Gestational intake of methyl donors and global LINE-1 DNA methylation in maternal and cord blood

Abstract

Maternal diet affects offspring DNA methylation in animal models, but evidence from humans is limited. We investigated the extent to which gestational intake of methyl donor nutrients affects global DNA methylation in maternal and umbilical cord blood. Among mother-infant pairs in Project Viva, a folate-replete US population, we estimated maternal intakes of vitamin B12, betaine, choline, folate, cadmium, zinc and iron periconceptionally and during the second trimester. We examined associations of these nutrients with DNA methylation, measured as %5-methyl cytosines (%5mC) in Long Interspersed Nuclear Element-1 (LINE-1), in first trimester (n = 830) and second trimester (n = 671) maternal blood and in cord blood at delivery (n = 516). Cord blood methylation was higher for male than female infants {mean [standard deviation (SD)] 84.8 [0.6] vs. 84.4 [0.7]%}. In the multivariable-adjusted model, maternal intake of methyl donor nutrients periconceptionally and during the second trimester of pregnancy was not positively associated with first trimester, second trimester or cord blood LINE-1 methylation. Periconceptional betaine intake was inversely associated with cord blood methylation [regression coefficient = −0.08% (95% confidence interval (CI): −0.14, −0.01)] but this association was attenuated after adjustment for dietary cadmium, which itself was directly associated with first trimester methylation and inversely associated with cord blood methylation. We also found an inverse association between periconceptional choline [−0.10%, 95% CI: −0.17, −0.03 for each SD (∼63 mg/day)] and cord blood methylation in males only. In this folate-replete population, we did not find positive associations between intake of methyl donor nutrients during pregnancy and DNA methylation overall, but among males, higher early pregnancy intakes of choline were associated with lower cord blood methylation.

Introduction

DNA methylation is a form of epigenetic “memory” that may mediate effects of maternal environmental factors, such as diet, on offspring health outcomes.^1–5 The nutrients vitamin B12, betaine, choline and folate are important in the one-carbon metabolism pathway that produces S-adenosylmethionine (SAM), which donates a methyl group in DNA methylation processes. Therefore, differential intake of these nutrients may lead to differential SAM production, changing methylation patterns and gene expression.

A number of animal studies show differences in offspring methylation and phenotype based on maternal dietary intake of these nutrients during pregnancy. In the viable yellow agouti mouse model, mothers on a diet supplemented with vitamin B12, betaine, choline and folic acid periconceptionally gave birth to male and female mice with hypermethylation of a metastable epiallele that were more likely to be brown and have lower incidence of obesity and cancer as well as greater longevity.^6–8 Ewes fed a diet deficient in vitamin B12, folate and methionine during pregnancy gave birth to offspring with hypomethylated DNA that were heavier and fatter, had increased blood pressure, and were more likely to be insulin resistant as adults.⁹ These associations appeared to be stronger in males, similar to a more recent study of the effects of in utero methyl donors on offspring glucose homeostasis in rats by the same authors.¹⁰

While these animal experiments are instructive, among humans it is unclear the extent to which maternal dietary intake of methyl donor nutrients during pregnancy affects DNA methylation in the mother or her child. In a study of 24 pregnant women in the UK, cord plasma homocysteine, which decreases with increased folate intake, was inversely associated (r = −0.69) with methylation in cord blood, measured by Long Interdispersed Nuclear Element 1 (LINE-1), a proxy for global DNA methylation.¹¹ In the same study, maternal folic acid intake during pregnancy and cord serum folate were positively correlated with cord LINE-1 (r = 0.31 and 0.21), but these estimates were not statistically significant. Among 107 pregnant Korean women, serum folate was directly, and homocysteine inversely, associated with percent placental methylation.¹² In a cross-sectional study of 120 mother-child pairs in Rotterdam, maternal periconceptional use of 400 µg folic acid supplements was associated with higher methylation of the IGF2 gene in the young offspring.¹³ While these studies suggest relations of methyl donor nutrients with aspects of DNA methylation, they were relatively small, did not assess diet at different timepoints in pregnancy, and were limited to offspring DNA methylation.

In this study, we aimed to determine the extent to which dietary intake of the methyl donor nutrients vitamin B12, betaine, choline and folate periconceptionally and during the second trimester of pregnancy affects global LINE-1 leukocyte methylation in maternal and umbilical cord blood in a healthy US population. We hypothesized that higher intakes of methyl donors would be associated with higher amounts of methylation in maternal and cord blood. Given the animal studies that suggest sex-specific effects and the fact that we observed higher methylation in male than female infants (see below), we also conducted sex-stratified analyses.

Results

Participants with first trimester methylation data did not differ in health or sociobehavioral characteristics from those with data from the second trimester or from cord blood. Mean (SD) methylation levels were 84.3 (0.6), 84.5 (0.4) and 84.6 (0.7) %5mC, respectively, in first trimester, second trimester and cord blood. Male infants had higher cord blood methylation levels than females (mean (SD) 84.8 (0.6) vs. 84.4 (0.7) %5mC, respectively, Table 1). There were no other clear associations between LINE-1 methylation and the health or sociobehavioral characteristics examined. The B-vitamins were moderately to strongly correlated with each other (rs = 0.55–0.89) and choline and methionine were strongly correlated (r_s = 0.74). Periconceptional cadmium intake was moderately correlated with periconceptional intake of betaine (r_s = 0.50) but weakly correlated with other nutrients. All other correlations among the principal methyl donor nutrients examined were weaker (r_s < 0.32).

Table 1.

Characteristics of Project Viva participants according to quartiles of cord blood LINE-1 DNA methylation

LINE-11 Quartile %5mC	Overall 84.6 (0.7) n = 534	1 83.8 (0.6) n = 133	2 84.4 (0.1) n = 134	3 84.8 (0.1) n = 134	4 85.4 (0.3) n = 133
	Mean (SD)		Mean (SD)			P for trend
Mother
Age, y	32.2 (5.1)	32.9 (4.7)	32.2 (5.0)	32.2 (4.8)	31.6 (6.0)	0.04
Prepregnancy BMI, kg/m2	24.7 (5.2)	24.2 (4.5)	24.8 (5.1)	24.6 (5.6)	25.1 (5.7)	0.23
Gestational weight gain, lb	34.0 (11.9)	34.0 (11.7)	35.8 (12.3)	32.2 (12.1)	33.9 (11.4)	0.42
Daily physical activity during pregnancy, hrs/wk	6.8 (5.9)	7.3 (6.7)	6.1 (4.0)	7.0 (6.8)	6.8 (5.5)	0.77
First trimester daily energy intake, calories	2118 (654)	2164 (687)	2133 (642)	2106 (620)	2066 (668)	0.21
Energy-adjusted total intake: weeks 0–4 of pregnancy, units/day
Vitamin B12, µg	10.4 (9.2)	10.6 (7.0)	9.6 (5.8)	11.3 (14.9)	10.0 (6.3)	0.97
Betaine, mg	235 (105)	255 (126)	237 (101)	230 (95)	218 (89)	0.004
Choline, mg	331 (63)	339 (65)	335 (65)	328 (61)	322 (62)	0.02
Folate, µg	786 (441)	836 (431)	813 (446)	745 (426)	749 (457)	0.06
Methyl Score2	9.9 (2.8)	10.5 (2.8)	10.1 (2.8)	9.6 (2.8)	9.5 (2.9)	0.003
Vitamin B2, mg	3.9 (5.6)	3.6 (2.9)	3.5 (1.7)	4.8 (10.1)	3.6 (3.2)	0.56
Vitamin B6, mg	5.0 (8.1)	5.5 (8.7)	4.0 (2.5)	6.1 (12.7)	4.3 (4.3)	0.59
Methionine, g	2.0 (0.4)	2.1 (0.4)	2.1 (0.5)	2.0 (0.4)	2.0 (0.4)	0.11
Cadmium, µg	15.4 (3.9)	16.3 (4.5)	15.6 (3.8)	14.9 (3.4)	14.7 (3.4)	0.001
Iron, mg	33.3 (16.6)	33.6 (13.1)	35.0 (23.1)	32.8 (13.6)	31.8 (14.6)	0.24
Zinc, mg	25.8 (10.6)	27.1 (11.5)	26.1 (10.7)	25.4 (10.0)	24.3 (9.9)	0.03
Second trimester daily energy intake, calories	2143 (600)	2165 (634)	2200 (587)	2076 (575)	2132 (602)	0.37
Energy-adjusted total second trimester intake, units/day
Vitamin B12, µg	10.2 (4.6)	10.7 (6.4)	9.9 (2.8)	10.4 (4.7)	9.9 (3.6)	0.34
Betaine, mg	230 (104)	245 (114)	232 (113)	223 (87)	218 (99)	0.04
Choline, mg	322 (66)	330 (78)	317 (58)	323 (62)	317 (62)	0.23
Folate, µg	1244 (397)	1261 (372)	1258 (369)	1262 (431)	1195 (415)	0.23
Methyl Score2	10.0 (3.0)	10.3 (3.0)	10.0 (2.9)	10.1 (2.8)	10.6 (3.1)	0.09
Cadmium, µg	15.2 (3.7)	16.0 (3.9)	15.1 (4.0)	15.1 (3.4)	14.5 (3.3)	0.004
Biological Father
Age, y	33.5 (6.0)	33.9 (5.1)	32.7 (5.8)	33.7 (6.0)	33.6 (6.8)	0.95
Infant
Birthweight for gestational age z-score	0.26 (0.95)	0.29 (0.90)	0.28 (0.91)	0.24 (0.99)	0.23 (1.00)	0.54
	N (%)		N (%)
Mother
Race/ethnicity						0.01
Asian	23 (4.3%)	1 (0.8%)	2 (1.5%)	8 (6.0%)	12 (9.0%)
Black	50 (9.4%)	15 (11.3%)	9 (6.7%)	13 (9.7%)	13 (9.8%)
Hispanic	40 (7.5%)	11 (8.3%)	9 (6.7%)	7 (5.2%)	13 (9.8%)
White	398 (74.5%)	102 (76.7%)	107 (79.9%)	98 (73.1%)	91 (68.4%)
More than 1 race/other	23 (4.3%)	4 (3.0%)	7 (5.2%)	8 (6.0%)	4 (3.0%)
Completed college education	375 (70.2%)	94 (70.7%)	98 (73.1%)	97 (72.4%)	86 (64.7%)	0.29
Multipara	285 (53.4%)	71 (53.4%)	70 (52.2%)	68 (50.8%)	76 (57.1%)	0.61
Smoker						0.85
During early pregnancy	50 (9.6%)	13 (9.9%)	13 (9.9%)	12 (9.2%)	12 (9.5%)
Former	110 (21.2%)	26 (19.9%)	30 (22.7%)	30 (22.9%)	24 (19.1%)
Never	360 (69.2%)	92 (70.2%)	89 (67.4%)	89 (67.9%)	90 (71.4%)
Any alcohol intake before learned that pregnant	361 (70.0%)	98 (74.2%)	92 (71.3%)	87 (68.0%)	84 (66.1%)	0.13
Infant
Male	279 (52.3%)	34 (25.6%)	66 (49.3%)	86 (64.2%)	93 (69.9%)	< 0.0001

¹LINE-1, Long Interspersed Nuclear Element 1 (%5mC, %5-methyl cytosines)

²Combines intakes of vitamin B12, betaine, choline and folate, each in quartiles (4–16 points).

Maternal periconceptional intake of methyl donor nutrients was not positively associated with cord blood LINE-1 methylation in a model adjusted for maternal intake of methyl donor nutrients and sociodemographic and weight-related covariates (Table 2). Periconceptional betaine intake was inversely associated with cord blood methylation [for every one standard deviation (∼105 mg/day) increment in intake, methylation was lower by −0.08 (95% CI: −0.14, −0.01) %5mC], but additional adjustment for maternal periconceptional cadmium intake attenuated the association (−0.04 %5mC, 95% CI: −0.11, 0.03). Periconceptional cadmium intake itself was positively associated with first trimester methylation (effect estimate for a one standard deviation (∼4 µg/day) increment: 0.06 %5mC, 95% CI: 0.01, 0.11) and inversely associated with cord blood methylation (effect estimate: −0.07 %5mC, 95% CI: −0.14, −0.002) in the fully adjusted model.

Table 2.

Associations of periconceptional methyl donor intakes with cord blood LINE-1 methylation among Project Viva participants

Model1	N	Methyl Score2		Vitamin B123		Betaine3		Choline3		Folate3
		Multivariable linear regression estimate (95% CI)
		β	p	β	p	β	p	β	p	β	p
1	516	−0.02 (−0.04, −0.003)	0.03	0.01 (−0.05, 0.07)	0.84	−0.08 (−0.14, −0.02)	0.01	−0.04 (−0.10, 0.02)	0.20	−0.04 (−0.10, 0.02)	0.18
1a	516	-		0.03 (−0.03, 0.09)	0.36	−0.08 (−0.14, −0.02)	0.01	−0.04 (−0.10, 0.02)	0.18	−0.02 (−0.09, 0.04)	0.45
2	516	−0.02 (−0.04, 0.002)	0.09	0.02 (−0.04, 0.08)	0.52	−0.07 (−0.13, −0.01)	0.02	−0.03 (−0.09, 0.03)	0.28	−0.02 (−0.08, 0.04)	0.54
3	499	−0.02 (−0.04, 0.001)	0.07	0.02 (−0.05, 0.09)	0.63	−0.08 (−0.14, −0.01)	0.02	−0.02 (−0.08, 0.03)	0.42	−0.03 (−0.10, 0.03)	0.34
4	499	−0.02 (−0.04, 0.01)	0.17	0.01 (−0.06, 0.08)	0.70	−0.04 (−0.11, 0.03)	0.24	−0.02 (−0.08, 0.04)	0.45	−0.03 (−0.10, 0.03)	0.32

Statistically significant results (p < 0.05) in bold.

¹M1: Unadjusted; M1a: Adjusts methyl donors for each other; M2: Add child's sex; M3: Add mother's age, race, smoking, gestational weight gain, education; M4: Add mother's cadmium intake

²Combines intakes of vitamin B12, betaine, choline and folate, each in quartiles (4–16 points)

³Estimate is %5mC difference in LINE-1 methylation for increment in 1 standard deviation (∼9 µg/day for B12, 105 mg/day for betaine, 63 mg/day for choline, 440 µg/day for folate).

There were no positive associations between maternal intake of methyl donor nutrients and LINE-1 methylation in first trimester, second trimester or cord blood (Table 3). However, we did observe effect modification by sex for associations of periconceptional betaine, choline and methyl donor score with cord blood methylation (interaction p-values 0.03, 0.01 and 0.01, respectively). Among males, in fully adjusted models including cadmium intake, for every SD increment of betaine intake, cord blood methylation was −0.08 %5mC, 95% CI: −0.17, 0.01 lower (data not shown). The estimate for choline was −0.10 %5mC, 95% CI: −0.17, −0.03. Owing to the effects of these two nutrients, we also observed an association of methyl score with cord blood methylation (−0.02 %5mC, 95% CI: −0.06, −0.001 per 1-unit increment). In contrast, the estimates for females were −0.01 (−0.11, 0.10) for betaine, 0.06 (−0.04, 0.16) for choline, and −0.003 (−0.04, 0.03) for methyl donor score. Even when restricting analyses to those with low folate intake, there were no positive associations between the nutrients and LINE-1 methylation; among those with periconceptional (n = 194) and second trimester (n = 68) folate intake below 600 µg/day, the multivariable estimates with cord blood LINE-1 methylation were −0.1 (95% CI: −0.5, 0.2) and −0.2 (95% CI: −0.6, 0.2), respectively.

Table 3.

Associations of methyl donor intakes at two timepoints in pregnancy with LINE-1 DNA methylation in maternal and cord blood among Project Viva participants¹

	LINE-1
	Trimester 1		Trimester 2		Cord blood
	Multivariable linear regression estimate (95% CI)
	β	p	β	p	β	p
Periconceptional dietary intake
Vitamin B122	−0.02 (−0.04, 0.01)	0.19	−0.001 (−0.03, 0.03)	0.96	0.01 (−0.06, 0.08)	0.70
Betaine2	−0.04 (−0.09, 0.01)	0.12	−0.03 (−0.06, 0.01)	0.13	−0.04 (−0.11, 0.03)	0.24
Choline2	−0.03 (−0.08, 0.01)	0.12	−0.001 (−0.03, 0.03)	0.97	−0.02 (−0.08, 0.04)	0.45
Folate2	−0.02 (−0.06, 0.03)	0.47	−0.01 (−0.04, 0.02)	0.52	−0.03 (−0.10, 0.03)	0.32
Second trimester dietary intake
Vitamin B122	-	-	0.02 (−0.003, 0.04)	0.09	−0.02 (−0.09, 0.06)	0.64
Betaine2	-	-	0.03 (−0.07, 0.00)	0.09	−0.02 (−0.10, 0.05)	0.50
Choline2	-	-	−0.01 (−0.04, 0.02)	0.62	−0.004 (−0.07, 0.06)	0.98
Folate2	-	-	−0.01 (−0.04, 0.02)	0.42	−0.02 (−0.08, 0.05)	0.61

Statistically significant results (p < 0.05) in bold.

¹Adjusted for other methyl donors, child's sex, mother's age, race, smoking, pregnancy weight gain, education, cadmium intake

²Estimate is %5mC difference in LINE-1 methylation for increment in 1 standard deviation (∼9 µg/day for B12, 105 mg/day for betaine, 63 mg/day for choline, 440 µg/day for folate).

Discussion

In this prospective cohort study, we did not find a positive association between maternal dietary intake of methyl donors and first trimester, second trimester or cord blood LINE-1 leukocyte methylation, despite adequate statistical power as reflected in narrow confidence intervals. We did find an inverse association between maternal periconceptional betaine intake and cord blood LINE-1 global DNA methylation, but this association appeared to be confounded by intake of cadmium.

Our findings of no other associations between methyl donor intake and LINE-1 methylation are consistent with some studies in non-pregnant adults.^14,15 For example, Friso et al. did not find an association between plasma folate and global genomic methylation in lymphocytes of 198 non-pregnant adults except among subjects with MTHFR minor variants, in whom low folate was associated with lower methylation.¹⁵

There are several potential explanations for the lack of association of methyl donor intake with DNA methylation in our study. First, due to folic acid fortification of the US food supply and generally high plane of nutrition among the mothers in Project Viva, there are few individuals in this population who were deficient in methyl donor nutrients, especially by the second trimester of pregnancy. For example, only 96 (9%) of women had second trimester folate intake <600 µg/day. It is possible that a threshold exists above which consuming more of these nutrients does not alter methylation levels. However, a relatively high proportion of women had low daily folate intake from weeks 0 to 4 of pregnancy [422 (39%) had folate <600 µg], so if there were truly an effect at levels currently defined as deficient, we would likely have observed it. Second, because much about methylation processes is still unknown, there may be unmeasured confounding by other factors. Authors of a recent combined analysis of five human investigations have identified covariates that affect LINE-1 methylation¹⁶ and may operate as confounders in epidemiology investigations. However, we have assessed the extent of confounding by those and many other carefully measured variables and have not identified many variables that substantially affect this association. Third, using an FFQ for dietary assessment likely introduced some measurement error into our analyses, especially since use of this FFQ in pregnancy was not validated for the nutrients we examined; however, the FFQ should accurately rank individuals' intake of these nutrients. We also used a detailed interview about maternal use of nutritional supplements periconceptionally. Fourth, LINE-1 methylation in leukocytes may not be a sensitive long-term measure of global genomic methylation, and the lack of association may be due to statistical noise of the methylation variable. However, we used the most technologically advanced and validated methods currently available for LINE-1 methylation analysis and have already demonstrated a relationship between higher LINE-1 and male sex and several other obesity-related risk factors in offspring in early life.¹⁷ This method for LINE-1 methylation analysis has been shown in previous investigations to be sensitive to environmental and host-related factors, including age,¹⁸ sex,^16,19 smoking²⁰ and tobacco smoke components,²¹ physical activity²² and environmental pollution.^23–26 Also, we measured DNA methylation only in blood DNA and we cannot exclude that methylation changes due to dietary components might be present in other tissues in the same individuals. Finally, we do not have gene- or tissue-specific methylation data, and there may be differences in gene- or tissue-specific methylation despite no differences in global LINE-1 in leukocytes.

Our null findings are important because they suggest that at the levels of folic acid intake found in a healthy US population, global DNA methylation in blood is not related to intake. This is reassuring considering the concern about potential adverse effects, possibly through epigenetic changes, of folic acid fortification of the food supply in the US and elsewhere.^27,28

One exception to our null findings was lower cord blood methylation in relation to higher periconceptional intakes of betaine and choline in males only. We consider these sex-specific observations to be hypothesis-generating. This was a post hoc analysis based on our initial observation that males had higher levels of cord blood methylation than females, rather than an a priori hypothesis. Nevertheless, given that this pattern is consistent with some, but not all, animal experiments,^7,9,10 we would recommend that investigators search for sex-specific determinants, as well as outcomes, of DNA methylation.

These inverse associations among males, and the initial finding that periconceptional maternal betaine intake was inversely related to cord blood methylation in the overall cohort were counter to our hypothesis, considering that some previous studies have found positive relationships between maternal methyl donor intake and offspring methylation.^9,11,12 The inverse association could possibly be due to changes in the expression or activity of DNA methyltransferase (DNMT), the enzyme that attaches methyl groups to DNA. This would be consistent with a study in rats in which a deficiency of choline, a precursor of betaine, during gestation caused increased global methylation in the brain and liver of offspring by upregulation of DNMT1 expression.²⁹ However, the fact that we did not find other associations of methyl donor intake at two timepoints with DNA methylation at three timepoints suggests that the findings may be due to chance. If we had corrected for multiple testing, these associations would not have been statistically significant by conventional standards (p < 0.05). Also, the betaine association was substantially attenuated after adjustment for maternal dietary cadmium intake, which itself was directly associated with first trimester and inversely associated with cord blood LINE-1.

We found that dietary cadmium was associated with first trimester and cord blood LINE-1 methylation. In the human diet, cadmium, a toxic heavy metal if ingested at high doses, comes mostly from plant-based foods grown in soils treated with fertilizer.³⁰ Betaine comes from similar sources, and intake was moderately correlated (r_s = 0.50) with cadmium intake in our study. Additionally, women with higher cadmium intakes in our study were older and consumed more methyl donors in general. Cadmium interferes with the activity of DNMT in animals and human cell cultures,³¹ so dietary intake of this mineral could affect methylation in human populations as well. One study found that long-term low exposure to cadmium increased DNMT activity and genomic methylation in human embryo lung fibroblast cells.³² A study in mouse testicular Leydig cells found that 24-h exposure to cadmium led to decreased expression of genes for DNMTs.³³ Another study in rat liver cells found that exposure to cadmium initially led to decreased, then later to increased DNMT activity and methylation.³⁴ Finally, investigators found that cadmium exposure in chick embryos caused downregulation of DNMT3A/3B gene expression.³⁵ More research must be conducted about the validity of cadmium intake measured by FFQ, cadmium's effects on DNMT activity and methylation, and potential intergenerational health effects in humans in vivo.

Strengths of this study include its prospective design, detailed dietary information, measurement of DNA methylation in both maternal and cord blood, adjustment for multiple confounding factors, and large sample size compared with previous studies. The findings from this study may or may not be generalizable to other populations.

In conclusion, in this folate-replete population, we did not find a positive association between maternal dietary intake of methyl donors and first trimester, second trimester or cord blood LINE-1 leukocyte methylation. Cadmium intake was a predictor of maternal and cord blood methylation. Future research should focus not only on effects of methyl donors in other populations but also on maternal cadmium exposure as a potential determinant of DNA methylation and offspring health.

Patients and Methods

Study sample.

We studied participants in Project Viva, an ongoing prospective pre-birth cohort study initiated in 1999. Women joined the study during their first prenatal visit at Harvard Vanguard Medical Associations, a large multi-specialty group practice in eastern Massachusetts. Eligibility criteria included fluency in English, gestational age less than 22 weeks at first prenatal visit, and singleton pregnancy. Additional details of recruitment and retention procedures have been published in reference ³⁶. Of the 2,¹²8 mother-infant pairs in the cohort, we obtained DNA from maternal blood samples in the first (mean [SD] 10.1 [2.2] weeks) and second (mean [SD] 27.9 [1.4] weeks) trimesters of pregnancy and umbilical cord blood samples at delivery in 914, 922 and 557 participants, respectively. We excluded individuals without periconceptional or second trimester dietary data, leaving 859, 870 and 516 individuals for analysis of periconceptional diet with first trimester, second trimester and cord blood methylation, respectively, and 840 and 484 individuals for analysis of second trimester diet with second trimester and cord blood methylation, respectively.

Measurements.

We collected venous whole blood samples at in-person study visits conducted with the mother at the end of the first and second trimesters of pregnancy and from the umbilical cord at delivery. We refrigerated samples immediately after collection and transferred them to the Channing Laboratory within 24 h, where they were spun and blood components were separated into aliquots for storage in liquid nitrogen (−80°C). To prepare the samples for pyrosequencing, we extracted high molecular weight genomic DNA from the white blood cells with commercially available PureGene Kits (Fisher, catalog #: A407-4, A416-4; Qiagen, catalog #: 158908, 158912, 158924).

At each first and second trimester study visit, to assess intake of methyl donors from food, we administered a semi-quantitative food frequency questionnaire (FFQ) modified from the well-validated instrument used in the Nurses' Health Study and other large cohorts^37,38 and further calibrated for use in pregnancy.³⁹ The second trimester FFQ also included questions about use of nutritional supplements including vitamin B12 and folic acid, which we used to calculate total second trimester intake of these nutrients. At the first trimester visit, we also conducted a 33-item detailed interview about use (frequency, brand/type, dosage and timing) of nutritional supplements including vitamin B12 and folic acid in early pregnancy. With this information, we calculated intake of vitamin B12 and folic acid from supplements in gestational weeks 0 to 4, i.e., in the periconceptional period. Assuming that mothers had relatively consistent intake of the methyl donor nutrients from food during the first trimester, we calculated total maternal intake of methyl donor nutrients periconceptionally by summing food and supplement contributions. Previous investigators have shown the methyl donor nutrients to be validly measured by similar FFQs.^40,41 We energy-adjusted micronutrient intake using a residuals method.⁴² Data from the FFQ and interviews with mothers indicated that this was a generally folate-replete population [mean (SD) intake 786 (441) µg/day periconceptionally and 1,244 (397) µg/day in the second trimester].

Because no methyl donor “score” exists based on physiology or association with health outcomes, we relied primarily on a regression approach in which we calculated individual effects of the four nutrients. However, we also created a methyl donor intake score by calculating the quartiles of each methyl donor nutrient separately. We then assigned a value of 1 to the first quartile, 2 to the second, and so on. The methyl donor score was the sum of these quartile values, so that a mother whose intake of each nutrient was in the smallest quartile had a score of 4, while a mother with intake in the highest quartile of each nutrient had a score of 16.

Using a combination of questionnaires and interviews, we collected information about a range of sociodemographic factors, lifestyle habits, and medical and reproductive history.³⁶ Mothers reported paternal weight and height. We calculated maternal gestational weight gain as the self-reported prepregnancy weight subtracted from the last clinically recorded weight before delivery. We calculated prepregnancy body mass index (BMI) using self-reported height and prepregnancy weight with the formula BMI = (weight in kilograms/height in meters²). We obtained infant birthweight from the hospital clinical record, and we calculated gestational age from the last menstrual period; if the estimate of gestational age from the second trimester ultrasound differed by > 10 d, we used it instead. We determined birthweight-for-gestational age z-score by use of US national reference data.⁴³ We obtained maternal white blood cell count at the medical visits closest to date of first and second trimester blood draw from clinical records. We also used the FFQ to obtain information on maternal intake of zinc, iron, cadmium, protein, vitamin E and vitamin C. Measurements of these nutrients during pregnancy by FFQ tend to be moderately to highly correlated with diet record values,^42,44 except for cadmium, for which we could identify no validation studies. Little is known about the quality of the cadmium variable as measured by the nutrient database and cadmium exposure may vary based on source of drinking water and plant-based foods as well as environmental exposure and differential absorption.⁴⁵

We quantitated DNA methylation using bisulfite-PCR and Pyrosequencing, using previously described primers and conditions.^23,24 We used PCR primers developed to amplify a large pool of repetitive elements to serve as a surrogate for diffuse genomic DNA methylation changes. To achieve this goal, the primers were designed toward a consensus LINE-1 sequence (i.e., a sequence repeated with no variation in a large proportion of LINE-1 repeated elements across the human genome). This method quantitatively assessed the proportion of methylated sites in LINE-1 repetitive elements dispersed throughout the genome. Non-CpG cytosine residues were used as built-in controls to verify bisulfite conversion. Compared with other common methods of DNA methylation analysis, pyrosequencing-based assays have the advantage of producing individual measures of methylation at more than one CpG dinucleotide, thus reflecting more accurately DNA methylation in the region. We measured methylation as a percentage of 5-methylated cytosines (%5mC) at each of four CpG dinucleotide positions that are repeated over the human genome. We tested each sample in two replicates, and used their average in the statistical analysis. We sent 48 of the cord blood samples before the others as a pilot study, and for these samples, we measured LINE-1 only at the first three CpG dinucleotide positions, so we adjusted accordingly in our analysis (see below).

Statistical analysis.

We estimated LINE-1 methylation by the following procedure. We fit a mixed effects model⁴⁶ allowing different mean levels for each position in each run. We also allowed different means per position and run for the 48 subjects for whom only three positions were available. This approach was necessary due to observed differences between the samples from these 48 subjects and the other subjects. We fit a random intercept as well as a general covariance structure allowing unique variance and unique covariance between positions within run and within run across positions. The empirical Bayes' estimates (predicted random intercepts) are interpreted as the underlying LINE-1 methylation level and used as such in the analysis.

We examined characteristics of participants by quartile of LINE-1 methylation in each of maternal and cord blood. We calculated Spearman correlation coefficients (r_s) to examine bivariate associations among maternal intakes of several nutrients involved in methylation pathways. We conducted multivariable linear regression of LINE-1 methylation on total maternal intake of methyl donor nutrients, first using quartiles of nutrient intake to assure linearity of exposure-outcome associations and then using methyl donor intake as a continuous variable to maximize power. We assessed the bivariate association between each nutrient and methylation outcome, then adjusted the nutrients for each other, and finally added variables that we considered a priori to be confounders or found to be associated with maternal dietary intake of methyl donors and/or DNA methylation. We present sequential multivariable models to illustrate the extent to which addition of covariates changed effect estimates. In addition, we added first trimester maternal dietary intake of metals cadmium, zinc and iron to the models since these metals have been found to be associated with methylation and/or genomic stability in animal models,⁴⁷ but of these only cadmium remained in the final multivariable model, since the other metals were not associated with methylation and did not materially change the effect estimate. The final model also included adjustment for mother's age, race, smoking, pregnancy weight gain and education; and in the overall (not sex-stratified) model, child's sex.

We additionally considered maternal prepregnancy BMI, physical activity, white blood cell count, prepregnancy alcohol consumption, season of diet measurement, and first trimester intake of protein, vitamin E, vitamin C and total calories, paternal age, household income, season and day of the week of LINE-1 measurement and child birthweight for gestational age z-score as potential covariates for the multivariable models, but none of these variables materially changed (i.e., by > 10%) exposure-outcome associations, so we did not include them in the models.

We examined maternal intake from food or supplements separately (vitamin B12 and folate/folic acid only) to see if one had a stronger effect than the other. We also examined associations of folate intake with LINE-1 methylation among those with periconceptional and second trimester folate intake below 600 µg/day to see if stronger associations existed among those with low folate intake. Finally, we separately examined whether vitamin B2, vitamin B6 and methionine, other nutrients involved in the methylation pathways, were individually associated with methylation.

To assess effect modification, we examined sex-specific associations and calculated an interaction term in overall analyses as sex x nutrient intake.

We used a complete case approach for all analyses since there were few missing data for the covariates.

We performed all calculations in SAS version 9.2 and code is available by request.

Institutional review boards of Harvard Pilgrim Health Care, Brigham and Women's Hospital and Beth Israel Deaconess Medical Center approved the study protocols.

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.