Abstract
Preterm birth is associated with medical problems affecting the neuroendocrine system, altering cortisol levels resulting in negative effects on newborn neurobehavior. Newborn neurobehavior is regulated by DNA methylation of NR3C1 and HSD11B2.
Aim:
Determine if methylation of HSD11B2 and NR3C1 is associated with neurobehavioral profiles in preterm infants.
Patients & methods:
Neurobehavior was measured before discharge from the hospital in 67 preterm infants. Cheek swabs were collected for DNA extraction.
Results:
Infants with the high-risk neurobehavioral profile showed more methylation than infants with the low-risk neurobehavioral profile at CpG3 for NR3C1 and less methylation of CpG3 for HSD11B2. Infants with these profiles were more likely to have increased methylation of NR3C1 and decreased methylation of HSD11B2 at these CpG sites.
Conclusion:
Preterm birth is associated with epigenetic differences in genes that regulate cortisol levels related to high-risk neurobehavioral profiles.
Keywords: : cortisol, DNA methylation, epigenetics, high-risk infants, NICU Network Neurobehavioral Scale, prematurity, programming
Preterm birth in the USA is a significant public health problem with a prevalence rate of approximately 10% (450,000 infants) per year [1]. Although, advances in perinatal and neonatal care have improved survival rates for preterm infants, this increase in survival is accompanied by a parallel increase in the risk for developmental impairment. Approximately 28–50% of infants born less than 32 weeks gestational age in the 1990s developed neuromotor, sensory, cognitive, language and/or behavioral impairments that require extensive healthcare, educational and psychosocial community resources through adulthood [2].
Developmental outcome in preterm infants
Neurodevelopmental impairment is a critically important outcome of preterm birth. Rates of cognitive impairment alone at 24 months in preterm infants, especially those born less than 32 weeks gestational age, range from 23 to 47% [2–4]. Behavior problems are also associated with lower cognitive scores [5]. Although improvements in cognitive scores have been seen by 8 years, preterm infants continued to score -0.5 to -1 standard deviations (SD) below their full-term peers. As many as 50–70% have impairments in executive functioning, visual motor skills, verbal memory, visual processing and/or adaptive tasks [6]. Even in children without specific cognitive deficits, those with chronic lung disease (CLD) and/or severe intraventricular hemorrhage (IVH) [7] are at increased risk of poor school achievement (24–41%), receipt of special education services (25–62%), repetition of grades (15–34%) and a 60–75% lower rate of graduation from high school. Cognitive impairments persist and remain significant in the areas of visual-perceptual tasks, reading and math achievement. In a meta-anaysis, preterm children were 0.48–0.76 SD behind term peers in reading, math, spelling and showed poor academic achievement [8].
Significant motor impairments in preterm infants include cerebral palsy (CP) and generally impaired gross motor function [6]. At school age, 10–20% have neurological ‘soft signs’ that do not indicate localized brain injury, but reflect disorders of speech, balance, tone, gait and fine or visual-motor skills that are also associated with low IQ, learning disabilities, attention deficits and behavioral disorders at 6–11 years of age [9]. Mild visual and hearing impairments affect 9–25% of these infants, moderate-to-severe sensory impairments affect 13%, and at 11–14 years, 27–32% show impaired functional outcomes involved in daily living and social participation [10].
Behavioral and psychological disorders are evident in toddlers and children (8–12 year olds) born preterm including social withdrawal and/or anxiety or depression, attention deficits [11–14] and increased risk for Autism Spectrum Disorders [15,16]. When these infants reach adolescence, they have more difficulty establishing social relationships, and continue to experience social withdrawal and anxiety disorders at 20 years of age [13].
Medical problems & the neuroendocrine system
Although the range of morbidity in preterm infants is due, in part, to the immaturity of their organ systems, preterm birth is often accompanied by severe medical problems, such as bronchopulmonary dysplasia, retinopathy of prematurity, infection and structural brain abnormalities [17–19]. These complications are inversely proportional to gestational age at birth and are often the result of hemodynamic instability, cardiovascular dysfunction and hypotension and are correlated with increased mortality and morbidity [20]. Such problems can also affect the neuroendocrine system and disrupt the hypothalamic pituitary adrenocortical (HPA) axis, specifically; affected brain regions include elements of the limbic system that stimulate the release of corticotropin-releasing hormone (CRH). These regions include the hypothalamus, amygdala and hippocampus. CRH activates the pituitary gland to release adrenocorticotropic hormone (ACTH) into the bloodstream, which in turn stimulates cells in the adrenal glands to produce cortisol which is also released into the bloodstream [21,22]. The association of illnesses with HPA function and dysregulation has been investigated intensively in preterm infants [23]. These illnesses affect the secretion of CRH [20,24] altering cortisol levels with consequent negative effects on newborn neurobehavior and later development including behavioral and cognitive disorders.
Cortisol levels and the effects of cortisol are regulated, in part, by the cross talk of the sensitive feedback loop between the hormone products of the hypothalamus, the pituitary and the adrenal gland. There are complex mechanisms for the regulation of hormone and hormone receptor expression and processing. One example is the regulation of cortisol responsiveness by epigenetic differences in the expression of the glucocorticoid receptor (GR) gene NR3C1 and 11β-hydroxysteroid dehydrogenase type 2 (HSD11B2). NR3C1 encodes GR, a nuclear receptor that binds cortisol and facilitates cortisol's transcriptional activity including regulation of HSD11B2. HSD11B2 converts cortisol to its inert form of cortisone, thus regulating levels of the active hormone in circulation. Regulation of the expression of these genes has been, in part, reported to occur through epigenetic mechanisms, particularly through DNA methylation of the promoter regions of these genes. In rodents, maternal care is related to DNA methylation, increased expression of the hippocampal NR3C1 promoter region and HPA responses to stress including lower levels of corticosterone [25]. Human infants born to mothers with depression during pregnancy show increased DNA methylation of NR3C1 in the comparable promoter region in blood and increased salivary cortisol levels at 3 months of age [26].
Measuring newborn neurobehavior
We have shown that DNA methylation of HSD11B2 and NR3C1 in the placenta is related to newborn neurobehavior [27–29] using the Neonatal Intensive Care Unit Network Neurobehavioral Scale (NNNS) [30]. The NNNS is a comprehensive assessment of both neurologic integrity and behavioral functioning, including signs of stress. It assesses the full range of infant neurobehavioral performance (orientation to auditory and visual stimuli); infant stress (color changes, tremors and startles), neurologic functioning (reflexes, tone); some features of gestational age; self-soothing capacities; states and their organization. The neurobehavioral, reflex and stress items on the NNNS are reduced to summary scores (Table 1; attention, handling, self-regulation, arousal, excitability, lethargy, hypertonicity, hypotonicity, nonoptimal reflexes, asymmetric reflexes, quality of movement and stress/abstinence) with alpha coefficients ranging from 0.87 to 0.90 [31]. Methods including latent profile [32,33] and latent modeling have been developed to group infants into mutually exclusive categories or profiles based on the summary scores, with each profile showing a different pattern of performance over the course of the exam [34–36]. Collapsing the multidimensional NNNS exam into coherently integrated, statistically validated profiles facilitates their use in statistical comparison of behavioral outcome to biological variables. The profiles have been used to identify both normal and abnormal groups of infants and predict long-term outcome [32]. The NNNS can be used as early as 34 weeks’ gestational age, depending on the medical status of the infant, through approximately 1-month post-term (44 weeks gestational age). It can be used with low-risk as well as extremely high-risk infants and is sensitive to a wide variety of medical conditions, prenatal exposures and demographic variables. In studies of preterm infants, the NNNS has been related to quality of care in the NICU, [37] medical problems [38] and white and gray matter abnormalities [39]. Especially important, the NNNS and NNNS-based profiles predict cognitive and motor scores at 18 months in preterm infants [40] and the development of CP and motor impairment [41]. Preterm infants and infants with prenatal cocaine exposure with an abnormal NNNS profile had an abnormal cranial ultrasound at 1 month, chronic neurological abnormalities and brain-related illness or CP by age 3, impaired cognitive and motor scores at 1 and 2 years, behavior problems at age 3, concept problems in school readiness at age 4 and low IQ at 4.5 years [32]. It is also both interesting and supportive of our analysis that in a low-risk sample, NNNS profiles predicted motor development and behavior problems at age 3 [33].
Table 1. . NICU Network Neurobehavioral Scale summary scores.
| NNNS summary scales | Description |
|---|---|
| Attention |
Ability to localize and track objects, faces and voices |
| Handling |
Handling strategies used during attention |
| Self-regulation |
Organize behavior in response to stimulation |
| Arousal |
Level of arousal during the examination |
| Excitability |
High levels of motor, state and physiologic reactivity |
| Lethargy |
Low levels of motor, state and physiologic reactivity |
| Hypertonicity |
Hypertonic responses in arms, legs, trunk or tone |
| Hypotonicity |
Hypotonic response in arms, legs, trunk or tone |
| Nonoptimal reflexes |
Number of poor reflex scores |
| Asymmetric reflexes |
Number of asymmetric reflex scores |
| Quality of movement |
Smoothness, maturity, lack of startles, tremors |
| Stress/abstinence | Number of stress signs observed |
NNNS: Neonatal Intensive Care Unit Network Neurobehavioral Scale.
DNA methylation & newborn neurobehavior
In previous work with full-term infants, our group has studied relations between DNA methylation of placental HSD11B2 and the NR3C1 promoter and NNNS scores at birth. We found that DNA methylation of placental HSD11B2 was related to quality of movement [27]. DNA methylation of the placental NR3C1 promoter was also related to quality of movement as well as to attention (ability to maintain alert states and track visual and auditory stimuli) [28]. DNA methylation of both placental NR3C1 and HSD11B2 in mothers with depression or anxiety during pregnancy was related to self-regulation (able to modulate responsivity to stimulation), hypotonia (low muscle tone) and lethargy (low level of arousal) [29]. DNA methylation of somatic HSD11B2 and NR3C1 has not been studied in preterm infants. Our studies suggest that epigenetic mechanisms could play a role in the regulation of cortisol levels in preterm infants [27–29]. Specifically, DNA methylation of HSD11B2 and NR3C1 has the potential to disrupt HPA activity and affect the neurobehavior of these infants.
The purpose of the current study was to examine DNA methylation of somatic HSD11B2 and NR3C1 in preterm infants. In addition, we wanted to determine if DNA methylation of these genes was associated with different profiles on the NNNS in this preterm population. The rationale for this study is based on the impact of medical problems on the preterm infant's neuroendocrine system, the accompanying stress that could be involved and the role of HSD11B2 and NR3C1 in regulating cortisol. In addition, DNA placental methylation of these genes is associated with neurobehavior in term infants and we wanted to determine if there were similar DNA somatic methylation effects in preterm infants. As described above, it has been well documented that preterm infants are at high risk for the later development of cognitive, motor and social impairment as well as behavior problem and mental health disorders. Given that profiles on the NNNS predict long-term outcome in preterm infants, this study is an opportunity to identify potential epigenetic pathways in these infants that could alter early neurobehavior that has been related to later developmental outcome.
Patients & methods
The sample included 67 preterm infants born 23–35 weeks gestational age (birthweight, 480–1495 g) enrolled between 2 weeks after birth and 2 weeks before hospital discharge from the Neonatal Intensive Care Unit (NICU) at Women & Infants Hospital of Rhode Island. Exclusion criteria included; non-English speaking or <18–year-old mother, infant congenital anomalies or transfer to another hospital. The hospital Institutional Review Board approved the study, and written informed consent was obtained from all participants. The NNNS was administered to the infants 3–4 days before discharge from the NICU by certified examiners. Cheek swabs to collect buccal cells for DNA extraction were collected following administration of the NNNS. Demographic and medical data were abstracted from the medical records. Demographic characteristics included infant gender, race, ethnicity, maternal education and partner status. Medical variables included birthweight, gestational age, length of stay, weight at discharge, gestational age at discharge, rate of weight gain, head circumference at birth, gestational age at full enteral feeding, necrotizing enterocolitis, intraventricular hemorrhage, periventricular leukomalacia, retinopathy of prematurity, sepsis, bronchopulmonary dysplasia and the Neonatal Therapeutic Intervention Scoring System (NTISS). The NTISS is a measure of therapeutic intensity and provides an index of neonatal illness severity and resource utilization throughout the length of stay in the NICU. The NTISS scores were used to assess illness severity and medical interventions [42].
Statistical analysis
One-Way analysis of variance and Pearson correlation was used for continuous variables, χ2 and odds ratios (OR) for dichotomous variables. The calculation of the NNNS profiles was based on a Gaussian-distributed recursively partitioned mixture model RPMM, [34–36] and was used to cluster infants into discrete profiles from the NNNS summary scores. RPMM is a hierarchical, model-based clustering algorithm that estimates the number of underlying classes (e.g., K) and the posterior probability of class membership for each subject across the K estimated classes. This methodology is similar to finite mixture models, but differs from this approach in its computational efficiency and its ability to estimate the number of classes without the need for sequential mixture model fitting. RPMM was used to fit the 67 infants. Based on the RPMM solution fit to these data, an empirical Bayesian procedure was used to predict profile membership for these infants. The profile analysis was conducted using the R ([43]; RPMM package). The Benjamini and Hochberg method (FDR q<0.10) was used to determine the percent of findings that could be a false discovery [44].
DNA methylation analysis
Genomic DNA was extracted from oral swab samples of each infant collected using Oragene Discover for assisted collection using the prepIT kit (DNA Genotek, (Kanata, ON, Canada), and subjected to bisulfite modification using the EZ DNA methylation Kit (Zymo Research, CA, USA). The DNA methylation status for both the NR3C1 exon 1F and HSD11B2 promoter regions was assessed using quantitative bisulfite pyrosequencing as previously described [27,28]. Amplification of the bisulfite treated DNA was accomplished with primers for NR3C1 (Forward-5′-TTTTTT TTT TGA AGT TTT TTT A-3′, Reverse-5′-biotin-CCC CCA ACT CCC CAA AAA-3)and for HSD11B2 (Forward-5-GGAAGTGGGGTTGTGYGTTTTTAGGTTTAAGTT-3′, Reverse-5′-biotin-ATACCCTTTACTAATCRCACCACC-3′). Sequencing primers were for NR3C1 (5′-GAG TGG GTT TGG AGT-3′) and for HSD11B2(5′-GGGGTAGAGATTTTAAGAA -3′). Sequencing primers were for NR3C1 (5′-GAG TGG GTT TGG AGT-3′) and for HSD11B2 (5′-GGGGTAGAGATTTTAAGAA -3′). We used sequencing primers to interrogate the first 4 of the 13 NR3C1 CpG sites in the 1F region (representing the following genomic coordinates: GRCh37/hg19 Chr 14: 142783501, 142783503, 142783513, 142783519), as this region has been shown to relate to regulation of the neuroendocrine system including the HPA axis and stress reactivity in rodent [25] and human studies, [26] and the four HSD11B2 CpG sites (representing the following genomic coordinates: GRCh37/hg19 Chr 1667464389, 67464395, 67464399 and 67464412). Percent of DNA methylation at each CpG site (1–4 for NR3C1 and 1–4 for HSD11B2) was quantified using the PyroMark MD instrument and the PyroMark Q-CpG software, version 1.0.11. Bisulfite conversion controls were included with each sequencing read. In order for the sample's DNA methylation percent to be called, the bisulfite conversion rate must be greater than 93%. For all samples examined, the conversion rate was greater than 95%. All samples were sequenced in triplicates from the same bisulfite-converted DNA template, and if the repeats differed by greater than 10% the sample was repeated. DNA methylation data are analyzed as a continuous measure based on percent of methylation at each CpG site.
Results
High-risk & low-risk neurobehavioral profiles
The RPMM analysis yielded two profiles (Figure 1) from the NNNS summary scores shown on the X axis. This was then converted to z (standard) scores shown on the Y axis. There were 38 infants (56.7%) characterized by positive neurobehavior (low-risk group) and 29 infants (43.3%) characterized by negative, problematic neurobehavior (high-risk group). The high-risk profile is virtually identical to the profile in the Liu et al. study (‘profile 5’) [32]. In the latter study, profile 5 predicted developmental outcome through 4 years of age in a sample of high-risk infants. On the specific summary scores, infants with the low-risk profile showed better attention that required less handling (Table 2). They had better self-regulation, they were less aroused, less excitable, had a better quality of movement and few signs of stress. Infants with the high-risk profile showed poor attention that required substantial handling. They had poor self-regulation, were highly aroused and excitable, showed poor quality of movement and substantial signs of stress. There were fewer females with the high-risk profile than in infants with the low-risk profile and more mothers of infants with the high-risk profile had less than a high school education than mothers of infants in the low-risk profile (Table 3). There were no differences in medical characteristics between infants in the two profile groups, including gestational age at birth (Table 4).
Figure 1. . Neonatal Intensive Care Unit Network Neurobehavioral Scale profiles for high- and low-risk infants.
Table 2. . NICU Network Neurobehavioral Scale summary scale scores by high- and low-risk profiles.
| NNNS summary scales | High-risk profile (n = 29, 43.3%), mean (SD) | Low-risk profile (n = 38, 56.7%), mean (SD) | p-value |
|---|---|---|---|
| Attention |
4.13 (0.83) |
4.99 (1.29) |
0.006 |
| Handling |
0.46 (0.20) |
0.26 (0.21) |
<0.001 |
| Self-regulation |
4.88 (0.58) |
5.86 (0.59) |
<0.001 |
| Arousal |
3.84 (0.72) |
3.30 (0.39) |
<0.01 |
| Excitability |
4.07 (1.89) |
1.21 (1.34) |
<0.001 |
| Lethargy |
4.90 (1.78) |
5.50 (1.89) |
0.188 |
| Hypertonicity |
0.10 (0.31) |
0.08 (0.27) |
0.733 |
| Hypotonicity |
0.21 (0.41) |
0.34 (0.58) |
0.292 |
| Nonoptimal reflexes |
5.93 (2.28) |
5.47 (1.91) |
0.376 |
| Asymmetrical reflexes |
1.03 (0.78) |
1.03 (0.89) |
0.969 |
| Quality of movement |
4.21 (0.58) |
4.68 (0.48) |
0.001 |
| Stress/abstinence | 0.15 (0.04) | 0.13 (0.05) | 0.044 |
NNNS: Neonatal Intensive Care Unit Network Neurobehavioral Scale; SD: Standard deviation.
Table 3. . Maternal & neonatal characteristics by high- and low-risk profiles.
| Characteristic | High-risk profile (n = 29, 43.3%) | Low-risk profile (n = 38, 56.7%) | p-value |
|---|---|---|---|
| Birth weight (g), M (SD) |
1082 (257) |
1017 (291) |
0.346 |
| Gestational age at birth (wks), M (SD) |
28.4 (2.6) |
28.3 (3.0) |
0.879 |
| Infant sex, female, n (%) |
11 (37.9) |
25 (65.8) |
0.023 |
| Infant race, n (%): | 0.435 | ||
| – White | 21 (72.4) | 23 (60.5) | – |
| – African–American | 2 (6.9) | 3 (7.9) | – |
| – Asian | 0 | 3 (7.9) | – |
| – Multiracial |
6 (20.7) |
9 (23.7) |
– |
| Low SES, n (%) |
4 (14.8) |
4 (10.8) |
0.712 |
| Maternal ethnicity, n (%): | |||
| – Hispanic |
3 (10.3) |
6 (15.8) |
0.721 |
| Maternal educational status, n (%): | |||
| – Less than high school |
14 (50.0) |
10 (26.3) |
0.048 |
| Maternal partner status, n (%): | |||
| – No partner | 4 (13.8) | 5 (13.2) | 0.940 |
M: Mean; SD: Standard deviation; SES: Socioeconomic status; wk: Week.
Table 4. . Infant medical outcomes by high- and low-risk profiles.
| Medical outcome | High-risk profile (n = 29, 43.3%) | Low-risk profile (n = 38, 56.7%) | p-value |
|---|---|---|---|
| Length of stay (d), M (SD) |
76 (32) |
81 (39) |
0.584 |
| Weight at discharge (g), M (SD) |
3010 (666) |
3010 (787) |
1.00 |
| Postmenstrual age at discharge (wk), M (SD) |
39.21 (2.46) |
39.80 (3.21) |
0.412 |
| Rate of weight gain (g/d), M (SD) |
25.27 (3.01) |
24.61 (3.75) |
0.441 |
| Head circumference (cm), M (SD) |
25.84 (2.67) |
25.23 (2.83) |
0.375 |
| GA at full enteral feeding (wk), M (SD) |
31.10 (2.09) |
30.93 (2.39) |
0.755 |
| Necrotizing enterocolitis (present), n (%) |
6 (20.7) |
4 (10.5) |
0.247 |
| Intraventricular hemorrhage (grade 3/4), n (%) |
1 (3.4) |
1 (2.6) |
0.846 |
| Periventricular leukomalacia (present), n (%) |
1 (3.4) |
1 (2.6) |
0.846 |
| Retinopathy of prematurity (stage 3, 4, 5), n (%) |
1 (3.6) |
3 (8.1) |
0.451 |
| Sepsis, n (%) |
3 (10.3) |
6 (15.8) |
0.517 |
| Bronchopulmonary dysplasia, n (%) |
14 (48.3) |
17 (44.7) |
0.773 |
| NTISS total score, M (SD) | 26.79 (7.30) | 26.76 (8.74) | 0.988 |
d: Day; GA: Gestational age; M: Mean; NTISS: Neonatal therapeutic intervention scoring system; SD: Standard deviation; wk: Week.
DNA methylation of NR3C1 & HSD11B2 is related to neurobehavioral profiles
The percent of DNA methylation at each of the CpG sites in the NR3C1 and HSD11B2 genes were compared between infants in the low- and high-risk profile groups. For NR3C1 (Figure 2) CPG sites 1 and 2, infants with the high-risk profile demonstrated less percent of methylation than infants with the low-risk, although these differences were not statistically significant. CpGs 3 and 4, though, demonstrated more methylation among infants with the high-risk profile than infants with the low-risk profile and reached statistical significance at CpG3. In fact, at CpG3, the methylation among infants with the high-risk profile was double that of the low-risk profile (high-risk M = 0.555; SD = 0.542; low-risk M = 0.264; SD = 0.412; p = 0.015; FDRq = 0.06). The opposite effect was observed for HSD11B2 (Figure 3), where the percent methylation at CPG3 was higher for infants with the low-risk profile (M = 0.938; SD = 0.435) than infants with the high-risk profile (M = 0.723; SD = 0.254; p = 0.021; FDRq = 0.08).
Figure 2. . Percentage methylation of NR3C1 by CpG site.
NNNS: Neonatal Intensive Care Unit Network Neurobehavioral Scale.
Figure 3. . Percentage methylation of HSD11B2 by CpG site.
NNNS: Neonatal Intensive Care Unit Network Neurobehavioral Scale.
Looking specifically at these two loci and using a median split of the percent of DNA methylation showed that infants with the high-risk profile were more likely to be above the median (more methylated group) than infants with the low-risk profile at CpG3 (OR: 4.27; CI = 1.53–12.01). For HSD11B2, infants with the high-risk profile were more likely to be below the median (less methylated group) than infants with the low-risk profile at CpG3 (OR: 3.05; CI = 1.10–8.44). Pairwise correlations between DNA methylation of NR3C1 CpG1–4 and HSD11B2 were all negative, and statistically significant at CpG4 (r = -0.324; p = 0.008).
Discussion
We studied DNA methylation of the promoter region of HSD11B2 and NR3C1 as this region relates to regulation of the neuroendocrine system including the HPA axis and stress reactivity. Our findings demonstrate differences in DNA methylation of the HSD11B2 and NR3C1 promoter at CpG3 between preterm infants with a high-risk neurobehavioral profile derived from summary scores on the NNNS compared with preterm infants with a low-risk neurobehavioral profile. Specifically, infants with the high-risk profile exhibited a greater extent of DNA methylation of NR3C1 at CpG3 than infants with the low-risk profile whereas for HSD11B2 infants with the high-risk profile exhibited less methylation than infants with the low-risk infant's profile. The false discovery rates of these findings were low, suggesting it was likely that these findings represent true discoveries, but like all findings from initial studies, we suggest replication in an independent cohort. We then conducted median split analysis at these two sites only. We found that, compared with infants with the low-risk profile, the likelihood of increased DNA methylation was higher for infants with the high-risk profile for NR3C1 at CpG3 but lower for HSD11B2 at CpG3. Thus, the median split analysis supported the mean comparison findings.
NR3C1 & HSD11B2 methylation may affect transcription factor binding
The findings for NR3C1 CpG3 and, to a lesser extent, the trend at CpG4, are consistent with findings from both rodent [25] and human work [26] as these CpG sites represent regions of the NR3C1 1F promoter that binds the nerve growth factor inducible protein transcription factor (NGF1-A) that is important for brain development including differentiation and neuronal plasticity [45]. In rodents, decreased methylation of the NGFI-A consensus binding site NR3C1 promoter results in increased expression of exon 17 NR3C1 hippocampal transcripts increasing negative HPA feedback and affecting stress regulation [25]. Human suicide victims also demonstrated increased methylation at NGF1-A consensus sites in the NR3C1 1F region in postmortem hippocampal samples, and in vitro studies demonstrated that methylation at these sites can inhibit expression in human cell as well [46]. Most human studies, though, cannot examine hippocampal tissue, but prior studies using peripheral samples demonstrated that DNA methylation of NR3C1 at CpG3 in the newborn is related to maternal depression during pregnancy and more importantly, to the a phenotype of cortisol stress reactivity in the same infants at 3 months of age [26]. Our findings here best align with this prior study of newborns. We have also found that newborn infants of mothers with depression during pregnancy showed more methylation of placental NR3C1 at CpG2 and these infants also showed poorer scores on the NNNS [29]. The absolute differences in the extent of methylation we observe are much smaller in peripheral samples than those observed in more selected hippocampal tissue, likely reflecting the cellular heterogeneity of our samples. Thus, while it is unlikely that all cells are demonstrating differences in methylation within the sample, some proportion appear to be, and it would be among those cells where we would suggest the increased methylation observed in this region among high-risk infants in our study could affect NR3C1 expression and later HPA-mediated stress reactivity. Likely, these cells represent those that are glucocorticoid responsive.
For HSD11B2, in silico analyses suggest that CpG3 may be part of a sequence region representing binding sites for the E2F1 transcription factor, which is involved in cell proliferation, cell cycle regulation, DNA synthesis and apoptosis. This CpG site is also adjacent to a GR-α-binding region, consistent with GR's role in the control of HSD11B2 expression through a negative feedback loop. This could suggest that methylation in this region affects HSD11B2 expression by altering the binding of E2F1 or GR. The role of these transcription factors and the potential disruption by methylation is consistent with the impact of illnesses associated with prematurity on the neuroendocrine system and disruption of the HPA axis. Moreover, control of HSD11B2 and NR3C1 are critical to the recovery and development of preterm infants. Disruption or enhancement of the transcription factors that elicit their expression could play an important role in establishing infants who are at risk or resilient following preterm birth. Although our current findings are not conclusive on this issue, they do suggest that further work to resolve the effects of methylation on the activity of these pathways is of interest.
NNNS profiles could reflect cortisol regulation & may have diagnostic value
It is both reasonable and noteworthy that HSD11B2 and NR3C1 have different methylation patterns related to newborn neurobehavior as these genes work in concert to regulate cortisol and it is well documented that cortisol affects infant behavior [21]. This is supported by our findings of a statistically significant negative correlation between HSD11B2 and NR3C1 at CpG3 as increased methylation of NR3C1, indicative of decreased expression would be expected to be related to decreased methylation of HSD11B2. These correlated patterns may represent the known feedback loop between GR and HSD11B2, and could suggest that in these infants, there may be compensatory reductions in methylation at HSD11B2 in response to reduced NR3C1 expression. The fact that this regulatory process is also reflected in the NNNS profiles speaks to the sensitivity of neurobehavior as a marker of physiological processes. In our previous work [34,36] we used placental DNA and found relations between DNA methylation of HSD11B2 and NR3C1 and NNNS profiles in healthy term infants. Here we used buccal cells and found an association between promoter methylation of these genes and NNNS profiles in preterm infants. This observation supports the utility of accessible samples such as saliva to assess these epigenetic marks. The generalizability and robustness of the relationship is suggested by these findings showing that relations have been observed in different populations with different underlying risks.
The profiles of neurobehavior based on the NNNS in these studies are very similar to previous observations. Both the low-risk and high-risk profiles in the present study are virtually identical to profiles that we have reported using different statistical methods [32,35] and in different populations. These two profiles represent extremes of positive and negative neurobehavior. The low-risk profile is that of the ‘optimal’ baby; attentive, alert, easy to handle, calm, not over-reactive, with fluid movements and minimal stress. The high-risk profile is quite the opposite; nonattentive, difficult to handle, very reactive, easily upset, jittery movements, stiff muscle tone and highly stressed. These profiles have now been observed in several populations including normal healthy infants, [34,36] low-risk infants, [33] term and preterm infants with prenatal cocaine exposure [32] and preterm infants in the present study. In addition, these profiles have been used with infants at birth through 5 weeks of age [32,33] here in preterm infants at NICU discharge and with placental DNA methylation of the same two genes [34,36] and now with somatic DNA methylation. In two of these studies, the profiles predicted 3.5 [33] and 4.5-year developmental outcomes [32]. This could suggest that the profiles capture ‘true’ characteristics or developmental typologies of infants that may not be evident in individual summary scores. It is also possible that some infants with the high-risk profile in the current study will have some form of developmental impairment in the long-term. If this hypothesis is confirmed, the high-risk profile could have diagnostic value, and be used to identify those infants in need of early intervention to potentially impact future deficits in health and behavioral outcomes.
The role of developmental programming
Our findings could suggest the possibility of a compensatory regulatory system involving HSD11B2 and NR3C1 (Figure 4A & B) to re-establish a balance in cortisol activity in infants demonstrating the high-risk profile. As described above, the reciprocity between HSD11B2 and NR3C1 could function to decrease methylation of the high-risk profile in HSD11B2 as this could result in reduced cortisol levels, representing a compensatory mechanism as a result of developmental programming.
Figure 4. . Proposed pathways leading to low-risk and high-risk and neurobehavioral profiles based on Neonatal Intensive Care Unit Network Neurobehavioral Scale summary scores.
The solid arrows in infants with the low-risk profile (A) indicate normal developmental processes and appropriate levels of cortisol. The dashed lines in infants with the high-risk profile (B) indicate disrupted processes including transcription factors and thick arrows for increased levels of cortisol.
Developmental programming or the resetting of physiological parameters due to external events, in this case prematurity, can endure into adulthood. Epigenetic regulation has been implicated as a key mechanism underlying developmental programming [47]. Modification of gene expression as a result of epigenetic differences could alter regulatory systems such as the regulation of cortisol through changes in DNA methylation of NR3C1 and HSD11B2 as seen in our study. We suggest that this regulatory system is differentially affected in infants with the high-risk profile versus infants with the low-risk profile. It is possible that developmental programming was impeded in infants with the high-risk profile interfering with compensatory mechanisms that would reduce cortisol levels with continued disruption of the HPA axis which then becomes manifested as the abnormal high-risk profile.
Impaired developmental programming could be due to the effects of methylation of NR3C1 on transcription factors that bind to this region. Specifically, NGF1-A is important for differentiation and for neuronal plasticity and neuronal plasticity is necessary for developmental programming. Decreased activity of this transcription factor could reduce neuronal plasticity and impede developmental programming. It is possible that decreased expression of GR would result in reduced binding potential for cortisol and reduction in its transcriptional activity, which is critical for appropriate neurodevelopmental programming. A compensatory decrease in methylation within the HSD11B2 region could allow for increased expression of this cortisol regulator to then inactivate the high levels of circulating cortisol resulting from the lack of GRs related to its increased methylation. Impaired developmental programming could also reduce the activity of the E2F1 transcription factor that binds to the CpG3 region of HSD11B2 thereby affecting cell proliferation, cell cycle regulation, DNA synthesis and apoptosis and through these potentially global effects could be responsible for the high-risk profile. The potential permanency of this epigenetic developmental programming could explain why many infants with the high-risk profile develop later impairment. Thus, epigenetic effects, especially those related to the HPA system may be one mechanism involved in the long-term outcome of preterm and other high-risk infants.
Stress & allostatic load
We also know that stress can impact the neuroendocrine system. Thus, in addition to the specific effects of medical problems on the infant's neuroendocrine system, the number of medical problems probably results in cumulative stress and further exacerbation of effects on the neuroendocrine system. Since there is abundant evidence that the NICU environment itself is stressful, the construct of allostatic load may apply here. Allostatic load refers to chronic, cumulative stress that becomes biologically embedded through repeated activation of the HPA system. This ‘wear and tear’ on stress response systems that has long-term effects on adult cardiovascular, metabolic, nervous and immune systems, increases the likelihood of disease, [48–50] psychological and behavioral abnormalities [51–54] and psychopathology [55]. Although this construct is not typically used in the current context, it is important to note that the mean gestational age at birth of our sample was 28.26 weeks with a mean length of stay of 11.5 weeks. This represents 28% of their lifespan and can be considered ‘chronic stress’ for these infants. This hypothesis is consistent with rodent work relating hippocampal transcripts to increased negative HPA feedback that affects stress regulation [25].
There is also a rich literature on prenatal stress although we cannot say with certainty what specific stressors, if any, were responsible for these infants’ preterm birth. Poor health outcomes have been related to prenatal stress including low birth weight, preterm birth and intrauterine growth retardation [56,57]. Low birth weight as a proxy for the quality of the intrauterine environment has also been associated with greater HPA reactivity in both childhood and adolescence [58]. It is also interesting that infants exposed to stress in utero show high reactivity, activity and irritability [59–61] reminiscent of the high-risk profile in the current study. In addition, in our study, more mothers of infants with the high-risk profile had less than a high school education than mothers of infants with the low-risk profile. Having less than a high school education could be a proxy for sociodemographic factors indicative of a high stress environment and contributed to the development of the high-risk profile [36].
Importance of this study
This is the first study of somatic DNA methylation of HSD11B2 and NR3C1 in preterm infants and is important for several reasons. We have shown that DNA methylation of these two genes that play a critically important role in regulating cortisol levels seems to be related to distinct neurobehavioral profiles. Moreover, these profiles replicate previous work including the high-risk abnormal profile that predicts long-term developmental outcome. It is well known that preterm infants are at high risk for the later development of cognitive, motor and social impairment as well as behavior problem and mental health disorders. We add to this literature the possibility of epigenetic pathways that could be involved in the developmental outcome of these infants and raise the role of developmental programming involvement. These findings are also important because the methylation effects that we found were relatively small, yet they were related to not only newborn neurobehavior, but to neurobehavioral profiles that have been related to long-term developmental outcome in similar populations. Even small epigenetic effects may have ‘big’ consequences for later development. Thus, epigenetics may play an important role in our understanding of the consequences of prematurity.
DNA methylation of NR3C1 in particular, has been studied in many populations and is related to newborn neurobehavior, [27–29] cortisol reactivity in infants, [26] environmental adversity including parental loss and child maltreatment, [62] childhood psychopathology and suicide [46]. There is an extensive literature on cortisol reactivity and the development of mental health disorders [63] and disturbances in HPA regulation have also been associated with affective and anxiety disorders [64–67]. We suggest that epigenetic differences that perturb the HPA axis could predispose infants to neurobehavioral profiles that interact with postnatal environmental factors leading to later mental health disorders [68].
Limitations
This study has limitations. This is an associational study and as such we cannot establish causal relations. Although specimens such as blood and buccal swabs are increasingly being used to study epigenetic processes in human populations, there is no direct evidence that these specimens indicate epigenetic mechanisms in the brain or that they directly represent the rodent work on which much of this is based [25]. It is possible that findings from different specimens indicate an epigenetic ‘footprint;’ that epigenetic processes were involved and that additional research is needed to elucidate the role of these ‘footprints’. Due to sample collection constraints, we were not able to measure gene expression or cortisol levels. Thus, we still lack direct evidence that cortisol levels differed between the two groups. However, we did measure an epigenetic mechanism that regulates cortisol levels. We only studied two genes and it is unlikely that only two genes are involved in these processes. Coordinated alterations in many genes may be particularly important for our understanding of neurobehavioral development. DNA methylation at the genome-wide level would complement the study of candidate genes.
Conclusion
We have presented a unique study that measures epigenetic mechanisms that regulate cortisol levels and are potentially responsible for differences in cortisol levels related to neurobehavior in preterm infants. Thus, epigenetics may play an important role in our understanding of neurobehavior related to prematurity. The fact that the NNNS exam and the NNNS profiles have predictive validity suggests that our findings could have implications for the long-term developmental outcome of preterm infants. We suggest that preterm birth can result in epigenetic differences in genes that regulate the HPA system and that disruption of this system can lead to abnormal neonatal neurobehavior and later developmental impairment.
Future perspective
The study of epigenetic processes involved in human behavior is just beginning and holds great promise for the future. Epigenetics is the quintessential gene–environment interaction as it enables us to study how environmental factors change gene expression at the cellular level. This enables us to understand the molecular underpinnings of both normal and abnormal behavior and development. This is not only a scientific ‘sea change’ but also opens the door to epigenetically based interventions to prevent disorders of behavior and development. For example, if increased cortisol levels are responsible for the high-risk profile described in this paper leads to adverse developmental outcomes, reducing cortisol levels could become a target for intervention.
Executive summary.
Background
Epigenetic differences related to cortisol levels has the potential to alter neurobehavior in preterm infants.
Results
Neurobehavior in preterm infants at hospital discharge shows low-risk versus high-risk neurobehavioral profiles.
Infants with the high-risk profile showed more DNA methylation than infants with the low-risk profile at CpG3 for NR3C1 and less methylation of CpG3 for HSD11B2.
Discussion
A compensatory regulatory system involving HSD11B2 and NR3C1 to re-establish a balance in cortisol activity in infants with the high-risk profile is involved.
Perturbations in this system due to reduced activity of transcription factors that impedes developmental programming increases cortisol levels resulting in the high-risk neurobehavioral profile.
Conclusion
Epigenetics may play an important role in our understanding of consequences of prematurity including long-term developmental outcome.
Footnotes
Financial & competing interests disclosure
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
No writing assistance was utilized in the production of this manuscript.
Ethical conduct of research
The authors state that they have obtained appropriate institutional review board approval or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved.
References
Papers of special note have been highlighted as: • of interest
- 1.March of Dimes: March of Dimes 2014 Premature Birth Report Card. 2014. www.marchofdimes.org/mission/prematurity-reportcard.aspx
- 2.Vohr BR, Wright LL, Poole WK, McDonald SA. Neurodevelopmental outcomes of extremely low birth weight infants <32 weeks’ gestation between 1993 and 1998. Pediatrics. 2005;116(3):635–643. doi: 10.1542/peds.2004-2247. [DOI] [PubMed] [Google Scholar]
- 3.Wilson-Costello D, Friedman H, Minich N, Fanaroff AA, Hack M. Improved survival rates with increased neurodevelopmental disability for extremely low birth weight infants in the 1990s. Pediatrics. 2005;115(4):997–1003. doi: 10.1542/peds.2004-0221. [DOI] [PubMed] [Google Scholar]
- 4.O'shea TM, Kuban KC, Allred EN, et al. Neonatal cranial ultrasound lesions and developmental delays at 2 years of age among extremely low gestational age children. Pediatrics. 2008;122(3):e662–e669. doi: 10.1542/peds.2008-0594. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Messinger D, Lambert B, Bauer CR, Bann CM, Hamlin-Smith K, Das A. The relationship between behavior ratings and concurrent and subsequent mental and motor performance in toddlers born at extremely low birth weight. J. Early Interv. 2010;32(3):214–233. doi: 10.1177/1053815110380917. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Stephens BE, Vohr BR. Neurodevelopmental outcome of the premature infant. Pediatric Clin. North Am. 2009;56(3):631–646. doi: 10.1016/j.pcl.2009.03.005. [DOI] [PubMed] [Google Scholar]; • Review of all domains of neurodevelopmental outcome of preterm infants.
- 7.Patrianakos-Hoobler AI, Msall ME, Huo D, Marks JD, Plesha-Troyke S, Schreiber MD. Predicting school readiness from neurodevelopmental assessments at age 2 years after respiratory distress syndrome in infants born preterm. Dev. Med. Child Neurol. 2010;52(4):379–385. doi: 10.1111/j.1469-8749.2009.03343.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Aarnoudse-Moens CS, Weisglas-Kuperus N, van Goudoever JB, Oosterlaan J. Meta-analysis of neurobehavioral outcomes in very preterm and/or very low birth weight children. Pediatrics. 2009;124(2):717–728. doi: 10.1542/peds.2008-2816. [DOI] [PubMed] [Google Scholar]
- 9.Breslau N, Chilcoat HD, Johnson EO, Andreski P, Lucia VC. Neurologic soft signs and low birthweight: their association and neuropsychiatric implications. Biol. Psychiatry. 2000;47(1):71–79. doi: 10.1016/s0006-3223(99)00131-6. [DOI] [PubMed] [Google Scholar]
- 10.Taylor HG, Klein N, Minich NM, Hack M. Middle-school-age outcomes in children with very low birthweight. Child Dev. 2000;71(6):1495–1511. doi: 10.1111/1467-8624.00242. [DOI] [PubMed] [Google Scholar]
- 11.Milligan DW. Outcomes of children born very preterm in Europe. Arch. Dis. Child Fetal Neonatal. Ed. 2010;95(4):F234–F240. doi: 10.1136/adc.2008.143685. [DOI] [PubMed] [Google Scholar]
- 12.Reef J, Van Meurs I, Verhulst FC, Van Der Ende J. Children's problems predict adults’ DSM-IV disorders across 24 years. J. Am. Acad. Child Adolesc. Psychiatry. 2010;49(11):1117–1124. doi: 10.1016/j.jaac.2010.08.002. [DOI] [PubMed] [Google Scholar]
- 13.Hack M, Youngstrom EA, Cartar L, et al. Behavioral outcomes and evidence of psychopathology among very low birth weight infants at age 20 years. Pediatrics. 2004;114(4):932–940. doi: 10.1542/peds.2003-1017-L. [DOI] [PubMed] [Google Scholar]
- 14.Hille ET, Dorrepaal C, Perenboom R, Gravenhorst JB, Brand R, Verloove-Vanhorick SP. Social lifestyle, risk-taking behavior, and psychopathology in young adults born very preterm or with a very low birthweight. J. Pediatr. 2008;152(6):793–800. doi: 10.1016/j.jpeds.2007.11.041. [DOI] [PubMed] [Google Scholar]
- 15.Limperopoulos C, Bassan H, Sullivan NR, et al. Positive screening for autism in ex-preterm infants: prevalence and risk factors. Pediatrics. 2008;121(4):758–765. doi: 10.1542/peds.2007-2158. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Kuban KC, O'shea TM, Allred EN, Tager-Flusberg H, Goldstein DJ, Leviton A. Positive screening on the Modified Checklist for Autism in Toddlers (M-CHAT) in extremely low gestational age newborns. J. Pediatr. 2009;154(4):535–540.e1. doi: 10.1016/j.jpeds.2008.10.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Watterberg KL, Gerdes JS, Cook KL. Impaired glucocorticoid synthesis in premature infants developing chronic lung disease. Pediatr. Res. 2001;50(2):190–195. doi: 10.1203/00006450-200108000-00005. [DOI] [PubMed] [Google Scholar]
- 18.Ng PC. Is there a “normal” range of serum cortisol concentration for preterm infants? Pediatrics. 2008;122(4):873–875. doi: 10.1542/peds.2008-0516. [DOI] [PubMed] [Google Scholar]
- 19.Witt CL. Adrenal insufficiency in the term and preterm neonate. Neonatal Netw. 1999;18(5):21–28. doi: 10.1891/0730-0832.18.5.21. [DOI] [PubMed] [Google Scholar]
- 20.Fernandez E, Montman R, Watterberg KL. ACTH and cortisol response to critical illness in term and later preterm newborns. J. Perinatol. 2008;28(12):797–802. doi: 10.1038/jp.2008.190. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Gunnar M, Vasquez D. Stress neurobiology and developmental psychopathology. In: Cicchette D, Cohen D, editors. Developmental Psychopathology. John Wiley & Sons,; Hoboken, NJ, USA: 2006. pp. 533–577. [Google Scholar]
- 22.Stansbury K, Gunnar MR. Adrenocortical activity and emotion regulation. Monogr. Soc. Res. Child Dev. 1994;59(2–3):108–134. [PubMed] [Google Scholar]
- 23.Fernandez EF, Watterberg KL. Relative adrenal insufficiency in the preterm and term infant. J. Perinatol. 2009;29(Suppl. 2):S44–S49. doi: 10.1038/jp.2009.24. [DOI] [PubMed] [Google Scholar]
- 24.Hanna CE, Keith LD, Colasurdo MA, et al. Hypothalamic pituitary adrenal function in the extremely low birth weight infant. J. Clin. Endocrinol. Metab. 1993;76(2):384–387. doi: 10.1210/jcem.76.2.8381799. [DOI] [PubMed] [Google Scholar]
- 25.Weaver IC, Cervoni N, Champagne FA, et al. Epigenetic programming by maternal behavior. Nat. Neurosci. 2004;7(8):847–854. doi: 10.1038/nn1276. [DOI] [PubMed] [Google Scholar]; • Shows that maternal behavior in the rodent results in changes in DNA methylation of NR3C1 in the hippocampus and in stress reactivity of offspring.
- 26.Oberlreander TF, Weinberg J, Papsdorf M, Grunau R, Misri S, Devlin AM. Prenatal exposure to maternal depression, neonatal methylation of human glucocorticoid receptor gene (NR3C1) and infant cortisol stress responses. Epigenetics. 2008;3(2):97–106. doi: 10.4161/epi.3.2.6034. [DOI] [PubMed] [Google Scholar]; • Human study of maternal depression during pregnancy, DNA methylation of NR3C1 in infants and cortisol stress reactivity.
- 27.Marsit CJ, Maccani MA, Padbury JF, Lester BM. Placental 11-beta hydroxysteroid dehydrogenase methylation is associated with newborn growth and a measure of neurobehavioral outcome. PLoS ONE. 2012;7(3):e33794. doi: 10.1371/journal.pone.0033794. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Bromer C, Marsit CJ, Armstrong DA, Padbury JF, Lester B. Genetic and epigenetic variation of the glucocorticoid receptor (NR3C1) in placenta and infant neurobehavior. Dev. Psychobiol. 2013;55(7):673–683. doi: 10.1002/dev.21061. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Conradt E, Lester BM, Appleton AA, Armstrong DA, Marsit CJ. The roles of DNA methylation of NR3C1 and 11beta-HSD2 and exposure to maternal mood disorder in utero on newborn neurobehavior. Epigenetics. 2013;8(12):1321–1329. doi: 10.4161/epi.26634. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Lester B, Tronick E. The Neonatal Intensive Care Unit Network Neurobehavioral Scale (NNNS) Pediatrics. 2004;113(3):631–699. [PubMed] [Google Scholar]
- 31.Fink NS, Tronick E, Olson K, Lester B. Healthy newborns’ neurobehavior: norms and relations to medical and demographic factors. J. Pediatr. 2012;161(6):1073–1079. doi: 10.1016/j.jpeds.2012.05.036. [DOI] [PubMed] [Google Scholar]
- 32.Liu J, Bann C, Lester B, et al. Neonatal neurobehavior predicts medical and behavioral outcome. Pediatrics. 2010;125(1):e90–e98. doi: 10.1542/peds.2009-0204. [DOI] [PMC free article] [PubMed] [Google Scholar]; • Introduces Neonatal Intensive Care Unit Network Neurobehavioral Scale profiles that predict developmental outcomes at 4.5 years.
- 33.Sucharew H, Khoury JC, Xu Y, Succop P, Yolton K. NICU Network Neurobehavioral Scale profiles predict developmental outcomes in a low-risk sample. Paediatr. Perinat. Epidemiol. 2012;26(4):344–352. doi: 10.1111/j.1365-3016.2012.01288.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Lesseur C, Armstrong D, Murphy M, et al. Sex-specific associations between placental leptin promoter DNA methylation and infant neurobehavior. Psychoneuroendocrinology. 2014;40:1–9. doi: 10.1016/j.psyneuen.2013.10.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Houseman EA, Christensen BC, Yeh RF, et al. Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions. BMC Bioinformatics. 2008;9:365. doi: 10.1186/1471-2105-9-365. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Appleton AA, Lester BM, Armstrong DA, Lesseur C, Marsit CJ. Examining the joint contribution of placental NR3C1 and HSD11B2 methylation for infant neurobehavior. Psychoneuroendocrinology. 2015;52:32–42. doi: 10.1016/j.psyneuen.2014.11.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Montirosso R, Del Prete A, Bellu R, Tronick E, Borgatti R. Neonatal Adequate Care for Quality of Life Study G. Level of NICU quality of developmental care and neurobehavioral performance in very preterm infants. Pediatrics. 2012;129(5):e1129–e1137. doi: 10.1542/peds.2011-0813. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Brown NC, Doyle LW, Bear MJ, Inder TE. Alterations in neurobehavior at term reflect differing perinatal exposures in very preterm infants. Pediatrics. 2006;118(6):2461–2471. doi: 10.1542/peds.2006-0880. [DOI] [PubMed] [Google Scholar]
- 39.Brown NC, Inder TE, Bear MJ, Hunt RW, Anderson PJ, Doyle LW. Neurobehavior at term and white and gray matter abnormalities in very preterm infants. J. Pediatr. 2009;155(1):32–38. doi: 10.1016/j.jpeds.2009.01.038. 38.e1. [DOI] [PubMed] [Google Scholar]
- 40.El-Dib M, Massaro AN, Glass P, Aly H. Neurobehavioral assessment as a predictor of neurodevelopmental outcome in preterm infants. J. Perinatol. 2012;32(4):299–303. doi: 10.1038/jp.2011.100. [DOI] [PubMed] [Google Scholar]
- 41.Stephens BE, Liu J, Lester B, et al. Neurobehavioral assessment predicts motor outcome in preterm infants. J. Pediatr. 2010;156(3):366–371. doi: 10.1016/j.jpeds.2009.09.042. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Gray JE, Richardson DK, Mccormick MC, Workman-Daniels K, Goldmann DA. Neonatal therapeutic intervention scoring system: a therapy-based severity-of-illness index. Pediatrics. 1992;90(4):561–567. [PubMed] [Google Scholar]
- 43.https://cran.r-project.org/ The Comprehensive R Archive Network.
- 44.Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B (Methodological) 1995;57(1):289–300. [Google Scholar]
- 45.Aloe L, Rocco ML, Bianchi P, Manni L. Nerve growth factor: from the early discoveries to the potential clinical use. J. Transl. Med. 2012;10:239. doi: 10.1186/1479-5876-10-239. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Mcgowan PO, Sasaki A, D'alessio AC, et al. Epigenetic regulation of the glucocorticoid receptor in human brain associates with childhood abuse. Nat. Neurosci. 2009;12(3):342–348. doi: 10.1038/nn.2270. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Gluckman PD, Hanson MA, Cooper C, Thornburg KL. Effect of in utero and early-life conditions on adult health and disease. N. Engl. J. Med. 2008;359(1):61–73. doi: 10.1056/NEJMra0708473. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Juster RP, Mcewen BS, Lupien SJ. Allostatic load biomarkers of chronic stress and impact on health and cognition . Neurosci. Biobehav. Rev. 2010;35(1):2–16. doi: 10.1016/j.neubiorev.2009.10.002. [DOI] [PubMed] [Google Scholar]
- 49.Mcewen BS. Protective and damaging effects of stress mediators. N. Engl. J. Med. 1998;338(3):171–179. doi: 10.1056/NEJM199801153380307. [DOI] [PubMed] [Google Scholar]; • Introduces the concept of allostatic load.
- 50.Mcewen BS, Stellar E. Stress and the individual. Mechanisms leading to disease. Arch. Intern. Med. 1993;153(18):2093–2101. [PubMed] [Google Scholar]
- 51.Meijer A. Child psychiatric sequelae of maternal war stress. Acta Psychiatr. Scand. 1985;72(6):505–511. doi: 10.1111/j.1600-0447.1985.tb02647.x. [DOI] [PubMed] [Google Scholar]
- 52.Stott DH. Follow-up study from birth of the effects of prenatal stresses. Dev. Med. Child Neurol. 1973;15(6):770–787. doi: 10.1111/j.1469-8749.1973.tb04912.x. [DOI] [PubMed] [Google Scholar]
- 53.Ward AJ. Prenatal stress and childhood psychopathology. Child Psychiat. Hum. Dev. 1991;22(2):97–110. doi: 10.1007/BF00707788. [DOI] [PubMed] [Google Scholar]
- 54.Dipietro J. The role of prenatal maternal stress in child development. Curr. Dir. Psychol. Sci. 2004;13(2):71–74. [Google Scholar]
- 55.McEwen BS. Early life influences on life-long patterns of behavior and health. Ment. Retard. Dev. Dis. Res. Rev. 2003;9(3):149–154. doi: 10.1002/mrdd.10074. [DOI] [PubMed] [Google Scholar]
- 56.Wadhwa PD, Sandman CA, Garite TJ. The neurobiology of stress in human pregnancy: implications for prematurity and development of the fetal central nervous system. Prog. Brain Res. 2001;133:131–142. doi: 10.1016/s0079-6123(01)33010-8. [DOI] [PubMed] [Google Scholar]
- 57.Wadhwa PD, Garite TJ, Porto M, et al. Placental corticotropin-releasing hormone (CRH), spontaneous preterm birth, and fetal growth restriction: a prospective investigation. Am. J. Obstet. Gynecol. 2004;191(4):1063–1069. doi: 10.1016/j.ajog.2004.06.070. [DOI] [PubMed] [Google Scholar]
- 58.Kajantie E, Raikkonen K. Early life predictors of the physiological stress response later in life. Neurosci. Biobehav. Rev. 2010;35(1):23–32. doi: 10.1016/j.neubiorev.2009.11.013. [DOI] [PubMed] [Google Scholar]
- 59.Field T. Stress and coping from pregnancy through the postnatal period. In: Cummings E, editor. Life-Span Developmental Psychology: Perspectives on Stress and Coping. Lawrence Erlbaum Associates; Hillsdale, NJ, USA: 1991. pp. 45–59. [Google Scholar]
- 60.Huizink AC, Robles De Medina PG, Mulder EJ, Visser GH, Buitelaar JK. Stress during pregnancy is associated with developmental outcome in infancy. J. Child Psychol. Psychiatry. 2003;44(6):810–818. doi: 10.1111/1469-7610.00166. [DOI] [PubMed] [Google Scholar]
- 61.Levy-Shiff R, Dimitrovsky L, Shulman S, Har-Even D. Cognitive appraisals, coping strategies, and support resources as correlates of parenting and infant development. Dev. Psychol. 1998;34(6):1417–1427. doi: 10.1037//0012-1649.34.6.1417. [DOI] [PubMed] [Google Scholar]
- 62.Tyrka AR, Price LH, Marsit C, Walters OC, Carpenter LL. Childhood adversity and epigenetic modulation of the leukocyte glucocorticoid receptor: preliminary findings in healthy adults. PLoS ONE. 2012;7(1):e30148. doi: 10.1371/journal.pone.0030148. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Gunnar M, Quevedo K. The neurobiology of stress and development. Ann. Rev. Psychol. 2007;58:145–173. doi: 10.1146/annurev.psych.58.110405.085605. [DOI] [PubMed] [Google Scholar]; • Review of literature on cortisol reactivity and mental health disorders.
- 64.Maes M, Meltzer HY, D'Hondt P, Cosyns P, Blockx P. Effects of serotonin precursors on the negative feedback effects of glucocorticoids on hypothalamic-pituitary-adrenal axis function in depression. Psychoneuroendocrinology. 1995;20(2):149–167. doi: 10.1016/0306-4530(94)00049-g. [DOI] [PubMed] [Google Scholar]
- 65.Meador-Woodruff JH, Greden JF, Grunhaus L, Haskett RF. Severity of depression and hypothalamic-pituitary-adrenal axis dysregulation: identification of contributing factors. Acta Psychiatr. Scand. 1990;81(4):364–371. doi: 10.1111/j.1600-0447.1990.tb05465.x. [DOI] [PubMed] [Google Scholar]
- 66.Van Praag H. Depression. Lancet. 1982;320(8310):1259–1264. doi: 10.1016/s0140-6736(82)90115-5. [DOI] [PubMed] [Google Scholar]
- 67.Nemeroff CB, Widerlov E, Bissette G, et al. Elevated concentrations of CSF corticotropin-releasing factor-like immunoreactivity in depressed patients. Science. 1984;226(4680):1342–1344. doi: 10.1126/science.6334362. [DOI] [PubMed] [Google Scholar]
- 68.Lester BM, Marsit CJ, Conradt E, Bromer C, Padbury JF. Behavioral epigenetics and the developmental origins of child mental health disorders. J. Dev. Orig. Health Dis. 2012;3(6):395–408. doi: 10.1017/S2040174412000426. [DOI] [PubMed] [Google Scholar]