Relationships between Maternal Folic Acid Supplementation and GATA4 Gene Polymorphisms in Patients with Non-Chromosomal Congenital Heart Disease: A Hospital-Based Case–Control Study in China
1
Infection Control Center, Xiangya Hospital, Central South University, Changsha 410017, China
2
National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410017, China
3
Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha 410017, China
*
Author to whom correspondence should be addressed.
Nutrients 2023, 15(20), 4478; https://doi.org/10.3390/nu15204478
Submission received: 15 September 2023
/
Revised: 9 October 2023
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Accepted: 10 October 2023
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Published: 23 October 2023
(This article belongs to the Section Micronutrients and Human Health)
Abstract
:This study aimed to investigate the relationships between maternal FA supplementation and nine single-nucleotide variants of the GATA4 gene in non-chromosomal CHD and further explore the gene–environment interactions associated with CHD. A total of 585 CHD patients and 600 controls were recruited in the case–control study. Maternal FA (FA-containing multivitamin) supplementation information and nine polymorphisms of the GATA4 gene were collected in this study. Adjusted ORs (aOR) and their 95% confidence intervals (CIs) were calculated using proper statistical methods to analyze the relationships between the two main exposures of interest with respect to CHD. After adjusting the suspicious confounding factors, a significantly increased risk for CHD in offspring was found with non-FA supplementation before/during the pregnancy to CHD in offspring (aOR = 1.58, 95% CI: 1.01–2.48). We suggested taking FA supplementation before/during the pregnancy to prevent CHD in offspring, especially in the preconception period (aOR = 0.53, 95% CI: 0.32–0.90). The genetic results showed that the polymorphisms of rs4841588, rs12458, and rs904018 under specific genotypes and genetic models were significantly related to CHD. The gene–environment interaction between rs10108052 and FA supplementation before/during pregnancy could increase the risk of CHD (aOR = 5.38, 95% CI: 1.67–17.09, Pinteraction = 0.004). Relationships between maternal FA supplementation and specific polymorphisms of the GATA4 gene, as well as the gene–environment interaction, were significantly associated with CHD in offspring.
1. Introduction
Congenital heart disease (CHD) is the most common type of birth defect and is the main cause of morbidity and mortality due to congenital defects. The overall prevalence has been estimated at 9.4 per 1000 live births worldwide [1]. Despite the improvement in surgical and clinical management, millions of newborns are affected by CHD every year [2]. The etiology of non-chromosomal CHD is complicated and multifactorial and mainly caused by a combination of genetic and environmental factors [3].
The transcription factor GATA4, as the predominant GATA family member, plays a key role in regulating embryonic cardiac development, is the critical modifier in the early stages of cardiac formation, and contributes to the development of the myocardium, endocardium, and conduction system [4]. Evidence has shown that a dysfunction of the GATA4 gene could increase the risk of CHD. The genotype and allele distributions of the polymorphisms at the GATA4 gene (such as rs867858, rs904018, and rs884662) are significantly different between CHD cases and controls and are associated with CHD [5,6]. Although the single-nucleotide variations in the GATA4 gene are firmly considered risk factors, studies have yielded conflicting results regarding specific mutations [7]. The disagreements across studies might be related to diverse populations, sample sizes, selection bias, and a lack of consideration of gene–environment interactions.
Maternal folic acid (FA) supplementation around the periconceptional period is generally established to prevent neural tube defects. Animal studies have indicated that FA deficiency might negatively affect the migration of the cardiac neural crest in the development of the embryonic heart and the subsequent formation of the cardiovascular system, but the precise role in CHD was still deficient and inconsistent [8]. Studies from Asia and Europe were more likely to support the preventive effect of FA supplementation on CHD [9,10,11]; otherwise, inconclusive results have been found in North America [12,13]. Xu et al. conducted a meta-analysis which included 20 original epidemiological studies to assess the correlation between maternal FA supplementation and CHD, and a significant decrease in risk was found [14]. Geographical heterogeneity was observed across the eligible studies, and it is worth noting that the risk in Chinese and European populations was more likely to be reduced by maternal FA supplementation.
To date, the FA–gene interactions associated with CHD are not fully understood, and folate-metabolizing genes have primarily been taken into account in previous studies. The MTHFR, MTR, and MTRR genes were found to be essential to FA cycle metabolism, and most studies have focused on the association of these folate-metabolizing genes with CHD [12,15]. However, a genome-wide association study (GWAS) showed that several newly identified folate-regulatory genes were related to plasma folate concentration, indicating that the genes associated with folate metabolism should be expanded [16,17]. Few studies have investigated the potential interactions between FA-related environmental factors and GATA4 polymorphisms, and relevant studies could connect the dots regarding CHD risk factors and reveal the mechanisms of CHD. Therefore, the purpose of this case–control study was to investigate the relationships between maternal FA supplementation and single-nucleotide polymorphisms (SNPs) of the GATA4 gene in non-chromosomal CHD and further explore the gene–environment interactions associated with CHD.
2. Materials and Methods
2.1. Study Participants
The participants in this study were recruited in Hunan Children’s Hospital (Changsha, China) between December 2018 and June 2021. A total of 585 unrelated CHD patients and 600 healthy individuals with ages ranging from days to 1 year were included. The CHD cases were all non-chromosomal CHD inpatients diagnosed by professional clinicians according to the ICD–10 (International Classification of Diseases, Tenth Revision), while those with chromosomal aberrations, syndrome CHD, or other congenital malformations were excluded. The controls were randomly selected from the healthy individuals without any congenital defects or cardiac disease in the children’s health care department. To minimize the genetic confounding factors, CHD cases and controls with any familial relationships were excluded. A sample of 3 mL of peripheral venous blood was collected from each individual, and maternal pregnancy information was obtained after permission was granted. Ethical consent was given by the ethics committee at the Xiangya School of Public Health, Central South University, in January 2018 (No. XYGW-2018-36).
2.2. Data/Sample Collection and Management
Maternal pregnancy information was collected via face-to-face investigation using structured questionnaires among the CHD and control groups. The standardized questionnaire included the following information: (1) demographic characteristic information (e.g., average pregnancy age and annual household income); (2) history of pregnancy and delivery (e.g., adverse pregnancy outcome); (3) history of disease (e.g., diabetes, hypertension, and influenza); and (4) personal lifestyle (e.g., smoking and drinking) before and during the pregnancy. Blood samples were collected and restored in an ultra-low-temperature refrigerator temporarily and sent to BoMiao Biological Technology (Beijing, China) for DNA extraction and genotyping via the MassArray sequencing technique.
2.3. Main Exposure and Covariates
Maternal FA (FA-containing multivitamin) supplementation and polymorphisms of the GATA4 gene were the two main interested forms of exposure in this study. Exposure information on maternal FA supplementation was evaluated via the initiation of supplement timing before/during the pregnancy for three time windows: preconception period (−12 to −1 weeks), the first trimester of pregnancy (0 to 12 weeks), and the middle/last trimesters of pregnancy (13 weeks to the conception). Single-nucleotide variants of the GATA4 gene were selected by screening the mutation variants in CHD patients in previous studies and searching the dbSNP database of NCBI (https://www.ncbi.nlm.nih.gov/SNP/), accessed on 15 May 2022. Eventually, nine common variants (rs4841588, rs884662, rs804287, rs3203358, rs867858, rs2645457, rs10108052, rs12458, and rs904018) of the GATA4 gene with minor allele frequency (MAF) ≥ 0.05 were included in this study.
2.4. Statistic Analysis
The distributions of baseline characteristics and genetic frequencies between the CHD and control groups were assessed using chi-squared tests. The Hardy–Weinberg equilibrium (HWE) and FDR adjustment were performed in the control group to test the representativeness of the population. The relationships between single-nucleotide variants of the GATA4 gene and CHD were calculated using genotype frequency distributions and genetic models (dominant, additive, and recessive). Genetic models were categorized into dominant models (“heterozygous + mutant” vs. wild type), additive models (“heterozygous vs. wild type” and “mutant vs. wild type”), and recessive models (mutant vs. “wild type + heterozygous”). FA supplementation was classified by two binary variables: took FA supplementation before or during pregnancy and took FA supplementation in the preconception period; the genetic variables of target loci defaulted to dominant models in the gene–environment interaction analyses. The associations between maternal FA supplementation and SNPs of target loci at the GATA4 gene and the gene–environment interactions with CHD were estimated by calculating the adjusted ORs (aORs) and their 95% confidence intervals (CIs) from unconditional logistic regression analyses. All statistical data analyses were performed using SPSS version 26.0 (SPSS Inc., Chicago, IL, USA) and R version 4.2.1. All tests were performed using the two-sided approach and significance was considered at p < 0.05.
3. Results
3.1. Maternal Baseline Characteristics
The distributions of basic characteristics between 585 CHD cases and 600 controls are listed in Table 1. Statistical differences were observed in the distributions of pregnancy age, economic status, history of adverse pregnancy, pre-gestational diabetes, gestational diabetes, gestational hypertension, influenza before/during pregnancy, smoking before/during pregnancy, passive smoking before/during pregnancy, and alcohol intake before/during pregnancy in the CHD and control groups (p < 0.05). The mothers in the CHD group were more likely to have a lower household income, to experience adverse pregnancy outcomes, to be affected by pregnancy complications, and to have a bad lifestyle before/during the pregnancy compared with the mothers in the control group.
3.2. Associations between Maternal FA Supplementation and CHD
We found that about 11% of the participants had never taken any FA supplementation before or during the pregnancy, and the majority of the mothers were supplemented in the first trimester of the pregnancy period (Table 2). After adjusting the suspicious confounding factors, a significantly increased risk of CHD in offspring was found among non-FA-supplementation mothers (aOR = 1.58, 95% CI: 1.01–2.48). We further classified the FA supplementation into three time periods. The unadjusted associations were statistically significant between FA supplementation in the preconception period (cOR = 0.29, 95% CI: 0.19–0.45) and the first trimester of pregnancy (cOR = 0.50, 95% CI: 0.34–0.74) with CHD. Adjustment for the confounding factors showed that FA supplementation in the preconception period (aOR = 0.53, 95% CI: 0.32–0.90) was significantly and protectively associated with CHD.
3.3. Associations between Polymorphisms of Target Loci at the GATA4 Gene and CHD
The HWE test showed that eight SNPs followed the HWE after the FDR adjustment (QFDR > 0.05), except for rs867858 (Table 3). The comparisons of genotypes and genetic models of target loci at the GATA4 gene between the CHD and control group are summarized in Table 4. After adjusting for the confounding factors, the polymorphisms of rs4841588 (GG/TT: aOR = 0.41, 95% CI: 0.24–0.70; additive model: aOR = 0.77, 95% CI: 0.62–0.95; recessive model: aOR = 0.41, 95% CI: 0.25–0.69), rs12458 (AA/GG: aOR = 2.20, 95% CI: 1.50–3.23; AG/GG: aOR = 1.87, 95% CI: 1.34–2.60; dominant model: aOR = 1.98, 95% CI: 1.45–2.69; additive model: aOR = 1.49, 95% CI: 1.23–1.81; recessive model: aOR = 1.48, 95% CI: 1.08–2.04), and rs904018 (additive model: aOR = 0.81, 95% CI: 0.66–0.99) of the GATA4 gene were significantly related to CHD (p < 0.05).
3.4. Interactions between the Polymorphisms of the GATA4 Gene and FA Supplementation with CHD
The effects of potential interactions between target loci at the GATA4 gene and FA supplementation with CHD were analyzed (Table 5). After adjusting for the confounding factors, the interaction between rs10108052 (aOR = 5.38, 95% CI: 1.67–17.09, Pinteraction = 0.004), and FA supplementation taken before/during pregnancy was found. The interactions between other comparisons were not associated with CHD.
4. Discussion
In this study, we investigated the relationships between maternal FA supplementation and nine polymorphisms of the GATA4 gene and the gene–environment interactions with CHD in 585 CHD patients and 600 healthy subjects from Chinese population. After adjusting for the confounding factors, a significantly increased risk for CHD in offspring was found with non-FA supplementation before/during pregnancy, which meant the data provided support for the hypothesis that maternal FA supplementation would decrease the risk of CHD in offspring. The initiation of FA supplementation timing was optimum in the preconception period (−12 to −1 weeks). The analyses of polymorphisms at the GATA4 gene associated with CHD showed that the polymorphisms of rs4841588, rs12458, and rs904018 under specific genotypes and genetic models were significantly related to CHD. In addition, we explored the gene–environmental interactions, which indicated that the synergistic interaction between the rs10108052 polymorphism and FA supplementation might increase the risk of CHD in offspring.
According to our results, it is suggested to supplement FA before pregnancy to prevent CHD in offspring. However, the findings were inconsistent with whether maternal FA supplementation would reduce the risk of CHD in offspring among studies in different geographical areas and with different study designs [14]. Studies from China have been more likely to prove the preventive effect of FA supplementation on CHD, but inconsistent results have been found in other countries. Mao showed that maternal FA supplementation before the pregnancy caused a 58% reduction in CHD based on a birth cohort study in Gansu Province (China) [18], which was consistent with our study. Qu conducted a large-sample case-control study including 8379 CHD cases and 6918 controls in Guangzhou Province (China), showing a significant protective association (aOR = 0.69, 95% CI: 0.62–0.76) between first-trimester maternal FA supplementation and CHD [19]. Similar results were observed in other studies of the Chinese population, as well as in Budapest (Hungary) and Atlanta (America) [20,21], which indicated that maternal FA supplementation before/during pregnancy would reduce the risk of CHD in offspring. However, null significant associations were found in the two unique large prospective birth cohorts from Denmark and Norway [22]. Several case–control studies from North America did not find any preventive effect of maternal FA supplementation before/during pregnancy with CHD after the policy of FA food fortification.
The discrepancy of results among different geographical areas might be mainly caused by the diet habits of different countries. The traditional Chinese diet has a lower intake of folate-rich food than that in the West. Since 1993, the Ministry of Health of China has been recommending that women who plan to be pregnant take 0.4 mg of FA tablets every day in order to prevent neural tube defects [23]. As far as we know, there are still some women who have never taken any FA supplementation before or during their pregnancy. This might be explained by unplanned pregnancies, the weak awareness of antenatal care, or the lack of publicity of health knowledge. In Canada, the United States, and other foreign countries, the FA fortification of staple food (mainly flour, pasta, and cornmeal) was mandatory in 1998, which might have improved circulating folate levels [24,25]. Liu found that the birth prevalence of CHD showed a temporal pattern of declining after the introduction of FA food fortification and confirmed that FA food fortification was related to a decreased risk of non-chromosomal CHD [26]. In the population with high circulating folate levels, the ineffective results between FA supplementation and CHD were more likely to be reported.
The GATA4 gene, as one of the most important transcription factors, is considered to be crucial for regulating cardiac formation. The irregular mutation of the GATA4 gene is related to CHD, but the regulatory mechanism of the GATA4 factor remains unclear. The genetic distributions of rs4841588 were opposite to those in the case–control study by Huang in the Chinese population [27]. The study by Li [6] identified that rs884662, and rs3203358 had a strong correlation with CHD, whereas these two SNPs did not show any positive effect on CHD in our present study. These inconsistencies might be related to the different sample source. To date, few studies have evaluated the associations with single-nucleotide variations in the GATA4 gene and CHD under specific genetic models. The relationships with rs4841588 under additive and recessive models, rs12458 under additive and recessive models, and rs904018 under additive models are significantly related to CHD and similar to the genotype distributions. Therefore, we suggest that, in China, rs12458 might contribute to an increased risk of CHD, and rs4841588 and rs904018 might have a protective effect on CHD.
The occurrence of CHD might be caused by a combination of genetic variations and maternal lifestyle factors before or during pregnancy. Gene–environment interactions could be deterministic for CHD in individuals who were exposed to environmental risk factors [28]. Recent studies have suggested that the genes related to FA metabolism (i.e., MTHFR, MTRR, MTR, and CBS) might have gene–environment interactions that influence individual susceptibility to CHD [29,30]. In addition to the genes that directly regulate folate metabolism, several lines of evidence based on GWAS suggest that other genes (i.e., SYT9, FIGN, and NBPF3) are linked to FA concentration [17,31]. Therefore, the exploration of genetic variations with FA should be expanded. In our study, the interaction between rs10108052 and maternal FA supplementation before or during pregnancy could significantly increase the risk of CHD; thus mothers with rs10108052 of the GATA4 gene are actively recommended to undergo FA supplementation in the preconception period.
Study Strengths and Limitations
We recruited a case–control study to investigate the relationships between maternal FA supplementation and single-nucleotide variants of the GATA4 gene in non-chromosomal CHD and further explored the gene–environment interactions associated with CHD. In this study, environmental factors and genetic factors of 1185 individuals were collected to explain the relationships with CHD. The main exposure of FA supplementation was classified according to the initiation of supplement use before/during the pregnancy to evaluate the effect of FA supplementation timing on CHD. The genetic distributions of nine SNPs of the GATA4 gene were estimated under specific genotypes and genetic models. As far as we know, this is the first study to explore the interactions between the SNPs of the GATA4 gene and maternal FA supplementation and CHD.
The present study still has several limitations. First, we only collected information on whether the mothers were undergoing FA supplementation and on the timing of supplementation initiation (before or during the conception period). The information about FA intake on the individual level and dietary FA was deficient. Second, the relationships between the risk factors and specific CHD subtypes were not analyzed due to the sample size. Third, the retrospective design of our study might have led to recall bias, which could have affected the accuracy of the data collection. Therefore, there is still space to investigate the effect of maternal FA supplementation and SNPs of the GATA4 gene and their interactions with CHD.
5. Conclusions
In this study, protective relationships were found between maternal FA supplementation and CHD, and specific polymorphisms of the GATA4 gene were associated with CHD in different genetic models. Furthermore, we evaluated the effect of interactions between maternal FA supplementation and GATA4 genetic polymorphisms, which indicated that the exploration of valuable gene–environment interactions between FA supplementation and genes in CHD has to be expanded. Since maternal exposure to related environmental factors may have synergistic or antagonistic interactions with specific gene loci, pregnancy health care—reducing maternal exposure to adverse factors and carrying out perinatal health examinations (including undergoing sufficient FA supplementation and gene sequencing)—is necessary for reducing the risk of congenital disease in offspring.
Author Contributions
All authors contributed to the study conception and design. Conceptualization, L.C. and J.Q.; writing—original draft preparation, L.C.; investigation, L.C. and M.S.; data curation, T.W.; writing—review and editing, T.Y. and J.Q.; supervision, J.Q.; funding acquisition, J.Q. All authors have read and agreed to the published version of the manuscript.
Funding
This study was supported by the National Natural Science Foundation Program of China (82073653 and 81803313), Hunan Outstanding Youth Fund Project (2022JJ10087), National Key Research and Development Project (2018YFE0114500), China Postdoctoral Science Foundation (2020M682644), Hunan Provincial Science and Technology Talent Support Project (2020TJ-N07), Hunan Provincial Key Research and Development Program (2018SK2063), Open Project from NHC Key Laboratory of Birth Defect for Research and Prevention (KF2020006), Natural Science Foundation of Hunan Province (2018JJ2551), Natural Science Foundation of Hunan Province of China (2022JJ40207), and Changsha Municipal Natural Science Foundation (kq2202470).
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Xiangya School of Public Health, Central South University (approval number: XYGW-2018-36).
Informed Consent Statement
Written informed consent was obtained from the patients to publish this paper.
Data Availability Statement
Data are unavailable due to privacy or ethical restrictions.
Acknowledgments
The authors would like to thank the editors and reviewers for their suggestions and all colleagues working in the Maternal and Child Health Promotion and Birth Defect Prevention Group. In addition, the authors are grateful to Nan Ren for his support during the entire revision.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Liu, Y.; Chen, S.; Zühlke, L.; Black, G.C.; Choy, M.-K.; Li, N.; Keavney, B.D. Global birth prevalence of congenital heart defects 1970–2017: Updated systematic review and meta-analysis of 260 studies. Int. J. Epidemiol. 2019, 48, 455–463. [Google Scholar] [CrossRef] [PubMed]
- Van der Linde, D.; Konings, E.E.; Slager, M.A.; Witsenburg, M.; Helbing, W.A.; Takkenberg, J.J.; Roos-Hesselink, J.W. Birth prevalence of congenital heart disease worldwide: A systematic review and meta-analysis. J. Am. Coll. Cardiol. 2011, 58, 2241–2247. [Google Scholar] [CrossRef] [PubMed]
- Nora, J.J.; Nora, A.H. The evolution of specific genetic and environmental counseling in congenital heart diseases. Circulation 1978, 57, 205–213. [Google Scholar] [CrossRef] [PubMed]
- Zhou, L.; Liu, J.; Xiang, M.; Olson, P.; Guzzetta, A.; Zhang, K.; Moskowitz, I.P.; Xie, L. Gata4 potentiates second heart field proliferation and Hedgehog signaling for cardiac septation. Proc. Natl. Acad. Sci. USA 2017, 114, E1422–E1431. [Google Scholar] [CrossRef] [PubMed]
- Pulignani, S.; Vecoli, C.; Sabina, S.; Foffa, I.; Ait-Ali, L.; Andreassi, M.G. 3′UTR SNPs and Haplotypes in the GATA4 Gene Contribute to the Genetic Risk of Congenital Heart Disease. Rev. Esp. Cardiol. (Engl. Ed.) 2016, 69, 760–765. [Google Scholar] [CrossRef]
- Li, D. Study on Differently Expressed Plasma microRNA of Congenital Heart Disease and Its Association with GATA4 Gene Target Sequence Polymorphism. Ph.D. Thesis, Shandong University, Jinan, China, 2015. [Google Scholar]
- Peng, T.; Wang, L.; Zhou, S.F.; Li, X. Mutations of the GATA4 and NKX2.5 genes in Chinese pediatric patients with non-familial congenital heart disease. Genetica 2010, 138, 1231–1240. [Google Scholar] [CrossRef]
- Tang, L.S.; Wlodarczyk, B.J.; Santillano, D.R.; Miranda, R.C.; Finnell, R.H. Developmental consequences of abnormal folate transport during murine heart morphogenesis. Birth Defects Research. Part. A Clin. Mol. Teratol. 2004, 70, 449–458. [Google Scholar] [CrossRef]
- Li, X.; Li, S.; Mu, D.; Liu, Z.; Li, Y.; Lin, Y.; Chen, X.; You, F.; Li, N.; Deng, K.; et al. The association between periconceptional folic acid supplementation and congenital heart defects: A case-control study in China. Prev. Med. 2013, 56, 385–389. [Google Scholar] [CrossRef]
- Elizabeth, K.E.; Praveen, S.L.; Preethi, N.R.; Jissa, V.T.; Pillai, M.R. Folate, vitamin B12, homocysteine and polymorphisms in folate metabolizing genes in children with congenital heart disease and their mothers. Eur. J. Clin. Nutr. 2017, 71, 1437–1441. [Google Scholar] [CrossRef]
- Obermann-Borst, S.A.; Isaacs, A.; Younes, Z.; van Schaik, R.H.; van der Heiden, I.P.; van Duyn, C.M.; Steegers, E.A.; Steegers-Theunissen, R.P. General maternal medication use, folic acid, the MDR1 C3435T polymorphism, and the risk of a child with a congenital heart defect. Am. J. Obstet. Gynecol. 2011, 204, 236.e1–236.e8. [Google Scholar] [CrossRef]
- Hobbs, C.A.; Cleves, M.A.; Karim, M.A.; Zhao, W.; MacLeod, S.L. Maternal folate-related gene environment interactions and congenital heart defects. Obstet. Gynecol. 2010, 116, 316–322. [Google Scholar] [CrossRef] [PubMed]
- Balderrábano-Saucedo, N.A.; Sánchez-Urbina, R.; Sierra-Ramírez, J.A.; García-Hernández, N.; Sánchez-Boiso, A.; Klunder-Klunder, M.; Arenas-Aranda, D.; Bravo-Hernández, G.; Noriega-Zapata, P.; Vizcaíno-Alarcón, A. Polymorphism 677C→T MTHFR gene in Mexican mothers of children with complex congenital heart disease. Pediatr. Cardiol. 2013, 34, 46–51. [Google Scholar] [CrossRef] [PubMed]
- Xu, A.; Cao, X.; Lu, Y.; Li, H.; Zhu, Q.; Chen, X.; Jiang, H.; Li, X. A Meta-Analysis of the Relationship Between Maternal Folic Acid Supplementation and the Risk of Congenital Heart Defects. Int. Heart J. 2016, 57, 725–728. [Google Scholar] [CrossRef] [PubMed]
- Wang, X.; Wei, H.; Tian, Y.; Wu, Y.; Luo, L. Genetic variation in folate metabolism is associated with the risk of conotruncal heart defects in a Chinese population. BMC Pediatr. 2018, 18, 287. [Google Scholar] [CrossRef]
- Wang, D.; Wang, F.; Shi, K.H.; Tao, H.; Li, Y.; Zhao, R.; Lu, H.; Duan, W.; Qiao, B.; Zhao, S.M.; et al. Lower Circulating Folate Induced by a Fidgetin Intronic Variant Is Associated with Reduced Congenital Heart Disease Susceptibility. Circulation 2017, 135, 1733–1748. [Google Scholar] [CrossRef] [PubMed]
- Tanaka, T.; Scheet, P.; Giusti, B.; Bandinelli, S.; Piras, M.G.; Usala, G.; Lai, S.; Mulas, A.; Corsi, A.M.; Vestrini, A.; et al. Genome-wide association study of vitamin B6, vitamin B12, folate, and homocysteine blood concentrations. Am. J. Hum. Genet. 2009, 84, 477–482. [Google Scholar] [CrossRef]
- Mao, B.; Qiu, J.; Zhao, N.; Shao, Y.; Dai, W.; He, X.; Cui, H.; Lin, X.; Lv, L.; Tang, Z.; et al. Maternal folic acid supplementation and dietary folate intake and congenital heart defects. PLoS ONE 2017, 12, e0187996. [Google Scholar] [CrossRef]
- Qu, Y.; Lin, S.; Zhuang, J.; Bloom, M.S.; Smith, M.; Nie, Z.; Mai, J.; Ou, Y.; Wu, Y.; Gao, X.; et al. First-Trimester Maternal Folic Acid Supplementation Reduced Risks of Severe and Most Congenital Heart Diseases in Offspring: A Large Case-Control Study. J. Am. Heart Assoc. 2020, 9, e015652. [Google Scholar] [CrossRef]
- Czeizel, A.E.; Vereczkey, A.; Szabó, I. Folic acid in pregnant women associated with reduced prevalence of severe congenital heart defects in their children: A national population-based case-control study. Eur. J. Obs. Biol. 2015, 193, 34–39. [Google Scholar] [CrossRef]
- Botto, L.D.; Mulinare, J.; Erickson, J.D. Occurrence of congenital heart defects in relation to maternal mulitivitamin use. Am. J. Epidemiol. 2000, 151, 878–884. [Google Scholar] [CrossRef]
- Øyen, N.; Olsen, S.F.; Basit, S.; Leirgul, E.; Strøm, M.; Carstensen, L.; Granström, C.; Tell, G.S.; Magnus, P.; Vollset, S.E.; et al. Association Between Maternal Folic Acid Supplementation and Congenital Heart Defects in Offspring in Birth Cohorts from Denmark and Norway. J. Am. Heart Assoc. 2019, 8, e011615. [Google Scholar] [CrossRef] [PubMed]
- Chen, L.; Pickett, K. To prevent neurotract malformation using folic acid-American policy Development and Chinese preventive measurement. Matern. Child Health Care China 2002, 17, 568–570. [Google Scholar] [CrossRef]
- Ray, J.G. Folic acid food fortification in Canada. Nutr. Rev. 2004, 62, S35–S39. [Google Scholar] [CrossRef]
- Herbert, V.; Bigaouette, J. Call for endorsement of a petition to the Food and Drug Administration to always add vitamin B-12 to any folate fortification or supplement. Am. J. Clin. Nutr. 1997, 65, 572–573. [Google Scholar] [CrossRef]
- Liu, S.; Joseph, K.S.; Luo, W.; León, J.A.; Lisonkova, S.; Van den Hof, M.; Evans, J.; Lim, K.; Little, J.; Sauve, R.; et al. Effect of Folic Acid Food Fortification in Canada on Congenital Heart Disease Subtypes. Circulation 2016, 134, 647–655. [Google Scholar] [CrossRef] [PubMed]
- Huang, X.; Qiu, X.; Zeng, X.; Huang, D.; Liu, S.; Guo, X.; Tan, C.; Qin, R.; Liu, L.; Yang, X.; et al. Study on the relationship between the NKX2.5, GATA4, TBX5 gene polymorphism and ventricular septal defect of Guangxi Zhuang and Han population. Matern. Child Health Care China 2016, 31, 2508–2513. [Google Scholar]
- Yu, L.-X.; Liu, L.-Y.; Xiang, Y.-J.; Wang, F.; Zhou, F.; Huang, S.-Y.; Zheng, C.; Ye, C.-M.; Zhou, W.-Z.; Yin, G.-S.; et al. XRCC5/6 polymorphisms and their interactions with smoking, alcohol consumption, and sleep satisfaction in breast cancer risk: A Chinese multi-center study. Cancer Med. 2021, 10, 2752–2762. [Google Scholar] [CrossRef]
- Christensen, K.E.; Zada, Y.F.; Rohlicek, C.V.; Andelfinger, G.U.; Michaud, J.L.; Bigras, J.L.; Richter, A.; Dubé, M.P.; Rozen, R. Risk of congenital heart defects is influenced by genetic variation in folate metabolism. Cardiol. Young 2013, 23, 89–98. [Google Scholar] [CrossRef]
- Wang, B.; Liu, M.; Yan, W.; Mao, J.; Jiang, D.; Li, H.; Chen, Y. Association of SNPs in genes involved in folate metabolism with the risk of congenital heart disease. J. Matern.-Fetal Neonatal Med. 2013, 26, 1768–1777. [Google Scholar] [CrossRef]
- Hazra, A.; Kraft, P.; Lazarus, R.; Chen, C.; Chanock, S.J.; Jacques, P.; Selhub, J.; Hunter, D.J. Genome-wide significant predictors of metabolites in the one-carbon metabolism pathway. Hum. Mol. Genet. 2009, 18, 4677–4687. [Google Scholar] [CrossRef]
Table 1.
Maternal baseline characteristics of CHD cases and controls.
| Maternal Baseline Characteristics | CHD Cases (n = 585) | Controls (n = 600) | χ2 | p Value | ||
|---|---|---|---|---|---|---|
| n | % | n | % | |||
| Pregnancy age (years) | 11.014 | 0.004 * | ||||
| ≤24 | 164 | 28.0 | 119 | 19.8 | ||
| 25–29 | 223 | 38.1 | 251 | 41.8 | ||
| ≥30 | 198 | 33.9 | 230 | 38.4 | ||
| Average annual household income (RMB) | 302.674 | <0.001 * | ||||
| ≤50,000 | 463 | 79.1 | 174 | 29.0 | ||
| 50,001–100,000 | 88 | 15.0 | 264 | 44.0 | ||
| 100,001–150,000 | 12 | 2.1 | 51 | 8.5 | ||
| >150,000 | 22 | 3.8 | 111 | 18.5 | ||
| BMI before pregnancy (kg/m2) | 2.345 | 0.506 | ||||
| <18.5 | 111 | 19.0 | 115 | 19.2 | ||
| 18.5–23.9 | 390 | 66.6 | 380 | 63.3 | ||
| 24.0–27.9 | 60 | 10.3 | 75 | 12.5 | ||
| ≥28.0 | 24 | 4.1 | 30 | 5.0 | ||
| History of adverse pregnancy | 15.844 | <0.001 * | ||||
| No | 258 | 44.1 | 334 | 55.7 | ||
| Yes | 327 | 55.9 | 266 | 44.3 | ||
| Pre-gestational diabetes | 20.358 | <0.001 * | ||||
| No | 523 | 89.4 | 577 | 96.2 | ||
| Yes | 62 | 10.6 | 23 | 3.8 | ||
| Gestational diabetes | 12.785 | <0.001 * | ||||
| No | 526 | 89.9 | 572 | 95.3 | ||
| Yes | 59 | 10.1 | 28 | 4.7 | ||
| Pre-gestational hypertension | 0.139 # | 0.750 | ||||
| No | 580 | 99.1 | 596 | 99.3 | ||
| Yes | 5 | 0.9 | 4 | 0.7 | ||
| Gestational hypertension | 22.077 | <0.001 * | ||||
| No | 532 | 90.9 | 584 | 97.3 | ||
| Yes | 53 | 9.1 | 16 | 2.7 | ||
| Influenza before/during pregnancy | 34.304 | <0.001 * | ||||
| No | 331 | 56.6 | 437 | 72.8 | ||
| Yes | 254 | 43.4 | 163 | 27.2 | ||
| Smoking before/during pregnancy | 19.807 | <0.001 * | ||||
| No | 533 | 91.1 | 583 | 97.2 | ||
| Yes | 52 | 8.9 | 17 | 2.8 | ||
| Passive smoking before/during pregnancy | 33.243 | <0.001 * | ||||
| No | 270 | 46.2 | 377 | 62.8 | ||
| Yes | 315 | 53.8 | 223 | 37.2 | ||
| Alcohol intake before/during pregnancy | 35.033 | <0.001 * | ||||
| No | 459 | 78.5 | 545 | 90.8 | ||
| Yes | 126 | 21.5 | 55 | 9.2 | ||
BMI, body mass index. * p < 0.05, the difference between the CHD cases and controls is significant. # Using Fisher’s exact test.
Table 2.
Association between FA supplementation and CHD.
| CHD Cases (n = 585) | Controls (n = 600) | cOR (95% CI) | p | aOR (95% CI) # | p | |||
|---|---|---|---|---|---|---|---|---|
| n | % | n | % | |||||
| FA supplementation before or during pregnancy | ||||||||
| Yes | 499 | 85.3 | 556 | 92.7 | 1.00 | - | 1.00 | - |
| No | 86 | 14.7 | 44 | 7.3 | 2.18 (1.49–3.19) | <0.001 * | 1.58 (1.01–2.48) | 0.044 * |
| Initiation of FA supplementation | ||||||||
| None | 86 | 14.7 | 44 | 7.3 | 1.00 | - | 1.00 | - |
| The preconception period | 87 | 14.9 | 154 | 25.7 | 0.29 (0.19–0.45) | <0.001 * | 0.53 (0.32–0.90) | 0.018 * |
| The first trimester of pregnancy | 387 | 66.1 | 397 | 66.2 | 0.50 (0.34–0.74) | <0.001 * | 0.64 (0.41–1.01) | 0.056 |
| The middle/last trimesters of pregnancy | 25 | 4.3 | 5 | 0.8 | 2.56 (0.92–7.14) | 0.073 | 1.66 (0.56–4.97) | 0.362 |
cOR indicates crude odds ratios based on univariate analysis; aOR indicates adjusted odds ratios based on multivariable logistic regression. * p < 0.05; the difference between case and control is significant. # Adjusted for pregnancy age, average annual household income, history of adverse pregnancy, pre-gestational diabetes, gestational diabetes, gestational hypertension, influenza before/during pregnancy, smoking before/during pregnancy, passive smoking before/during pregnancy, and alcohol intake before/during pregnancy.
Table 3.
HWE of target loci of the GATA4 gene in the control group.
| Target Loci | MAF # | Major Allele | Minor Allele | Genotype | Controls (n) | χ2 | p | QFDR |
|---|---|---|---|---|---|---|---|---|
| rs4841588 | 0.325 | T | G | TT/TG/GG | 272/244/84 | 5.777 | 0.016 | 0.072 |
| rs884662 | 0.282 | T | C | TT/TC/CC | 414/176/10 | 3.220 | 0.077 | 0.164 |
| rs804287 | 0.250 | C | T | CC/CT/TT | 329/223/48 | 1.372 | 0.249 | 0.374 |
| rs3203358 | 0.155 | C | G | CC/CG/GG | 558/42/0 | 0.789 | 0.631 | 0.811 |
| rs867858 | 0.361 | A | C | AA/AC/CC | 242/204/154 | 55.834 | <0.001 | 0.009 |
| rs2645457 | 0.359 | T | G | TT/TG/GG | 576/24/0 | 0.250 | 1.000 | 1.000 |
| rs10108052 | 0.379 | A | G | AA/AG/GG | 159/298/143 | 0.021 | 0.934 | 1.000 |
| rs12458 | 0.400 | G | A | GG/GA/AA | 203/274/123 | 2.951 | 0.091 | 0.164 |
| rs904018 | 0.368 | A | G | AA/AG/GG | 273/247/80 | 3.999 | 0.042 | 0.126 |
# MAF: minor allele frequency, based on the global population of 1000 Genomes Project.
Table 4.
Associations between polymorphisms of target loci at the GATA4 gene and CHD.
| Target Loci | CHD Cases (N = 585) (%) | Controls (N = 600) (%) | cOR (95% CI) | p | aOR (95% CI) # | p |
|---|---|---|---|---|---|---|
| rs4841588 | ||||||
| TT | 317 (54.1) | 272 (45.3) | 1.00 | - | 1.00 | - |
| GT | 239 (40.9) | 244 (40.7) | 0.84 (0.66–1.07) | 0.157 | 0.98 (0.74–1.31) | 0.909 |
| GG | 29 (5.0) | 84 (14.0) | 0.30 (0.19–0.47) | <0.001 * | 0.41 (0.24–0.70) | <0.001 * |
| Dominant model | - | - | 0.70 (0.56–0.88) | 0.002 * | 0.85 (0.65–1.12) | 0.244 |
| Additive model | - | - | 0.66 (0.55–0.78) | <0.001 * | 0.77 (0.62–0.95) | 0.016 * |
| Recessive model | - | - | 0.32 (0.21–0.50) | <0.001 * | 0.41 (0.25–0.69) | <0.001 * |
| rs884662 | ||||||
| TT | 408 (69.7) | 414 (69.0) | 1.00 | - | 1.00 | - |
| CT | 162 (27.7) | 176 (29.3) | 0.93 (0.73–1.20) | 0.597 | 0.87 (0.65–1.18) | 0.372 |
| CC | 15 (2.6) | 10 (1.7) | 1.52 (0.68–3.43) | 0.310 | 1.89 (0.68–5.27) | 0.225 |
| Dominant model | - | - | 0.97 (0.75–1.24) | 0.781 | 0.91 (0.68–1.23) | 0.551 |
| Additive model | - | - | 1.01 (0.81–1.26) | 0.959 | 0.97 (0.75–1.27) | 0.844 |
| Recessive model | - | - | 1.55 (0.69–3.49) | 0.286 | 1.96 (0.71–5.46) | 0.197 |
| rs804287 | ||||||
| CC | 344 (58.8) | 329 (54.8) | 1.00 | - | 1.00 | - |
| TC | 196 (33.5) | 223 (37.2) | 0.84 (0.66–1.07) | 0.164 | 0.81 (0.61–1.09) | 0.165 |
| TT | 45 (7.7) | 48 (8.0) | 0.90 (0.58–1.38) | 0.622 | 0.99 (0.58–1.70) | 0.972 |
| Dominant model | - | - | 0.85 (0.68–1.07) | 0.168 | 0.84 (0.64–1.11) | 0.217 |
| Additive model | - | - | 0.90 (0.75–1.08) | 0.249 | 0.91 (0.73–1.13) | 0.391 |
| Recessive model | - | - | 0.96 (0.63–1.46) | 0.844 | 1.08 (0.64–1.83) | 0.779 |
| rs3203358 | ||||||
| CC | 546 (93.3) | 558 (93.0) | 1.00 | - | 1.00 | - |
| GC | 39 (6.7) | 42 (7.0) | 0.95 (0.60–1.49) | 0.820 | 1.07 (0.63–1.80) | 0.814 |
| Dominant model | - | - | 0.95 (0.60–1.49) | 0.820 | 1.07 (0.63–1.80) | 0.814 |
| Additive model | - | - | 0.95 (0.60–1.49) | 0.820 | 1.07 (0.63–1.80) | 0.814 |
| rs2645457 | ||||||
| TT | 556 (95.0) | 576 (96.0) | 1.00 | - | 1.00 | - |
| GT | 29 (5.0) | 24 (4.0) | 1.25 (0.72–2.18) | 0.425 | 1.27 (0.63–2.55) | 0.500 |
| Dominant model | - | - | 1.25 (0.72–2.18) | 0.425 | 1.27 (0.63–2.55) | 0.500 |
| Additive model | - | - | 1.25 (0.72–2.18) | 0.425 | 1.27 (0.63–2.55) | 0.500 |
| rs10108052 | ||||||
| AA | 189 (32.3) | 159 (26.5) | 1.00 | - | 1.00 | - |
| AG | 249 (42.6) | 298 (49.7) | 0.70 (0.54–0.92) | 0.010 * | 0.86 (0.62–1.19) | 0.356 |
| GG | 147 (25.1) | 143 (23.8) | 0.87 (0.63–1.18) | 0.362 | 0.99 (0.68–1.44) | 0.948 |
| Dominant model | - | - | 0.76 (0.59–0.97) | 0.028 * | 0.90 (0.66–1.22) | 0.497 |
| Additive model | - | - | 0.92 (0.79–1.07) | 0.289 | 0.99 (0.82–1.20) | 0.907 |
| Recessive model | - | - | 1.07 (0.82–1.40) | 0.604 | 1.09 (0.79–1.50) | 0.608 |
| rs12458 | ||||||
| GG | 130 (22.3) | 203 (33.8) | 1.00 | - | 1.00 | - |
| GA | 281 (48.0) | 274 (45.7) | 1.60 (1.22–2.11) | <0.001 * | 1.87 (1.34–2.60) | <0.001 * |
| AA | 174 (29.7) | 123 (20.5) | 2.21 (1.61–3.04) | <0.001 * | 2.20 (1.50–3.23) | <0.001 * |
| Dominant model | - | - | 1.79 (1.38–2.32) | <0.001 * | 1.98 (1.45–2.69) | <0.001 * |
| Additive model | - | - | 1.49 (1.27–1.75) | <0.001 * | 1.49 (1.23–1.81) | <0.001 * |
| Recessive model | - | - | 1.64 (1.26–2.14) | <0.001 * | 1.48 (1.08–2.04) | 0.016 * |
| rs904018 | ||||||
| AA | 333 (56.9) | 273 (45.5) | 1.00 | - | 1.00 | - |
| AG | 196 (33.5) | 247 (41.2) | 0.65 (0.51–0.83) | <0.001 * | 0.80 (0.60–1.08) | 0.144 |
| GG | 56 (9.6) | 80 (13.3) | 0.57 (0.39–0.84) | 0.004 * | 0.67 (0.42–1.06) | 0.085 |
| Dominant model | - | - | 0.63 (0.50–0.80) | <0.001 * | 0.77 (0.58–1.02) | 0.064 |
| Additive model | - | - | 0.72 (0.61–0.85) | <0.001 * | 0.81 (0.66–0.99) | 0.046 * |
| Recessive model | - | - | 0.69 (0.48–0.99) | 0.043 * | 0.73 (0.47–1.14) | 0.171 |
cOR indicates crude odds ratios based on univariate analysis; aOR indicates adjusted odds ratios based on multivariable logistic regression. * p < 0.05; the difference between case and control is significant. # Adjusted for pregnancy age, average annual household income, history of adverse pregnancy, pre-gestational diabetes, gestational diabetes, gestational hypertension, influenza before/during pregnancy, smoking before/during pregnancy, passive smoking before/during pregnancy, and alcohol intake before/during pregnancy.
Table 5.
Interactions between the polymorphisms of the GATA4 gene and FA supplementation with CHD.
| Target Loci | Took FA Supplementation before or during Pregnancy | Took FA Supplementation in the Preconception Period | ||
|---|---|---|---|---|
| aOR (95% CI) # | p | aOR (95% CI) # | p | |
| rs4841588 | 0.60 (0.24–1.50) | 0.277 | 0.87 (0.26–2.90) | 0.824 |
| rs884662 | 2.43 (0.98–6.01) | 0.055 | 1.16 (0.35–3.85) | 0.804 |
| rs804287 | 0.45 (0.18–1.11) | 0.084 | 0.40 (0.12–1.33) | 0.133 |
| rs3203358 | 1.12 (0.12–10.52) | 0.921 | 1.96 (0.10–39.99) | 0.663 |
| rs2645457 | NA | NA | NA | NA |
| rs10108052 | 4.94 (1.57–15.59) | 0.006 * | 3.20 (0.67–15.29) | 0.146 |
| rs12458 | 1.82 (0.69–4.85) | 0.228 | 1.92 (1.54–6.92) | 0.317 |
| rs904018 | 0.70 (0.29–1.69) | 0.422 | 0.58 (0.18–1.88) | 0.363 |
NA: not available. * p < 0.05; the difference between the CHD cases and controls is significant. # Adjusted for pregnancy age, average annual household income, history of adverse pregnancy, pre-gestational diabetes, gestational diabetes, gestational hypertension, influenza before/during pregnancy, smoking before/during pregnancy, passive smoking before/during pregnancy, and alcohol intake before/during pregnancy.
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Chen, L.; Yang, T.; Wang, T.; Sun, M.; Qin, J. Relationships between Maternal Folic Acid Supplementation and GATA4 Gene Polymorphisms in Patients with Non-Chromosomal Congenital Heart Disease: A Hospital-Based Case–Control Study in China. Nutrients 2023, 15, 4478. https://doi.org/10.3390/nu15204478
AMA Style
Chen L, Yang T, Wang T, Sun M, Qin J. Relationships between Maternal Folic Acid Supplementation and GATA4 Gene Polymorphisms in Patients with Non-Chromosomal Congenital Heart Disease: A Hospital-Based Case–Control Study in China. Nutrients. 2023; 15(20):4478. https://doi.org/10.3390/nu15204478
Chicago/Turabian StyleChen, Letao, Tubao Yang, Tingting Wang, Mengting Sun, and Jiabi Qin. 2023. "Relationships between Maternal Folic Acid Supplementation and GATA4 Gene Polymorphisms in Patients with Non-Chromosomal Congenital Heart Disease: A Hospital-Based Case–Control Study in China" Nutrients 15, no. 20: 4478. https://doi.org/10.3390/nu15204478
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