Abstract
Purpose
The objective of this study was to identify predictors of epigenetic age and its association with neurodevelopmental outcomes in children born preterm.
Methods
A prospective cohort of children born 24-to-32-weeks gestation, well characterized with clinical data, brain imaging, and neurodevelopmental assessments at 3 years (Bayley-III cognitive and motor composite), were included. Epigenetic age (in weeks) was determined using the Pediatric Buccal Epigenetic Clock and corrected for chronological age to give epigenetic age difference (EAD). The associations between EAD, clinical risk factors and neurodevelopmental outcomes were examined using multivariable linear regression.
Results
A total of 102 study participants (47% males) were included. Epigenetic age showed a positive association with chronological age at term-equivalent age and 3 years. Neonatal morbidity was associated with a lower EAD (epigenetic age deceleration) at 3 years (β = −10.1, P = .03). Sex modified the association between EAD and extreme prematurity (P = .08), retinopathy of prematurity (P = .04), and infection (P = .06), with males having a higher EAD (epigenetic age acceleration). EAD at 3 years was associated with cognitive (β = 0.1, P = .004) and motor (β = 0.2, P = .001) scores at 3 years, with a higher EAD (epigenetic age acceleration) being associated with better outcomes.
Conclusion
Epigenetic age acceleration was associated with better cognitive and motor outcomes in children born preterm. Epigenetic modification is an important biological mechanism that contributes to the variability in neurodevelopmental outcomes in the preterm population.
Keywords: Epigenetic Age, Neurodevelopment, Pediatric buccal epigenetic age, Prematurity
Introduction
Preterm birth is associated with increased risk of mortality, morbidity, and disability.1, 2, 3 Several risk factors can alter the neurodevelopmental trajectory of children born preterm, including gestational age, sex, neonatal morbidities (infection, bronchopulmonary dysplasia, retinopathy of prematurity, and brain injury), and environmental factors (socioeconomic status and parenting style).4, 5, 6, 7, 8, 9, 10, 11, 12 Although neonatal morbidities and environmental factors are predictive of neurodevelopment, they do not fully account for the variability in cognitive and motor outcomes. Children born preterm with similar clinical and environmental risk factors can have different neurodevelopmental trajectories, suggesting that underlying biological factors may contribute to the variability in outcomes.
Epigenetic mechanisms, including DNA methylation, histone modification, and chromatin modeling, play an important role in development and healthy aging. DNA methylation is the most commonly studied epigenetic mechanism in human population studies. In DNA methylation, the addition of a methyl group to a cytosine phosphate guanine site can influence gene transcription, leading to gene silencing or gene activation, depending on genomic location.13 DNA methylation is responsive to environmental stressors, and alterations in this epigenetic response may contribute to differences in health outcomes. Preterm birth is a form of early-life adversity, given the risk for morbidities, exposure to painful procedures, and separation from parents. These environmental stressors of prematurity may be associated with epigenetic modifications, which, in turn, influence neurodevelopmental outcomes. Previous epigenetic studies in the preterm population have examined DNA methylation patterns using epigenome-wide association studies and single-gene epigenetic studies.14, 15, 16, 17, 18, 19, 20 These studies have found different DNA methylation patterns in children born preterm compared with children born at term.14,16,19 Epigenetic changes were found in genes involved in neuronal function and signaling including SLC6A4, SLC7A5, SLC1A2, NPBWR1, and LRG1.16, 17, 18, 19 Sex differences have also been observed in epigenome wide association studies, with males and females born preterm having distinct patterns of DNA methylation.21, 22, 23 Although epigenome-wide association studies demonstrate a link between prematurity and DNA methylation, these studies can be difficult to interpret because of the large number of genes examined and the lack of a clinical correlate. Furthermore, previous epigenome-wide association studies have not fully accounted for neonatal morbidities and other preterm risk factors, which may influence DNA methylation at multiple gene loci.
DNA methylation has also been utilized to develop biomarkers, such as epigenetic clocks, which measure DNA methylation at specific cytosine phosphate guanine sites to determine epigenetic age, a measure of biological aging.24 Epigenetic age can be compared with chronological age, and deviations in epigenetic age (acceleration or deceleration) can be linked to adverse health outcomes. For instance, deviations in epigenetic age have been associated with childhood internalizing and externalizing disorders.25,26 The Pediatric Buccal Epigenetic (PedBE) and Neonatal Epigenetic Estimator of Age (NEOage) clocks were developed specifically for pediatric and neonatal populations. In preterm infants, epigenetic age based on the NEOage clock was found to be highly correlated with both postmenstrual and postnatal age in the neonatal period.27 A second study found an accelerated NEOage epigenetic age in preterm infants who had neonatal morbidities, in particular, bronchopulmonary dysplasia.28 These studies were limited to the neonatal period, and studies examining epigenetic age and long-term outcomes are lacking. Epigenetic age based on PedBE has been shown to be predictive of chronological age from 0 to 20 years of age.29 However, there are limited studies using the PedBE clock in the preterm population.30 Our group has previously assessed epigenetic age based on PedBE clock in preterm infants with infection and found an accelerated epigenetic age at term-equivalent age in extreme preterm infants (less than 28 weeks gestational age) compared with very preterm (28-32 weeks gestational age) infants.31 Furthermore, an accelerated epigenetic age was associated with lower cognitive and language scores at 18 months.31 This preliminary study was limited by small sample size, inclusion of infants born preterm with concurrent infection, and lack of long-term follow-up. We have expanded on this early work by studying PedBE epigenetic age in a larger group of children born preterm who were followed to early childhood. The objective of this study was to examine the association between epigenetic age, neonatal morbidities, and neurodevelopmental outcomes at 3 years in children born preterm. We hypothesized that neonatal morbidities would be associated with differences in epigenetic age and that epigenetic age would be predictive of cognitive and motor outcomes in children born preterm.
Materials and Methods
Study participants
Study participants were enrolled from a prospective cohort of extreme and very preterm infants born 24 to 32 weeks gestational age, recruited from Neonatal Intensive Care Units at 3 hospitals in Canada from 2015 to 2019. This preterm cohort is well characterized with detailed neonatal clinical data, brain magnetic resonance imaging (MRI) scans, and structured neurodevelopmental assessment at 3 years of age. Participants were excluded from this cohort if they had congenital brain malformations, congenital infections, or major congenital anomaly. Study participants with buccal epithelial cell DNA collected at term-equivalent age or at 3 years were included in the study. The study was approved by the Institutional Research Ethics Boards. Written informed consent was obtained by the parent.
Clinical data and neurodevelopmental assessment
Clinical data and daily records from the Neonatal Intensive Care Unit admission were obtained through prospective chart review. For each participant the following data were collected: sex, very preterm (28-32-week gestation), extreme preterm (less than 28-week gestation), neonatal morbidity (postnatal infection, bronchopulmonary dysplasia, retinopathy of prematurity, and brain injury), age at MRI, and age at buccal epithelial cell DNA collection. Neurodevelopmental outcomes were assessed at 3 years by a Psychologist using standardized measures of child development (Bayley Scales of Infant and Toddler Development, 3rd edition).
Neuroimaging
Brain MRI scans were performed early life (soon after birth) and at term-equivalent age without pharmacological sedation on a Siemens 3T MRI scanner. A single channel neonatal head coil was used. Anatomical images were reviewed by a pediatric neuroradiologist to assess for the presence of brain injury and classified as white matter injury (mild, moderate, and severe) and intraventricular hemorrhage (grades 1-3, periventricular hemorrhagic infarct). Brain injury was further classified as severe brain injury (white matter injury moderate or severe, intraventricular hemorrhage grade 3, periventricular hemorrhagic infarct).
Epigenetic data
Buccal epithelial cell samples were collected from study participants at the term equivalent or 3 year assessment. DNA was extracted using the Isohelix Buccal Prep Plus DNA isolation kits, followed by bisulfate conversion using the Zymo EZ DNA methylation kit. DNA methylation was profiled using the Infinium Methylation EPIC Bead Chip.32 Sample quality control checks were conducted as previously described.33,34 We performed background correction, dye-bias normalization and functional normalization.35,36 Chronological age was defined as postnatal age, calculated as the number weeks from birth to buccal sample collection. Epigenetic age was calculated using the PedBE and NEOage clocks29 and presented as postnatal age in weeks. Epigenetic age difference (EAD) was calculated by subtracting chronological age from epigenetic age to control for variability in participant age. A higher EAD was interpreted as epigenetic age acceleration, and a lower EAD was interpreted as epigenetic age deceleration. We used absolute age difference (or absolute age acceleration) as the most suitable measure of epigenetic age acceleration for our study, given that we had a single group. This is in contrast to the use of residual based acceleration, which can be more advantageous for data sets that include case-control study designs.37 In our statistical models that used EAD, we controlled for chronological age to minimize the risk of confounding.
Statistical analysis
Statistical analysis included descriptive statistics to examine clinical characteristics of the full study cohort and by sex. Multivariable linear regression models were conducted to examine predictors of EAD at term-equivalent age and at 3 years including sex, extreme prematurity, and neonatal morbidities. The association between EAD (term-equivalent age, 3 years) and neurodevelopmental outcomes at 3 years was assessed using multivariable linear regression models. All models were adjusted for factors associated with EAD (chronological age and buccal epithelial cell proportion), and neurodevelopmental outcomes (sex, extreme prematurity, and neonatal morbidity). Statistical significance was set at an alpha level of less than 0.05 for main effects and less than 0.1 for interaction effects.38
Results
The study sample included 102 children born preterm with a mean gestational age of 27.2 (SD 2.3) weeks and 47% males. The sample included 10 twins (10%), and 92 were singletons (90%). A higher proportion of preterm males (88%) received antenatal magnesium sulfate, compared with females (65%). Males and females were similar in all other clinical, neuroimaging, and socioeconomic factors. Mean EAD at term-equivalent age was −3.2 (SD 5.0), and the mean EAD at 3 years of age was 49.7 (SD 26.1). Detailed clinical features of the study cohort are presented in Table 1.
Table 1.
Clinical characteristics of the study cohort
| Variable | Full Cohort (N = 102) | Males (N = 48) | Females (N = 54) | P Value |
|---|---|---|---|---|
| Mean gestational age | 27.2 (2.3) | 27.5 (2.4) | 27.0 (2.2) | .3 |
| Extreme preterm | 60 (58%) | 26 (53%) | 36 (64%) | .2 |
| Very preterm | 42 (41%) | 23 (47%) | 19 (35%) | .2 |
| Antenatal corticosteroids | 83 (81%) | 42 (88%) | 41 (76%) | .2 |
| Antenatal magnesium sulfate | 77 (75%) | 42 (88%) | 35 (65%) | .03∗ |
| Maternal education | .7 | |||
| Primary or secondary | 24 (24%) | 11 (23%) | 14 (25%) | |
| Post-secondary | 63 (62%) | 29 (60%) | 34 (64%) | |
| Graduate | 14 (14%) | 8 (17%) | 6 (11%) | |
| Infection | 35 (34%) | 14 (29%) | 21 (39%) | .4 |
| Retinopathy of prematurity (ROP) | 42 (41%) | 21 (44%) | 21 (39%) | .6 |
| Bronchopulmonary dysplasia (BPD) | 32 (31%) | 13 (27%) | 19 (35%) | .4 |
| Severe brain injury (WMI >2, IVH >3) | 14 (14%) | 8 (17%) | 6 (11%) | .4 |
| Mean chronological age at term-equivalent age (weeks) | 15.0 (4.6) | 14.4 (3.9) | 15.5 (5.1) | .3 |
| Mean epigenetic age term-equivalent age (weeks) | 12.2 (6.5) | 12.4 (6.6) | 12.1 (6.5) | .9 |
| Mean epigenetic age difference term-equivalent age (weeks) | −3.2 (5.0) | −2.9 (4.3) | −3.4 (5.3) | .8 |
| Mean buccal cell proportion term-equivalent age | 97.9 (2.6) | 98.4 (0.9) | 97.6 (3.3) | .3 |
| Mean chronological age at 3 years (weeks) | 161.7 (7.6) | 161.1 (8.4) | 162.2 (6.8) | .5 |
| Mean epigenetic age 3 years (weeks) | 211.4 (28.8) | 207.1 (28.2) | 215.2 (28.9) | .2 |
| Mean epigenetic age difference 3 years (weeks) | 49.7 (26.1) | 46.0 (24.6) | 53.1 (27.2) | .2 |
| Mean buccal cell proportion 3 years | 92.4 (9.1) | 91.2 (11.2) | 93.3 (6.7) | .3 |
Stastitically significant P < .05
To examine the accuracy of the PedBE and NEOage clocks in predicting chronological age in the preterm population, we performed correlation analyses. One outlier with a high chronological age at 3 years was removed from the regression analyses. Epigenetic age based on NEOage was correlated with chronological age at term-equivalent age (r = 0.7, MAE = 1.9, P < .001), but not at 3 years of age (r = 0.05, MAE = 147.9, P = .7), because the later time point falls outside of the age range of the clock. To allow for consistent comparison of epigenetic age across both time points, the PedBE clock was used for subsequent analyses. NEOage epigenetic age and its association with chronological age at term-equivalent age and 3 years, as well as regression models are presented in Supplemental Figure 1 and Supplemental Tables 1 and 2. Epigenetic age based on the PedBE clock showed a positive linear association with chronological age at term equivalent (r = 0.7, MAE = 4.8, P < .001) and 3 years (r = 0.4, MAE = 49.8, P < .001) of age (Figure 1). The relationship between epigenetic age and chronological age was similar in males (r = 0.7, P < .001) and females (r = 0.6 P < .001) at term-equivalent age and also similar in males (r = 0.5, P < .007) and females (r = 0.4, P = .005) at 3 years of age (Figure 1). A positive association between epigenetic age and chronological age was seen in children born very preterm (r = 0.9, P < .001) and extreme preterm (r = 0.5, P < .001) at term-equivalent age, and in children born very preterm (r = 0.2, P = .3) and extreme preterm (r = 0.6, P < .001) at 3 years (Figure 1).
Figure 1.
Epigenetic age based on PedBE clock is correlated with chronological age in children born preterm. Epigenetic age based on PedBE clock shows a positive linear association with chronological age at term-equivalent age (A) in males and females born preterm (B) and in children born extreme and very preterm (C). A positive linear association between epigenetic age based on PedBE clock and chronological age was also seen at 3 years of age (D) in males and females born preterm (E) and in children born extreme and very preterm (F).
To examine predictors of EAD, multivariable regression analyses were performed. There were no significant predictors of EAD at term-equivalent age (Table 2). Neonatal morbidity was a significant predictor of EAD at 3 years (β = −10.1, P = .03), with the presence of a neonatal morbidity being associated with a 10 week decrease in EAD. Regression models exploring the contribution of individual neonatal morbidities to EAD at term-equivalent age and 3 years revealed several main effects (Figure 2, Table 3). Infection was associated with a 12 week lower EAD at 3 years (β = −12.6 P = .02). Retinopathy of prematurity was a predictor of EAD at 3 years (β = −12.5, P = .01), with the presence of retinopathy of prematurity being associated with a 12 week lower EAD. Exploratory analysis of interaction effects revealed that sex modified several associations. Specifically, sex modified the association between term-equivalent EAD and extreme prematurity (P = .05) and retinopathy of prematurity (ROP) (P = .03), with males having a higher EAD in the presence of these factors. Sex modified the association between 3 year EAD and infection, with males with infection having a higher EAD (P = .08). No significant associations were seen for bronchopulmonary dysplasia or severe brain injury.
Table 2.
Linear regression models examining predictors of PedBE epigenetic age difference
| PedBE Epigenetic Age Difference at Term-Equivalent Age | Beta | Standardized Beta | 95% CI | P Value |
|---|---|---|---|---|
| Male | 0.9 | 0.5 | [−1.6, 3.5] | .5 |
| Extreme preterm birth | −0.5 | −0.3 | [−3.4, 2.3] | .7 |
| Neonatal morbidity (any) | 1.2 | 0.6 | [−1.6, 3.5] | .4 |
| PedBE Epigenetic Age Difference at 3 years | Beta | Standardized Beta | 95% CI | P Value |
|---|---|---|---|---|
| Male | −9.0 | −4.3 | [−18.7, 0.8] | .08 |
| Extreme preterm birth | −0.8 | −0.4 | [−11.8, 8.7] | .9 |
| Neonatal morbidity (any) | −10.1 | −5.0 | [−19.7, −0.2] | .03∗ |
Stastitically significant P < .05.
Figure 2.
Epigenetic age difference and preterm risk factors in males and females born preterm. Epigenetic age difference at term-equivalent age is increased in males born extremely preterm (A) and males with retinopathy of prematurity (B). Epigenetic age difference at 3 years is increased in males with infection (C).
Table 3.
Linear regression models examining predictors of PedBE epigenetic age difference
| PedBE Epigenetic Age Difference at Term-Equivalent Age |
PedBE Epigenetic Age Difference at 3 Years |
||||||||
|---|---|---|---|---|---|---|---|---|---|
| Beta | Standardized Beta | 95% CI | P Value | Beta | Standardized Beta | 95% CI | P Value | ||
| Model 1 | |||||||||
| Male | −2.0 | −1.0 | [−5.0, 1.0] | .2 | Male | −8.0 | −4.0 | [−17.7, 1.7] | .1 |
| Extreme preterm | −1.7 | −0.8 | [−5.0, 1.5] | .3 | Extreme preterm | −5.4 | −2.7 | [−15.5, 4.7] | .3 |
| Males born extreme preterm | 4.7 | 2.0 | [−0.05, 9.5] | .05∗ | |||||
| Model 2 | |||||||||
| Male | 0.9 | 0.5 | [−1.5, 3.5] | .4 | Male | −14.7 | −7.3 | [−25.5, −3.9] | .008∗ |
| Infection | −0.2 | −0.1 | [−3.1, 2.6] | .9 | Infection | −20.0 | −9.7 | [−32.7, −7.3] | .002∗ |
| Males with infection | 18.5 | 6.4 | [−2.2, 39.3] | .08∗ | |||||
| Model 3 | |||||||||
| Male | −1.8 | −0.9 | [−4.7, 1.1] | .2 | Male | −6.7 | −3.3 | [−16.0, 2.7] | .2 |
| ROP | −2.9 | −1.5 | [−6.3, 0.5] | .1 | ROP | −12.5 | −6.3 | [−22.0, −3.1] | .01∗ |
| Males with ROP | 5.7 | 2.3 | [0.6, 10.7] | .03∗ | |||||
| Model 4 | |||||||||
| Male | 1.2 | 0.6 | [−1.3, 3.6] | .3 | Male | −7.6 | −3.9 | [−17.1, 1.8] | .1 |
| BPD | 2.0 | 0.6 | [−0.9, 4.9] | .2 | BPD | −5.2 | −2.5 | [−15.6, 5.2] | .3 |
| Model 5 | |||||||||
| Male | 1.0 | 0.5 | [−1.5, 3.6] | .4 | Male | −7.0 | −3.5 | [−16.8, 2.7] | .1 |
| Severe brain injury | 0.6 | 0.5 | [−3.2, 4.4] | .7 | Severe brain injury | −10.9 | −3.5 | [−26.8, 4.9] | .2 |
Stastitically significant P < .05.
To examine the association between EAD and neurodevelopmental outcomes at 3 years multivariable regression analyses were performed (Table 4). EAD at term-equivalent age was not associated with cognitive or motor scores at 3 years. EAD at 3 years was associated with cognitive (β = 0.1, P = .004) and motor (β = 0.2 P = .001) scores at 3 years, with a higher EAD being associated with better outcomes (Figure 3).
Table 4.
Linear regression models examining the association between PedBE epigenetic age difference and outcomes at 3 years
| Cognitive Outcomes 3 Years Model 1 |
Beta | Standardized Beta | 95% CI | P Value |
|---|---|---|---|---|
| Epigenetic age difference term-equivalent age | −0.1 | −0.6 | [−0.5, 0.3] | .5 |
| Extreme preterm birth | 2.9 | 1.4 | [−4.5, 10.4] | .4 |
| Male sex | −3.0 | −1.5 | [−9.2, 3.0] | .3 |
| Neonatal morbidity (any) | −7.7 | −3.5 | [−14.7, −0.8] | .03∗ |
| Model 2 | ||||
| Epigenetic age difference 3 years | 0.1 | 2.6 | [0.03, 0.2] | .004∗ |
| Extreme preterm birth | 0.9 | 0.5 | [−3.3, 5.2] | .7 |
| Male sex | −2.6 | −1.3 | [−6.6, 1.2] | .2 |
| Neonatal morbidity (any) | −6.7 | −3.1 | [−11.3, −2.0] | .005∗ |
| Motor Outcomes 3 years Model 1 |
||||
| Epigenetic age difference term-equivalent age | 0.2 | 0.7 | [−0.6, 0.9] | .7 |
| Extreme preterm birth | 1.2 | 0.8 | [−7.0, 10.6] | .7 |
| Male sex | −3.0 | −1.5 | [−11.2, 5.2] | .5 |
| Neonatal morbidity (any) | −7.9 | −3.6 | [−17.3, 1.5] | .1 |
| Model 2 | ||||
| Epigenetic age difference 3 years | 0.2 | 4.6 | [0.08, 0.3] | .001∗ |
| Extreme preterm birth | −3.0 | −1.5 | [−9.5, 3.5] | .4 |
| Male sex | −5.4 | −2.7 | [−10.7, −0.04] | .05 |
| Neonatal morbidity (any) | −4.0 | −1.9 | [−10.7, 2.8] | .2 |
Stastitically significant P < .05.
Figure 3.
Predictors of neurodevelopmental outcomes in children born preterm. Epigenetic age difference is a predictor of cognitive (A) and motor (B) scores at 3 years in children born preterm. Graphs show the standardized beta coefficients for the independent variables in the linear regression models. × Denotes statistically significant variable.
In our sample 10 participants (10%) were twins (5 twin pairs). To assess for possible nonindependence of data, the analyses was repeated by including only 1 twin from each twin pair. The results were unchanged and neonatal morbidity remained a predictor of EAD at 3 years (P = .04), and EAD at 3 years was a predictor of cognitive (P = .01) and motor (P = .002) outcomes at 3 years. These results are presented as in Supplemental Tables 3 and 4.
Discussion
In this study we found that EAD was associated with neurodevelopmental outcomes in children born preterm. Epigenetic age acceleration was favorable and was associated with better cognitive and motor outcomes at 3 years of age. Children born preterm with similar risk factors and brain injury can have different neurodevelopmental trajectories. Although great strides have been made in identifying preterm risk factors, including neonatal morbidities, brain injury, and environmental factors, they do not fully explain the variability in outcomes in the preterm population. Our results suggest that epigenetic mechanisms contribute to the variability in neurodevelopment in children born preterm. DNA methylation in response to environmental stressors of prematurity may differ in children born preterm, which may lead to differences in neurodevelopmental outcomes. Our findings are in contrast to the adult epigenetic literature, which has found epigenetic age acceleration to be associated with poor health outcomes.39 These contrasting findings may be explained by differences in DNA methylation in pediatric and adult populations, reflecting epigenetic changes across the life course. Genome-wide DNA methylation increases in the neonatal and childhood periods, remains stable in adulthood, and decreases in late adulthood.13 It has also been reported that certain life periods (eg, childhood, puberty) are associated with higher rates of epigenetic changes.13 A positive linear relationship between epigenetic age and chronological age is expected in the pediatric age group.13 In our preterm cohort, we saw a positive linear relationship between epigenetic age and chronological age at the neonatal and 3 year time points. This relationship between epigenetic age and chronological was observed in males and females born preterm and in children born extreme and very preterm.
Epigenetic changes can occur from interactions between the genome and the environment. The environmental stressors of prematurity (eg, neonatal morbidities, painful procedures, and separation from parents), may alter DNA methylation in children born preterm. In our study, the presence of a neonatal morbidity was associated with EAD at 3 years of age. When we examined these risk factors and neonatal morbidities separately, we found that infection and ROP were associated with EAD. Consistent with this, a previous epigenome-wide association study by Everson et al,18 also found differences in DNA methylation patterns in children born preterm in the presence of neonatal morbidities. This prior study reported a dose response, with increasing number of neonatal morbidities being associated with DNA hypermethylation. Similarly, a second epigenome-wide association study by Hodge et al,21 found that bronchopulmonary dysplasia and antenatal steroid exposure were associated with differential DNA methylation of genes involved in the hypothalamic pituitary adrenal axis. It is well established that neonatal morbidities are associated with adverse neurodevelopmental outcomes in children born preterm.5,6,8 However, the biological mechanism underlying this association is not well understood. In our study, we observed a negative association between neonatal morbidities and EAD and a positive association between EAD and neurodevelopmental outcomes. These results suggest that the epigenetic response may be an important underlying mechanism that can help explain the variability in neurodevelopmental outcomes in the preterm population. The environmental stressors of prematurity may lead to epigenetic modification of genes involved in neural function or development,7,16 resulting in differences in neurodevelopment. An alternative explanation for our findings is that preterm infants born with a lower epigenetic age may be more vulnerable to neonatal morbidities, which can, in turn, lead to poor neurodevelopmental outcomes.
Preterm birth may be a key environmental stressor that leads to differences in DNA methylation. Previous epigenome-wide association studies have also found different DNA methylation patterns in children born preterm.14,16, 17, 18, 19 Sparrow et al,19 found that infants born preterm had distinct patterns of DNA methylation of genes involved in neural function when compared with term infants. Similarly, a study by Cruickshank et al,14 found genome wide differences in DNA methylation in neonates born extremely preterm compared with those born at term. This study also found that certain DNA methylation patterns were conserved over time in children born preterm suggesting an “epigenetic legacy” associated with preterm birth.14 In studies using epigenetic clocks, alterations in epigenetic age have been reported in pediatric populations exposed to early life adversity or child maltreatment.30,40,41 These studies were primarily in healthy, term born children. Epigenetic studies using epigenetic clocks in the preterm population are limited.27,28,30 A previous study by our group utilized the PedBE clock in infants born preterm who also had postnatal infection.31 In this study, infants born preterm had epigenetic age acceleration at term-equivalent age, which was associated with worse neurodevelopmental outcomes and slower brain growth.31 It is important to note that this study only included infants born preterm who had postnatal infection (with a sample size of 35), and only early outcomes at 18 months were assessed. A second study by Paniagua et al,28 also found epigenetic age acceleration in infants born preterm in the presence of neonatal morbidities. It is possible that epigenetic age and DNA methylation patterns differ in the neonatal period compared with early childhood. In this study, we found that epigenetic age at term-equivalent age was not associated with neurodevelopmental outcomes at 3 years of age, whereas the 3 year epigenetic age was a predictor of outcomes. There may be rapid epigenetic changes from the neonatal period to childhood, which may help explain the contrasting findings of epigenetic age at term-equivalent and 3 years of age.
We found interesting sex differences suggesting that males and females have different epigenetic responses to environmental stressors. Sex modified the association between EAD and extreme prematurity, retinopathy of prematurity, and infection. Males who were born extremely preterm (as opposed to very preterm) or with infection or ROP had epigenetic age acceleration. This was surprising because we expected extreme prematurity and neonatal morbidities to be associated with EAD. However, in males, these preterm risk factors were associated with epigenetic age acceleration. We propose that males born extremely preterm or with neonatal morbidities may methylate differently and have epigenetic age acceleration as an adaptive response to the environmental stressors of prematurity. Previous studies in the preterm population have also found differences in DNA methylation in males and females born preterm.21, 22, 23 The preterm literature often reports a preterm male disadvantage, with worse outcomes in males compared with females born preterm.42,43 However there are instances in which females born preterm are more vulnerable, for instance, early-life pain exposure has been shown to be associated with slower brain maturation and worse neurodevelopmental outcomes in females born preterm.44 These epigenetic sex differences may underlie the observed differences in morbidity, clinical course, and neurodevelopmental outcomes of males and females born preterm.42, 43, 44
The strengths of our study include the well-characterized prospective preterm cohort, which allowed us to examine associations between epigenetic age, specific neonatal morbidities, brain injury, and neurodevelopmental outcomes. Our methodology focused on epigenetic age using the PedBE clock, a tool developed specifically for assessing epigenetic age in the pediatric population. We acknowledge some limitations of this study. First, we could not fully control for prenatal exposures and intergenerational methylation changes that have been previously reported.45,46 We also did not control for the age of parents, and there are reports of parental age influencing offspring DNA methylation patterns.13 Although the NEOage clock has been found to be highly correlated with chronological age during the neonatal period,27 our study found poor correlation between NEOage epigenetic age and chronological age at 3 years. Therefore, our analysis used only the PedBE epigenetic age given the positive association with chronological age at both time points. We could not perform a longitudinal analysis for epigenetic age given the smaller sample size at term-equivalent age. Therefore, we presented cross-sectional results at 2 time points. Of the study participants, 82 (80%) were singletons, and there were 10 twin pairs (9.8%). Finally, our study examined neurodevelopmental outcomes at 3 years of age, and later follow-up at school age may have been more informative in identifying neurodevelopmental concerns.
In conclusion, our results show that epigenetic age is related to neurodevelopmental outcomes in children born preterm. Epigenetic age acceleration was associated with better cognitive and motor outcomes in early childhood. The presence of neonatal morbidities was associated with epigenetic age deceleration. The epigenetic response is an important biological mechanism that helps explain the variability in neurodevelopmental outcomes in children born preterm. Further studies are needed to understand how epigenetic patterns change over time and its influence on long-term neurodevelopmental outcomes in the preterm population.
Data Availability
Data will be made available to research team for research purposes (eg, meta-analyses) and can be requested by emailing the corresponding author.
Conflict of Interest
The authors declare no conflicts of interest.
Acknowledgments
Funding
This study was funded by the Canadian Institutes of Health Research operating grants MOP-79262 (S.P.M.), MOP-86489 (R.E.G.), and Kids Brain Health Network. S.P.M. was supported by the Bloorview Children’s Hospital Chair in Pediatric Neuroscience (to 2022) and is currently supported by the James and Annabel McCreary Chair in Pediatrics. R.E.G. is supported by an investigator salary award from the BC Children's Hospital Research Institute.
Author Contributions
Rhandi Christensen conducted the data analysis, interpreted the results, and drafted the manuscript. Beryl Zhuang, Michael S. Kobor, Chaini Konwar, and Julia MacIsaac processed the epigenetic samples, contributed to data interpretation, and critically reviewed the manuscript. Vann Chau, Anne Synnes, Ting Guo, Ruth E. Grunau, and Steven P. Miller contributed to data interpretation and critically reviewed the manuscript. All authors approved the final version of the manuscript and agree to be accountable for all aspects of the work.
ORCID
Rhandi Christensen: https://orcid.org/0000-0001-9463-6293
Ethics Declaration
The study was approved by the Institutional Research Ethics Board. Written informed consent was obtained from all study participants. All data were deidentified.
Footnotes
Additional Information
The online version of this article (https://doi.org/10.1016/j.gimo.2025.103479) contains supplemental material, which is available to authorized users.
Additional Information
Supplementary Figures.
References
- 1.Bell E.F., Hintz S.R., Hansen N.I., et al. Mortality, in-hospital morbidity, care practices, and 2-year outcomes for extremely preterm infants in the US, 2013-2018. JAMA. 2022;327(3):248–263. doi: 10.1001/jama.2021.23580. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Sci live births, by weeks of gestation. Government of Canada. https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid = 1310042501
- 3.Pierrat V., Marchand-Martin L., Marret S., et al. Neurodevelopmental outcomes at age 5 among children born preterm: EPIPAGE-2 cohort study. BMJ. 2021;373 doi: 10.1136/bmj.n741. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Benavente-Fernández I., Synnes A., Grunau R.E., et al. Association of socioeconomic status and brain injury with neurodevelopmental outcomes of very preterm children. JAMA Netw Open. 2019;2(5) doi: 10.1001/jamanetworkopen.2019.2914. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Chau V., Brant R., Poskitt K.J., Tam E.W.Y., Synnes A., Miller S.P. Postnatal infection is associated with widespread abnormalities of brain development in premature newborns. Pediatr Res. 2012;71(3):274–279. doi: 10.1038/pr.2011.40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Christensen R., Chau V., Synnes A., Guo T., Grunau R.E., Miller S.P. Preterm neurodevelopmental trajectories from 18 months to 4.5 years. J Pediatr. 2023;258 doi: 10.1016/j.jpeds.2023.113401. [DOI] [PubMed] [Google Scholar]
- 7.Christensen R., Miller S.P., Gomaa N.A. Home-ics: how experiences of the home impact biology and child neurodevelopmental outcomes. Pediatr Res. 2024;96(6):1475–1483. doi: 10.1038/s41390-024-03609-2. [DOI] [PubMed] [Google Scholar]
- 8.Guo T., Duerden E.G., Adams E., et al. Quantitative assessment of white matter injury in preterm neonates: association with outcomes. Neurology. 2017;88(7):614–622. doi: 10.1212/WNL.0000000000003606. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Manuck T.A., Sheng X., Yoder B.A., Varner M.W. Correlation between initial neonatal and early childhood outcomes following preterm birth. Am J Obstet Gynecol. 2014;210(5):426.e1–426.e9. doi: 10.1016/j.ajog.2014.01.046. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Rees P., Callan C., Chadda K.R., et al. Preterm brain injury and neurodevelopmental outcomes: a meta-analysis. Pediatrics. 2022;150(6) doi: 10.1542/peds.2022-057442. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Schmidt B., Asztalos E.V., Roberts R.S., et al. Impact of bronchopulmonary dysplasia, brain injury, and severe retinopathy on the outcome of extremely low-birth-weight infants at 18 months: results from the trial of indomethacin prophylaxis in preterms. JAMA. 2003;289(9):1124–1129. doi: 10.1001/jama.289.9.1124. [DOI] [PubMed] [Google Scholar]
- 12.Treyvaud K., Anderson V.A., Howard K., et al. Parenting behavior is associated with the early neurobehavioral development of very preterm children. Pediatrics. 2009;123(2):555–561. doi: 10.1542/peds.2008-0477. [DOI] [PubMed] [Google Scholar]
- 13.Jones M.J., Goodman S.J., Kobor M.S. DNA methylation and healthy human aging. Aging Cell. 2015;14(6):924–932. doi: 10.1111/acel.12349. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Cruickshank M.N., Oshlack A., Theda C., et al. Analysis of epigenetic changes in survivors of preterm birth reveals the effect of gestational age and evidence for a long term legacy. Genome Med. 2013;5(10):96. doi: 10.1186/gm500. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Fitzgerald E., Boardman J.P., Drake A.J. Preterm birth and the risk of neurodevelopmental disorders—is there a role for epigenetic dysregulation? Curr Genomics. 2018;19(7):507–521. doi: 10.2174/1389202919666171229144807. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Chau C.M.Y., Ranger M., Sulistyoningrum D., Devlin A.M., Oberlander T.F., Grunau R.E. Neonatal pain and COMT Val158Met genotype in relation to serotonin transporter (SLC6A4) promoter methylation in very preterm children at school age. Front Behav Neurosci. 2014;8:409. doi: 10.3389/fnbeh.2014.00409. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Everson T.M., Marsit C.J., Michael O’Shea T., et al. Epigenome-wide analysis identifies genes and pathways linked to neurobehavioral variation in preterm infants. Sci Rep. 2019;9(1):6322. doi: 10.1038/s41598-019-42654-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Everson T.M., O’Shea T.M., Burt A., et al. Serious neonatal morbidities are associated with differences in DNA methylation among very preterm infants. Clin Epigenet. 2020;12(1):151. doi: 10.1186/s13148-020-00942-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Sparrow S., Manning J.R., Cartier J., et al. Epigenomic profiling of preterm infants reveals DNA methylation differences at sites associated with neural function. Transl Psychiatry. 2016;6(1) doi: 10.1038/tp.2015.210. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Mckinnon K., Conole E.L.S., Vaher K., et al. Epigenetic scores derived in saliva are associated with gestational age at birth. Clin Epigenet. 2024;16(1):84. doi: 10.1186/s13148-024-01701-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Hodge K.M., Zhabotynsky V., Burt A.A., et al. Epigenetic associations in HPA axis genes related to bronchopulmonary dysplasia and antenatal steroids. Pediatr Res. 2024;96(2):510–518. doi: 10.1038/s41390-024-03116-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Jackson W.M., Santos H.P., Hartwell H.J., et al. Differential placental CpG methylation is associated with chronic lung disease of prematurity. Pediatr Res. 2022;91(6):1428–1435. doi: 10.1038/s41390-021-01868-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Santos H.P., Enggasser A.E., Clark J., et al. Sexually dimorphic methylation patterns characterize the placenta and blood from extremely preterm newborns. BMC Biol. 2023;21(1):173. doi: 10.1186/s12915-023-01662-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10) doi: 10.1186/gb-2013-14-10-r115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Dammering F., Martins J., Dittrich K., et al. The pediatric buccal epigenetic clock identifies significant ageing acceleration in children with internalizing disorder and maltreatment exposure. Neurobiol Stress. 2021;15 doi: 10.1016/j.ynstr.2021.100394. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.McGill M.G., Pokhvisneva I., Clappison A.S., et al. Maternal prenatal anxiety and the fetal origins of epigenetic aging. Biol Psychiatry. 2022;91(3):303–312. doi: 10.1016/j.biopsych.2021.07.025. [DOI] [PubMed] [Google Scholar]
- 27.Graw S., Camerota M., Carter B.S., et al. NEOage clocks—epigenetic clocks to estimate post-menstrual and postnatal age in preterm infants. Aging. 2021;13(20):23527–23544. doi: 10.18632/aging.203637. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Paniagua U., Lester B.M., Marsit C.J., et al. Epigenetic age acceleration, neonatal morbidities, and neurobehavioral profiles in infants born very preterm. Epigenetics. 2023;18(1) doi: 10.1080/15592294.2023.2280738. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.McEwen L.M., O’Donnell K.J., McGill M.G., et al. The PedBE clock accurately estimates DNA methylation age in pediatric buccal cells. Proc Natl Acad Sci U S A. 2020;117(38):23329–23335. doi: 10.1073/pnas.1820843116. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Musci R.J., Raghunathan R.S., Johnson S.B., et al. Using epigenetic clocks to characterize biological aging in studies of children and childhood exposures: a systematic review. Prev Sci. 2023;24(7):1398–1423. doi: 10.1007/s11121-023-01576-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Gomaa N., Konwar C., Gladish N., et al. Association of pediatric buccal epigenetic age acceleration with adverse neonatal brain growth and neurodevelopmental outcomes among children born very preterm with a neonatal infection. JAMA Netw Open. 2022;5(11) doi: 10.1001/jamanetworkopen.2022.39796. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Moran S., Arribas C., Esteller M. Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences. Epigenomics. 2016;8(3):389–399. doi: 10.2217/epi.15.114. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Merrill S.M., Gladish N., Fu M.P., et al. Associations of peripheral blood DNA methylation and estimated monocyte proportion differences during infancy with toddler attachment style. Attach Hum Dev. 2023;25(1):132–161. doi: 10.1080/14616734.2021.1938872. [DOI] [PubMed] [Google Scholar]
- 34.Konwar C., Asiimwe R., Inkster A.M., et al. Risk-focused differences in molecular processes implicated in SARS-CoV-2 infection: corollaries in DNA methylation and gene expression. Epigenetics Chromatin. 2021;14(1):54. doi: 10.1186/s13072-021-00428-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Fortin J.P., Labbe A., Lemire M., et al. Functional normalization of 450k methylation array data improves replication in large cancer studies. Genome Biol. 2014;15(12):503. doi: 10.1186/s13059-014-0503-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Triche T.J., Weisenberger D.J., Van Den Berg D., Laird P.W., Siegmund K.D. Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res. 2013;41(7) doi: 10.1093/nar/gkt090. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Teschendorff A.E., Horvath S. Epigenetic ageing clocks: statistical methods and emerging computational challenges. Nat Rev Genet. 2025;26(5):350–368. doi: 10.1038/s41576-024-00807-w. [DOI] [PubMed] [Google Scholar]
- 38.Thiese M.S., Ronna B., Ott U. P value interpretations and considerations. J Thorac Dis. 2016;8(9):E928–E931. doi: 10.21037/jtd.2016.08.16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Oblak L., van der Zaag J., Higgins-Chen A.T., Levine M.E., Boks M.P. A systematic review of biological, social and environmental factors associated with epigenetic clock acceleration. Ageing Res Rev. 2021;69 doi: 10.1016/j.arr.2021.101348. [DOI] [PubMed] [Google Scholar]
- 40.Chang O.D., Meier H.C.S., Maguire-Jack K., Davis-Kean P., Mitchell C. Childhood maltreatment and longitudinal epigenetic aging: NIMHD social epigenomics program. JAMA Netw Open. 2024;7(7) doi: 10.1001/jamanetworkopen.2024.21877. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Smeeth D., Ecker S., Chervova O., et al. War exposure and DNA methylation in Syrian refugee children and adolescents. JAMA psychiatry. 2025;82(2):191–200. doi: 10.1001/jamapsychiatry.2024.3714. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Christensen R., Chau V., Synnes A., et al. Preterm sex differences in neurodevelopment and brain development from early life to 8 years of age. J Pediatr. 2024;276 doi: 10.1016/j.jpeds.2024.114271. [DOI] [PubMed] [Google Scholar]
- 43.Boghossian N.S., Geraci M., Edwards E.M., Horbar J.D. Sex differences in mortality and morbidity of infants born at less than 30 weeks’ gestation. Pediatrics. 2018;142(6) doi: 10.1542/peds.2018-2352. [DOI] [PubMed] [Google Scholar]
- 44.Selvanathan T., Ufkes S., Guo T., et al. Pain exposure and brain connectivity in preterm infants. JAMA Netw Open. 2024;7(3) doi: 10.1001/jamanetworkopen.2024.2551. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Fransquet P.D., Macdonald J.A., Ryan J., Greenwood C.J., Olsson C.A. Exploring perinatal biopsychosocial factors and epigenetic age in 1-year-old offspring. Epigenomics. 2023;15(18):927–939. doi: 10.2217/epi-2023-0284. [DOI] [PubMed] [Google Scholar]
- 46.Pilkay S., Riffer A., Carroll A. Trauma context exerts intergenerational effects on child mental health via DNA methylation. Epigenetics. 2024;19(1) doi: 10.1080/15592294.2024.2333654. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data will be made available to research team for research purposes (eg, meta-analyses) and can be requested by emailing the corresponding author.