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. 2019 Jun 17;111(13):920–931. doi: 10.1002/bdr2.1536

Exploring associations of maternal sleep during periconceptional period with congenital heart disease in offspring

Anda Zhao 1,†, Kena Zhao 1,†, Yuanqing Xia 1, Yong Yin 2, Jianzhen Zhu 3, Haifa Hong 2,✉, Shenghui Li 1,✉
PMCID: PMC7432175  PMID: 31206252

Abstract

Background

In general, the existing evidence points to a role for maternal sleep in pregnancy complications and fetal growth, however, little has been focused on birth defects. We aimed to explore the association between periconceptional poor sleep and the risk of congenital heart disease (CHD), and to examine if daytime napping could to some extent change the association.

Methods

A case–control study was conducted in Shanghai Children's Medical Center, in which, a total of 524 cases (262 simple CHD vs. 262 severe CHD), along with 262 controls.

Results

In the multivariable logistic analysis, poor sleep could increase the risk of both simple CHD (OR = 2.486, 95% CI = 1.619–3.818) and severe CHD (OR = 1.950, 95% CI = 1.269–2.997), while routine daytime nap could decrease risk of simple CHD (OR = 0.634, 95% CI = 0.435–0.923). In the stratified analysis, the concurrence with routine daytime nap could weaken the risk of simple CHD caused by poor sleep (OR = 3.183, 95% CI: 1.830–5.537 decreased to OR = 2.236, 95% CI: 1.200–4.165). The examinations were repeated in ventricular septal defect and tetralogy of Fallot, and the established associations can be verified. Moreover, all these findings were also similarly observed in both propensity‐score‐adjusted and propensity‐score‐matched analyses.

Conclusions

Poor maternal sleep around periconceptional period seems to be an independent risk factor for CHD. The concurrence with daytime nap could to some extent reduce the risk in simple CHD. The results individually and collectively put forward the importance of maternal sleep in embryonic heart development.

Keywords: case–control study, congenital heart disease, daytime nap, maternal sleep, pregnancy

1. INTRODUCTION

Sleep is a major physiological process, which may serve important functions including energy restoration, metabolic regulation, immune enhancement, and so forth (Krueger, Frank, Wisor, & Roy, 2016). Due to the hormone‐related physiological changes in immunity and metabolism, poor sleep, characterized by chronic sleep deprivation and sleep disorders, was highly prevalent in pregnant women (Wilkerson & Uhde, 2018; Yang et al., 2018). As suggested by studies, poor sleep has potentially increased the risk of pregnancy complications, such as gestational diabetes mellitus and pregnancy‐induced hypertension (Li, Zhao, Hua, & Li, 2018; Palagini et al., 2014; Wilkerson & Uhde, 2018). Meanwhile, a number of studies support that poor sleep, in general, may have negative effect on the fetus resulting in altered gestational length and growth restriction (Abeysena, Jayawardana, & Seneviratne Rde, 2009; Abeysena, Jayawardana, & Seneviratne Rde, 2010; Kajeepeta et al., 2014). Of note is that, up to now, very few studies have focused on birth defects.

To the best of our knowledge, only one epidemiologic study explored the topic (Li et al., 2015). The study evaluated the association of periconceptional poor sleep with risk of neural tube defects (NTDs) based on a case–control design, where it was found that frequent poor sleep (≥4 days/week) could independently increase the risk of NTDs by 3.1 times (Li et al., 2015). The finding encouraged us to do more to probe into the role of maternal sleep in embryonic early development. This is further highlighted by research progress in melatonin. Studies within the last decade suggested that melatonin, a sleep–wake and other circadian rhythm messenger linking mother and fetus, has essential functions in fetal maturation and placenta/uterine homeostasis (Reiter, Tan, Korkmaz, & Rosales‐Corral, 2014; Voiculescu, Zygouropoulos, Zahiu, & Zagrean, 2014). Besides, two recent studies jointly revealed that melatonin is involved in embryonic cardiac development (Kudova, Vasicek, Ciz, & Kubala, 2016; Nogueira & Sampaio, 2017). Since it has been confirmed that sleep disturbance during pregnancy could interfere the rhythm and amplitude of melatonin secretion (Shimada, Seki, Samejima, Hayase, & Shirai, 2016; Suzuki et al., 1993), it is now an opportune time to explore the relationship of maternal sleep with heart development and disease. We hypothesized that poor sleep exposure during the key period of embryonic heart development might increase the risk of congenital heart diseases (CHD), cardiovascular malformations caused by abnormal cardiac morphogenesis during early embryonic development (Bouma & Mulder, 2017). Moreover, a recent study suggested that daytime napping could be a beneficial countermeasure to the sleep disruption among pregnant women (Ebert, Wood, & Okun, 2015). We would observe whether daytime napping could weaken or reverse the risk, in which, any significant finding would help better understanding the link of maternal sleep to CHD in offspring.

To extend an insight into the association between maternal sleep and birth defects, a hospital‐based case–control study was specifically designed to examine our hypothesis of an association between periconceptional poor sleep and the risk of CHD, and furthermore, to examine if daytime napping could to some extent change the association. The examination would be conducted in both simple CHD and severe CHD, and then repeated in ventricular septal defect (VSD) and tetralogy of Fallot (TOF), the most frequent single type of simple CHD and severe CHD, respectively.

2. METHODS

2.1. Ethics statement

The ethical application and consent procedure of this study were approved by the Ethics Committee of Shanghai Jiao Tong University School of Medicine (Approval number: SJUPN‐201717).

2.2. Study design and subjects

A hospital‐based case–control study was conducted in Shanghai Children's Medical Center through June 2016 to December 2017. 262 children with severe CHD and 262 children with simple CHD, along with 262 control children without any birth defects, were enrolled in this study. All the children were younger than 2 years old, and those with any of the following conditions were excluded from the study: (a) cases with other congenital deformities except CHD, such as Down syndrome, 22q11.2 microdeletion, Noonan syndrome, Williams syndrome, Marfan syndrome, and etc.; (b) death of mother; (c) mother diagnosed with mental disorder; and (d) inability to locate the mother for interview.

The CHD diagnoses were based on the codes of the International Classification of Diseases, Tenth Revision, Clinical Modification. A team of experts, including pediatric cardiothoracic surgeons and fetal ultrasonologists, made evaluation and ensured the accuracy of the final diagnosis. To discuss the relationship of maternal sleep with the risk of different subgroup of CHD, the CHD cases were divided into simple and severe CHD groups with reference to previous studies (Wu et al., 2010; Yeh et al., 2015). In this study, simple CHD includes VSD, atrial septal defect, congenital pulmonary valve stenosis, congenital stenosis of aortic valve, patent ductus arteriosus, coarctation of aorta, and stenosis of pulmonary artery. To avoid the enrollment of nonsignificant clinical forms or spontaneously resolved defects, only those children who had a CHD‐specific admission were recruited (Chou et al., 2016). Severe CHD in this study includes double outlet right ventricle, transposition of the great arteries, common ventricle, single ventricle, atrioventricular septal defect, TOF, congenital tricuspid stenosis, hypoplastic left heart syndrome, atresia of pulmonary artery, and total anomalous pulmonary venous return. The children in the control group were recruited from the pediatric patients admitted into the same hospital during the same period when the cases were recruited. Among the 262 controls, 132 came from pediatric respiratory medicine, 91 from pediatric general surgery, and 39 from pediatric gastroenterology.

2.3. Measures

Information on sociodemographic characteristics and health‐related behaviors was retrospectively collected through the Periconceptional Behaviors and Environmental Exposure Questionnaire (PBEQ). For both cases and controls, only those mothers who signed informed consent were invited to fill out the questionnaires by themselves while they were in hospital.

2.3.1. Sleep assessment

Periconceptional Sleep Questionnaire (PSQ), as a part of PBEQ, was specifically designed to evaluate maternal sleep characteristics during periconceptional period. The PSQ was developed on previous literature review (Abeysena et al., 2009; Li et al., 2015; O'Brien et al., 2013; Okun, Luther, Wisniewski, & Wisner, 2013; Okun, Schetter, & Glynn, 2011; Wang, Lin, Lin, Chen, & Lin, 2010), pilot studies and reliability assessment. The Cronbach's alpha coefficient of the PSQ was 0.86, which indicates that the internal consistency is good and acceptable. The PSQ has six questions in total to evaluated maternal sleep characteristics around 1 month before the conception and the whole pregnancy. One is to ask about sleep duration: “how much was your total sleep duration in a typical day?”. Four are regarding signs and symptoms of sleep quality: “How often did you have difficulty falling asleep in 30 minutes during a typical week?”; “How often did you have difficulty in maintaining/reinitiating sleep during a typical week?”; “How often did you snore loudly during a typical week?”; and “How often did you take medicine to help sleep during a typical week?”. The response was assessed on a 4‐point Likert scale (“quite often” if occurred 3 or more nights per week, “sometimes” for 1–2 nights per week, “occasionally” for <1 night per week, and “never”). The last one is about routine daytime nap: “Did you have a daytime nap in general?”, and the response was “yes” or “no.”

The optimal duration of sleep needed in pregnancy is still unknown. The cut‐off of short sleep duration differed in the literature, and <8 hr/d, comparatively, was a common choice (Abeysena et al., 2009; Abeysena et al., 2010; Lee & Gay, 2004; Mizutani, Suzuki, Kondo, & Yamagata, 2007; Yu et al., 2017). In addition, 8 hr/d is close to the bottom 25th centile of the distribution of total sleep duration of our data. Therefore, short sleep duration was defined as sleep duration <8 hr/d in the present study. Each item regarding sleep quality was scored as yes = 1 if responses were “quite often” or “sometimes” and no = 0 if responses were “occasionally” or “never.” Based on the scoring system, the index of sleep quality was summed up as 0–4, which was further dichotomized as 0–2 versus 3–4 and the latter was defined as poor sleep quality. Considering the overlap and collinearity existed between short sleep duration and poor sleep quality, we combined them into a single variable named as “poor sleep”, defined as sleep duration < 8 hr/d and/or the index of sleep quality being 3–4.

2.4. Potential confounding variables

2.4.1. Demographic and obstetric characteristics

Maternal ethnic was categorized as Han ethnicity versus others; maternal age at delivery was grouped as <35 years old versus ≥35 years; maternal educational level was grouped into three categories: middle school and below, high school, and college and above; marital status was categorized as married versus unmarried/divorced/widowed; residence was categorized as urban versus suburban/rural; maternal prepregnancy obesity, was grouped into yes versus no (obesity was defined as body mass index (BMI) ≥ 28 [Zhou, 2002], BMI was calculated as weight in kilograms divided by height in meters squared based on prepregnancy height and weight); multiple birth was grouped as yes versus no; family history of CHD was grouped as yes versus no; and infant gender was categorized as male versus female.

2.4.2. Maternal health indicators and behaviors

Maternal prepregnancy diabetes (defined as pregestational diabetes, yes vs. no), maternal prepregnancy hypertension (defined as pregestational hypertension, yes vs. no), maternal folic acid supplementation (defined as taking folic acid supplements before and/or during pregnancy, yes vs. no), maternal smoking (defined as smoking before and/or during pregnancy, yes vs. no), and maternal drinking (defined as drinking before and/or during pregnancy, yes vs. no).

2.5. Statistical analysis

Statistical description was made by use of percentage for categorical variables, and the Chi‐square test and Fisher's exact test were employed to compare difference between groups.

To identify the relationship between maternal sleep and CHD, univariate and multivariable logistic regression models were established. Adjustments were made following a two‐step procedure: modela was adjusted for demographic and obstetric characteristics; and in modelb, maternal health indicators and behaviors were simultaneously controlled. To examine if daytime napping could change the strength of the associations, the likelihood ratio test and stratified analysis were further specifically applied.

The present study adopted a propensity score method for adjustment and matching (Deb et al., 2016; Heinze & Juni, 2011). A multivariable logistic regression model was developed to estimate the propensity score, in which all potential confounding variables related to CHD were included in the model. In propensity‐score‐adjusted analysis, propensity scores were used as a covariate. The strength of propensity‐score‐adjusted analysis lies in taking all covariates along with their interactions as one covariate into account, which could reduce potential selection bias between cases and controls, making the regression models more parsimonious and accurate (Deb et al., 2016; Heinze & Juni, 2011). In propensity‐score‐matched analysis, controls were matched 1:1 to cases based on a greedy nearest neighbor matching algorithm on propensity score with a caliper equaling to 0.1.

We conducted propensity score analyses using R version 3.5.1 (The R Foundation for Statistical Computing). All other analyses were performed with the Statistical Package for the Social Sciences (SPSS) (IBM‐SPSS Statistics version 23.0, Inc., Chicago, IL). Statistical significance level was set at p value <.05 (two sided).

3. RESULTS

3.1. The characteristics of the sample

Table 1 summarizes the sample information on demographic and obstetric characteristics by CHD cases versus controls. The following variables were significantly different between CHD cases and control group before propensity score matched: residence, maternal education level, and folic acid supplementation (all p < .05). In addition, infant gender and family history of CHD were also different, however, only between simple CHD cases and controls (both p < .05). After accounting for propensity score matching, no significant differences were observed between cases and controls.

Table 1.

The description of characteristics by CHD cases versus controls, (n, %)

Simple CHD Severe CHD
Unmatched Matched Unmatched Matched
Cases (n = 262) Controls (n = 262) p Cases (n = 187) Controls (n = 187) p Cases (n = 262) Controls (n = 262) p Cases (n = 186) Controls (n = 186) p
Demographic and obstetric characteristics
Maternal ethnic
Han 250, 95.4 255, 97.3 .243 180, 96.3 180, 96.3 1.000 251, 95.8 255, 97.3 .337 179, 96.2 181, 97.3 .557
Other 12, 4.6 7, 2.7 7, 3.7 7, 3.7 11, 4.2 7, 2.7 7, 3.8 5, 2.7
Maternal age at delivery
<35 years old 240, 91.6 240, 91.6 1.000 172, 92.0 172, 92.0 1.000 233, 88.9 240, 91.6 .302 171, 91.9 166, 89.2 .375
≥35 years old 22, 8.4 22, 8.4 15, 8.0 15, 8.0 29, 11.1 22, 8.4 15, 8.1 20, 10.8
Maternal education
Middle school and below 83, 31.7 58, 22.1 .014 48, 25.7 51, 27.3 .866 101, 38.5 58, 22.1 <.001 65, 34.9 52, 28.0 .294
High school 55, 21.0 48, 18.3 44, 23.5 40, 21.4 62, 23.7 48, 18.3 33, 17.7 41, 22.0
College and above 124, 47.3 156, 59.5 95, 50.8 96, 51.3 99, 37.8 156, 59.5 88, 47.3 93, 50.0
Marital status
Married 258, 98.5 257, 98.1 1.000a 184, 98.4 184, 98.4 1.000a 253, 96.6 257, 98.1 .279 182, 97.8 182, 97.8 1.000a
Unmarried/divorced/widowed 4, 1.5 5, 1.9 3, 1.6 3, 1.6 9, 3.4 5, 1.9 4, 2.2 4, 2.2
Residence
Urban 110, 42.0 166, 63.4 <.001 87, 46.5 95, 50.8 .408 99, 37.8 166, 63.4 <.001 86, 46.2 91, 48.9 .604
Suburban/rural 152, 58.0 96, 36.6 100, 53.5 92, 49.2 163, 62.2 96, 36.6 100, 53.8 95, 51.1
Maternal prepregnancy obesity
Yes 6, 2.3 9, 3.4 .432 6, 3.2 5, 2.7 .760 17, 6.5 9, 3.4 .108 4, 2.2 7, 3.8 .359
No 256, 97.7 253, 96.6 181, 96.8 182, 97.3 245, 93.5 253, 96.6 182, 97.8 179, 96.2
Multiple births
Yes 15, 5.7 23, 8.8 .178 13, 7.0 11, 5.9 .673 29, 11.1 23, 8.8 .381 15, 8.1 21, 11.3 .293
No 247, 94.3 239, 91.2 174, 93.0 176, 94.1 233, 88.9 239, 91.2 171, 91.9 165, 88.7
Infant gender
Male 128, 48.8 168, 64.1 <.001 98, 52.4 100, 53.5 .836 160, 61.1 168, 64.1 .470 126, 67.7 110, 59.1 .085
Female 134, 51.1 94, 35.9 89, 47.6 87, 46.5 102, 38.9 94, 35.9 60, 32.3 76, 40.9
Family history of CHD
Yes 20, 7.6 6, 2.3 .005 3, 1.6 6, 3.2 .502a 11, 4.2 6, 2.3 .218 5, 2.7 6, 3.2 .760
No 242, 92.4 256, 97.7 184, 98.4 181, 96.8 251, 95.8 256, 97.7 181, 97.3 180, 96.8
Maternal health indicators and behaviors
Diabetes/hypertension
Yes 2, 0.8 7, 2.7 .176a 2, 1.1 1, 0.5 1.000a 8, 3.1 7, 2.7 .793 4, 2.2 5, 2.7 1.000a
No 260, 99.2 255, 97.3 185, 98.9 186, 99.5 254, 96.9 255, 97.3 182, 97.8 181, 97.3
Folic acid supplementation
Yes 204, 77.9 222, 84.7 .044 147, 78.6 152, 81.3 .518 186, 71.0 222, 84.7 <.001 142, 76.3 148, 79.6 .453
No 58, 22.1 40, 15.3 40, 21.4 35, 18.7 76, 29.0 40, 15.3 44, 23.7 38, 20.4
Smoking/drinking
Yes 25, 9.5 26, 9.9 .883 18, 9.6 19, 10.2 .863 40, 15.3 26, 9.9 .065 16, 8.6 22, 11.8 .304
No 237, 90.5 236, 90.1 169, 90.4 168, 89.8 222, 84.7 236, 90.1 170, 91.4 164, 88.2
Sleep characteristics
Short sleep duration
Yes 87, 33.2 55, 21.0 .002 63, 33.7 35, 18.7 .001 79, 30.2 55, 21.0 .016 57, 30.6 40, 21.5 .045
No 175, 66.8 207, 79.0 124, 66.3 152, 81.3 183, 69.8 207, 79.0 129, 69.4 146, 78.5
Poor sleep quality
Yes 22, 8.4 6, 2.3 .002 17, 9.1 4, 2.1 .004 14, 5.3 6, 2.3 .068 10, 5.4 3, 1.6 .048
No 240, 91.6 256, 97.7 170, 90.9 183,97.9 248, 94.7 256, 97.7 176, 94.6 183, 98.4
Poor sleep
Yes 94, 35.9 56, 21.4 <.001 69, 36.9 36, 19.3 <.001 83, 31.7 56, 21.4 .008 60, 32.3 40, 21.5 .019
No 168, 64.1 206, 78.6 118, 63.1 151, 80.7 179, 68.3 206, 78.6 126, 67.7 146, 78.5
Daytime nap
Yes 132, 50.4 163, 62.2 .006 93, 49.7 115, 61.5 .022 132, 50.4 163, 62.2 .006 97, 52.2 113, 60.8 .094
No 130, 49.6 99, 37.8 94, 50.3 72, 38.5 130, 49.6 99, 37.8 89, 47.8 73, 39.2
Open in a new tab

CHD = congenital heart disease.

a

The Fisher's exact test was used; in the other cases, the Chi‐square test was applied.

Maternal sleep characteristics were also shown in Table 1. The prevalence of short sleep duration was significantly higher in cases (simple CHD: 33.2%; severe CHD: 30.2%) than in control group (21.0%) before propensity score matched (both p < .05). So was the poor sleep (simple CHD: 35.9%; severe CHD: 31.7%; controls: 21.4%, both p < .05). The prevalence of poor sleep quality was different in simple CHD versus controls (8.4% vs. 2.3%, p < .05), but not in severe CHD versus controls (5.3% vs. 2.3%, p > .05). The proportion of mothers who had daytime naps was significantly lower in cases (simple CHD: 50.4%; severe CHD: 50.4%) than in control group (62.2%) (both p < .05). After propensity score matching, the significant difference between CHD cases and controls were still kept in most cases.

Information on the characteristics of VSD versus controls and TOF versus controls was also described, as demonstrated in Table S1.

3.2. The associations of maternal sleep with CHD

3.2.1. Simple CHD versus controls

As shown in Table 2, the final adjusted model (modelb) demonstrated that both short sleep duration (OR = 2.289, 95% CI: 1.484–3.532), poor sleep quality (OR = 4.738, 95% CI: 1.691–13.277), poor sleep (OR = 2.486, 95% CI: 1.619–3.818) could increase the risk of simple CHD; while routine daytime nap (OR = 0.634, 95% CI: 0.435–0.923) could decrease the risk. After accounting for propensity score matching, the results were generally kept. When the analyses were repeated in VSD, similar results were obtained in most cases (Table S2).

Table 2.

Associations of maternal sleep during periconceptional period with CHD in offspring

Before propensity score analysis After propensity score analysis
Crude OR (95% CI) Adjusted OR (95%CI) Propensity‐score‐adjusted Propensity‐score‐matched
Modela Modelb OR (95% CI) OR (95% CI)
Simple CHD
Short sleep duration
Yes vs. no 1.871 (1.263–2.772)** 2.203 (1.439–3.371)*** 2.289 (1.484–3.532)*** 2.165 (1.425–3.287)*** 2.206 (1.370–3.553)**
Poor sleep quality
Yes vs. no 3.911 (1.559–9.811)** 3.633 (1.404–9.404)** 4.738 (1.691–13.277)** 4.067 (1.570–10.535)** 4.575 (1.509–13.869)**
Poor sleep
Yes vs. no 2.058 (1.396–3.036)*** 2.394 (1.572–3.646)*** 2.486 (1.619–3.818)*** 2.239 (1.542–3.519)*** 2.543 (1.534–3.392)***
Daytime nap
Yes vs. no 0.617 (0.436–0.873)** 0.640 (0.442–0.929)* 0.634 (0.435–0.923)* 0.646 (0.448–0.931)* 0.619 (0.411–0.934)*
Severe CHD
Short sleep duration
Yes vs. no 1.625 (1.092–2.418)* 1.890 (1.229–2.907)** 1.862 (1.206–2.876)** 1.831 (1.197–2.800)** 1.613 (1.009–2.577)*
Poor sleep quality
Yes vs. no 2.409 (0.911–6.368) 1.925 (0.692–5.352) 2.079 (0.736–5.871) 2.065 (0.741–5.575) 3.466 (0.938–12.803)
Poor sleep
Yes vs. no 1.706 (1.151–2.529)** 1.964 (1.283–3.004)** 1.950 (1.269–2.997)** 1.918 (1.259–2.921)** 1.738 (1.091–2.769)*
Daytime nap
Yes vs. no 0.617 (0.436–0.873)** 0.729 (0.504–1.055) 0.759 (0.522–1.103) 0.771 (0.532–1.117) 0.704 (0.467–1.063)
Open in a new tab

CHD = congenital heart disease.

Modela: adjusted for maternal ethic, maternal age at delivery, maternal education, marital status, residence, maternal prepregnancy obesity, multiple births, infant gender, and family history of CHD.

Modelb: based on modela, further adjusted for prepegnancy diabetes/hypertension, folic acid use, and smoking/drinking.

*

p value <.05

**

p value <.01

***

p value <.001.

The likelihood ratio test of the interaction between routine daytime nap and poor sleep did not achieve statistical significance level (p > .05). However, in the stratified analysis (Table 3), the concurrence with routine daytime nap could decrease the risk of simple CHD caused by poor sleep (OR = 3.183 with 95% CI: 1.830–5.537 decreased to OR = 2.236 with 95% CI: 1.200–4.165 in the final adjusted model). The trend is similar either after propensity‐score‐adjusted analysis or after propensity‐score‐matched analysis. The analysis was also repeated in VSD and the similar results were obtained (Table S3).

Table 3.

Stratified analysis for maternal daytime nap and sleep status during periconceptional period on risk of CHD in offspring

Poor sleep Daytime nap Simple CHD
Before propensity score analysis After propensity score analysis
Cases Controls Crude OR (95% CI) Adjusted OR (95% CI) Propensity‐score‐adjusted Propensity‐score‐matched
Modela Modelb Cases Controls OR (95% CI) Cases Controls OR (95% CI)
No Yes 99 137 1 1 1 99 137 1 72 100 1
No 69 69 1.384 (0.907–2.111) 1.309 (0.830–2.064) 1.324 (0.836–2.097) 69 69 1.324 (0.848–2.068) 46 51 1.253 (0.759–2.067)
Yes Yes 33 26 1.756 (0.988– 3.122) 2.139 (1.158–3.952) * 2.236 (1.200–4.165) * 33 26 2.121 (1.156–3.892) * 21 15 1.944 (0.938–4.029)
No 61 30 2.814 (1.693–4.676) *** 3.049 (1.772–5.246) *** 3.183 (1.830–5.537) *** 61 30 2.968 (1.737–5.074) *** 48 21 3.175 (1.750–5.759) ***
Poor sleep Daytime nap Severe CHD
Before propensity score analysis After propensity score analysis
Cases Controls Crude OR (95%CI) Adjusted OR (95% CI) Propensity‐score‐adjusted Propensity‐score‐matched
Modela Modelb Cases Controls OR (95% CI) Cases Controls OR (95% CI)
No Yes 96 137 1 1 1 96 137 1 70 95 1
No 83 69 1.717 (1.137–2.593) * 1.309 (0.839–2.041) 1.384 (0.893–2.144) 83 69 1.290 (0.830–2.004) 56 51 1.490 (0.914–2.430)
Yes Yes 36 26 1.976 (1.120–3.487) * 2.213 (1.179–4.091) * 2.263 (1.232–4.154) ** 36 26 2.140 (1.176–3.894) * 27 18 2.036 (1.040–3.985) *
No 47 30 2.236 (1.320–3.787) ** 2.110 (1.203–3.699) ** 2.184 (1.250–3.616) ** 47 30 2.086 (1.193–3.648) * 33 22 2.036 (1.093–3.790) *
Open in a new tab

CHD = congenital heart disease.

Modela: adjusted for maternal ethic, maternal age at delivery, maternal education, marital status, residence, maternal prepregnancy obesity, multiple births, infant gender, and family history of CHD.

Modelb: based on modela, further adjusted for prepegnancy diabetes/hypertension, folic acid use, and smoking/drinking.

*

p value <0.05

**

p value <0.01

***

p value <0.001

3.2.2. Severe CHD versus controls

After controlling for all possible confounders in modelb, it was indicated that short sleep duration and poor sleep could increase the risk of severe CHD by 86.2% (95% CI: 1.206–2.876) and 95.0% (95% CI: 1.269–2.997), respectively. By contrast to simple CHD, routine daytime nap was not found to be a protective factor for severe CHD. After propensity score analyses, the results were generally kept. In the likelihood ratio test and stratified analysis (Table 3), it was demonstrated that the concurrence with routine daytime nap could not decrease the risk of severe CHD caused by poor sleep. When the analyses were repeated in TOF, similar results were obtained in most cases (Table S3).

4. DISCUSSION

This study, for the first time, explored the associations between maternal poor sleep around periconceptional period and the risk of CHD based on a case–control study. Of particular crucial was that daytime napping might to some extent weaken the association in simple CHD. The findings, in most cases, can be verified in VSD and TOF, the most frequent single type of simple CHD and severe CHD, respectively. We also applied propensity score analysis to confirm the association, the similar results further enforced the evidence that maternal sleep is involved in embryonic heart development. The present study extended our attention on the association between maternal sleep and birth defects, and the findings point to an important area for future research.

As summarized by a systematic review, the global incidence of CHD increased nearly 15 times, from 0.6‰ during 1930–1934 to 9.1‰ in 1995, based on 114 studies all around the world over the past 60 years (van der Linde et al., 2011). In China, the rising trend similarly existed, and the prevalence of CHD among newborns has reached up to 11.1‰ (Qu et al., 2016). Therefore, CHD has been a severe public health challenge worldwide and also in China. Increasing attention should be focused on potential risk factors associated with CHD, especially those factors that can be modified.

As far as we know, to date this is the only one population‐based study to explore the association between maternal sleep and CHD in offspring. We found that poor sleep, defined as short sleep duration and/or poor sleep quality, during periconceptional period could increase the risk of CHD. A previous study demonstrated that maternal frequent poor sleep ≥4 days/week, compared to <1day/week, was significantly associated with an increased risk of NTDs by 3.1 times (Li et al., 2015). The study took demographic characteristics and some lifestyle factors into account when examining the associations; therefore, it suggests maternal poor sleep during periconceptional period could be an independent risk factor for NTDs. Similarly, the present study established the relationship between maternal poor sleep and CHD, which is also independent of demographic characteristics, lifestyle factors, and even maternal health indicators as well. Taken together, the only two studies so far consistently suggest poor maternal sleep should be involved in disrupted embryonic development and then birth defects, which points to an important role of maternal sleep in embryonic development, and would probably drive an emerging area of research interest.

More importantly, in the present study, maternal habitual daytime nap during periconceptional period was found to be independently associated with a decreased risk of simple CHD, indicating maternal daytime nap could be a protective factor for simple CHD. To test the association, stratified analysis was further applied to examine the association between poor sleep and daytime nap, in which it suggested that the concurrence with daytime napping might to some extent weaken the risk of simple CHD caused by poor sleep. The significance of the findings lies not only in the verification of the protective impact of daytime nap but also proposing a promising approach to counteract the risk caused by poor sleep on CHD. Meanwhile, the findings provide further support for the role of maternal sleep in CHD.

Presently, limited studies have paid attention to daytime nap in pregnant women and its possible health effect (Ebert et al., 2015; Okun & Coussons‐Read, 2007; Tsai, Kuo, Lee, Lee, & Landis, 2013). As demonstrated by a previous study, it seemed that pregnant women need more daytime nap, nearly twice the frequency of nonpregnant women (1.80 vs. 0.94 naps per week) (Okun & Coussons‐Read, 2007). Given the substantial degree of sleep deprivation/disruption during pregnancy (Okun & Coussons‐Read, 2007; Yang et al., 2018), we speculate that daytime nap among pregnant women may be a compensatory strategy to the poor sleep. As expected, two prospective studies obtained a relatively consistent result that daytime nap during pregnancy was a beneficial action to offset the disturbed sleep, with a minimal negative impact on nocturnal sleep (Ebert et al., 2015; Tsai, Lin, Kuo, Lee, & Landis, 2013). Our finding further extended the understanding that daytime nap during pregnancy may not only be a countermeasure to the sleep disruption, but also even be involved the inversion of the adverse health risk related to gestational poor sleep. Besides these, previous studies suggested that daytime nap participates in the process of emotion regulation, which would exert a positive influence on mood and emotion (Cellini, Lotto, Pletti, & Sarlo, 2017; Peth et al., 2012). It has been reported that maternal exposure to emotional stress and anxiety during pregnancy could increase the risk of CHD and other birth defects (Carmichael et al., 2017; Zhu et al., 2013). Therefore, another possible pathway underlying the implication of daytime nap in a reduced risk of adverse pregnancy outcomes could be through a specific neurophysiological process which helps to relieve maternal stress and anxiety.

Although this is the first epidemiologic study exploring the relationship between maternal sleep and offspring CHD, several potential biological mechanisms are feasible in theory to support the relationship. Accumulating studies demonstrated that short sleep duration and sleep disorders could increase the risk of impaired glucose metabolism, lead to hyperglycemia, hyperinsulinemia, and gestational diabetes (Wang et al., 2017; Zhong et al., 2018), and meanwhile, it has been generally confirmed that hyperglycemia and hyperinsulinemia have an adverse effect on early embryonic development, could increase the risk of CHD and other congenital anomalies (Bateman et al., 2015; Oyen et al., 2016). Except for this, previous studies have put forward another explanation that disturbed sleep during early pregnancy could contribute to an increased inflammatory response, and then as a result, impaired uterine/placenta blood blow and functioning (Okun et al., 2013; Okun & Coussons‐Read, 2007). Furthermore, the research progress in melatonin, a circadian rhythm messenger between mother and fetal, revealed that it played an important role in cardiac differentiation and the maturation of embryonic stem cells, and the formation of the embryo S‐shaped heart as well (Ansari, Agathagelidis, Lee, Korf, & von Gall, 2009; Bian et al., 2017; Kudova et al., 2016; Nogueira & Sampaio, 2017).

To the best of our knowledge, this is the first epidemiological study to examine the impacts of maternal sleep on offspring CHD. We established the population‐based association between poor maternal sleep around periconceptional period and the risk of CHD, and further found that routine daytime nap might to some extent weaken the associations in simple CHD. In this case–control study, to control recall bias as far as possible, all the children recruited were younger than 2 years old. When we evaluated the associations between maternal sleep and CHD in offspring, a series of factors including demographic characteristics, pregnancy complications, and lifestyle behaviors were taken into accounts. To increase the statistical power and achieve an effect size for the entire population, we included the propensity score as a covariate to adjust the differences in characteristics between cases and controls. Moreover, to reduce potential selection bias and minimize the confounding association caused by differences in characteristics, controls were individually matched to cases by using the propensity score. Further, to verify the stability and reliability of the results, we repeated all analyses in the single type of CHD, including VSD and TOF for comparison. The results of various statistical analyses being prominently consistent give strengthened evidence that maternal sleep plays a key role in CHD of offspring.

Several limitations should be acknowledged in interpreting the results. First, maternal sleep characteristics collected were not detailed enough, compared with most studies using validated sleep questionnaires (Heazell et al., 2018; McCowan et al., 2017). However, the items cover the most important core sleep characteristics, including sleep duration, sleep quality, and daytime nap. A previous study demonstrated that maternal poor sleep was significantly associated with an increased risk of NTDs by 3.1 times (Li et al., 2015). The results of our study similarly established the relationship between maternal poor sleep during periconception and birth defects in offspring. We, even more, also found that daytime napping might be a countermeasure to the adverse effect of maternal poor sleep. Second, the critical period of heart development is the first 8 weeks of pregnancy, however, the sleep parameters in this study were during periconceptional period, which covers 1 month before the conception and the whole pregnancy. Third, sample size was somewhat limited for further stratified analyses. However, all analyses were repeated and almost all the results can be verified in VSD and TOF, the most frequent single type of simple CHD and severe CHD. Fourth, although recall bias is inevitable in a case–control study, we recruited the children were all younger than 2 years old to control recall bias as far as possible. Fifth, the need of sleep duration varies in different individuals, however, we set the only one cut‐off point to define short sleep duration. There might be a chance for misclassification bias. Finally, there was potential residual confounding, even though a wide range of confounders has been taken into accounts. However, considering the results were mostly consistent between simple and severe CHD, and can be verified in VSD and TOF, even after accounting for propensity score matching, the evidence regarding association between maternal sleep and CHD has been strengthened to some extent.

5. CONCLUSIONS

The present study is the first to observe the association between maternal sleep and CHD. The findings provided epidemiological evidence for the relationship of maternal poor sleep with an increased risk of CHD, fortunately, the concurrence with daytime nap might to some extent weaken the risk in simple CHD. They jointly revealed the important role of maternal sleep in embryonic heart development, which extends our understanding on the health effect of maternal sleep. The findings also should be of clinically significance in considering that many aspects of sleep are modifiable.

Supporting information

Table S1 The description of characteristics by VSD/TOF Cases vs. Controls, (n, %)

Table S2. Associations of maternal sleep during periconceptional period with VSD/TOF in offspring

Table S3. Stratified analysis for maternal daytime nap and sleep status during periconceptional period on risk of VSD/TOF in offspring

Click here for additional data file. (39.2KB, docx)

ACKNOWLEDGMENTS

We gratefully acknowledge the persons who took the time to participate in our study. Collected data: Dr. Qihua Fu, Yimei Ma, Sha Sha, Department of Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine.

Zhao A, Zhao K, Xia Y, et al. Exploring associations of maternal sleep during periconceptional period with congenital heart disease in offspring. Birth Defects Research. 2019;111:920–931. 10.1002/bdr2.1536

Funding information the Key Project from Shanghai Municipal Science and Technology Commission, Grant/Award Number: 18411951600; National Natural Science Foundation of China, Grant/Award Numbers: 81673183, 81874266; the Research Funds from Shanghai Jiao Tong University School of Medicine, Grant/Award Number: 20170509‐1; the Science and Technology Funds from Pudong New Area, Shanghai, Grant/Award Number: PKJ2017‐Y01; the Scientific Research Development Funds from Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Grant/Award Number: HX0251

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1 The description of characteristics by VSD/TOF Cases vs. Controls, (n, %)

Table S2. Associations of maternal sleep during periconceptional period with VSD/TOF in offspring

Table S3. Stratified analysis for maternal daytime nap and sleep status during periconceptional period on risk of VSD/TOF in offspring

Click here for additional data file. (39.2KB, docx)

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