Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

A Matched Case-Control Study on the Association Between Colds, Depressive Symptoms during Pregnancy and Congenital Heart Disease in Northwestern China

Subjects

Abstract

The purpose of this study was to explore the association between colds, depressive symptoms during pregnancy and offspring congenital heart disease (CHD). A 1:2 matching case-control study was conducted in Northwest China. Information was gathered by a structured questionnaire and was reviewed by investigators on the spot. Multivariate logistic regressions and nonlinear mixed effect model were performed. 614 cases and 1228 controls were available in this study. After adjusting for potential confounders, the colds during the entire pregnancy were associated with increased risk of offspring CHD (OR = 1.44(1.12–1.85)). Similarly, there was a higher depression score in CHD group than the control group (OR = 1.89(1.48–2.41)). In addition, the women with both colds and higher depression scores had a higher risk of offspring CHD (OR = 2.72(1.87–3.93)) than their counterparts with only colds (OR = 1.48(1.04–2.09)) or with only higher depression scores (OR = 1.94(1.37–2.74)). The combined effects were significant in the multiplication model (OR = 2.04(1.47–2.83)) but not in the additive model (S = 1.40(0.70–2.81), AP = 0.19(−0.15–0.53) and RERI = 0.55(−0.54–1.64)). In conclusion, the colds and depressive symptoms during pregnancy were found associated with increased risk of offspring CHD and we found for the first time that there existed a statistically multiplying interaction effect of colds and depression on increasing risk of offspring CHD.

Introduction

Congenital heart disease (CHD) referred to a cardiovascular malformation caused by abnormal cardiovascular development in the fetus. The prevalence of CHD was about 6.8 to 9.0 per 1000 live births worldwide1,2,3. It had become the most common form of human birth defects, accounting for nearly one-third of all major birth defects2. In addition, the CHD resulted in more than 40% of prenatal deaths4 and remained the leading cause of birth defect related to infant morbidity, mortality, and long-term disability5. It greatly affected the life quality of children and brought heavy medical burden to families and society6. In China, the prevalence of CHD was about 8 to 10 per 1000 live births7, which contributed a lot to the total number of CHD in the world because of the large population of China. Therefore, studying the control and prevention of CHD in Chinese population was not only beneficial to China but also provided evidence beyond China to some extent.

However, its underlying causes were still unclear8. Therefore, it was important to explore the possible etiological factors, especially where it could be modified, which may be a key step in the implementation of primary prevention. Over the past decade, a large number of epidemiological surveys had shown that a multitude of environment risk factors greatly contributed to the CHD. For example, maternal drinking, smoking, taking medicine and exposure to some physical factors during pregnancy were associated with the increased risk of offspring CHD9,10. On the other hand, pregnancy medical examination and taking folic acid may reduce the risk of offspring CHD5.

Increasing numbers of studies also had explored the association of various maternal illnesses, such as hypertension, diabetes mellitus, thyroid disorders and upper respiratory tract infection during pregnancy with CHD11,12. Due to changes in the mother’s internal environment during peri-conception, the body’s immune system will be reduced, and infectious diseases, such as cold and fever were more likely to occur13,14. There was growing evidence that maternal infection during pregnancy may be associated with spontaneous abortion, stillbirth and congenital defects in future generations15,16.

Physiological and psychological diseases often interact17,18. As the most common disease during pregnancy, the effect of colds on pregnant women’s mood was noteworthy. It was indicated that the prevalence of antenatal depression or anxiety during pregnancy was 7 to 11 per 100 pregnant women in high-income countries19,20, and was 11 to 27 per 100 in China21. Previous studies found maternal psychosocial stress during pregnancy was a possible risk factor for adverse birth outcomes22,23,24. For instance, the evidence from a population-based cohort study had shown that maternal psychosocial adversity during pregnancy was associated with offspring gestational age and birth weight25. It was also well established that maternal anxiety during pregnancy could result in offspring asthma24.

However, most studies had only reported one type of diseases, such as infectious diseases and febrile diseases instead of a specific disease, when analyzing the association between common diseases during pregnancy and offspring CHD. It was difficult to distinguish between the independent and joint effects between maternal colds and fever10. In terms of psychological factors, most studies focused on psychological stress and anxiety. Research on depression was mainly focused on postpartum depression or the association between depression and mother’s cardiovascular disease26,27. Other studies had included clinical trials of the efficacy of antidepressant drugs in psychiatric patients28. As far as we know, there was no specific epidemiological study on the association between depressive symptoms during pregnancy in the general population and offspring CHD. In addition, there was no article on exploring the interaction of colds and depressive symptoms during pregnancy on offspring CHD.

Therefore, this study focused on the association of common diseases during pregnancy that was colds and depressive symptoms, and offspring CHD. Because the prevalence of the CHD was increasing, we had reason to believe that even relatively small increases in effect would produce many cases. In addition, the combination of physiological factors and psychological factors followed the biopsychosocial model by Engel GL29. This study sought to better understand that association by assessing the interaction of colds, depressive symptoms on offspring CHD.

Results

Characteristics of the participants

Flow-chart of study participants was shown in Fig. 1. 1842 subjects were available in this study, including 614 cases and 1228 controls. Maternal characteristics during pregnancy among infants with and without CHD were listed in Table 1. Compared with controls, case mothers were likely to more young, have lower education level, have a lower economic level, drink, take less folic acid, take medicine, have abnormal results of the prenatal examination, have negative life events and family history of offspring CHD (all P < 0.05). There were no statistical differences between two groups in maternal smoking, pregnancy reaction, chronic disease, natural abortion history and baby gender.

Figure 1
figure1

Flow-chart of study participants.

Table 1 Maternal characteristics during pregnancy among infants with and without CHD.

Association of congenital heart disease with colds or depressive symptoms

Table 2 showed the association between colds, depressive during pregnancy and offspring CHD. 319 (51.95%) cases and 560 (45.60%) controls were affected with colds during the entire pregnancy. Of these, 27 (4.40%) cases and 36 (2.93%) controls reported there was a fever when colds occurring. There were significant increases of CHD associated with colds during the entire pregnancy in Model 1, Model 2 and Model 3. The OR with 95% CI were 1.29(1.06–1.57), 1.35(1.05–1.73) and 1.44(1.12–1.85), respectively. Stratified analysis proved that the estimate between CHD risk and colds without fever was similar in Model 1, Model 2 and Model 3 (OR = 1.22(1.01–1.48), 1.27(1.01–1.63) and 1.35(1.05–1.73)). In addition, the ORs assessing colds in three months before pregnancy and the second trimester on CHD were 3.12(1.17–8.31) and 1.34(1.01–1.80) after adjusting for potential confounders (Model 3). The average depression scores of the cases and controls were 33.04 ± 6.28 points and 33.78 ± 5.26 points, respectively. There was a higher depression score in CHD group than the control group (P < 0.05) (Table 2). Only 49 (2.66%) subjects were classified as having depressive symptoms according to the cutoff value of 50. Finally, we divided it by 34 by calculating the Youden index, with 531 (43.24%) cases and 351 (57.17%) controls having the higher depression scores. Compared with continuous variables, classification variables could discover a higher risk in all models (OR = 1.75(1.44–2.13), 1.83(1.43–2.32) and 1.89(1.48–2.41)) (Table 2).

Table 2 Association between the offspring CHD and maternal exposure to colds and depressive symptoms during pregnancy.

Interactive effect analysis

Tables 3 and 4 evaluated the interactions of colds and depressive symptoms on offspring CHD. Table 3 showed 502 (27.25%) subjects caught a cold with lower depressive scores and 505 (27.42%) subjects only had a higher depression score without colds. There were 377 (20.47%) subjects with colds and higher depression scores. Compared with those who neither colds nor higher depression scores, the women with both colds and higher depression scores subjects had a significantly increased risk of offspring CHD (OR = 2.72(1.87–3.93)) after adjusting for potential confounders. In addition, the women with only colds and lower depressive scores had a significantly higher risk of offspring CHD (OR = 1.48(1.04–2.09)). Similarly, the women with only higher depression scores and no colds also had a significantly increased risk of offspring CHD (OR = 1.94(1.37–2.74)). When analyses were stratified by fever, the combined effects were significant for colds without fever (OR = 2.54(1.76–3.65)) but not for colds with fever (OR = 1.75(0.65–4.73)). Table 4 evaluated the interactions of colds and depressive symptoms by multiplication model and additive model. After adjusting for potential confounders, the variable of depression*colds had a significantly increased risk of offspring CHD (OR = 2.04(1.47–2.83)). Similarly, the variable of depression*colds without fever had a significantly increased risk of offspring CHD (OR = 2.21(1.57–3.09)). On the other hand, there was an increased risk of offspring CHD (S = 1.40(0.70–2.81), AP = 0.19(−0.15–0.53) and RERI = 0.55(−0.54–1.64)), but the differences did not reach the level of significance in the additive model (P > 0.05). When data were stratified by fever, there were still not statistically significant.

Table 3 Interactive effect (OR(95%CI)) of maternal colds and depressive symptoms on the occurrence of CHD.
Table 4 Multiplication models and additive models of maternal colds and depressive symptoms on the occurrence of CHD.

Sensitivity analysis

Sensitivity analysis for interactive effects was based on a series of basic demographic characteristics. Tables 5 and 6 presented some of the results of sensitivity analysis. Compared with those who neither colds nor higher depression scores, both colds and higher depression scores subjects had significantly increased ORs of CHD among populations with different levels of economic activity, different levels of taking folic acid, and different baby sex (all P < 0.05). According to the ORs of the variable of depression*colds in Table 6, there were statistically multiplication effects in all subgroups (all P < 0.05). According to the S, AP and RERI, there were additive intersecting trends in all subgroups but the differences were not statistically significant (P > 0.05). The results of grouping by other demographic characteristics were similar, which were not shown in this article.

Table 5 Sensitivity analysis of interactive effect (OR(95%CI)) between maternal colds and depressive symptoms on CHD.
Table 6 Sensitivity analysis of multiplication models and additive models between maternal colds and depressive symptoms on CHD.

Discussion

Our findings showed that the colds during pregnancy were associated with increased risk of offspring CHD. Similarly, there was a higher depression score in CHD group. The women with both colds and higher depression scores had a higher risk of offspring CHD than their counterparts with only colds and lower depressive scores or with only higher depression scores and no colds. There was a statistically multiplying interaction effect of colds and depressive symptoms on increasing risk of offspring CHD. Stratified analysis also presented the stability of association of colds and depressive symptoms with CHD. These findings implied the importance of strengthening the guidance of pregnancy against the prevalence of offspring CHD.

After adjusting for potential confounders, we still observed a significantly increased risk of CHD with colds, which was in accordance with several studies. Oster et al. and Csaky-Szunyogh M et al. reported an association between maternal influenza during periconceptional period and a higher risk of offspring CHD30,31. Liu F et al. also reported that catching a cold was independent risk factors for isolated CHD32. The association was may stable in our study, because after stratification of fever and exposure time, the results showed the trends of increased risk except for colds with fever. Obviously, low incidence of fever caused by colds in our study could explain this situation. It was similar to previous evidence about exposure time. Liu F et al. indicated the maternal health condition, especially in the early trimester of pregnancy, was very important for the risk of heart defects32. There may be false exposure categories about three months before pregnancy and first trimester during pregnancy. The biological effects by which maternal colds with or without fever may result in heart defects were unclear. One of the most common assumptions was apoptosis, which was thought to be related to heart morphology and could be altered by fever and infection33.

In this study, depressive status in cases and controls were measured with SDS. There were several reasons for us to choose it. Firstly, previous studies had assessed the SDS as a valid and reliable instrument in identifying depression21,34. Secondly, we focused on early detection of depressive symptoms. The ZSDS score could not conduct clinically diagnose, but instead showed levels of depressive symptoms that might be clinically significant. In this study, due to the design of epidemiology, it was not possible to diagnose depression according to the clinical design standard manual. An easy, short-term, inexpensive self-report screening scale was optional. However, the results showed that most mothers were below the cutoff value of the scale itself, which may be due to small sample sizes and different populations. Of course, recall bias caused by investigation design was probably the most important reason. Moreover, individuals in China sometimes tended to be reluctant to show their negative emotions for fear of discrimination35.

The current study also showed that maternal depression scores, whether continuous or categorical, were associated with an increased risk of offspring CHD. Chou et al. observed a higher risk of offspring CHD in women with epilepsy and mood disorders during pregnancy36. Previous studies had suggested that stress during pregnancy were independently associated with increased risks of adverse birth outcomes22,24. In addition, perinatal depressive symptoms were associated with an increased risk of premature birth in a systematic review37. The mechanisms by which maternal depression may result in CHD remained unclear. It may be through hypothalamic-pituitary-adrenal axis hyperactivity and release of corticotrophin from the placenta. High levels of corticotropin-releasing hormone had been observed in relation to fetal growth restriction and birth defects38,39.

In the present study, we found for the first time that there existed a positive joint effect between maternal colds and depressive symptoms on the incidence of offspring CHD. The risk of CHD increased much more in women with both colds and higher depression scores subjects than those only having a cold or only having a higher depression score during pregnancy. Similarly, a positive combined effect still was found between maternal colds without fever and depression. Based on the interaction effect analysis, we found that the combined effect of the colds and depression could be multiplying effect. Sensitivity analysis indicated the stability of the results. Previous studies had examined the interaction between depression and other characteristics. For instance, it was well-established in some studies about the multiplying effect between smoking exposure and depressive symptoms on promoting atherosclerosis and heart disease26,40. Lou et al. found an additive effect of depression and nicotine addiction on the chronic obstructive pulmonary disease in a prospective cohort study41. In addition, a multiplying effect between memory disorders and depressive symptoms was observed to be associated exacerbate cognitive impairment in Alzheimer’s disease42.

The mechanism of interaction between colds and depressive symptoms for CHD may be complex and remain unclear. One hypothesis was that depression leads to a dysfunction in the anti-inflammatory response43,44. Some people had hypothesized that this inflammatory response explained the association between depression and cardiovascular diseases45. Colds were the most common type of respiratory infection and also increased systemic inflammation, which may explain the amplified association between colds, depressive symptoms, and offspring CHD. Closer interdisciplinary cooperation between physical and mental health care may help to determine possible common risk factors for depression and colds46. Future research should explore more potential mechanisms to further understand how colds and depression work together to increase the risk of offspring CHD.

This study tried to verify the association of offspring CHD with colds, depressive symptoms during pregnancy using a series of logical analysis of the data from the case-control study. Firstly, we established three logistic regression models in this study to investigate possible association before and after controlling for potential affecting factors. Then, the combined effect of colds and depressive symptoms was further explored by two methods, which were multiplication model and additive model, respectively. To our knowledge, ours was the first study to explore the interaction between pregnancy colds and depressive symptoms on offspring CHD. Further, we performed a series of sensitivity analyses to verify stability of this association. However, due to limitation of case-control study, we couldn’t draw causal inferences. In addition, there was an inconsistency between the cases and the controls, indicating the existence of selection bias. So we adjusted for possible covariates in our statistical analysis. Similarly, recall bias could have potentially affected our results because the interview was often based on parental recall after the birth of the child, especially when it came to assessing depression. Although controls were randomly matched at the rate of 1:2 by age of child, we may have missed some depressive symptom40. Moreover, colds and fever were largely determined by maternal self-reports, which it may lead to false exposure classification, and it was possible to miss a fever because women may be reluctant to disclose such information47. It may underestimate the effects of colds with fever during pregnancy and offspring CHD. Unfortunately, laboratory evidence or clinical diagnosis was difficult to obtain in retrospective studies. Finally, these findings come from only one study in China and need to be replicated in other ethnic groups.

In conclusion, our study found that the colds, depressive symptoms during pregnancy were associated with increased risk of offspring CHD, and we found for the first time that there existed a statistically multiplying interaction effect of colds and depression on increasing risk of CHD. These findings highlight the importance of considering pregnancy depressive status not only as potential risk factors for CHD but also in how the mental health may interact with physical health, which was valuable for pre-conception counseling and the identification of high-risk pregnant women. However, further studies of large samples are needed to confirm these results, to examine possible biological mechanisms, and to fully understand the effects of colds and depression on CHD through prospective studies.

Methods

Data and participants

The data used here was from a 1:2 matching case-control study with the purpose of exploring possible exposures in relation to offspring CHD. The study was conducted in Shaanxi province, Northwest China, which was a less developed region compared with Midwestern China. The study was carried out at 6 tertiary hospitals from January 2014 to December 2016: First Affiliated Hospital of Xi’an Jiaotong University, Xijing Hospital of Fourth Military Medical University, People’s Hospital of Shaanxi Province, Northwest Women’s and Children’s Hospital of Xi’an Jiaotong University, Second Affiliated Hospital of Xi’an Jiaotong University and Tangdu Hospital of Fourth Military Medical University, among which the first four hospitals were national birth defects monitoring hospitals. Ethics permission was approved by the ethics committee of the Xi’an Jiaotong University Health Science Center (No. 20120008). It was conducted in accordance with the relevant guidelines and regulations and we had obtained all participants’ written informed consents.

Cases were included based on the following inclusion criteria: termination of pregnancy lasted from January 2014 to December 2016; from 28 weeks after pregnancy to 7 days after birth, the perinatal children (including single live births and stillbirths) diagnosed with CHD according to the ICD-10 classification criteria, and pregnancy less than 28 weeks but diagnosed with CHD by ultrasound; exclude other deformities other than CHD. All birth defects were diagnosed by the doctors from the monitoring hospitals according to the relevant birth defect diagnostic criteria and classified according to the ICD-10 standard. Each classification had a corresponding diagnostic standard48. Controls were defined as singleton newborn infants without birth defects. At the same time, controls were randomly matched at the rate of 1:2 by age of child. The exclusion criteria included preterm infants (birth at less than 37 weeks gestation) with isolated patent ductus arteriosus or patent foramen ovale in, or the diameters of pulmonary artery end or patent foramen less than 3 mm in full-term infants in 24 hours after birth32,49. In addition, when the investigator explained the problem, parents who were unable to accurately answer the questionnaire due to mental illness or serious illness were also excluded.

From January 2014 to December 2016, Information was gathered by a structured questionnaire and was reviewed by investigators on the spot. Family data during pregnancy were reported by a face-to-face interview with mothers, including socio-demographic characteristics, lifestyles, environmental factors, disease and health conditions, nutrients and drug usage, physical examination, life events, pregnancy history and family history. Information on neonatal gender, weight, gestational age, birth outcomes and final diagnosis were collected by medical records at local hospitals. To minimize recall bias of exposure by mothers to the greatest extent, the interviews were conducted less than one year after the expected date of delivery. The questionnaire in this survey was designed by Xi’an Jiaotong University Health Science Center and modified according to a pilot study. All interviewed were conducted by well-trained doctoral and master students in epidemiology. The questionnaires and clinical diagnosis for all the cases and controls were examined by the experienced cardiovascular epidemiologists and obstetricians. We focused on exploring the association between maternal colds, depressive symptoms and offspring CHD in the present study. The datasets analyzed in the current paper were available from the corresponding author on reasonable request.

Defining maternal colds and depressive symptoms

Data on maternal colds during different trimesters of pregnancy were gathered from mother’s self-report or through medical records if they sought medical care. Mothers were also asked to indicate whether fever had been present when they had a cold. According to the Chinese medical Guide50, the exposure to colds was divided into two types. One was the common cold, which was a mild viral infection of the upper respiratory tract (nose and throat). The other was influenza, generally more severe than the common cold. Influenza was an acute respiratory infection caused by the influenza virus, which included some additional symptoms such as fever, chills and muscle soreness. Fever referred to a pathological body temperature greater than 38 degrees.

For all cases and controls, the exposure time of depressive symptoms was defined as the entire pregnancy. Depressive symptoms were assessed with the developed Zung Self-rating Depression Scale (SDS)51. It was one of the most simple and common screening tools to discern subjective feelings of depression from nondepressive participants52. It had been well-validated and used in other Chinese studies53. The SDS was composed of 20 items covering somatic, affective and psychological features of depression. Every item had 1–4 scales. The standard score we ultimately need was obtained by multiplying the original total score by 1.25. The higher the score, the higher the likelihood of depression. More than 50 scores were considered to have moderate or strong symptoms of depression51. The Cronbach’s α of the SDS was 0.87. SDS was collected after subjects were interviewed. In this study, the Cronbach’s α of the SDS was 0.84 and Spearman-Brown Coefficient was 0.80. We examined depressive symptoms in both continuous and categorical form. For the categorical variable, there were two groups based on their standard total score: lower depression score group (<34) and higher depression score group (≥34). The cutoff value of 34 was determined by calculating the Youden index because the vast majority of people were in the subclinical stage in our study54,55.

Covariates

Covariates in this study were selected on the basis of some evidences in the literature about examining the risk factors of CHD or other birth defects10,32,36,39,49,56,57. These covariates included maternal age (<30 and ≥30 years), education (<Junior school, Junior school, High school and >High school), economic status (Poor, Moderate, Rich), baby gender (Male or Female) and other variables classified into No or Yes, such as maternal smoking, drinking, taking folic acid, taking medicine, pregnancy reaction, abnormal prenatal examination, negative life events, chronic disease, natural abortion history and family history of CHD. Demographic and Health Survey wealth index was used to measure economic status, which was obtained by the principal component analysis58. Maternal smoking referred to smoking any cigarette and drinking was defined as drinking any liquor (>50 ml) at least one time during pregnancy. Taking folic acid referred to the folic acid supplement more than 30 days during pregnancy. Taking medicine was defined as taking any drugs during pregnancy, including antibiotics, anticancer drugs, antidepressants, hormones, hypoglycemic agents, antihypertensive drugs, salicylates. As for pregnancy reaction, mothers were asked whether there had been a severe early pregnancy reaction during pregnancy. Abnormal prenatal examination referred that normal B ultrasound, heart color ultrasound, four-dimensional B ultrasound or down’s screening had abnormal results. Negative life events were measured by maternal reports of the death of a close relative or friend, divorce or separation, poor living environment or working environment, relationship difficulties. In addition, chronic disease was defined as having any physical diseases during pregnancy, including gestational hypertension, hypotension, diabetes, hyperthyroidism, hypothyroidism, anemia, viral hepatitis and so on. All these exposures were asked based on the entire pregnancy.

Statistical Analysis

The categorical variables were expressed as frequency and percentages. Depression score was expressed as mean ± SD. Chi-square test (χ2), Fisher exact test, and t-test were used for comparison between two groups. Multivariate logistic regressions were performed to investigate the association between colds, depression, and CHD controlling the confounders step by step. With CHD considered the dependent variable and colds or depressive symptoms considered the independent variable, respectively, we established three logistic regression models in this study: Model 1 wasn’t adjusted to any covariate. Model 2 was adjusted for all covariates (maternal age, education, economic status, maternal smoking, drinking, taking folic acid, taking medicine, pregnancy reaction, abnormal prenatal examination, negative life events, chronic disease, natural abortion history, family history of CHD and baby gender). Model 3 included all the outcome variables (colds and depressive symptoms) besides the covariates in Model 2. Similarly, stratified analysis of fever and exposure time was assessed using logistic regression.

The separate and combined effects of colds and depressive symptoms were also evaluated by logistic regression. The combined effect was further explored by two methods. On the one hand, we established the multiplication model by using logistic regression. We added the variable of colds*depressive (categorical) on the base of Model 259,60. When odds ratio (OR) > 1, there was a positive biological multiplying effect. On the other hand, we used nonlinear mixed effect model method to establish the additive model61. The synergy index (S), the attributable proportion (AP), and relative excess risk due to interaction (RERI) were used to assess biological interactions in the additive model. S was the ratio of the combined effect to the sum of the individual effects. AP showed the proportion of the interaction effect in the total effect. RERI referred to the difference between the combined action of cold and depressive symptoms and the sum of individual effects. When S > 1, RERI > 0, or AP > 0, there was a positive biological additive effect. All analyses were adjusted for the covariates described above. In addition, we performed a series of sensitivity analyses that used the same approach based on three covariates: economic status, taking folic acid and baby gender.

All data were double-entered using EpiData version 3.1 (CDC, Atlanta, GA, USA). All statistical analyses were performed using SAS software, version 9.4 (SAS Institute) and were evaluated using 2-tailed 95% confidence intervals (CI). P < 0.05 was regarded as a statistic significance.

References

  1. 1.

    Abid, D. et al. Congenital heart disease in 37,294 births in Tunisia: birth prevalence and mortality rate. Cardiology in the young 24, 866–871 (2014).

    Article  Google Scholar 

  2. 2.

    van der Linde, D. et al. Birth prevalence of congenital heart disease worldwide: a systematic review and meta-analysis. Journal of the American College of Cardiology 58, 2241–2247, https://doi.org/10.1016/j.jacc.2011.08.025 (2011).

    Article  PubMed  Google Scholar 

  3. 3.

    Bernier, P.-L., Stefanescu, A., Samoukovic, G. & Tchervenkov, C. I. In Seminars in Thoracic & Cardiovascular Surgery: Pediatric Cardiac Surgery Annual. 26–34 (Elsevier).

  4. 4.

    Smith, T., Rajakaruna, C., Caputo, M. & Emanueli, C. MicroRNAs in congenital heart disease. Annals of translational medicine 3 (2015).

  5. 5.

    Donofrio, M. et al. American Heart Association Adults With Congenital Heart Disease Joint Committee of the Council on Cardiovascular Disease in the Young and Council on Clinical Cardiology, Council on Cardiovascular Surgery and Anesthesia, and Council on Cardiovascular and Stroke Nursing. Diagnosis and treatment of fetal cardiac disease: a scientific statement from the American Heart Association. Circulation 129, 2183–2242 (2014).

    Article  Google Scholar 

  6. 6.

    Blue, G. M., Kirk, E. P., Sholler, G. F., Harvey, R. P. & Winlaw, D. S. Congenital heart disease: current knowledge about causes and inheritance. The Medical journal of Australia 197, 155–159 (2012).

    Article  Google Scholar 

  7. 7.

    Song, X. et al. Depression and its influencing factors among mothers of children with birth defects in china[J]. Maternal and child health journal 16, 1–6 (2012).

    Article  Google Scholar 

  8. 8.

    Bateman, D. N. et al. A case control study to examine the pharmacological factors underlying ventricular septal defects in the North of England. European journal of clinical pharmacology 60, 635–641, https://doi.org/10.1007/s00228-004-0829-x (2004).

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Malik, S. et al. Maternal smoking and congenital heart defects. Pediatrics 121, e810–e816 (2008).

    Article  Google Scholar 

  10. 10.

    Ou, Y. et al. Risk factors of different congenital heart defects in Guangdong, China. Pediatric research 79, 549–558, https://doi.org/10.1038/pr.2015.264 (2016).

    Article  PubMed  Google Scholar 

  11. 11.

    van der Lugt, N. M. et al. Fetal, neonatal and developmental outcomes of lithium-exposed pregnancies. Early human development 88, 375–378 (2012).

    Article  Google Scholar 

  12. 12.

    Liu, S. et al. Association between maternal chronic conditions and congenital heart defects: a population-based cohort study. Circulation, CIRCULATIONAHA. 113, 001054 (2013).

    Google Scholar 

  13. 13.

    Botto, L. D. et al. Congenital heart defects after maternal fever. American journal of obstetrics and gynecology 210, 359 e351–359 e311, https://doi.org/10.1016/j.ajog.2013.10.880 (2014).

    Article  Google Scholar 

  14. 14.

    Powrie, R., Michael Greene & William Camann, eds. De Swiet’s medical disorders in obstetric practice. (John Wiley & Sons, 2010).

  15. 15.

    Wheeler, S. M., Dotters-Katz, S., Heine, R. P., Grotegut, C. A. & Swamy, G. K. Maternal Effects of Respiratory Syncytial Virus Infection during Pregnancy. Emerging infectious diseases 21, 1951–1955, https://doi.org/10.3201/eid2111.150497 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Kourtis, A. P., Read, J. S. & Jamieson, D. J. Pregnancy and infection. New England Journal of Medicine 370, 2211–2218 (2014).

    Article  Google Scholar 

  17. 17.

    Denhardt, D. T. Effect of stress on human biology: Epigenetics, adaptation, inheritance, and social significance. Journal of cellular physiology 233, 1975–1984, https://doi.org/10.1002/jcp.25837 (2018).

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Holzhauer, C. G., Wemm, S. & Wulfert, E. Distress tolerance and physiological reactivity to stress predict women’s problematic alcohol use. Experimental and clinical psychopharmacology 25, 156–165, https://doi.org/10.1037/pha0000116 (2017).

    Article  PubMed  Google Scholar 

  19. 19.

    Gaynes, B. N. et al. Perinatal depression: Prevalence, screening accuracy, and screening outcomes: Summary. (2005).

  20. 20.

    Steel, Z. et al. The global prevalence of common mental disorders: a systematic review and meta-analysis 1980-2013. International journal of epidemiology 43, 476–493, https://doi.org/10.1093/ije/dyu038 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Liang, J. J. et al. Depressive symptoms and risk factors in Chinese patients with premature ventricular contractions without structural heart disease. Clinical cardiology 32, E11–17, https://doi.org/10.1002/clc.20460 (2009).

    Article  PubMed  Google Scholar 

  22. 22.

    Tegethoff, M., Greene, N., Olsen, J., Schaffner, E. & Meinlschmidt, G. Stress during pregnancy and offspring pediatric disease: A National Cohort Study. Environmental health perspectives 119, 1647–1652, https://doi.org/10.1289/ehp.1003253 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Entringer, S. et al. Prenatal psychosocial stress exposure is associated with insulin resistance in young adults. American journal of obstetrics and gynecology 199, 498 e491–497, https://doi.org/10.1016/j.ajog.2008.03.006 (2008).

    CAS  Article  Google Scholar 

  24. 24.

    Cookson, H., Granell, R., Joinson, C., Ben-Shlomo, Y. & Henderson, A. J. Mothers’ anxiety during pregnancy is associated with asthma in their children. The Journal of allergy and clinical immunology 123, 847–853 e811, https://doi.org/10.1016/j.jaci.2009.01.042 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Tegethoff, M., Greene, N., Olsen, J., Meyer, A. H. & Meinlschmidt, G. Maternal psychosocial adversity during pregnancy is associated with length of gestation and offspring size at birth: evidence from a population-based cohort study. Psychosomatic medicine 72, 419–426, https://doi.org/10.1097/PSY.0b013e3181d2f0b0 (2010).

    Article  PubMed  Google Scholar 

  26. 26.

    Carroll, A. J. et al. Interaction between smoking and depressive symptoms with subclinical heart disease in the Coronary Artery Risk Development in Young Adults (CARDIA) study. Health psychology: official journal of the Division of Health Psychology, American Psychological Association 36, 101–111, https://doi.org/10.1037/hea0000425 (2017).

    MathSciNet  Article  Google Scholar 

  27. 27.

    Janssen, I. et al. Relation of persistent depressive symptoms to coronary artery calcification in women aged 46 to 59 years. American Journal of Cardiology 117, 1884–1889 (2016).

    Article  Google Scholar 

  28. 28.

    Ban, L. et al. Maternal depression, antidepressant prescriptions, and congenital anomaly risk in offspring: a population-based cohort study. BJOG: an international journal of obstetrics and gynaecology 121, 1471–1481, https://doi.org/10.1111/1471-0528.12682 (2014).

    CAS  Article  Google Scholar 

  29. 29.

    Engel, G. L. The clinical application of the biopsychosocial model. The American journal of psychiatry 137, 535–544, https://doi.org/10.1176/ajp.137.5.535 (1980).

    CAS  Article  PubMed  Google Scholar 

  30. 30.

    Oster, M. E., Riehle-Colarusso, T., Alverson, C. J. & Correa, A. Associations between maternal fever and influenza and congenital heart defects. The Journal of pediatrics 158, 990–995, https://doi.org/10.1016/j.jpeds.2010.11.058 (2011).

    Article  PubMed  Google Scholar 

  31. 31.

    Csaky-Szunyogh, M., Vereczkey, A., Kosa, Z., Gerencser, B. & Czeizel, A. E. Risk and protective factors in the origin of conotruncal defects of heart–a population-based case-control study. American journal of medical genetics. Part A 161A, 2444–2452, https://doi.org/10.1002/ajmg.a.36118 (2013).

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Liu, F. et al. Prevalence of Congenital Heart Disease in Xinjiang Multi-Ethnic Region of China. PloS one 10, e0133961, https://doi.org/10.1371/journal.pone.0133961 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Edwards, M. J. Apoptosis, the heat shock response, hyperthermia, birth defects, disease and cancer. Where are the common links? Cell stress & chaperones 3, 213–220 (1998).

    CAS  Article  Google Scholar 

  34. 34.

    Rajala, U., Keinanen-Kiukaanniemi, S. & Kivela, S. L. Non-insulin-dependent diabetes mellitus and depression in a middle-aged Finnish population. Social psychiatry and psychiatric epidemiology 32, 363–367 (1997).

    CAS  Article  Google Scholar 

  35. 35.

    Parker, G., Cheah, Y. C. & Roy, K. Do the Chinese somatize depression? A cross cultural study. Social psychiatry and psychiatric epidemiology 36, 287–293 (2011).

    Article  Google Scholar 

  36. 36.

    Chou, H. H. et al. Association of maternal chronic disease with risk of congenital heart disease in offspring. CMAJ: Canadian Medical Association journal = journal de l’Association medicale canadienne 188, E438–E446, https://doi.org/10.1503/cmaj.160061 (2016).

    Article  PubMed  Google Scholar 

  37. 37.

    Garcia, E. R. & Yim, I. S. A systematic review of concepts related to women’s empowerment in the perinatal period and their associations with perinatal depressive symptoms and premature birth. BMC pregnancy and childbirth 17, 347, https://doi.org/10.1186/s12884-017-1495-1 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Wadhwa, P. D. et al. Placental corticotropin-releasing hormone (CRH), spontaneous preterm birth, and fetal growth restriction: a prospective investigation. American journal of obstetrics and gynecology 191, 1063–1069, https://doi.org/10.1016/j.ajog.2004.06.070 (2004).

    CAS  Article  PubMed  Google Scholar 

  39. 39.

    Feng, Y. et al. Maternal lifestyle factors in pregnancy and congenital heart defects in offspring: review of the current evidence. Italian journal of pediatrics 40, 85, https://doi.org/10.1186/s13052-014-0085-3 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Carroll, A. J. et al. Association of the Interaction Between Smoking and Depressive Symptom Clusters With Coronary Artery Calcification: The CARDIA Study. Journal of dual diagnosis 13, 43–51, https://doi.org/10.1080/15504263.2017.1287455 (2017).

    Article  PubMed Central  Google Scholar 

  41. 41.

    Lou, P. et al. Interaction of Depression and Nicotine Addiction on the Severity of Chronic Obstructive Pulmonary Disease: A Prospective Cohort Study. Iranian journal of public health 45, 146–157 (2016).

    PubMed  PubMed Central  Google Scholar 

  42. 42.

    Oba, H. et al. Interaction between memory impairment and depressive symptoms can exacerbate anosognosia: a comparison of Alzheimer’s disease with mild cognitive impairment. Aging & mental health, 1–7, https://doi.org/10.1080/13607863.2018.1442411 (2018).

  43. 43.

    Berk, M. et al. So depression is an inflammatory disease, but where does the inflammation come from? BMC medicine 11, 200 (2013).

    Article  Google Scholar 

  44. 44.

    Miller, A. H. & Raison, C. L. The role of inflammation in depression: from evolutionary imperative to modern treatment target. Nature Reviews Immunology 16, 22 (2016).

    CAS  Article  Google Scholar 

  45. 45.

    Case, S. M. & Stewart, J. C. Race/ethnicity moderates the relationship between depressive symptom severity and C-reactive protein: 2005–2010 NHANES data. Brain, behavior, and immunityBrain, behavior, and immunity 41, 101–108 (2014).

    Article  Google Scholar 

  46. 46.

    Adam, Y., Meinlschmidt, G. & Lieb, R. Associations between mental disorders and the common cold in adults: a population-based cross-sectional study. Journal of psychosomatic research 74, 69–73, https://doi.org/10.1016/j.jpsychores.2012.08.013 (2013).

    Article  PubMed  Google Scholar 

  47. 47.

    Toh, S. et al. Antidepressant use during pregnancy and the risk of preterm delivery and fetal growth restriction. Journal of clinical psychopharmacology 29, 555–560, https://doi.org/10.1097/JCP.0b013e3181bf344c (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Sipek, A. et al. Incidence of congenital heart defects in the Czech Republic–current data. Ceska gynekologie 75, 221–242 (2010).

    CAS  PubMed  Google Scholar 

  49. 49.

    Li, H. et al. An artificial neural network prediction model of congenital heart disease based on risk factors: A hospital-based case-control study. Medicine 96, e6090, https://doi.org/10.1097/MD.0000000000006090 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Yi-min, L. & Zi-feng, Y. Reading on guidelines for diagnosis and treatment of influenza(2011 Edition). Chinese Journal of Practical Internal Medicine 32, 105–108 (2012).

  51. 51.

    Zung, W. W. The differentiation of anxiety and depressive disorders: a biometric approach. Psychosomatics 12, 380–384 (1971).

    CAS  Article  Google Scholar 

  52. 52.

    Thurber, S., Snow, M. & Honts, C. R. The Zung self-rating depression scale: convergent validity and diagnostic discrimination. Assessment 9, 401–405 (2002).

    Article  Google Scholar 

  53. 53.

    Li, C. J., Zhang, G., Zhang, Y., Cong, H. & Zhang, B. L. Clinical efficacy of Xinkeshu Pian on coronary heart disease and mood disorder complications after PCI. International journal of clinical and experimental medicine 7, 1887–1890 (2014).

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    Auer, R. et al. Association between lifetime marijuana use and cognitive function in middle age: the Coronary Artery Risk Development in Young Adults (CARDIA) study. JAMA internal medicine 176, 352–361 (2016).

    Article  Google Scholar 

  55. 55.

    Yaffe, K. et al. Early adult to mid-life cardiovascular risk factors and cognitive function. Circulation, CIRCULATIONAHA. 113, 004798 (2014).

    Google Scholar 

  56. 56.

    Liang, Q. et al. The influence of maternal exposure history to virus and medicine during pregnancy on congenital heart defects of fetus. Environmental science and pollution research international 24, 5628–5632, https://doi.org/10.1007/s11356-016-8198-4 (2017).

    CAS  Article  PubMed  Google Scholar 

  57. 57.

    Lee, L. J. et al. Role of maternal occupational physical activity and psychosocial stressors on adverse birth outcomes. Occupational and environmental medicine 74, 192–199, https://doi.org/10.1136/oemed-2016-103715 (2017).

    Article  PubMed  Google Scholar 

  58. 58.

    Filmer, D. & Pritchett, L. H. Estimating wealth effects without expenditure data—or tears: an application to educational enrollments in states of India. Demography 38, 115–132 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Menard, S. Applied logistic regression analysis. Vol. 106 (SAGE publications, 2018).

  60. 60.

    Vogiatzoglou, A. et al. Cognitive function in an elderly population: interaction between vitamin B12 status, depression, and apolipoprotein E epsilon4: the Hordaland Homocysteine Study. Psychosomatic medicine 75, 20–29, https://doi.org/10.1097/PSY.0b013e3182761b6c (2013).

    CAS  Article  PubMed  Google Scholar 

  61. 61.

    Demidenko, E. Nonlinear mixed effects model. Mixed models, 433–486 (2013).

Download references

Acknowledgements

This work was supported by two grants from the National Natural Science Foundation of China (No. 81230016) and Birth Defect Control and Prevention Project of Shaanxi Commission of Health and Family Planning (No. sxwsjswzfcght 2016–013).The authors thank all the monitoring hospitals for providing CHD cases and controls for the study, and the authors particularly appreciated the families in the study and the professionals who supported the study.

Author information

Affiliations

Authors

Contributions

S.D. and H.Y. contributed to the design of the study. L.G. and D.Z. performed the data cleansing and wrote the main manuscript text. L.G., R.Z. and S.L. prepared Tables 1–4. R.L. and H.W. prepared Tables 5–6. All authors reviewed the manuscript and had approved the submitted version.

Corresponding authors

Correspondence to Shaonong Dang or Hong Yan.

Ethics declarations

Competing Interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Guo, L., Zhao, D., Zhang, R. et al. A Matched Case-Control Study on the Association Between Colds, Depressive Symptoms during Pregnancy and Congenital Heart Disease in Northwestern China. Sci Rep 9, 589 (2019). https://doi.org/10.1038/s41598-018-36968-y

Download citation

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing