einstein et al. 2010; Fu et al. 2006, 2009).

Figure 3
Developmental exposures are focused through the lens of epigenetic mechanisms to influence later-life disease outcomes and susceptibilities.

Epigenetic modifications operate as an “on/off switch” to regulate gene expression, as with imprinting, or as a rheostat control to increase or decrease expression. Epigenetic modifications that function as a rheostat are often driven by environmental conditions, particularly those that are extreme, and are thought to function as a cellular memory of previous environmental conditions (Hanson et al. 2011), so that future environments can be physiologically anticipated. In this context, epigenetic modifications may represent accessible molecular markers of early-life events that may be more stable than direct measures of gene expression. The epigenetic modifications most often studied include the histone code, DNA CpG methylation, and microRNA (miRNA) expression levels.

The histone code contains the most capacity for epigenetic modification (Ruthenburg et al. 2007). Each cell contains approximately 3 × 108 nucleosomes, each consisting of histone core proteins that can be differentially acetylated, methylated, or phosphorylated. Presumably, cells and tissues adjust their histone codes during development in response to ambient conditions to fine tune the regulation of gene expression in anticipation of the future extrauterine environment.

DNA methylation occurs on cytosines in CpG sequences and is associated with specific early-life events, including maternal malnutrition, in vitro fertilization, transplacental exposures, and IUGR (Gomes et al. 2009; Heijmans et al. 2008; Katari et al. 2009; Perera et al. 2009; Steegers-Theunissen et al. 2009; Tobi et al. 2009). Although many different early-life events lead to similar adult phenotypes, the epigenetic modifications that occur in these exposures may be different; in other words, the end result may be the same, but the pathways giving rise to that result may be different.

miRNAs are a class of small RNAs 21–25 nucleotides in length that act as posttranscriptional regulators of gene expression (Du and Zamore 2007). These small RNAs bind to the 3´-untranslated regions of target mRNA transcripts and disrupt the translational machinery or lead to transcript degradation, depending on the level of sequence complimentarity. Humans have approximately 1,000 miRNAs, each of which can interact with a family of target RNAs, creating a regulatory mechanism with the potential to modulate about 60% of the protein-coding genes (Sayed and Abdellatif 2011).

Placenta. The placenta, a readily accessible human tissue that may reflect the fetal environment, could prove to be a very useful tool for understanding the mechanisms underpinning the developmental origins of disease; it could also serve as a tissue source for biomarkers to predict later disease risk. The placenta grows and develops throughout gestation in a dynamic, highly orchestrated manner (Myatt and Roberts 2006). Placental vascular development is important for the transfer of flow-limited substrates and for fetal cardiovascular loading and heart fitness. Placental growth is under the control of imprinted genes that may up- or down-regulate growth depending on the parent of origin. Placental 11-β-hydroxysteroid dehydrogenase-2 (11βHSD2) activity is developmentally regulated, responds to nutrients and oxygen levels, and regulates fetal exposure to maternal cortisol (Li et al. 2011). Oxidative stress is increasingly seen as a regulator of placental and fetal growth and development (Myatt 2010). Pregnancies complicated by obesity, preeclampsia, and IUGR are associated with increased placental oxidative and nitrative stress, leading to covalent modifications of placental proteins based on observational studies in humans (Myatt 2010).

The placenta has a distinct DNA methylation profile that changes throughout gestation in response to environmental cues (Chu et al. 2011; Novakovic et al. 2011). Placental epigenetic biomarkers have emerged as an active and informative area for the identification of early-life indicators of later-life disease (Maccani and Marsit 2009). Infant growth restriction has been associated with distinct patterns of placental DNA methylation (Banister et al. 2011). Placentas from large-for-gestational-age newborns had differential methylation of the glucocorticoid receptor (GR) gene (Filiberto et al. 2011). Maternal smoking has been associated with down-regulation of the placental miRNAs miR-16, miR-21, and miR-126a (Maccani et al. 2010), and reduced expression of miR-16 and miR-21 has been associated with SGA newborns (Maccani et al. 2011). Therefore, the placenta is a source of integrated molecular information about the developmental life history of the fetus and its environmental interactions.

In utero and postnatal epigenetic modifications that predict end points such as obesity, insulin resistance, and hypertension. In mice, differences in micronutrient intake during pregnancy induced differences in the coat color of offspring due to hypomethylation of the 5´ end of the agouti gene (Wolff et al. 1998), whereas a protein-restricted diet during pregnancy led to hypomethylation of promoter regions of metabolically important regulators—the GR and peroxisome proliferator-activated receptor (PPAR) α genes (Lillycrop et al. 2007). In this rodent model, hypomethylation of GR and PPARα was accompanied by an increase in their expression and that of their target genes, PEPCK (phosphoenolpyruvate carboxykinase) and AOX (acyl CoA oxidase), and the metabolic processes that they control, namely, glucogeneogenesis and β-oxidation. Altered methylation status of the liver PPARα promoter in juvenile offspring was due to hypomethylation of four specific CpG dinucleotides, two of which predicted the level of the mRNA transcript and persisted into adulthood (Lillycrop et al. 2008). The differentially methylated CpGs corresponded to transcription factor binding sites, which suggests that changes in the epigenetic regulation of genes established during development will induce altered transcription in response to specific stimuli and modify the capacity of the tissue to respond to metabolic challenge. Other animal studies have shown that folic acid supplementation (Wolff et al. 1998), neonatal overfeeding (Plagemann et al. 2009), constrained intrauterine blood supply (Pham et al. 2003), and maternal behavior (Weaver et al. 2004) alter the epigenetic regulation of genes in the offspring and that these changes are associated with an altered phenotype.

Hypomethylation of the imprinted IGF2 (insulin-like growth factor 2) gene has been observed in genomic DNA isolated from whole blood from 60 individuals who were exposed periconceptually to famine in utero during the Dutch famine compared with their unexposed same-sex siblings (Heijmans et al. 2008). In two independent cohorts, the methylation status of a single CpG site in the promoter region of the retinoid X receptor α (RXRα) in the umbilical cord was positively associated with childhood adiposity in both boys and girls, such that RXRα promoter methylation explained more than one-fifth of the variance in childhood fat mass (Godfrey et al. 2011). These human studies indicate that epigenetic marks may allow identification of individuals at increased risk of chronic disease in later life before the onset of clinical disease, thus facilitating targeted intervention strategies.

PPARγ and obesity. Rates of obesity have increased in infants, young children, and adolescents (Koebnick et al. 2010; McCormick et al. 2010; Taveras et al. 2009), suggesting that obesity is being programmed prenatally or in early childhood. Growing evidence supports a contribution of endocrine-disrupting chemicals (EDCs) in the obesity epidemic, and mechanisms are being revealed for at least a few EDCs (Janesick and Blumberg 2011a). Obesogens are chemicals that promote obesity by increasing the number of fat cells (and fat storage into existing fat cells), by changing the amount of calories burned at rest, by altering energy balance to favor storage of calories, and by altering the mechanisms through which the body regulates appetite and satiety (reviewed by Janesick and Blumberg 2011a). PPARγ plays an important role in nearly all aspects of adipocyte biology and is thought to be the master regulator of adipogenesis (Evans et al. 2004; Tontonoz and Spiegelman 2008). Activation of PPARγ2 in preadipocytes increases their differentiation into adipocytes, and PPARγ is required for adipocyte differentiation in vitro and in vivo (Rosen et al. 1999). The ligand-binding pocket of PPARγ is large and considered to be promiscuous (Maloney and Waxman 1999). A number of chemicals act as PPARγ ligands, many of which are obesogenic (Janesick and Blumberg 2011b).

One obesogen for which a mechanism of action is known is the organotin tribuytltin (TBT) (Grun et al. 2006). In mice, a single prenatal exposure to TBT during gestation resulted in premature accumulation of fat in adipose tissues and increased size of the fat depot relative to overall body mass (Grun et al. 2006). In mouse pups born to TBT-treated mothers, the liver, testis, mammary gland, and inguinal adipose tissue, which normally do not store lipids before feeding commences, all had stored fat at birth (Grun et al. 2006). TBT has a nanomolar affinity for both RXR and PPARγ, activates PPARγ–RXR heterodimer binding to DNA, and directly regulates transcription of its target genes (Grun et al. 2006; Kanayama et al. 2005; Tontonoz and Spiegelman 2008).

Mature adipocytes are generated from multipotent stromal cells (MSCs) found in almost all fetal and adult tissues (da Silva Meirelles et al. 2006). MSCs can differentiate into bone or adipose tissue, a balance mediated by PPARγ (reviewed by Takada et al. 2009). Intriguingly, exposure to the environmental obesogen TBT or the pharmaceutical obesogen rosiglitazone have been reported to induce the differentiation of MSCs into adipocytes at the expense of bone via PPARγ activation (Kirchner et al. 2010). Moreover, pregnant dams treated with a single dose of TBT or rosiglitazone produced pups with MSCs that in vitro differentiated into adipocytes about twice as frequently as did MSCs from controls (Kirchner et al. 2010). Thiazolidinedione antidiabetic drugs such as rosiglitazone are potent activators of PPARγ (Lehmann et al. 1995) and are known to increase weight and fat cell number in humans (Shadid and Jensen 2003).

MSCs derived from mice exposed to TBT in utero have exhibited alterations in the methylation status of the CpG islands of adipogenic genes such as AP2 and PPARγ. This altered methylation was associated with an increased number of preadipocytes in the MSC compartment and an increased frequency with which MSCs differentiate into adipocytes upon adipogenic stimulation (Kirchner et al. 2010). Understanding how adipocyte number is programmed at the genomic level will be of critical importance in understanding how the set point for adipocyte number is modified by chemicals, dietary factors, or the intrauterine environment.

In utero and postnatal indicators that predict diseases caused by arsenic exposure. Early-life exposure to inorganic arsenic produces a wide range of malignant and nonmalignant diseases in humans. Exposure to arsenic from naturally contaminated drinking water affects roughly 140 million people worldwide (Pilsner et al. 2009). For example, in Bangladesh, where exposure began in the early 1970s, there is a generation of women and men who have been exposed to arsenic for their entire lives. The placenta is not a barrier to arsenic; thus, children are born with blood concentrations of arsenic and its toxic metabolites similar to those present in their mothers (Hall et al. 2007). Increased lung cancer and bronchiectasis have been reported in young Chilean men and women who were exposed to arsenic only during prenatal and early postnatal life (Smith et al. 2006). Long-term follow-up of a cohort of thousands of infants exposed to arsenic-contaminated milk powder in 1955 in Japan suggests increased rates of leukemia and skin, liver, and pancreatic cancers (Yorifuji et al. 2010). In Thailand, among babies of mothers who experienced varying degrees of arsenic exposure, gene expression profiles were indicative of the activation of molecular networks associated with inflammation, apoptosis, stress, and metal exposure (Fry et al. 2007). In arsenic-exposed adults, DNA hypomethylation has been associated with the subsequent risk of developing arsenic-induced skin lesions (Pilsner et al. 2009). Collectively, this body of work in human populations strongly suggests that developmental exposure to arsenic may induce alterations in fetal cellular functioning that may have major public health consequences, including cancer, in later life (Ren et al. 2011; Tokar et al. 2011).

Arsenic as a transplacental carcinogen in mice. Comparing exposure levels between mice and humans is complicated by differences in metabolism and excretion (Carter et al. 2003; States et al. 2011). A transplacental model has been developed in which mice receive inorganic arsenic in the drinking water only during pregnancy. In this model arsenic acts as either a complete carcinogen or enhances cancer response to other agents in offspring as adults (Tokar et al. 2011). Transplacental exposure to arsenic in mice has been reported to produce tumors or stimulate response to other agents in various target tissues, including sites concordant with human targets of arsenic. Developing theory posits that cancer often is a stem cell (SC)-based disease. SCs are particularly active during the perinatal period. In fact, Tokar et al. (2011) reported that mouse skin carcinomas stimulated in adult offspring by prenatal arsenic exposure were remarkably enriched in cancer SCs. These authors reported that during malignant transformation by arsenic in vitro, a survival selection of SCs occurred, which resulted in an overabundance of cancer SCs (compared with other carcinogens) as cancer phenotype was acquired (Tokar et al. 2011). Thus, it appears that arsenic targets long-lived SC populations—perhaps through epigenetic modifications—to cause or to facilitate carcinogenic events during adulthood as a possible mechanism of the developmental basis of adult disease (Tokar et al. 2011).

Arsenic exposure and cardiovascular disease in mice. Myocardial infarction in infants exposed to high levels of arsenic during fetal life (Rosenberg 1974) was the first indication that in utero arsenic exposure could cause advanced atherosclerosis. In the apoplipoprotein E–knockout mouse, a commonly used model for human atherosclerosis, both in utero (Srivastava et al. 2007) and postweaning (Srivastava et al. 2009) arsenic exposures accelerate and exacerbate atherosclerosis. The postweaning studies have shown a clear linear dose response, further confirming the cardiovascular risk from arsenic exposure. These animal model results are consistent with early epidemiologic findings in Taiwan (Chen et al. 1996) and recent findings in Bangladesh (Chen et al. 2011) that show an arsenic dose response related to mortality from cardiovascular disease.


When we consider all of the human and animal evidence together (Table 1), many questions and research directions arise as to how we should develop and improve upon strategies to identify the individuals in whom early-life events predict adult diseases:

Table 1
Examples of in utero exposures that result in adverse health outcomes later in life.
  • Can epigenetic biomarkers be used to identify mechanisms, so we can treat the cause and not the symptom?
  • How do we assess the information stored in the histone code?
  • Can epigenetic biomarkers be used as integrative measures of mixed exposures?
  • How do we prioritize the investigation of different epigenetic mechanisms (DNA methylation, histone modifications, miRNAs) involving different sites within the epigenome?
  • What are the most critical developmental time points for producing a later-life effect of an exposure?
  • What are nonepigenetic mechanisms by which early-life events alter adult disease risk, and are there early markers for these?
  • How relevant is sex and tissue specificity?

Development of improved molecular and computational approaches that can generate and sift through exponentially increasing amounts of data is an overarching research priority. Just as important, understanding the epigenetic and nonepigenetic mechanisms that underlie the relationship between early-life events and adult disease risk will be essential in terms of a) interpreting the data, b) prioritizing the findings, and c) designing specific interventions that treat causes—both environmental and physiologic—and not only markers of adverse consequences of early-life events.

The pleiotropic effects of arsenic exposure on human health and in animal models illustrate an important area for further research. Animal studies suggest that the varied disease outcomes resulting from similar exposures are dependent on the disease predilection of the animal. Thus, in utero arsenic exposure induces cancer in adulthood in cancer-susceptible mouse strains and cardiovascular disease in atherosclerosis-prone strains. These observations suggest that the particular disease manifestation of arsenic exposure in humans may be dependent on the genetic predisposition of the individual as well as the life-stage timing of exposure, and that arsenic exposure may accelerate an underlying predilection to pathology. For example, a single nucleotide polymorphism near the gene for arsenic methyltransferase has been associated with an increased risk for arsenic-induced skin lesions (Pierce et al. 2012). It is not clear whether cessation of arsenic exposure can reduce the disease incidence, but emerging human data indicate that short, high-level arsenic exposure in early life is linked to cancer (Yorifuji et al. 2010), again emphasizing life-stage timing of exposure. Future research focusing on plasma biomarkers of disease in animal models and translating these to human populations will be of great benefit in identifying disease risk and in developing potential intervention strategies. In addition, large-scale genetic association studies in arsenic-exposed humans may provide valuable clues about disease susceptibility in both arsenic-exposed and unexposed populations.

How do we define the scope of this problem? We envision three distinct research strategies that can be used to define the scope and importance of early-life exposures predisposing later-life disease. The first approach would identify changes in known epigenetic targets already shown to be responsive following exposures of interest using high throughput in vitro systems and regulating on the result. However, this approach may be premature and too narrow scientifically because of our limited understanding of the underlying biology. A second approach would add a number of preliminary end points (such as epigenetic modifications) and pathway measures (such as DNA methylation capability) onto the very early part of a 2-year rodent bioassay beginning with an in utero exposure; early biomarkers could then be associated with later-life disease. This approach would produce large amounts of data, but at great cost in time and resources. An intermediate approach would be to assemble a comprehensive list of possible end points and pathways that might plausibly link exposure to a response (to the degree that this is known now) and then test a number of known “bad actors” (and their inactive congeners) to see which combinations of pathways and end points can separate the known actives from the known inactives. This approach would be limited to pathways that are currently known, but an advantage would be that it would generate some survey-scale data relatively quickly. Undoubtedly, all three of these research approaches will be used going forward to provide risk assessors with sufficiently robust data to begin the risk characterization of the relationship between early-life exposure and later-life disease.

How do we incorporate this emerging knowledge into risk assessment practices and health protective policies? The emerging science described here may be instructive for risk assessment and public health decision making. To be useful in these processes, science must address questions of interest to risk managers, including

  • What adverse effects will result from exposure (hazard identification)?
  • At what level of exposure will these effects occur (exposure/dose–response relationships)?
  • How certain are we of these effects (risk characterization)?
  • These questions are discussed below, exploring the extent to which early-life indicators of later-life disease may inform risk assessment.

Hazard identification. Both animal and human evidence now support a causative relationship between early-life exposures and a wide variety of later-life diseases (Schug et al. 2011). There is also an emerging understanding of the underlying mechanisms through which this can occur. The examples presented above focus primarily, but not exclusively, on epigenetic mechanisms and include exposures to chemicals, malnutrition, and other environmental stressors. As stated by Burdge and Lillycrop (2010),

[the] process that underlies induction of differential risk of disease by variation in the prenatal environment reflects environmental cues acting through developmental plasticity, which generate a range of genotypes from a single genome …. Recent findings show that altered epigenetic regulation of specific genes is central to the process by which different phenotypes are generated and hence differential risk of disease.

Chemicals and other environmental agents appear to have the ability to miscue the developing organism, resulting in maladaptation associated with increased disease (Janesick and Blumberg 2011a; Joss-Moore et al. 2010a). Alterations in DNA methylation, chromatin remodeling, and miRNA expression can modify SC fate and produce persistent changes in gene expression, resulting in downstream effects on cell, tissue, and organism functions in later life. These perturbed epigenetic targets can be used as early indicators of adverse health effects. Confidence in these early events as indicators is based on a systems biology level of understanding, compared with alterations in single isolated events. For specific, well-developed examples, such as IGF2 hypomethylation in Dutch famine victims and increased methylation of RXRα at birth associated with childhood adiposity, the case for causal links between these upstream molecular events and later-life effects is compelling. In certain situations, evidence may be sufficient to describe the upstream event or indicator itself as an adverse effect.

Exposure/dose–response relationships. For arsenic and obesogens, quantitative relationships between exposure or dose response and various indicators are evident, thus establishing their role as causal biomarkers of exposures. Currently, the quantitative relationships between these indicators and risks of disease must be experimentally or observationally determined. Given the complex and multifactorial nature of disease risks, it is not yet possible to predict quantitatively the risk of disease from the indicator exposure response or dose response alone. Complicating factors are a) the specific disease is contingent on interactions with the environment throughout the life course; b) the timeframe of environmental exposure, relative to the stage of in utero development, can influence the type of disease observed in later life; and c) identification of appropriate indicators from among the variety of associative and secondary changes occurring within the body is clearly complicated and difficult. Fortunately, this field is rapidly developing, and further advances will be aided by the increasing mechanistic understanding of human variability in response and the role of complex environmental exposures in disease risks.

Risk characterization. For the examples discussed above, evidence for a causal relationship between specific early indicators and adverse outcomes is provided by experimental evidence in animals and supported by observational human data. Taken together, the evidence for a causal relationship between early-life exposures, specific early indicators, and later-life disease is consistent, coherent across various types of studies, and biologically plausible. These examples illustrate the types of data and approaches that can inform risk assessment, as well as the substantial challenges that remain for general use of these types of data in risk assessment.


The examples presented here of early-life exposures that result in later-life disease provide both proof of concept and insights into the value of specific information. Although insufficient data are available on a substantial number of chemicals or end points for widespread application in current risk assessments, this new knowledge paves the way to a deeper understanding of the underlying biology and evaluation of potential public health risks. To further the use of these types of data in future risk assessments, additional basic biological research aimed at increasing knowledge of inherent epigenetic and gene regulatory structures of targeted genomic regions and their roles in disease must be supported. These new approaches have significant potential in generating novel causal biomarkers of exposure and of increasing understanding of susceptibility and responses to complex environmental exposures, thus serving as sophisticated indicators of potential risks.


The authors thank the staff, particularly K. Sawyer, and members of the NRC Standing Committee on Emerging Science for Environmental Health Decisions.


Publication was supported by a grant from the National Institutes of Health (P20ES018169) and the U.S. Environmental Protection Agency (EPA) (RD-83459401-1) to the Brown University Formative Center for the Evaluation of Environmental Impacts on Fetal Development.

This document has been reviewed by the National Health and Environmental Effects Research Laboratory, U.S. EPA, and approved for publication. Approval does not signify that the contents reflect the views of the agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.

R.E.C. is employed by Pfizer Global Research and Development, Groton, CT. B.B. holds several patents related to nuclear receptor sequence and function that have been licensed to for-profìt entities and generate annual royalty payments. K.B. occasionally acts as an expert consultant for chemical and pharmaceutical companies. The other authors declare they have no actual or potential competing financial interests.


  • Adami HO, Lagiou P, Trichopoulos D. Breast cancer following diethylstilbestrol exposure in utero: insights from a tragedy. Eur J Epidemiol. 2012;27:1–3. [PubMed]
  • Almond D. Is the 1918 Influenza pandemic over? Long-term effects of in utero influenza exposure in the post-1940 U.S. population. J Polit Econ. 2006;114:672–712.
  • Anway MD, Leathers C, Skinner MK. Endocrine disruptor vinclozolin induced epigenetic transgenerational adult-onset disease. Endocrinology. 2006;147(12):5515–5523. [PubMed]
  • Armitage JA, Khan IY, Taylor PD, Nathanielsz PW, Poston L. Developmental programming of the metabolic syndrome by maternal nutritional imbalance: how strong is the evidence from experimental models in mammals? J Physiol. 2004;561(pt 2):355–377. [PubMed]
  • Atzori L, Antonucci R, Barberini L, Locci E, Marincola FC, Scano P, et al. 1H NMR-based metabolomic analysis of urine from preterm and term neonates. Front Biosci. 2011;3:1005–1012. (Elite Ed) [PubMed]
  • Baharnoori M, Bhardwaj SK, Srivastava LK. Neonatal behavioral changes in rats with gestational exposure to lipopolysaccharide: a prenatal infection model for developmental neuropsychiatric disorders. Schizophr Bull. 2010;38(3):444–456. [PMC free article] [PubMed]
  • Banister CE, Koestler DC, Maccani MA, Padbury JF, Houseman EA, Marsit CJ. Infant growth restriction is associated with distinct patterns of DNA methylation in human placentas. Epigenetics. 2011;6(7):920–927. [PMC free article] [PubMed]
  • Bartha JL, Comino-Delgado R, Gonzalez-Mena C, Lopez I, Arrabal J. Umbilical blood flow and neonatal morphometry: a multivariate analysis. Eur J Obstet Gynecol Reprod Biol. 1998;79(1):27–33. [PubMed]
  • Baserga M, Hale MA, Wang ZM, Yu X, Callaway CW, McKnight RA, et al. Uteroplacental insufficiency alters nephrogenesis and downregulates cyclooxygenase-2 expression in a model of IUGR with adult-onset hypertension. Am J Physiol Regul Integr Comp Physiol. 2007;292(5):R1943–1955. [PubMed]
  • Bergmann RL, Bergmann KE, Dudenhausen JW. Undernutrition and growth restriction in pregnancy. Nestle Nutr Workshop Ser Pediatr Program. 2008;61:103–121. [PubMed]
  • Brown AS, Patterson PH. Maternal infection and schizophrenia: implications for prevention. Schizophr Bull. 2011;37(2):284–290. [PMC free article] [PubMed]
  • Bruin JE, Gerstein HC, Holloway AC. Long-term consequences of fetal and neonatal nicotine exposure: a critical review. Toxicol Sci. 2010;116(2):364–374. [PMC free article] [PubMed]
  • Buehler MR. A proposed mechanism for autism: an aberrant neuroimmune response manifested as a psychiatric disorder. Med Hypotheses. 2011;76(6):863–870. [PubMed]
  • Burdge GC, Lillycrop KA. Nutrition, epigenetics, and developmental plasticity: implications for understanding human disease. Annu Rev Nutr. 2010;30:315–339. [PubMed]
  • Carter DE, Aposhian HV, Gandolfi AJ. The metabolism of inorganic arsenic oxides, gallium arsenide, and arsine: a toxicochemical review. Toxicol Appl Pharmacol. 2003;193(3):309–334. [PubMed]
  • Chen CJ, Chiou HY, Chiang MH, Lin LJ, Tai TY. Dose-response relationship between ischemic heart disease mortality and long-term arsenic exposure. Arterioscler Thromb Vasc Biol. 1996;16(4):504–510. [PubMed]
  • Chen Y, Graziano JH, Parvez F, Liu M, Slavkovich V, Kalra T, et al. 2011. Arsenic exposure from drinking water and mortality from cardiovascular disease in Bangladesh: prospective cohort study. BMJ 342d2431 doi:[Online 5 May 2011]10.1136/bmj.d2431 [PubMed] [Cross Ref]
  • Chu T, Handley D, Bunce K, Surti U, Hogge WA, Peters DG. 2011. Structural and regulatory characterization of the placental epigenome at its maternal interface. PLoS One 62e14723 doi:[Online 23 February 2011]10.1371/journal.pone.0014723 [PMC free article] [PubMed] [Cross Ref]
  • Cleasby ME, Kelly PA, Walker BR, Seckl JR. Programming of rat muscle and fat metabolism by in utero overexposure to glucocorticoids. Endocrinology. 2003;144(3):999–1007. [PubMed]
  • da Silva Meirelles L, Chagastelles PC, Nardi NB. Mesenchymal stem cells reside in virtually all post-natal organs and tissues. J Cell Sci. 2006;119(pt 11):2204–2213. [PubMed]
  • Dauphine DC, Ferreccio C, Guntur S, Yuan Y, Hammond SK, Balmes J, et al. Lung function in adults following in utero and childhood exposure to arsenic in drinking water: preliminary findings. Int Arch Occup Environ Health. 2011;84(6):591–600. [PMC free article] [PubMed]
  • Davis EF, Newton L, Lewandowski AJ, Lazdam M, Kelly BA, Kyriakou T, et al. Pre-eclampsia and offspring cardiovascular health: mechanistic insights from experimental studies. Clin Sci (Lond) 2012;123(2):53–72. [PMC free article] [PubMed]
  • de Rooij SR, Painter RC, Phillips DI, Osmond C, Michels RP, Bossuyt PM, et al. Hypothalamic–pituitary–adrenal axis activity in adults who were prenatally exposed to the Dutch famine. Eur J Endocrinol. 2006;155(1):153–160. [PubMed]
  • Desai M, Babu J, Ross MG. Programmed metabolic syndrome: prenatal undernutrition and postweaning overnutrition. Am J Physiol Regul Integr Comp Physiol. 2007;293(6):R2306–R2314. [PubMed]
  • Desai M, Gayle D, Babu J, Ross MG. Programmed obesity in intrauterine growth-restricted newborns: modulation by newborn nutrition. Am J Physiol Regul Integr Comp Physiol. 2005;288(1):R91–R96. [PubMed]
  • Dessi A, Atzori L, Noto A, Visser GH, Gazzolo D, Zanardo V, et al. Metabolomics in newborns with intrauterine growth retardation (IUGR): urine reveals markers of metabolic syndrome. J Matern Fetal Neonatal Med. 2011;24(suppl 2):35–39. [PubMed]
  • Dolinoy DC, Huang D, Jirtle RL. Maternal nutrient supplementation counteracts bisphenol A-induced DNA hypomethylation in early development. Proc Natl Acad Sci USA. 2007;104(32):13056–13061. [PubMed]
  • Du T, Zamore PD. Beginning to understand microRNA function. Cell Res. 2007;17(8):661–663. [PubMed]
  • Einstein F, Thompson RF, Bhagat TD, Fazzari MJ, Verma A, Barzilai N, et al. 2010. Cytosine methylation dysregulation in neonates following intrauterine growth restriction. PLoS One 51e8887 doi:[Online 26 January 2010]10.1371/journal.pone.0008887 [PMC free article] [PubMed] [Cross Ref]
  • Eriksson JG, Kajantie E, Thornburg KL, Osmond C, Barker DJ. Mother’s body size and placental size predict coronary heart disease in men. Eur Heart J. 2011;32(18):2297–2303. [PMC free article] [PubMed]
  • Evans RM, Barish GD, Wang YX. PPARs and the complex journey to obesity. Nat Med. 2004;10(4):355–361. [PubMed]
  • Fan Z, Zhang ZX, Li Y, Wang Z, Xu T, Gong X, et al. Relationship between birth size and coronary heart disease in China. Ann Med. 2010;42(8):596–602. [PMC free article] [PubMed]
  • Filiberto AC, Maccani MA, Koestler D, Wilhelm-Benartzi C, Avissar-Whiting M, Banister CE, et al. Birthweight is associated with DNA promoter methylation of the glucocorticoid receptor in human placenta. Epigenetics. 2011;6(5):566–572. [PMC free article] [PubMed]
  • Fok TF, Hon KL, Ng PC, Wong E, So HK, Lau J, et al. Use of anthropometric indices to reveal nutritional status: normative data from 10,226 Chinese neonates. Neonatology. 2009;95(1):23–32. [PubMed]
  • Fraites MJ, Cooper RL, Buckalew A, Jayaraman S, Mills L, Laws SC. Characterization of the hypothalamic-pituitary-adrenal axis response to atrazine and metabolites in the female rat. Toxicol Sci. 2009;112(1):88–99. [PubMed]
  • Fry RC, Navasumrit P, Valiathan C, Svensson JP, Hogan BJ, Luo M, et al. 2007. Activation of inflammation/NF-κB signaling in infants born to arsenic-exposed mothers. PLoS Genet 311e207 doi:[Online 23 November 2007]10.1371/journal.pgen.0030207 [PubMed] [Cross Ref]
  • Fu Q, McKnight RA, Yu X, Callaway CW, Lane RH. Growth retardation alters the epigenetic characteristics of hepatic dual specificity phosphatase 5. FASEB J. 2006;20(12):2127–2129. [PubMed]
  • Fu Q, Yu X, Callaway CW, Lane RH, McKnight RA. Epigenetics: intrauterine growth retardation (IUGR) modifies the histone code along the rat hepatic IGF-1 gene. FASEB J. 2009;23(8):2438–2449. [PubMed]
  • Godfrey KM, Sheppard A, Gluckman PD, Lillycrop KA, Burdge GC, McLean C, et al. Epigenetic gene promoter methylation at birth is associated with child’s later adiposity. Diabetes. 2011;60(5):1528–1534. [PMC free article] [PubMed]
  • Gomes MV, Huber J, Ferriani RA, Amaral Neto AM, Ramos ES. Abnormal methylation at the KvDMR1 imprinting control region in clinically normal children conceived by assisted reproductive technologies. Mol Hum Reprod. 2009;15(8):471–477. [PubMed]
  • Grun F, Blumberg B. Environmental obesogens: organotins and endocrine disruption via nuclear receptor signaling. Endocrinology. 2006;147 (6 suppl):S50–S55. [PubMed]
  • Grun F, Watanabe H, Zamanian Z, Maeda L, Arima K, Cubacha R, et al. Endocrine-disrupting organotin compounds are potent inducers of adipogenesis in vertebrates. Mol Endocrinol. 2006;20(9):2141–2155. [PubMed]
  • Gustafsson P, Kallen K. Perinatal, maternal, and fetal characteristics of children diagnosed with attention-deficit-hyperactivity disorder: results from a population-based study utilizing the Swedish Medical Birth Register. Dev Med Child Neurol. 2011;53(3):263–268. [PubMed]
  • Hall M, Gamble M, Slavkovich V, Liu X, Levy D, Cheng Z, et al. Determinants of arsenic metabolism: blood arsenic metabolites, plasma folate, cobalamin, and homocysteine concentrations in maternal-newborn pairs. Environ Health Perspect. 2007;115:1503–1509. [PMC free article] [PubMed]
  • Hanson M, Godfrey KM, Lillycrop KA, Burdge GC, Gluckman PD. Developmental plasticity and developmental origins of non-communicable disease: theoretical considerations and epigenetic mechanisms. Prog Biophys Mol Biol. 2011;106(1):272–280. [PubMed]
  • Heijmans BT, Tobi EW, Stein AD, Putter H, Blauw GJ, Susser ES, et al. Persistent epigenetic differences associated with prenatal exposure to famine in humans. Proc Natl Acad Sci USA. 2008;105(44):17046–17049. [PubMed]
  • Heinonen K, Raikkonen K, Pesonen AK, Andersson S, Kajantie E, Eriksson JG, et al. Longitudinal study of smoking cessation before pregnancy and children’s cognitive abilities at 56 months of age. Early Hum Dev. 2011;87(5):353–359. [PubMed]
  • Hemachandra AH, Klebanoff MA, Duggan AK, Hardy JB, Furth SL. The association between intrauterine growth restriction in the full-term infant and high blood pressure at age 7 years: results from the Collaborative Perinatal Project. Int J Epidemiol. 2006;35(4):871–877. [PubMed]
  • Henriksen T, Clausen T. The fetal origins hypothesis: placental insufficiency and inheritance versus maternal malnutrition in well-nourished populations. Acta Obstet Gynecol Scand. 2002;81(2):112–114. [PubMed]
  • Hines EP, White SS, Stanko JP, Gibbs-Flournoy EA, Lau C, Fenton SE. Phenotypic dichotomy following developmental exposure to perfluorooctanoic acid (PFOA) in female CD-1 mice: low doses induce elevated serum leptin and insulin, and overweight in mid-life. Mol Cell Endocrinol. 2009;304(1–2):97–105. [PubMed]
  • Janesick A, Blumberg B. Endocrine disrupting chemicals and the developmental programming of adipogenesis and obesity. Birth Defects Res C Embryo Today. 2011a;93(1):34–50. [PubMed]
  • Janesick A, Blumberg B. Minireview: PPARgamma as the target of obesogens. J Steroid Biochem Mol Biol. 2011b;127:4–8. [PMC free article] [PubMed]
  • Jarvelin MR, Sovio U, King V, Lauren L, Xu B, McCarthy MI, et al. Early life factors and blood pressure at age 31 years in the 1966 northern Finland birth cohort. Hypertension. 2004;44(6):838–846. [PubMed]
  • Johansson S, Iliadou A, Bergvall N, Tuvemo T, Norman M, Cnattingius S. Risk of high blood pressure among young men increases with the degree of immaturity at birth. Circulation. 2005;112(22):3430–3436. [PubMed]
  • Joss-Moore LA, Lane RH. The developmental origins of adult disease. Curr Opin Pediatr. 2009;21(2):230–234. [PMC free article] [PubMed]
  • Joss-Moore LA, Metcalfe DB, Albertine KH, McKnight RA, Lane RH. Epigenetics and fetal adaptation to perinatal events: diversity through fidelity. J Anim Sci. 2010a;88 (13 suppl):E216–E222. [PubMed]
  • Joss-Moore LA, Wang Y, Campbell MS, Moore B, Yu X, Callaway CW, et al. Uteroplacental insufficiency increases visceral adiposity and visceral adipose PPARgamma2 expression in male rat offspring prior to the onset of obesity. Early Hum Dev. 2010b;86(3):179–185. [PMC free article] [PubMed]
  • Kajantie E, Osmond C, Barker DJ, Eriksson JG. Preterm birth–a risk factor for type 2 diabetes? The Helsinki birth cohort study. Diabetes Care. 2010;33(12):2623–2625. [PMC free article] [PubMed]
  • Kanayama T, Kobayashi N, Mamiya S, Nakanishi T, Nishikawa J. Organotin compounds promote adipocyte differentiation as agonists of the peroxisome proliferator-activated receptor gamma/retinoid X receptor pathway. Mol Pharmacol. 2005;67(3):766–774. [PubMed]
  • Katari S, Turan N, Bibikova M, Erinle O, Chalian R, Foster M, et al. DNA methylation and gene expression differences in children conceived in vitro or in vivo. Hum Mol Genet. 2009;18(20):3769–3778. [PMC free article] [PubMed]
  • Ke X, Lei Q, James SJ, Kelleher SL, Melnyk S, Jernigan S, et al. Uteroplacental insufficiency affects epigenetic determinants of chromatin structure in brains of neonatal and juvenile IUGR rats. Physiol Genomics. 2006;25(1):16–28. [PubMed]
  • Kirchner S, Kieu T, Chow C, Casey S, Blumberg B. Prenatal exposure to the environmental obesogen tributyltin predisposes multipotent stem cells to become adipocytes. Mol Endocrinol. 2010;24(3):526–539. [PubMed]
  • Koebnick C, Smith N, Coleman KJ, Getahun D, Reynolds K, Quinn VP, et al. Prevalence of extreme obesity in a multiethnic cohort of children and adolescents. J Pediatr. 2010;157(1):26–31 e22. [PubMed]
  • Lahti J, Raikkonen K, Kajantie E, Heinonen K, Pesonen AK, Jarvenpaa AL, et al. Small body size at birth and behavioural symptoms of ADHD in children aged five to six years. J Child Psychol Psychiatry. 2006;47(11):1167–1174. [PubMed]
  • Lassiter TL, Ryde IT, Mackillop EA, Brown KK, Levin ED, Seidler FJ, et al. Exposure of neonatal rats to parathion elicits sex-selective reprogramming of metabolism and alters the response to a high-fat diet in adulthood. Environ Health Perspect. 2008;116:1456–1462. [PMC free article] [PubMed]
  • Leasure JL, Giddabasappa A, Chaney S, Johnson JE, Jr, Pothakos K, Lau YS, et al. Low-level human equivalent gestational lead exposure produces sex-specific motor and coordination abnormalities and late-onset obesity in year-old mice. Environ Health Perspect. 2008;116:355–361. [PMC free article] [PubMed]
  • Lehmann JM, Moore LB, Smith-Oliver TA, Wilkison WO, Willson TM, Kliewer SA. An antidiabetic thiazolidinedione is a high affinity ligand for peroxisome proliferator-activated receptor γ (PPARγ). J Biol Chem. 1995;270(22):12953–12956. [PubMed]
  • Li JN, Ge YC, Yang Z, Guo CM, Duan T, Myatt L, et al. The Sp1 transcription factor is crucial for the expression of 11beta-hydroxysteroid dehydrogenase type 2 in human placental trophoblasts. J Clin Endocrinol Metab. 2011;96(6):E899–E907. [PubMed]
  • Lillycrop KA, Phillips ES, Torrens C, Hanson MA, Jackson AA, Burdge GC. Feeding pregnant rats a protein-restricted diet persistently alters the methylation of specific cytosines in the hepatic PPAR alpha promoter of the offspring. Br J Nutr. 2008;100(2):278–282. [PMC free article] [PubMed]
  • Lillycrop KA, Rodford J, Garratt ES, Slater-Jefferies JL, Godfrey KM, Gluckman PD, et al. Maternal protein restriction with or without folic acid supplementation during pregnancy alters the hepatic transcriptome in adult male rats. Br J Nutr. 2010;103(12):1711–1719. [PubMed]
  • Lillycrop KA, Slater-Jefferies JL, Hanson MA, Godfrey KM, Jackson AA, Burdge GC. Induction of altered epigenetic regulation of the hepatic glucocorticoid receptor in the offspring of rats fed a protein-restricted diet during pregnancy suggests that reduced DNA methyltransferase-1 expression is involved in impaired DNA methylation and changes in histone modifications. Br J Nutr. 2007;97(6):1064–1073. [PMC free article] [PubMed]
  • Lithell HO, McKeigue PM, Berglund L, Mohsen R, Lithell UB, Leon DA. Relation of size at birth to non-insulin dependent diabetes and insulin concentrations in men aged 50–60 years. BMJ. 1996;312(7028):406–410. [PMC free article] [PubMed]
  • Loaiza S, Coustasse A, Urrutia-Rojas X, Atalah E. Birth weight and obesity risk at first grade in a cohort of Chilean children. Nutr Hosp. 2011;26(1):214–219. [PubMed]
  • Lopez-Jaramillo P, Garcia RG, Lopez M. Preventing pregnancy-induced hypertension: are there regional differences for this global problem? J Hypertens. 2005;23(6):1121–1129. [PubMed]
  • Maccani MA, Avissar-Whiting M, Banister CE, McGonnigal B, Padbury JF, Marsit CJ. Maternal cigarette smoking during pregnancy is associated with downregulation of miR-16, miR-21, and miR-146a in the placenta. Epigenetics. 2010;5(7):583–589. [PMC free article] [PubMed]
  • Maccani MA, Marsit CJ. Epigenetics in the placenta. Am J Reprod Immunol. 2009;62(2):78–89. [PMC free article] [PubMed]
  • Maccani MA, Padbury JF, Marsit CJ. 2011. miR-16 and miR-21 expression in the placenta is associated with fetal growth. PLoS One 66e21210 e21210. doi:[Online 15 June 2011]10.1371/journal.pone.0021210 [PMC free article] [PubMed] [Cross Ref]
  • Maloney EK, Waxman DJ. trans-Activation of PPARα and PPARγ by structurally diverse environmental chemicals. Toxicol Appl Pharmacol. 1999;161(2):209–218. [PubMed]
  • Martin JA. Preterm births–United States, 2007. MMWR Surveill Summ. 2011;60(suppl):78–79. [PubMed]
  • McCormick DP, Sarpong K, Jordan L, Ray LA, Jain S. Infant obesity: are we ready to make this diagnosis? J Pediatr. 2010;157(1):15–19. [PubMed]
  • Meyer U, Nyffeler M, Yee BK, Knuesel I, Feldon J. Adult brain and behavioral pathological markers of prenatal immune challenge during early/middle and late fetal development in mice. Brain Behav Immun. 2008;22(4):469–486. [PubMed]
  • Meyer U, Weiner I, McAlonan GM, Feldon J. The neuropathological contribution of prenatal inflammation to schizophrenia. Expert Rev Neurother. 2011;11(1):29–32. [PubMed]
  • Moreno JL, Kurita M, Holloway T, Lopez J, Cadagan R, Martinez-Sobrido L, et al. Maternal influenza viral infection causes schizophrenia-like alterations of 5-HTA and mGlu receptors in the adult offspring. J Neurosci. 2011;31(5):1863–1872. [PMC free article] [PubMed]
  • Myatt L. Review: Reactive oxygen and nitrogen species and functional adaptation of the placenta. Placenta. 2010;31(suppl):S66–S69. [PMC free article] [PubMed]
  • Myatt L, Roberts VH. Cambridge, UK: Cambridge University Press,130–142; 2006. Placental mechanisms and developmental origins of health and disease. In: Developmental Origins of Health and Disease (Gluckman P, Hanson M, eds)
  • Nelson RE. Testing the fetal origins hypothesis in a developing country: evidence from the 1918 Influenza Pandemic. Health Econ. 2010;19(10):1181–1192. [PubMed]
  • Newbold RR, Padilla-Banks E, Jefferson WN. Environmental estrogens and obesity. Mol Cell Endocrinol. 2009;304(1–2):84–89. [PMC free article] [PubMed]
  • Novakovic B, Yuen RK, Gordon L, Penaherrera MS, Sharkey A, Moffett A, et al. 2011. Evidence for widespread changes in promoter methylation profile in human placenta in response to increasing gestational age and environmental/stochastic factors. BMC Genomics 12529 doi:[Online 28 October 2011]10.1186/1471-2164-12-529 [PMC free article] [PubMed] [Cross Ref]
  • O’Regan D, Kenyon CJ, Seckl JR, Holmes MC. Prenatal dexamethasone ‘programmes’ hypotension, but stress-induced hypertension in adult offspring. J Endocrinol. 2008;196(2):343–352. [PubMed]
  • Painter RC, Osmond C, Gluckman P, Hanson M, Phillips DI, Roseboom TJ. Transgenerational effects of prenatal exposure to the Dutch famine on neonatal adiposity and health in later life. BJOG. 2008;115(10):1243–1249. [PubMed]
  • Painter RC, Roseboom TJ, Bleker OP. Prenatal exposure to the Dutch famine and disease in later life: an overview. Reprod Toxicol. 2005;20(3):345–352. [PubMed]
  • Patterson PH. Maternal infection and immune involvement in autism. Trends Mol Med. 2011;17(7):389–394. [PMC free article] [PubMed]
  • Patterson RM, Pouliot MR. Neonatal morphometrics and perinatal outcome: who is growth retarded? Am J Obstet Gynecol. 1987;157(3):691–693. [PubMed]
  • Perera F, Tang WY, Herbstman J, Tang D, Levin L, Miller R, et al. 2009. Relation of DNA methylation of 5’-CpG island of ACSL3 to transplacental exposure to airborne polycyclic aromatic hydrocarbons and childhood asthma. PLoS One 42e4488 doi:[Online 16 February 2009]10.1371/journal.pone.0004488 [PMC free article] [PubMed] [Cross Ref]
  • Pham TD, MacLennan NK, Chiu CT, Laksana GS, Hsu JL, Lane RH. Uteroplacental insufficiency increases apoptosis and alters p53 gene methylation in the full-term IUGR rat kidney. Am J Physiol Regul Integr Comp Physiol. 2003;285(5):R962–R970. [PubMed]
  • Pierce BL, Kibriya MG, Tong L, Jasmine F, Argos M, Roy S, et al. 2012. Genome-wide association study identifies chromosome 10q24.32 variants associated with arsenic metabolism and toxicity phenotypes in Bangladesh. PLoS Genet 82e1002522 doi:[Online 23 February 2012]10.1371/journal.pgen.1002522 [PMC free article] [PubMed] [Cross Ref]
  • Pilgaard K, Faerch K, Carstensen B, Poulsen P, Pisinger C, Pedersen O, et al. Low birthweight and premature birth are both associated with type 2 diabetes in a random sample of middle-aged Danes. Diabetologia. 2010;53(12):2526–2530. [PubMed]
  • Pilsner JR, Liu X, Ahsan H, Ilievski V, Slavkovich V, Levy D, et al. Folate deficiency, hyperhomocysteinemia, low urinary creatinine, and hypomethylation of leukocyte DNA are risk factors for arsenic-induced skin lesions. Environ Health Perspect. 2009;117:254–260. [PMC free article] [PubMed]
  • Plagemann A, Harder T, Brunn M, Harder A, Roepke K, Wittrock-Staar M, et al. Hypothalamic proopiomelanocortin promoter methylation becomes altered by early overfeeding: an epigenetic model of obesity and the metabolic syndrome. J Physiol. 2009;587(pt 20):4963–4976. [PubMed]
  • Ravelli AC, van der Meulen JH, Michels RP, Osmond C, Barker DJ, Hales CN, et al. Glucose tolerance in adults after prenatal exposure to famine. Lancet. 1998;351(9097):173–177. [PubMed]
  • Ravelli GP, Stein ZA, Susser MW. Obesity in young men after famine exposure in utero and early infancy. N Engl J Med. 1976;295(7):349–353. [PubMed]
  • Ren X, McHale CM, Skibola CF, Smith AH, Smith MT, Zhang L. An emerging role for epigenetic dysregulation in arsenic toxicity and carcinogenesis. Environ Health Perspect. 2011;119:11–19. [PMC free article] [PubMed]
  • Roseboom T, de Rooij S, Painter R. The Dutch famine and its long-term consequences for adult health. Early Hum Dev. 2006;82(8):485–491. [PubMed]
  • Roseboom TJ, van der Meulen JH, Osmond C, Barker DJ, Ravelli AC, Bleker OP. Plasma lipid profiles in adults after prenatal exposure to the Dutch famine. Am J Clin Nutr. 2000;72(5):1101–1106. [PubMed]
  • Roseboom TJ, van der Meulen JH, Ravelli AC, Osmond C, Barker DJ, Bleker OP. Effects of prenatal exposure to the Dutch famine on adult disease in later life: an overview. Mol Cell Endocrinol. 2001;185(1–2):93–98. [PubMed]
  • Rosen ED, Sarraf P, Troy AE, Bradwin G, Moore K, Milstone DS, et al. PPAR gamma is required for the differentiation of adipose tissue in vivo and in vitro. Mol Cell. 1999;4(4):611–617. [PubMed]
  • Rosenberg HG. Systemic arterial disease and chronic arsenicism in infants. Arch Pathol. 1974;97(6):360–365. [PubMed]
  • Rubin MM. Antenatal exposure to DES: lessons learned...future concerns. Obstet Gynecol Surv. 2007;62(8):548–555. [PubMed]
  • Ruthenburg AJ, Li H, Patel DJ, Allis CD. Multivalent engagement of chromatin modifications by linked binding modules. Nat Rev Mol Cell Biol. 2007;8(12):983–994. [PubMed]
  • Sayed D, Abdellatif M. MicroRNAs in development and disease. Physiol Rev. 2011;91(3):827–887. [PubMed]
  • Schug TT, Janesick A, Blumberg B, Heindel JJ. Endocrine disrupting chemicals and disease susceptibility. J Steroid Biochem Mol Biol. 2011;127(3–5):204–215. [PMC free article] [PubMed]
  • Seckl JR, Cleasby M, Nyirenda MJ. Glucocorticoids, 11β-hydroxysteroid dehydrogenase, and fetal programming. Kidney Int. 2000;57(4):1412–1417. [PubMed]
  • Seckl JR, Meaney MJ. Glucocorticoid programming. Ann NY Acad Sci. 2004;1032:63–84. [PubMed]
  • Shadid S, Jensen MD. Effects of pioglitazone versus diet and exercise on metabolic health and fat distribution in upper body obesity. Diabetes Care. 2003;26(11):3148–3152. [PubMed]
  • Simmons RA, Templeton LJ, Gertz SJ. Intrauterine growth retardation leads to the development of type 2 diabetes in the rat. Diabetes. 2001;50(10):2279–2286. [PubMed]
  • Simonetti GD, Schwertz R, Klett M, Hoffmann GF, Schaefer F, Wuhl E. Determinants of blood pressure in preschool children: the role of parental smoking. Circulation. 2011;123(3):292–298. [PubMed]
  • Smith AH, Marshall G, Yuan Y, Ferreccio C, Liaw J, von Ehrenstein O, et al. Increased mortality from lung cancer and bronchiectasis in young adults after exposure to arsenic in utero and in early childhood. Environ Health Perspect. 2006;114:1293–1296. [PMC free article] [PubMed]
  • Srivastava S, D’Souza SE, Sen U, States JC. In utero arsenic exposure induces early onset of atherosclerosis in ApoE-/- mice. Reprod Toxicol. 2007;23(3):449–456. [PMC free article] [PubMed]
  • Srivastava S, Vladykovskaya EN, Haberzettl P, Sithu SD, D’Souza SE, States JC. Arsenic exacerbates atherosclerotic lesion formation and inflammation in ApoE-/- mice. Toxicol Appl Pharmacol. 2009;241(1):90–100. [PubMed]
  • States JC, Barchowsky A, Cartwright IL, Reichard JF, Futscher BW, Lantz RC. Arsenic toxicology: translating between experimental models and human pathology. Environ Health Perspect. 2011;119:1356–1363. [PMC free article] [PubMed]
  • Steegers-Theunissen RP, Obermann-Borst SA, Kremer D, Lindemans J, Siebel C, Steegers EA, et al. 2009. Periconceptional maternal folic acid use of 400 µg per day is related to increased methylation of the IGF2 gene in the very young child. PLoS One 411e7845 doi:[Online 16 November 2009]10.1371/journal.pone.0007845 [PMC free article] [PubMed] [Cross Ref]
  • Susser E, Neugebauer R, Hoek HW, Brown AS, Lin S, Labovitz D, et al. Schizophrenia after prenatal famine. Further evidence. Arch Gen Psychiatry. 1996;53(1):25–31. [PubMed]
  • Takada I, Kouzmenko AP, Kato S. Wnt and PPARγ signaling in osteoblastogenesis and adipogenesis. Nat Rev Rheumatol. 2009;5(8):442–447. [PubMed]
  • Taveras EM, Rifas-Shiman SL, Belfort MB, Kleinman KP, Oken E, Gillman MW. Weight status in the first 6 months of life and obesity at 3 years of age. Pediatrics. 2009;123(4):1177–1183. [PMC free article] [PubMed]
  • Thiering E, Bruske I, Kratzsch J, Thiery J, Sausenthaler S, Meisinger C, et al. Prenatal and postnatal tobacco smoke exposure and development of insulin resistance in 10 year old children. Int J Hyg Environ Health. 2011;214(5):361–368. [PubMed]
  • Tobi EW, Lumey LH, Talens RP, Kremer D, Putter H, Stein AD, et al. DNA methylation differences after exposure to prenatal famine are common and timing- and sex-specific. Hum Mol Genet. 2009;18(21):4046–4053. [PMC free article] [PubMed]
  • Tokar EJ, Qu W, Waalkes MP. Arsenic, stem cells, and the developmental basis of adult cancer. Toxicol Sci. 2011;120(suppl 1):S192–S203. [PMC free article] [PubMed]
  • Tontonoz P, Spiegelman BM. Fat and beyond: the diverse biology of PPARgamma. Annu Rev Biochem. 2008;77:289–312. [PubMed]
  • Torrens C, Poston L, Hanson MA. Transmission of raised blood pressure and endothelial dysfunction to the F2 generation induced by maternal protein restriction in the F0, in the absence of dietary challenge in the F1 generation. Br J Nutr. 2008;100(4):760–766. [PubMed]
  • Tsirka AE, Gruetzmacher EM, Kelley DE, Ritov VH, Devaskar SU, Lane RH. Myocardial gene expression of glucose transporter 1 and glucose transporter 4 in response to uteroplacental insufficiency in the rat. J Endocrinol. 2001;169(2):373–380. [PubMed]
  • Varvarigou AA. Intrauterine growth restriction as a potential risk factor for disease onset in adulthood. J Pediatr Endocrinol Metab. 2010;23(3):215–224. [PubMed]
  • Walther FJ. Growth and development of term disproportionate small-for-gestational age infants at the age of 7 years. Early Hum Dev. 1988;18(1):1–11. [PubMed]
  • Waterland RA. Assessing the effects of high methionine intake on DNA methylation. J Nutr. 2006;136 (6 suppl):1706S–1710S. [PubMed]
  • Weaver IC, Cervoni N, Champagne FA, D’Alessio AC, Sharma S, Seckl JR, et al. Epigenetic programming by maternal behavior. Nat Neurosci. 2004;7(8):847–854. [PubMed]
  • Wolff GL, Kodell RL, Moore SR, Cooney CA. Maternal epigenetics and methyl supplements affect agouti gene expression in Avy/a mice. FASEB J. 1998;12(11):949–957. [PubMed]
  • Yorifuji T, Tsuda T, Grandjean P. Unusual cancer excess after neonatal arsenic exposure from contaminated milk powder. J Natl Cancer Inst. 2010;102(5):360–361. [PMC free article] [PubMed]
  • Yuan Y, Marshall G, Ferreccio C, Steinmaus C, Selvin S, Liaw J, et al. Acute myocardial infarction mortality in comparison with lung and bladder cancer mortality in arsenic-exposed region II of Chile from 1950 to 2000. Am J Epidemiol. 2007;166(12):1381–1391. [PubMed]

Articles from Environmental Health Perspectives are provided here courtesy of National Institute of Environmental Health Science