Objective
In this study, we aimed to investigate relationships between maternal prepregnancy obesity and gestational diabetes mellitus and placental leptin DNA methylation.
Study Design
This study comprises data on 535 mother-infant dyads enrolled in the Rhode Island Child Health Study, a prospective cohort study of healthy term pregnancies. Prepregnancy body mass index was calculated from self-reported anthropometric measures and gestational diabetes mellitus diagnoses gathered from inpatient medical records. DNA methylation of the leptin promoter region was assessed in placental tissue collected at birth using quantitative bisulfite pyrosequencing.
Results
In a multivariable regression analysis adjusted for confounders, infants exposed to gestational diabetes mellitus had higher placental leptin methylation (β = 1.89, P = .04), as did those demonstrating prepregnancy obesity (β = 1.17, P = .06). Using a structural equations model, we observed that gestational diabetes mellitus is a mediator of the effects of prepregnancy obesity on placental leptin DNA methylation (β = 0.81, 95% confidence interval, 0.27–2.71).
Conclusion
Our results suggest that the maternal metabolic status before and during pregnancy can alter placental DNA methylation profile at birth and potentially contribute to metabolic programming of obesity and related conditions.
Maternal obesity and gestational diabetes mellitus (GDM) constitute 2 common, often comorbid pregnancy complications. In line with the developmental origins of health and disease hypothesis, increasing evidence suggests that these conditions modify the intrauterine environment and augment the offspring’s risk of obesity and diabetes in adult life. Epigenetic marks have been proposed as a mechanism for this developmental programming because they respond to environmental stimuli, but they are also mitotically stable. Because of the high tissue specificity of epigenetic mechanisms, though, it is critical to appropriately focus studies in relevant tissues. The placenta, a metabolically active organ that regulates the intrauterine environment and is crucial for fetal growth and development, is such a tissue.
Leptin is an adipokine central for energy homeostasis that functions as a satiety signal. During pregnancy, leptin is produced by the placenta in which it has pleiotropic functions, including regulating growth and nutrient exchange. Leptin ( LEP ) gene expression is inversely correlated with promoter DNA methylation and has been proposed as mediator of metabolic programming. In male rodents, in utero exposure to a low protein diet is associated with Lep promoter hypomethylation in adipose tissue, changes in body composition, and increased food intake.
In humans, in utero famine exposure has been associated with LEP promoter hypermethylation in the blood of adult men compared with their nonexposed siblings. In humans and rodents, maternal overnutrition produces similar adverse metabolic offspring phenotypes to undernutrition. Hence, in this study, we sought to investigate associations between maternal prepregnancy obesity and GDM and placental LEP DNA methylation in a birth cohort of healthy newborns.
Materials and Methods
Study population
Study participants are part of the Rhode Island Child Health Study (RICHS), which enrolls mother-infant dyads following delivery at Women and Infants Hospital of Rhode Island. Term infants born small for gestational age (SGA; less than the 10th percentile) or large for gestational age (LGA; greater than the 90th percentile), based on birthweight percentiles are selected, and infants appropriate for gestational age (10th or greater and 90th or less percentiles) matched on sex, gestational age (±3 days), and maternal age (±2 years) to SGA and LGA participants are enrolled. Only singleton, viable infants without congenital or chromosomal abnormalities were recruited.
Additional exclusion criteria include maternal age younger than 18 years and life-threatening conditions. Postrecruitment infants were reclassified into birthweight groups using sex-specific growth charts. In this analysis, we examined the first 535 RICHS participants enrolled between September 2009 and October 2012 with placental LEP methylation information. A structured chart review served to collect information from inpatient medical record from delivery, and mothers completed an interviewer-administered questionnaire.
Self-report of weight and height obtained during the interview served to calculate maternal prepregnancy body mass index (BMI). GDM status was obtained from medical charts. All subjects provided written informed consent. Protocols were approved by the institutional review boards for Women and Infants Hospital of Rhode Island and Dartmouth College and carried out in accordance with the Declaration of Helsinki.
LEP DNA methylation analysis and genotyping
Placental samples were collected from all subjects within 2 hours following delivery. Twelve fragments of placental parenchyma, 3 from each quadrant, were obtained 2 cm from the umbilical cord and free of maternal decidua. Collected tissue was immediately placed in RNAlater solution (Life Technologies, Grand Island, NY) and stored at 4°C. After at least 72 hours, tissue segments from each placental region were blotted dry, snap frozen in liquid nitrogen, homogenized by pulverization using a stainless steel cup and piston unit (Cellcrusher, Cork, Ireland), and stored at –80°C until needed.
DNA was extracted from homogenized placental samples using the DNAeasy blood and tissue kit (QIAGEN, Inc, Valencia, CA) and quantified using the ND 2000 spectrophotometer (Thermo Fisher Scientific Inc, Waltham, MA). DNA (500 ng) was sodium bisulfite modified using the EZ DNA methylation kit (Zymo Research, Irvine, CA). For DNA methylation detection, bisulfite pyrosequencing was used. Bisulfite polymerase chain reaction (PCR) conditions, primer sequences (Integrated DNA Technologies, Inc, Coralville, IA), and pyrosequencing assays are detailed in the Appendix ( Supplemental Table ).
We measured DNA methylation at 23 CpGs in the LEP promoter using the PyroMark MD (QIAGEN) and genotyped the single-nucleotide polymorphism rs2167270 (+19 G>A) in the region. Genotype calls were made by comparing peak heights; triplicate wells were called independently and compared for quality control. All procedures were performed following the manufacturer’s protocols.
Statistical analysis
Pairwise Pearson correlations were used to compare continuous LEP methylation between the 23 CpG loci analyzed. Self-reported gestational weight gain data were combined with prepregnancy BMI to construct a categorical variable following the Institute of Medicine cutoffs. Bivariate analyses were performed using a Student’s t test, 1-way analysis of variance or Pearson’s correlation, as appropriate. χ 2 tests were used to assess frequency distributions. Multivariable analyses were completed using linear regression models, with continuous LEP methylation as the outcome and maternal and infant characteristic as predictor variables.
A structural equation model (SEM) was used to assess mediation effects between predictors using Mplus, version 7.11 (Muthén and Muthén, Los Angeles, CA). A bootstrap method was used to estimate the mediational effect. All other analyses were conducted in R 3.0.1. The multivariable regression and SEM were adjusted for potential confounders: rs2167270 genotype, infant sex, maternal age, and birthweight group. Confounders included in the models were significantly associated with methylation in the bivariate analysis and also associated with methylation at a level of P = .1 in a fully adjusted multivariable linear model (data not shown) or are part of the RICHS matching criteria (maternal age and birthweight group). All tests were 2 sided and statistical significance was determined at P < .05.
Results
Study population
The study population characteristics are summarized in Table 1 . In accordance with the study design, all infants were born at term with overrepresentation of LGA and SGA and even distribution by sex. The majority of infants were born to white mothers (74.1%) who ranged between 18 and 40 years of age (mean, 30 years). The prevalence of maternal prepregnancy obesity and GDM in this study was 26% (n = 135) and 10% (n = 47), respectively. In addition, among study participants with medical chart diagnosis of GDM, 61% were obese before pregnancy. There were no significant differences between the sample of participants analyzed in this study and the larger RICHS cohort in terms of maternal age, prepregnancy maternal obesity, GDM, infant sex, or birthweight group.
| Variable | n | % | Mean | SD | Missing data |
|---|---|---|---|---|---|
| Infant characteristics | |||||
| Gestational age | 535 | 39.0 | 1.0 | ||
| Birthweight | 535 | 3486.6 | 696.6 | ||
| AGA | 281 | 52.5 | |||
| LGA | 140 | 26.2 | |||
| SGA | 114 | 21.3 | |||
| Sex | |||||
| Male | 270 | 50.5 | |||
| Female | 265 | 49.5 | |||
| Delivery method | |||||
| Cesarean section | 273 | 51.0 | |||
| Vaginal | 262 | 49.0 | |||
| Genotype (rs2167270) | |||||
| G/G, G/A | 457 | 85.4 | |||
| A/A | 78 | 14.6 | |||
| Maternal characteristics | |||||
| Age, y | 535 | 29.6 | 5.6 | ||
| BMI, kg/m 2 | 529 | 26.8 | 7.1 | 6 | |
| Prepregnancy obesity | 6 | ||||
| No (BMI <30 kg/m 2 ) | 394 | 74.5 | |||
| Yes (BMI ≥30 kg/m 2 ) | 135 | 25.5 | |||
| Gestational diabetes mellitus | 56 | ||||
| No | 432 | 90.2 | |||
| Yes | 47 | 9.8 | |||
| Gestational weight gain | 9 | ||||
| Inadequate | 101 | 19.2 | |||
| Adequate | 135 | 25.7 | |||
| Excessive | 290 | 55.1 | |||
| Ethnicity | 3 | ||||
| Other | 138 | 25.9 | |||
| White | 394 | 74.1 | |||
| Tobacco use during pregnancy | 7 | ||||
| No | 502 | 95.1 | |||
| Yes | 26 | 4.9 | |||
| History of diabetes type 1 | 59 | ||||
| No | 471 | 98.9 | |||
| Yes | 5 | 1.1 | |||
| History of diabetes type 2 | 59 | ||||
| No | 472 | 99.2 | |||
| Yes | 4 | 0.8 | |||
| Pregnancy hypertension | 10 | ||||
| No | 490 | 93.3 | |||
| Yes | 35 | 6.7 | |||
Placental LEP DNA methylation
There was a high degree of intercorrelation of DNA methylation at each of the 23 CpGs (mean r = 0.7); thus, we used the mean across the region. Mean LEP methylation was normally distributed and ranged from 9% to 45%. Genotypes frequencies at rs2167270 were in Hardy-Weinberg equilibrium, with 15% of the infants homozygous for the variant allele (A) and 44% heterozygous and 41% and homozygous for the dominant allele (G).
Infant and maternal predictors of placental LEP DNA methylation
The results of the bivariate analyses between LEP methylation and maternal and infant characteristics are presented in Table 2 . As previously reported, placental LEP methylation extent was higher in infants with the A/A genotype and in males. Strikingly, we did not observe associations with infant birthweight. We observed higher methylation in placentas from infants born to prepregnancy obese mothers ( P = .03) and from those diagnosed with GDM ( P = .01). Subsequently, we constructed a multivariable linear regression model to predict LEP methylation adjusted for all significant covariates from the bivariate analyses and the study matching criteria ( Table 3 ). Consistently, we observed associations between placental LEP methylation and infant sex and genotype.
| Variable | n | Mean LEP | SD | P value |
|---|---|---|---|---|
| Infant | ||||
| Birthweight group | .80 | |||
| AGA | 281 | 24.1 | 6.0 | |
| LGA | 140 | 23.9 | 5.8 | |
| SGA | 114 | 23.7 | 6.3 | |
| Sex | < .0001 | |||
| Female | 265 | 22.8 | 5.5 | |
| Male | 270 | 25.1 | 6.3 | |
| Genotype | .006 | |||
| Any G | 457 | 23.7 | 6.0 | |
| A/A | 78 | 25.7 | 5.8 | |
| Maternal | ||||
| Prepregnancy obesity | .03 | |||
| No | 394 | 23.6 | 5.9 | |
| Yes | 135 | 24.9 | 6.2 | |
| Gestational diabetes mellitus | .01 | |||
| No | 432 | 23.7 | 5.8 | |
| Yes | 47 | 26.2 | 6.4 | |
| Ethnicity | .51 | |||
| Other | 138 | 24.3 | 6.5 | |
| White | 394 | 23.9 | 5.9 | |
| Tobacco use during pregnancy | .51 | |||
| No | 502 | 24.0 | 6.1 | |
| Yes | 26 | 24.6 | 4.9 | |
| Gestational weight gain | .46 | |||
| Low | 101 | 24.2 | 6.7 | |
| Adequate | 135 | 23.3 | 5.3 | |
| High | 290 | 24.0 | 6.0 | |
| Pregnancy hypertension | .76 | |||
| No | 490 | 24.0 | 6.0 | |
| Yes | 35 | 24.3 | 6.4 | |
| History of type 2 diabetes | .40 | |||
| No | 472 | 23.9 | 5.9 | |
| Yes | 4 | 21.5 | 4.9 | |
| History of type 1 diabetes | .95 | |||
| No | 471 | 23.9 | 5.9 | |
| Yes | 5 | 24.1 | 4.7 | |
| r | ||||
| Maternal age | –0.05 | .26 | ||
| Variable | Estimate | SE, (n = 473) | P value |
|---|---|---|---|
| Prepregnancy obesity | |||
| No | Reference | ||
| Yes | 1.17 | 0.62 | .06 |
| Gestational diabetes mellitus | |||
| No | Reference | ||
| Yes | 1.89 | 0.92 | .04 |
| Maternal age, y | –0.07 | 0.05 | .15 |
| Infant sex | |||
| Female | Reference | ||
| Male | 2.28 | 0.53 | < .0001 |
| Infant genotype (rs2167270) | |||
| G/G and G/A | Reference | ||
| A/A | 2.17 | 0.73 | .003 |
| Birthweight group | |||
| AGA | Reference | ||
| LGA | –0.70 | 0.61 | .25 |
| SGA | –0.25 | 0.70 | .73 |
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