Risks of Macrosomia Associated with Catechol- O -Methyltransferase Genotypes and Genetic–Epigenetic Interactions among Children with and without Gestational Diabetes Exposure

Abstract

Background:

Gestational diabetes mellitus (GDM) is a major macrosomia risk factor. Variations in the catechol-O-methyltransferase (COMT; rs4680) genotypes are associated with heightened susceptibility to environmental exposures and nutritional conditions. However, macrosomia risks associated with COMT genetics, epigenetics, and the interaction between genetic and epigenetics among children with and without exposure to GDM are unknown.

Methods:

Data from women/children pairs (n = 1087) who participated in the Tianjin Gestational Diabetes Birth Cohort were used to examine the odds of being born with macrosomia associated with COMT-genotypes, 55 CpG sites located on the COMT gene, and genetic and epigenetic interactions. Odds of macrosomia associated with COMT genetic, epigenetic, genetic and epigenetic interactions, and moderations with GDM were tested using adjusted logistic regression models.

Results:

Overall, 16.1% (n = 175) of children were born with macrosomia. Models showed that children with at least one copy of the minor allele (A) had higher odds of macrosomia (odds ratio, 1.82; 95% confidence interval 1.25–2.64) compared with children with the GG-genotype. After false discovery rate corrections, none of the 55 CpG sites located on the COMT gene was associated with odds of macrosomia. The genetic and epigenetic associations were not modified by exposure to GDM.

Conclusion:

Findings suggest carriers of the COMT GG-genotype had lower odds of macrosomia, and this association was not modified by epigenetics or exposure to GDM.

Introduction

Macrosomia, defined as a birthweight greater than 4000 g regardless of gestational age, is associated with increased cardiometabolic disease risks.¹ Gestational diabetes mellitus (GDM) is a major contributor to risk of macrosomia.¹ Insights into the mechanisms through which GDM triggers the development of macrosomia in some children, but not in others, could help prevent macrosomia and its associated adverse cardiometabolic outcomes.

Variation in the genetic makeup of some individuals influences their susceptibility to nutritional conditions. For example, impulse regulation and genes that encode dopamine are associated with obesity.^2,3 The catechol-O-methyltransferase (COMT) gene (rs4680) is associated with differential susceptibility to stimuli, mainly because of its role in degrading dopamine.⁴ Carriers of COMT A-allele (i.e., carriers of the AA and AG genotypes) have higher dopamine levels and associated advantages in memory and attention tasks. Meanwhile, carriers of the COMT G-allele have lower dopamine levels and are more likely to have poorly regulated impulses.^4–6 However, gene/environment studies suggest that under stressful conditions these advantages reverse, and when compared with A-allele carriers, carriers of the GG-genotype have improved impulse control.^6,7 Previous studies showed that GDM can affect DNA methylation in the offspring and modify susceptibility to multiple metabolic diseases.^8,9 DNA methylation, which results from the transfer of a methyl group by DNA methyltransferase to the cytosine residue within 5′—C(cytosine)—phosphate—G(guanine)—3′ (CpG) dinucleotides to form 5-methylcytosine, promotes gene silencing that can impact phenotypes without changing the genetic code itself.¹⁰ Yet, to our knowledge, this is the first study to examine the associations of macrosomia with the COMT gene, its potential interacting genetic and epigenetic influences, and potential differences associated with GDM exposures.

This study examined the following. (1) The associations of COMT-genotypes with macrosomia. (2) The associations of methylation of 55 CpG sites located on the COMT gene (i.e., COMT-methylation) with macrosomia. (3) The two-way interaction of COMT-genotypes and COMT-methylation with macrosomia. (4) Whether the presence of GDM modified the risks for macrosomia associated with COMT-genotypes, COMT-methylation, or with the two-way interaction of COMT-genotypes and COMT-methylation.

Methods

Data from the Tianjin Gestational Diabetes Birth Cohort were used. Recruitment and methodology details of this 4-year randomized clinical trial carried out at the Tianjin Women's and Children's Health Center in Tianjin, China, are described elsewhere.^8,11,12 Women diagnosed with GDM at 26–30 weeks of gestation between 2005 and 2009 were invited to participate in the Tianjin Gestational Diabetes Mellitus Prevention Program (TGDMPP). Women were recruited between August 2009 and July 2011 (n = 1180) and randomly assigned (1:1) to a 4-year lifestyle intervention (n = 586) or a usual care group (n = 594). In addition, an observational cohort of women without GDM (i.e., non-GDM) and their children were recruited (n = 705). Women in the non-GDM cohort were recruited to match the birth dates and sex of the usual care group. The study was approved by the Human Subjects Committee of Tianjin Women's and Children's Health Center. All participants provided written informed consent. The study protocol was also approved by the Institutional Review Board of Pennington Biomedical Research Center (IRB #2014-031-PBRC).

This study utilizes data from 545 women with GDM assigned to the usual care group and 542 women in the non-GDM group with single-nucleotide polymorphism (SNP) data available at baseline. Of the 1087 participants in whom genotype data were attempted, it was carried out successfully in 1077 participants. Participants with missing genotype data (n = 10) were excluded from analyses.

Measurements

Women completed a self-administered questionnaire and underwent a physical examination at baseline. Questionnaires collected women's age, education, pregnancy outcomes (including prepregnancy BMI, pregnancy weight gain, child sex, and gestational age), and children's birthweight and birth date. Women's body weight and height were measured by study personnel using standardized protocols. All women completed glucose tests after overnight fast of at least 12 hours and the 75-g 2-hour oral glucose tolerance test (OGTT).

Macrosomia was defined as birthweight ≥4000 g independent of gestational age and sex or ≥90th percentile according to local gestational age- and sex-specific references.^13,14 GDM was defined as fasting glucose of ≥7.0 mmol/L or 2-h glucose ≥7.8 mmol/L in the 2-hour 75 g OGTT test, based on the 1999 World Health Organization criteria.¹⁵

DNA Extraction and Genotyping

Genomic DNA was extracted from blood leukocytes using the QIAamp Blood Kit (Qiagen, Hilden, Germany). SNP in the COMT rs4680 gene was genotyped as AA, AG, or GG by quantitative real-time TaqMan PCR (Applied Biosystems, Foster CIty, CA). The success rate of genotyping was >98%. For quality control, 10% of the samples were regenotyped, with >99% concordance.

The HumanMethylationEPIC (“EPIC”) array from Illumina with the default settings was used for methylation data preprocessing and quality control (Illumina, San Diego, CA). Methylation data for each CpG island were z-standardized before testing.

Proportions of CD4+ T lymphocytes, CD8+ T lymphocytes, B lymphocytes, natural killer cells, monocytes, and granulocytes were estimated to account for cell composition.¹⁶ Biases were addressed by deriving surrogate variables from intensity data for non-negative internal control probes using principal component analysis.¹⁷

Statistical Analysis

Logistic regression models were used to test odds of macrosomia associated with (1) genotypes, (2) COMT-methylation, (3) the interactions of genotypes with COMT-methylation, and (4) interactions with GDM: (4a) two-way interaction of COMT genotypes and GDM with macrosomia, (4b) two-way interactions of COMT methylation and GDM with macrosomia, and (4c) three-way interaction of COMT genotypes, COMT methylation, and GDM with macrosomia. Tests for the association of COMT genotypes with macrosomia (1) for all modes of inheritance were performed using SNPstats (https://www.snpstats.net). Akaike and Bayesian Information Criteria were used to identify the best mode of inheritance for subsequent tests. Tests were adjusted for maternal age, height, prepregnancy BMI, pregnancy weight gain, and level of education, as well as child sex and gestational age. Epigenetic tests and test of interactions with epigenetic data adjusted for the influence of the genotypes, the aforementioned covariates, blood cell type proportions and compositions, and 10 principal components representing processing batch effects (>90% of variance). All interactions were tested including multiplicative interaction terms. Calculations were conducted using STATAIC (v16). Multiple testing in the estimation of the epigenetic associations was accounted for by controlling for false discovery rate (FDR <0.05).¹⁸

Results

Women and child characteristics are shown in Table 1. Children with macrosomia (n = 175, 16.1%) were born at older mean gestational ages (p < 0.001) to women with higher prepregnancy BMI (p < 0.001), and higher weight gain during pregnancy (p < 0.001).

Table 1.

Descriptive Characteristics Overall and by Macrosomia Status

	Overall	Macrosomia at birth	Healthy weight at birth
	N (%)	N (%)	N (%)
Child sex at birth^*	1087	175 (16.1)	912 (83.9)
Male	561 (51.6)	104 (18.5)	457 (81.5)
Female	526 (48.4)	71 (13.5)	455 (86.5)
Gestational age (mean, SD)^**	39.09 ± 1.37	39.58 ± 1.23	38.99 ± 1.37
Maternal age (mean, SD)	30.08 ± 3.21	29.87 ± 2.84	30.12 ± 3.27
Maternal height (mean, SD)	118.42 ± 9.63	119.20 ± 9.07	118.27 ± 9.72
Prepregnancy mean BMI^** (mean, SD)	22.15 ± 3.11	23.57 ± 3.21	21.88 ± 3.01
Weight gain^** (mean, SD)	17.28 ± 6.22	19.79 ± 6.22	16.79 ± 6.11
Mother's education^a
≤High-school equivalent	157 (16.0)	25 (15.8)	132 (16.0)
Bachelor equivalent	727 (74.0)	117 (74.1)	610 (73.9)
Graduate school or higher	99 (10.1)	16 (10.1)	83 (10.1)
Gestational diabetes^**
No gestational diabetes	542 (49.9)	64 (11.8)	478 (88.2)
Gestational diabetes	545 (50.1)	111 (20.4)	434 (79.6)
COMT^a
AA	77 (7.1)	14 (18.2)	63 (81.8)
AG	460 (42.8)	88 (19.1)	372 (80.9)
GG	540 (50.1)	73 (13.5)	467 (86.5)

Numbers do not add up to 1087 due to missing data.

p < 0.05, ^**p < 0.001.

COMT, catechol-O-methyltransferase; SD, standard deviation

Odds of macrosomia were significantly associated with COMT-genotypes in the tests that examine the dominant, codominant, overdominant, and log-additive modes of inheritance (p < 0.05). Tests of the dominant mode of inheritance showed that when compared with carriers of the GG-genotype, children with at least one copy of the A-allele had higher odds of macrosomia (odds ratio, 1.82; 95% confidence interval 1.25–2.64) (Table 2). Models testing the interaction of genotypes with maternal GDM showed that exposure to GDM did not modify the odds of being born with macrosomia associated with COMT-genotypes (p < 0.05).

Table 2.

Odds of Macrosomia Associated with Catechol-O-Methyltransferase-Genotypes and Interactions with Gestational Diabetes Mellitus by Inheritance Model

	Coefficients^a
Modes of inheritance	OR	95% CI	p	AIC	BIC
Dominant model
GG		REF.
AG/AA	1.82	1.25–2.64	0.0015	768.4	822.1
AG/AA^*GDM		—	0.94
Codominant model
GG		REF.
AG	1.81	1.23–2.66	0.0062	770.4	829
AA	1.90	0.94–3.83
AG/AA^*GDM		—	0.21
Overdominant model
GG/AA		REF.
AG	1.66	1.15–2.40	0.0074	771.4	825.1
GG/AG^*GDM		—	0.33
Recessive model
GG/AG		REF.
AA	1.43	0.73–2.79	0.31	777.6	831.2
AA^*GDM		—	0.10
Log-additive
—	1.54	1.16–2.06	0.0032	769.9	823.6
Interaction			0.21

Adjusted for maternal age, height, prepregnancy BMI, weight gain, parity, education, gestational diabetes status, child sex, gestational age, and GDM status.

AIC, Akaike's Information Criteria; BIC, Bayesian Information Criteria; CI, confidence interval; GDM, gestational diabetes mellitus; OR, odds ratio; REF., reference.

Of the 55 methylated CpG sites examined, we found odds of macrosomia were associated with interaction between the COMT-genotypes and 4 CpG sites (cg24914853, cg11712482, cg24547396, cg27399558). Table 3 shows the odds for these significant interactions and the subsequent tests of interactions with GDM status (see Supplementary Table S1 for the tests of the odds of macrosomia associated with the interaction between COMT-genotypes and all 55 methylated CpG sites). Tests of interactions with GDM status showed that exposure to GDM modified the odds of macrosomia associated with the interaction between COMT A-allele and cg27399558. No other COMT-genotype and methylation site was significantly associated with children's odds of macrosomia. None of the epigenetic associations or interactions remained significant after FDR corrections for multiple tests.

Table 3.

Odds of Macrosomia Associated with the Interaction of Catechol-O-Methyltransferase-Genotypes with Selected Catechol-O-Methyltransferase-Methylation Sites and Their Respective Interactions with Gestational Diabetes Mellitus Tested Based on the Dominant Mode of Inheritance

	Genetic × epigenetic associations^a				Interactions with GDM^b
	OR	95% CI	p	FDR	OR	95% CI	p
cg24914853	0.71	0.51–0.98	0.04		0.97	0.74–1.26	0.80
GG		REF.				REF.
Ga/aa	1.78	1.19–2.66	0.005		1.74	1.17–2.59	0.01
cg24914853 × Ga/aa	1.89	1.27–2.81	0.002	0.67		—
GDM	1.75	1.09–2.18	0.020		1.72	1.08–2.75	0.02
cg24914853 × Ga/aa × GDM		—			1.23	0.83–1.82	0.31
cg11712482	0.79	0.58–1.09	0.15		1.00	0.76–1.32	0.99
GG		REF.				REF.
Ga/aa	1.63	1.09–2.43	0.02		1.66	1.12–2.47	0.01
cg11712482 × Ga/aa	1.61	1.07–2.41	0.02	0.73		—
GDM	1.67	1.05–2.66	0.03		1.0	1.04–2.66	0.03
cg11712482 × Ga/aa × GDM		—			1.06	0.69–1.64	0.79
cg24547396	0.93	0.65–1.32	0.67		1.16	0.84–1.60	0.36
GG		REF.				REF.
Ga/aa	1.58	1.05–2.36	0.03		1.62	1.09–2.41	0.02
cg24547396 × Ga/aa	1.62	1.09–2.42	0.02	0.74		—
GDM	1.67	1.04–2.67	0.03		1.67	1.04–2.66	0.03
cg24547396 × Ga/aa × GDM		—			1.08	0.69–1.67	0.74
cg27399558	0.80	0.59–1.08	0.14		0.85	0.65–1.10	0.21
GG		REF.				REF.
Ga/aa	1.70	1.13–2.55	0.01		1.69	1.13–2.53	0.01
cg27399558 × Ga/aa	1.52	1.02–2.27	0.04	0.77		—
GDM	1.66	1.04–2.66	0.03		1.55	0.97–2.49	0.07
cg27399558 × Ga/aa × GDM		—			1.66	1.08–2.56	0.02

Adjusted for maternal age, height, prepregnancy BMI, weight gain, parity, education, child sex, gestational age, gestational diabetes status, blood cell type proportions, and 10 principal components representing processing batch effects (>90% of variance).

Adjusted for maternal age, height, prepregnancy BMI, weight gain, parity, education, child sex, gestational age, blood cell-type proportions, and 10 principal components representing processing batch effects (>90% of variance).

CI, confidence interval; FDR, false discovery rate; REF., reference.

Discussion

This study demonstrated that children with at least one copy of the A-allele (i.e., genotypes AA and AG) of the COMT-gene have higher odds of macrosomia compared with carriers of the GG genotypes. We found that COMT-methylation did not modify the association of COMT-genotypes with odds of macrosomia. These null associations were not modified by exposure to GDM. These findings are an important contribution to characterizing the genetic and epigenetic variants underlying the risks of macrosomia.

In a previous study of mostly white children ages 2.5 to 3.5 years, carriers of the AA genotype had significantly higher BMI percentiles when compared with other genotypes.¹⁹ In contrast, a study in adults found an increased risk for obesity among carriers of the GG-genotype.²⁰ Our findings support the findings from a study among young children and introduce evidence suggesting that the association of A-allele carriers with obesity may begin at birth. Longitudinal studies are needed to investigate if the associations of the COMT-genotypes with risks of obesity change across the life span.

We found no evidence suggesting that alterations to the epigenome associated with a higher risk for macrosomia or modified the expressions of COMT-genotypes. Multiple studies have found that epigenetic changes associated with exposure to adverse in utero environments can alter the offspring's physiology and vulnerability to disease risk in adulthood.^8,9 This includes a meta-analysis of the association between maternal GDM and cord blood DNA methylation that found maternal GDM was associated with lower cord blood methylation levels within two regions.⁹ However, our results suggest that the epigenetic alterations of COMT do not influence odds of macrosomia, or any effects due to methylation are overpowered by the larger influence of the COMT gene.

Findings should be interpreted with caution given the limitations of the sample that included only Chinese children (which is not generalizable to other populations), and other major limitations introduced by the use of self-administered questionnaire, such as potential for recall bias. Future studies should verify the association identified among carriers of the GG-genotype with longitudinal racially/ethnically diverse samples, preferably using either anthropometric measures or medical chart review. Lastly, this study did not test the association of a genetic risk score that combined the information from the 55 methylation CpG sites due to data and sample limitations. Future studies should consider including the COMT gene and the 55 methylation CpG sites that expand from the COMT gene in assessments of the risks of macrosomia associated with polygenic risk scores.

In conclusion, this is the first study to show that variations in the COMT-gene are associated with odds of macrosomia. Results suggest that this association is not affected by methylation in 55 Cpg sites that expand from the COMT-gene or exposure to gestational diabetes. These findings are an important contribution that advances our understanding of the complex genetic and epigenetic contributions to the etiology of macrosomia.

Footnotes

Disclaimer

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Funding Information

This study was supported by grants from the European Foundation for the Study of Diabetes (EFSD)/Chinese Diabetes Society (CDS)/Lilly program for Collaborative Research between China and Europe, and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK, R01DK100790). This project also used core facilities supported by the NORC Center Grant P30 DK072476, and the COBRE Center Grant P30 GM118430. L.A. was supported by the American Heart Association during the completion of this project (17SFRN33660752) and is currently supported by the NIDDK (5R01DK115937-02). G.H. was partly supported by the grant from the National Institute of General Medical Sciences (U54GM104940). L.H. was partially supported by the American Heart Association Children's Strategically Focused Research Network (17SFRN33660752). H.L was support by the Natural Science Foundation of Tianjin, China (19JCYBJC28000). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the article.

Author Disclosure Statement

No competing financial interests exist.

Supplementary Material

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