To investigate the association between maternal body mass index (BMI) and major, structural congenital anomalies.
Cohort study using prospectively collected data.
Data on all singleton pregnancies booked at five maternity units in the north of England between 01 January 2003 and 31 December 2005 and data on congenital anomalies notified to the Northern Congenital Abnormality Survey were linked using key variables. Maternal pre-gestational diabetic status was derived from the Northern Diabetes in Pregnancy Survey. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated by maximum-likelihood logistic regression models, with missing values modelled as explicit categories.
There was a total of 41 013 singleton pregnancies during the study period, of which 682 were affected by a structural congenital anomaly, a total prevalence of 166 (95% CI: 154, 179) per 10 000 registered births. Overall, the risk of a congenital anomaly was significantly increased among the maternal underweight (BMI⩽18.5 kg m–2; aOR=1.60, 95% CI: 1.09, 2.36; P=0.02) and maternal obese groups (BMI⩾30 kg m–2; aOR=1.30, 95% CI: 1.03, 1.63; P=0.03), but not for maternal overweight (BMI=25–29.9 kg m–2; aOR=0.85, 95% CI: 0.68, 1.06; P=0.15), compared with mothers of recommended BMI. Maternal obesity was associated with significantly increased risk of ventricular septal defect (aOR=1.56, 95% CI: 1.01, 2.40; P=0.04), cleft lip (aOR=3.71, 95% CI: 1.05, 13.10; P=0.04) and eye anomalies (aOR=11.36, 95% CI: 2.25, 57.28; P=0.003). Maternal underweight was associated with significantly increased risks of atrial septal defect (aOR=2.86, 95% CI: 1.18, 6.96; P=0.02), genital anomalies (aOR=6.30, 95% CI: 1.58, 25.08; P=0.009) and hypospadias (aOR=8.77, 95% CI: 1.42, 54.29; P=0.02).
We found an overall increased risk of congenital anomalies in women who are obese and women who are underweight compared with women of recommended weight. Women should be made aware of these risks and supported to optimize their weight before pregnancy.
Obesity is a major public health and economic concern. Globally, 1.6 billion adults age 15 or above were overweight (body mass index (BMI) ⩾25 kg m–2) and over 400 million adults were obese (BMI ⩾30 kg m–2) in 2005.1 In the United Kingdom, almost a quarter of adults (24%), both men and women, were obese in 2007.2
The prevalence of overweight and obesity among women of childbearing age (16–44 years) is also increasing. Within the United Kingdom, there has been an increase in obesity among women of childbearing age from 12.0% in 1993 to 18.5% in 2006.3
Obesity in pregnancy is known to be associated with a number of adverse clinical outcomes for both the mother and baby. Health implications for the mother include increased risk of insulin resistance and gestational diabetes, hypertensive disorders, and increased caesarean section rates.4, 5, 6 For the infant, the health implications of maternal obesity include increased birthweight, stillbirth and neonatal death, and shoulder dystocia during delivery.7, 8, 9 (see Box 1 for definitions of obstetric terminology).
Congenital anomalies are a diverse range of conditions present at birth that affect approximately 2–4% of all deliveries. They are a leading cause of stillbirth and infant mortality as well as being important contributors to preterm birth and morbidity in the first year of life and beyond. Studies, mainly from the United States of America also suggest an association between maternal obesity and congenital anomalies, in particular neural tube defects,10, 11, 12 and cardiac anomalies.13, 14, 15, 16 Although maternal obesity has been associated with other congenital anomaly subtypes, the evidence for these links is less consistent.17 Maternal underweight has also been linked with the occurrence of specific congenital anomalies, for example, gastroschisis.18 A recent systematic review and meta-analysis also suggested that maternal overweight may also be implicated.17
The aim of this cohort study is to investigate whether maternal BMI at the first antenatal visit is associated with the occurrence of major, structural (non-chromosomal) congenital anomalies in the northeast region of the United Kingdom.
Materials and methods
Data on all singleton pregnancies occurring between 01 January 2003 and 31 December 2005, booked and delivered in five maternity units in the northeast of England, were included in the study. Multiple pregnancies were excluded as they are known to have a higher congenital anomaly risk than singletons.19 The five hospitals were chosen as they have electronically stored maternity care information for recent years.20 The five participating maternity units included both tertiary referral centres in major urban areas and smaller district general hospitals. Overall, they account for around half of all deliveries in the northeast region of England. The women delivering in these units are likely to be typical of the regional population as a whole.
Congenital anomaly data
Congenital anomaly data were extracted from the Northern Congenital Abnormality Survey (NorCAS), a population-based register of congenital anomalies that has been operating since 1985. The NorCAS is a voluntary collaborative survey, which collects data prospectively on congenital anomalies arising within the population of approximately three million living in the former Northern Health region, which includes the catchment populations of the five participating hospitals and an average of 30 000 total annual births during the study period.21 The geographical area covered by NorCAS is shown in Figure 1.
Case definition, classification and ascertainment
The NorCAS collects data on congenital anomalies whether occurring as late miscarriages (gestational age ⩾20 weeks), terminations of pregnancy for fetal anomaly after prenatal diagnosis, or registered births (live and stillbirths), and whether diagnosed antenatally or not. Cases born to mothers resident at birth within the boundaries of the former Northern health region, even if they were delivered outside the region, are captured by the NorCAS. Cases are notified to the register from multiple sources including antenatal ultrasound, fetal medicine records, cytogenetic laboratories, the regional cardiology centre, pathology departments and pediatric surgery to ensure a high case ascertainment. All cases of congenital heart disease are confirmed by autopsy, surgery, echocardiography or cardiac catheterization. Once notified, cases are verified for duplication and then entered onto the register. Further details of data collection have been published previously.22 The NorCAS has a high case ascertainment as evidenced by the regular cross-validations carried out with the UK Office for National Statistics and with regional cytogenetic and pediatric cardiology databases.23, 24
The age limit for registration onto NorCAS during the study period was 12 years. NorCAS records up to six congenital anomalies per case and adopts the exclusion criteria for minor anomalies used by the European Surveillance of Congenital Anomalies (EUROCAT).25 NorCAS is a member of the British Isles Network of Congenital Anomaly Registers26 and EUROCAT. All anomalies are coded using the WHO International Classification of Diseases version 10 (ICD 10).
Congenital anomalies were categorized by congenital anomaly group (the organ system affected), subtype (the individual condition) and syndrome (where applicable) according to the EUROCAT guidelines.25
Cases included all singleton deliveries (including terminations of pregnancy for fetal anomaly at any gestation, stillbirths of ⩾24 weeks gestation and live births) with at least one EUROCAT-classified congenital anomaly notified to the NorCAS with a date of delivery between 01 January 2003 and 31 December 2005 and delivered in one of the five hospitals. Cases associated with a known teratogen, chromosomal anomaly, monogenic syndrome, microdeletion, association or sequence were excluded.
Information on diabetes status of the mother
Information on maternal pre-gestational diabetes status was derived from the Northern Survey of Diabetes in Pregnancy (NorDIP),27 a collaborative survey of all pregnancies in women with diabetes diagnosed at least 6 months before the index pregnancy. NorDIP coordinators in each hospital notify pregnancies in women with pre-gestational diabetes, and data collection is undertaken by clinicians within the unit.
The NorDIP and NorCAS are maintained on a central database held at the Regional Maternity Survey Office in Newcastle upon Tyne.21
The hospital data were matched to the data held by the NorCAS and the NorDIP by staff in the information departments in each of the five hospitals. Data linkage was achieved by fuzzy matching using five key variables: mother's surname, mother's postcode at booking, infant date of birth, infant sex and birthweight. ‘Fuzzy’ matching involved first linking the data sets using all five variables, then by matching four variables, three, two and finally by using one variable.
The index of multiple deprivation, a UK census-derived area-based measure of socioeconomic deprivation, was determined from the mother's residential postcode and was added to the linked data set by staff at the NorthEast Public Health Observatory. The index of multiple deprivation is based on seven census domains: income deprivation, employment deprivation, health deprivation and disability, education, skills and training deprivation, barriers to housing and services, living environment deprivation, and crime.28
The NorCAS has exemption from the National Information Governance Board for Health and Social Care from a requirement for consent for inclusion on the register and has ethics approval (04/MRE04/25), as part of the British Isles Network of Congenital Anomaly Registers network, to undertake studies involving the use of the data. This study was given a favourable ethical opinion from the Northumberland Research Ethics Committee (07/Q0902/2) and Research and Development approval from each of the participating hospitals.
Variables were treated as categorical to account for potentially non-linear relationships. BMI was categorized according to the WHO classification: underweight BMI ⩽18.5 kg m–2; recommended weight BMI=18.5–24.9 kg m–2; overweight BMI=25–29.9 kg m–2; and obese BMI ⩾30 kg m–2. Maternal age at delivery was separated into three categories: <20 years, 20–29 years and ⩾30 years. Cigarette smoking status was dichotomized into current smokers and non/ex-smokers. The index of multiple deprivation was ranked and divided into tertiles for this study.
Unadjusted odds ratios (ORs), adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated by maximum-likelihood logistic regression models, with missing values modelled as explicit categories.29 Adjusted models included maternal age at delivery, ethnicity, maternal BMI at the first antenatal visit, maternal history of pre-gestational diabetes, cigarette smoking status at the first antenatal visit and index of multiple deprivation. ORs for the risk of a structural congenital anomaly were calculated for all maternal and fetal factors and for maternal BMI. ORs were calculated for all congenital anomaly groups and subtypes with five or more recorded cases. This cut-off was chosen to comply with current disclosure guidance.30 Interactions between maternal BMI and other maternal variables in predicting a structural congenital anomaly were examined by the inclusion of cross-product terms. Prevalence estimates for the total population, and stratified by BMI, were calculated for congenital anomaly groups and subtypes with five or more recorded cases.
As a smaller proportion of the cases had missing BMI than the non-cases, stratified prevalence estimates were weighted to correct for the resultant under-representation of the denominator. Weighting was determined for each congenital anomaly group and subtype as the ratio of all case pregnancies (or non-case pregnancies) to case pregnancies (or non-case pregnancies) with non-missing BMI multiplied by the ratio of all pregnancies (case and non-case) to pregnancies with a non-missing BMI.
Statistical analyses were performed using Stata 10 1 (StataCorp, College Station, TX, USA) and P<0.05 was considered statistically significant.
There was a total of 40 934 singleton pregnancies identified during the 3-year study period, of which 682 were affected by a structural congenital anomaly, a total prevalence of 166 (95% CI: 154, 179) per 10 000 registered births (Table 1). Cardiovascular anomalies were the most common congenital anomaly group identified, being present in half of the case pregnancies (341), followed by urinary anomalies (113; 16.6%), nervous system anomalies (71; 10.4%), digestive system anomalies (63; 9.2%) and orofacial clefts (59; 8.7%) (Table 1). In all, 585 (85.8%) of the case pregnancies ended in live birth, 84 (12.3%) in termination of pregnancy for fetal anomaly and 12 (1.8%) in fetal death (>20 weeks gestation).
Table 2 shows the distribution of maternal and fetal variables among cases (that is, pregnancies affected by a congenital anomaly) and non-cases. Mothers with pre-gestational diabetes and mothers who smoked cigarettes during pregnancy were both at significantly greater odds of a pregnancy affected by a congenital anomaly (diabetes: P<0.001, smoking: P=0.02).
Of the fetal factors, indeterminate sex (P<0.001), very low gestational age at delivery (P<0.001) and low birth weight (P<0.001) were significantly more common among pregnancies affected by a congenital anomaly, although fetal sex was not significant when cases of indeterminate sex were excluded (P=0.38).
Maternal BMI was missing for one-quarter of the participants (23.5% of cases; 25.0% of non-cases), resulting in 30 703 singleton pregnancies with known BMI, which included 522 cases. Those with missing BMI were older (P<0.001), less likely to smoke (P<0.001), less likely to live in a deprived area (P<0.001) and delivered smaller infants or fetuses (P<0.001) of a shorter gestational age (P<0.001). Table 3 shows the estimated prevalence of congenital anomaly by BMI category, correcting for unbalanced missing values.
Table 4 presents the ORs of a pregnancy being affected by a structural congenital anomaly by maternal BMI. There were no differences between the unadjusted ORs and the adjusted ORs for any of the comparisons examined, hence only adjusted ORs are presented (Table 4). The overall risk of a congenital anomaly were significantly increased among the mothers who were underweight (aOR=1.60, 95% CI: 1.09–2.36; P=0.02) and obese (aOR=1.30, 95% CI: 1.03–1.63; P=0.03), but not for those who were overweight (aOR=0.85, 95% CI: 0.68–1.06; P=0.16), compared with mothers of recommended BMI (Table 4). Considering the congenital anomaly groups and subtypes, maternal obesity was associated with a significantly increased risk of ventricular septal defect (aOR=1.56, 95% CI: 1.01, 2.40; P=0.04), cleft lip (aOR=3.71, 95% CI: 1.05, 13.10; P=0.04) and eye anomalies (aOR=11.36, 95% CI: 2.25, 57.28; P=0.003). Maternal underweight was associated with a significantly increased risk of both atrial septal defect (aOR=2.86, 95% CI: 1.18, 6.96; P=0.02) and genital anomalies (aOR=6.30, 95% CI: 1.58, 25.08; P=0.009), in particular hypospadias (aOR=8.77, 95% CI: 1.42, 54.29; P=0.02). There was no significant increased risk for maternal overweight (Table 4). No significant evidence of interaction was observed between maternal BMI and any of the other variables in the adjusted model.
This cohort study describes the relationship between maternal BMI at the first antenatal visit and the risk of a pregnancy being affected by a structural congenital anomaly over a 3-year period using data from the northeast of England. Only two previous studies from the United Kingdom have considered maternal weight and congenital anomaly risk, and both predate the current rise in obesity levels. Richards31 found an increased risk of anencephaly in women who were heavier than controls, and Wald et al.32 found that maternal serum alpha-fetoprotein, a marker for neural tube defects, was higher in lighter women. This is the first UK study to examine the relationship between maternal BMI and risk of congenital anomaly. After adjustment for available risk factors, we found that the overall risk of a structural congenital anomaly was greater for women who were obese or underweight at the start of pregnancy compared with women of recommended weight, but not for women who were overweight. More specifically, maternal obesity was associated with an increased risk of ventricular septal defects, cleft lip and eye anomalies while maternal underweight was associated with atrial septal defect, genital anomalies and hypospadias. No other significant associations were found between maternal BMI and any other congenital anomaly group or subtype. We analysed 23 congenital anomaly groups/subtypes and four categories of BMI. However, with such a large number of comparisons, we expect some significant association to occur by chance. In addition, as the number of cases in certain groups was small, the study had limited statistical power in these groups to detect a difference, for example, for limb reduction defects.
There are now a number of studies, mainly from the United States, suggesting an association between maternal obesity and congenital anomaly risk, particularly for neural tube defects and cardiovascular anomalies.10, 11, 12, 13, 14, 15, 16 In a recent meta-analysis, Stothard et al.17 showed increased risks in obese women for cleft palate, hydrocephaly and limb anomalies in addition to neural tube defects and cardiovascular anomalies. In this study, we found an increased risk of ventricular septal defects among women who were obese. Cedergren and Kallen15 also found an association between maternal obesity and ventricular septal defects.
Maternal underweight was associated with significantly increased odds of both atrial septal defects (ASDs) and genital anomalies. Although few previous studies have shown associations between maternal underweight and congenital anomalies, there are exceptions. Watkins et al.16 found an increased risk of ASDs in women who were underweight, while the study by Waller et al.12 found a raised OR for septal defects (ASDs were not specifically reported) although this did not reach statistical significance. Maternal underweight has also been associated with the occurrence of gastroschisis.18 To our knowledge, the risk of genital anomalies has not previously been examined with respect to maternal underweight.
Our study has several strengths. We have used data on congenital anomalies from a long-standing, high-quality register rather than that recorded in the hospital data. The NorCAS contributes to established United Kingdom and European networks that use similar inclusion criteria, and have a consistent approach to data collection, coding and recording. We have included congenital anomalies arising within live births, stillbirths, termination of pregnancy for fetal anomaly after prenatal diagnosis and late miscarriages, thus reducing ascertainment bias. Twelve percent of cases reported here resulted in a termination of pregnancy, highlighting the importance of including these cases in similar studies. As the NorCAS includes cases diagnosed beyond the first year of life, those congenital anomalies that are only detectable well after birth have also been captured. We have analysed a range of selected, major congenital anomalies that are well defined and ascertained. We were able to subdivide the congenital anomalies into groups and subtypes, thus anomalies with potentially different aetiologies were not being combined. When the same exclusion criteria were applied to the NorCAS data for the whole region, the total prevalence figure found in this study is similar to that reported by NorCAS. Further, with accurate data on maternal pre-gestational diabetes status from the NorDIP, we were able to take account of this confounder in our analyses.
However, there were also a number of study limitations. The BMI data were routinely collected by the five hospitals and, at the time of the data collection, is likely to have been derived from self reported height and, in some cases, weight. Fattah et al.33 showed that approximately a fifth of women booking for antenatal care in their sample underestimated their BMI, mainly because of underreporting of weight. BMI was missing for almost a quarter of our sample. It is not clear whether these data are missing because they were not collected at the time of the first antenatal visit, or whether they were recorded in the notes, but were not added to the hospital information systems. The loss of such a proportion of the sample reduced study power. This explains the wide CIs on many of the results, particularly for individual subtypes and indicates why this study was unable to confirm some of the findings of a recent systematic review,17 in spite of achieving similar point estimates for both neural tube defects and cardiovascular anomalies. Thus, as for many congenital anomaly studies, a lack of significant association should not be taken as evidence of no relationship.
We have presented risks associated with maternal BMI category by individual subtype where possible. While congenital anomalies are frequently associated within the same infant, we have not attempted to account for such clustering because of the relatively small number of cases. This approach is consistent with that of other studies in the field.16
As a smaller proportion of the cases had missing BMI than the non-cases, stratified prevalence estimates were weighted to correct for the resultant under-representation of the denominator. Although this process will have corrected for the numerator–denominator bias, there may still be bias if the BMI profile of the women with missing BMI was different to the women with known BMI.
As our study was limited to routinely collected data, information on some key data items, which are known to increase the risk of congenital anomalies, was not available. For example, we were not able to include information on maternal diet. The nutritional status of a woman during pregnancy is an established risk factor for many reproductive outcomes. In particular, the link between folic acid intake during the periconceptional period and the occurrence of neural tube defects is well established.34 Some of the hospitals did collect information on maternal folic acid status but, disappointingly, the data were too limited to be included in our analyses. The collection of such data on all pregnancies needs urgently to be improved if we are to gain important information on whether such factors influence the association of maternal BMI and congenital anomaly risk and to understand whether, and how, public health messages are acted on.
Finally, this study estimated standard errors using maximum-likelihood methods, which can provide biased results when the case and comparison groups are highly unbalanced.35 While exact methods offer a potential solution, these could not be used because of prohibitive computational requirements.
Several mechanisms linking maternal obesity to the occurrence of congenital anomalies have been suggested. Maternal pre-gestational diabetes is a known risk factor for congenital anomalies, especially nervous system and cardiac anomalies.36 Thus, undiagnosed diabetes and dysglycaemia in obese pregnant women is one potential explanation for the increased risk of congenital anomalies. Wentzel37 has suggested that diabetes-induced congenital anomalies result from disturbance in micronutrient metabolism and oxidative stress. However, including data on known maternal diabetes status in our study only fractionally reduced the ORs, most likely because of the very small number of cases.
Maternal obesity has also been associated with reduced folate levels,38 and the protective effect of folic acid in reducing the risk of a neural tube defect may not be observed in obese women.39 Similar nutritional deficiencies may explain the association between maternal underweight and congenital anomalies. Unfortunately, we did not have sufficient data on folate consumption, or any other vitamin or mineral supplementation, to test this hypothesis further.
It has previously been suggested that difficulties in the antenatal detection of congenital anomalies by ultrasound in obese women may explain the higher prevalence of congenital anomalies.40, 41 However, as our study includes terminations of pregnancy for fetal anomaly, this is an unlikely explanation for our findings. Furthermore, we found no significant difference in the proportion of terminations between mothers who were underweight, normal weight, overweight or obese (P=0.71).
Our study found an overall increased risk of congenital anomalies in women who are obese and women who are underweight, compared with women of recommended BMI. These findings suggest that interventions are needed to support women to achieve a healthy weight before becoming pregnant, not only for women who are obese but also for women who are underweight. We would suggest that future studies should consider the complete BMI range, as the effect of overweight remains unclear and all congenital anomaly subtypes as information on risk is still lacking for many. Further research on mechanisms is also essential if potential interventions, especially for those women who are unable to optimize their weight before pregnancy, are to be developed.
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We thank staff within the information departments in each of the five participating hospitals, Gillian Bryant at NEPHO and the Link Clinicians in the Northern region for their continued support of the NorCAS and NorDIP. Funding for this study was provided by BDF-Newlife. NorCAS is funded by the Healthcare Quality Improvement Partnership and JR by a Personal Award Scheme Career Scientist Award from the UK National Institute of Health Research.
The authors declare no conflict of interest.
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