Preconception environmental factors and placental morphometry in relation to pregnancy
outcome
Salavati, Nastaran
DOI:
10.33612/diss.109922073
IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from
it. Please check the document version below.
Document Version
Publisher's PDF, also known as Version of record
Publication date:
2020
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
Salavati, N. (2020). Preconception environmental factors and placental morphometry in relation to
pregnancy outcome. Rijksuniversiteit Groningen. https://doi.org/10.33612/diss.109922073
Copyright
Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).
Take-down policy
If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum.
and placental morphometry in
relation to pregnancy outcome
ISBN/EAN: 978-94-6375-696-9 Copyright © 2019 Nastaran Salavati
All rights reserved. No part of this thesis may be reproduced, stored or transmitted in any way or by any means without the prior permission of the author, or when applicable, of the publishers of the scientific papers.
Cover: Sahar Salavati (drawing) Sofie Bernhagen (layout design) Layout and design: Eduard Boxem | www.persoonlijkproefschrift.nl Printing: Ridderprint BV | www.ridderprint.nl
Financial support for this thesis was kindly provided by:
University Medical Center Groningen, University of Groningen, Research Foundation Obstetrics and Gynaecology, University Medical Center of Groningen, Danone Nutricia Research, Chipsoft, Moeders voor Moeders,
and placental morphometry in
relation to pregnancy outcome
Proefschrift
ter verkrijging van de graad van doctor aan de Rijksuniversiteit Groningen
op gezag van de
rector magnificus prof. dr. C. Wijmenga en volgens besluit van het College voor Promoties.
De openbare verdediging zal plaatsvinden op maandag 27 januari 2020 om 16.15 uur
door
Nastaran Salavati
geboren op 28 december 1991Prof. dr. E.M. van der Beek Co-promotores Dr. M.K. Bakker Dr. S.J. Gordijn Beoordelingscommissie Prof. dr. A. Hoek
Prof. dr. E.J.M. Feskens Prof. dr. T.J. Roseboom
Paranimfen Sahar Salavati Arman Salavati
1. General introduction 9
Part I: Preconception environmental factors and pregnancy outcome 21
2. The association of air pollution with congenital anomalies: an exploratory study in the Northern Netherlands
Salavati N., Strak M., Burgerhof J.G.M., De Walle H.E.K., Erwich J.J.H.M. International Journal of Hygiene and Environmental Health, 2018; 1061-1067
23
3. Cohort Profile: The Dutch Perined-Lifelines birth cohort
Salavati N., Bakker M.K., Van der Beek E.M., Erwich J.J.H.M. (PLOS ONE: in press)
47
4. Association of preconception macronutrient intake with birth weight across strata of maternal BMI in a linked population-based birth cohort
Salavati N., Bakker M.K., Lewis F., Vinke P.C., Mubarik F., Erwich J.J.H.M., Van der Beek E.M. (Submitted)
75
5. The association of food groups and birth weight: analyses in Perined-Lifelines linked birth cohort
Salavati N., Vinke P.C., Lewis F., Bakker M.K., Erwich J.J.H.M., Van der Beek E.M. (Manuscript in preparation)
105
Part II: Placental development and pregnancy outcome 123
6. The relationship between human placental morphometry and ultrasonic measurements of utero-placental blood flow and fetal growth
Salavati N., Sovio U., Plitman Mayo R., Charnock-Jones D.S., Smith G.C.S. Placenta, 2016;28:41-48
125
7. Birth weight to placenta weight ratio and its relationship to ultrasonic measurements, maternal and neonatal morbidity: A prospective cohort study of nulliparous women
Salavati N., Gordijn S.J., Sovio U., Zill-E-Huma R., Gebril A., Charnock-Jones D.S., Scherjon S.A., Smith G.C.S. Placenta, 2018; 63:45-52
149
8. The possible role of placental morphometry in the detection of fetal growth restriction
Salavati N., Smies M., Ganzevoort W., Charles A.K., Erwich J.J.H.M., Plösch T., Gordijn S.J. Frontiers in Physiology, 2019; 9:1884
171
9. General discussion 197
Appendices 225
Abbreviations 226
English Summary 228
Dutch Summary (Nederlandse samenvatting) 231
List of contributing authors 235
Acknowledgements (dankwoord) 240
About the author 245
General
Introduction
GENERAL INTRODUCTION
Fetal size and growth trajectories are important indicators of fetal health and are determined by maternal, placental and fetal factors. Normal fetal growth requires an adequate supply and effective transport of nutrients and oxygen across the placenta, which is dependent on normal placental structure, placental function and adequate maternal supply.
Barker showed with the concept of fetal origins of adult disease, that size at birth is related to the risk of developing diseases in later life 1. In particular, it has been shown that low birth weight is associated with increased risk of coronary heart disease, diabetes, hypertension and stroke in adulthood 1. The fetal origins of adult disease concept has been supported by several large birth registries and human cohorts where women and their offspring faced severe malnutrition in the form of famine 2–4. However, low birth weight is not necessarily a prerequisite for adverse outcome 5,6. Also those with “normal” birth weight may be at increased risk of adverse pregnancy outcome when they have encountered adverse exposures (e.g. placental insufficiency, inadequate maternal diet and suboptimal maternal health).
For long, the diagnosis of fetal growth restriction (FGR) has mainly been based on birth weight below a reference cut-off, most commonly the 10th percentile (p10), adjusted for gestational age 7. Birth weight below the p10 indicates that the birth weight is within the lowest 10% birth weight compared to the reference population. However, this does not necessarily mean that the fetus suffered from FGR, but the fetus is small for gestational age (SGA) and may be healthy. Fetuses who are too small according to the reference chart, may be physiologically small and are grown appropriate according to their individual growth potential (based upon their genetic and epigenetic inheritance at conception, or even transgenerational effects of adverse exposures when their grandmother was pregnant of their mother 8), and therefore may not be at a high risk from diseases related to FGR. On the other hand, many fetuses with FGR remain unnoticed since they are not necessarily too small in the population based reference chart, but they are too small according to their individual growth potential.
Causes of fetal growth restriction can be divided into maternal (e.g. anemia, malnourishment, (transgenerational) exposure to severe environmental factors), fetal (e.g. infections, maldevelopment) and placental (e.g. poor implantation, morphological abnormalities) factors 9,10.
1
PART 1:
Preconception environmental factors and pregnancy outcome
Although the fetal genetic makeup is complete at conception and regulates growth and organ development, a range of environmental exposures can alter the course of growth and development of the fetus 11. It is becoming more and more apparent that all non-communicable illnesses are a result of the interaction between genes and environment. In fact, recent evidence confirms that modifiable environmental factors may explain 70-80 percent of illness 12,13. In other words, modifiable determinants within our environment are in interaction with our genome, and depending on the resilience, it may maintain health or increase disease susceptibility 14. In the environmental domain it is important to receive determinants that are required for healthy survival and avoid those that are harmful 15. During pregnancy in particular, it appears that the processes that direct growth and development of the fetus are profoundly sensitive to nutritional requirements and vulnerable to environmental insults. Insufficient intake of required nutrients or adverse exposures during critical phases of development may have serious and life-long consequences 16. Depending on the stage of organ development impact may be different. To illustrate, periconception intake of folic acid is advised to prevent neural tube defects 17. The neural tube is developed during the third week of gestation, therefore it is advised to already start taking folic acid supplements when women are trying to conceive until 12 weeks of gestation 18.Air pollution is one of the environmental factors that has been acknowledged for its adverse influence on fetal development. Several studies have shown an association between exposure to air pollutants and an increased risk of fetal growth restriction 19, low birth weight 19, preterm birth and neonatal mortality 20. There is substantial evidence that oxidative stress and inflammation are involved in the mechanisms underlying the effects of air pollutants which can contribute to epigenetic changes, including alteration of DNA methylation 21,22. Such epigenetic modifications could impair normal embryo development and lead to congenital anomalies. Despite the evidence, inconsistencies and uncertainties remain about the effects of specific air pollutants. Accordingly, exposures within the air environment are something to be carefully considered, especially by those who aim to become pregnant. Nutritional requirements during pregnancy, and also prior to pregnancy, are also shown to be associated with fetal growth and development. This association has been established after some important historical events.
In the 1800s, maternal food intake was deliberately limited with the aim to restrict fetal growth and ease deliveries in women who had contracted pelvises. The poor survival rate of those undergrown fetuses led to subsequent studies which identified maternal factors associated with poor fetal growth 23. In 1944-45, after 4 years of German occupation of the Netherlands, an extreme food shortage occurred in the West Netherlands which persisted for over 5 months during the winter. The, so called, Dutch famine winter provided the unique opportunity to study the effects of a short but severe period of undernutrition during different stages of gestation on the offspring.
Among the group of women exposed to famine during the first trimester of pregnancy, preterm births and stillbirths increased, and also the risk of cardiovascular diseases and obesity later in life was increased among the offspring 24. However, the offspring of these women had normal birth weight, albeit placental weight was increased in these pregnancies 25. Contrary, those women exposed to famine during mid and late gestation delivered babies who had lower birth weight than unexposed babies and had an increased risk of impaired glucose tolerance as adults than the offspring of normally fed women 26. Although reduced birth weight is the most easily measured proxy for intrauterine deprivation, it is not the cause of later adult diseases, as previously mentioned. The fact that placental weight increased in pregnancies of women exposed to famine at the first trimester of pregnancy, while birth weight was within normal ranges, can be interpreted as compensatory mechanism by the placenta for the reduction in maternal nutrient intake. There was possibly no opportunity (e.g. past a window that allows placental size adaptation) to adapt to the relative short period of under nutrition when famine occurred in mid or late gestation, resulting in low birth weights offspring.
There are specific maternal dietary and lifestyle factors that appear to influence fetal growth. Maternal macronutrient intake during pregnancy and its association with birth weight has been subject of several studies. High consumption of fruits, vegetables, low-fat dairy products and lean meats, throughout gestation is associated with a decreased risk of giving birth to a small-for-gestational age (SGA) infant 27,28. On the other hand, maternal diet characterized by high consumption of red and processed meats and high-fat dairy products increases the risk of giving birth to a SGA infant 27. Godfrey et al. showed specifically that high carbohydrate intake in early pregnancy was associated with lower birth and placental weight, especially when combined with a low intake of high quality protein (e.g. animal protein) in late pregnancy 29.
1
Largely due to these historical examples, the focus on maternal lifestyle and dietary intake during pregnancy and the association with pregnancy outcome has increased. The formation of most organs (including the placenta) occurs however, when women are not yet aware of being pregnant, between the third and seventh week of gestation, risking several teratogenic effects during this time period 30. In addition, maternal nutritional status at conception influences pregnancy outcome and long-term health of both women and their offspring, by affecting the way energy is partitioned between maternal and fetal needs. This emphasizes the importance of shifting the focus from during pregnancy only also towards the preconception period. Defining this period has been done previously 31, suggesting that biologically this preconception phase may start in women around 26 weeks prior to conception when primordial follicles leave their resting state. However, because the most active phase of ovarian follicular development starts around 14 weeks preconception 32, this definition is more often used 31.
Literature regarding preconception dietary intake and its association with pregnancy is limited. Nevertheless, it has been shown that high pre-pregnancy maternal BMI shows a strong positive association with both neonatal adiposity and large-for-gestational age (LGA) births, and with the risk of preeclampsia 33–38. This probably reflects the general inflammatory status of the mother and also the condition of the uterine environment 39, and thus highlights the importance of maternal physiology and body composition, already from preconception onwards 36. Thus, increasing the knowledge on maternal dietary intake and environmental factors (e.g. exposure to air pollution) during preconception and pregnancy outcome, and consequently improving dietary habits before conception, appears to be an important priority to improve fetal growth and subsequent health outcomes.
PART 2:
Placental development and pregnancy outcome
Placental size, weight and shape are all subject to wide variations 40. Several studies have described the relationship between placental morphometry and adverse pregnancy outcomes, including fetal growth restriction (FGR). Small placental size 41, decreased placental surface area 42 and small placental volume 43, have been associated with increased risk of FGR. Smaller surface area and a more oval shape are more common in pregnancies complicated by preeclampsia 43,44. Moreover, low birth weight to placental weight-ratio (BWPW-ratio) (i.e. grams of the fetus per gram placenta 45), often described as “placental efficiency” 46, has been appointed by a
study as associated with a higher risk of delivering a term small for gestational age (SGA) infant 47.
Besides the fact that several studies have examined the association between placental morphometry and pregnancy outcomes, there is increasing evidence that features of placental gross morphology are linked biologically to the functional capacity of the placenta 48. However, studies investigating this association have mainly focused on the inter-relationship between antenatal utero-placental Doppler blood flow velocimetry and the post-natal microscopic and ultrastructural characteristics of the placenta and placental bed instead of the gross morphology of the placenta. Nevertheless, it is plausible that utero-placental blood flow may also be related to the gross morphology of the placenta.
Consequently, it is important to increase the understanding of the association between utero-placental blood flow, fetal growth and the gross morphology of the placenta, in relation to pregnancy outcome. Performing studies on morphology of the placenta (after birth) create the opportunity to help finding the neonate who has increased risk of morbidity (e.g. who suffered undetected growth restriction) and should be monitored more closely during postnatal care.
AIMS AND OUTLINE OF THE THESIS
The aim of this thesis was to gain more insight in both the relation between preconception environmental factors (including exposure to air pollution and maternal dietary intake) and in the relation of placental morphometry with pregnancy outcome. Therefore, the following studies have been conducted.
Part 1. Preconception environmental factors and pregnancy outcome.
With the increased understanding of the potential harmful effects of exposure to air pollutants during pregnancy on pregnancy outcome (and more specifically the risk of congenital anomalies), there are increasing numbers of studies focusing on these associations. However, results are inconsistent and most studies have focused only on the association of air pollution with congenital heart defects and orofacial clefts. In chapter 2 we aimed to identify, using an exploratory study design, congenital anomalies that may be sensitive to maternal exposure to specific air pollutants during the periconceptional period.
As previously mentioned, literature investigating the link between maternal nutritional status before conception and pregnancy outcome is still scarce. The studies that have investigated the association between preconception dietary intake in relation
1
to pregnancy outcome were either animal studies, limited in value due small sample sizes or focused solely on selected macronutrient intake rather than complete (macro) nutrient composition or dietary intake (e.g. food groups). Therefore, we investigated the association between preconception diet and pregnancy outcomes in a linked birth cohort. In chapter 3 we described the methodology of establishing the Perined-Lifelines linked birth cohort, and included the characteristics of the study population. The aim of chapter 4 was to assess the association between preconception maternal macronutrient intake and birth weight of the offspring, across strata of maternal BMI in the Perined-Lifelines Linked birth cohort. Chapter 5 evaluated the association between preconception food group intake and birth weight of the offspring, in the Perined-Lifelines linked birth cohort.
Part 2. Placenta morphometry and pregnancy outcome.
Placental function can be assessed in vivo by utero-placental Doppler flow velocimetry and fetal growth can be assessed by serial ultrasonic biometry. High resistance pattern of flow in the umbilical artery is widely used as an indicator of placental dysfunction 49. It is known that FGR may be the result of placental dysfunction, whereby the placenta may exhibit altered nutrient transfer capacity to compensate for suboptimal function. As described previously, diagnostic process of FGR remains difficult. We hypothesized that placental morphometry and BWPW-ratio may be of value in diagnosing FGR. In this part, we aim to increase the understanding of the relation between placental morphometry, and BWPW-ratio (“placental efficiency”), to ultrasound markers of fetal growth restriction and both maternal and neonatal morbidity.
In chapter 6 we investigated the inter-relationships between size and shape of the placenta (assessed following birth), utero-placental Doppler flow velocimetry and the rate of growth of the fetal abdomen measured between 20 and 36 weeks’ gestational age. With this study we aimed to increase the understanding of the (patho-)physiological association between utero-placental Doppler measurements (corresponding to placental function) and the gross morphology of the placenta after birth. Chapter 7 elaborates on the relationship between both a relatively smaller placenta (high birth weight -placenta weight ratio) and a relatively larger placenta (low birth weight-placental weight ratio), with ultrasonic measurements of utero-placental blood flow and neonatal and maternal morbidity. Our hypothesis was that failure of placental development, reflected by high BWPW-ratio, is associated with fetal growth restriction and increased umbilical artery pulsatility index. In chapter 8, a
literature review was presented in which the possible use of placental morphometry in detection of fetal growth restriction has been investigated. Finally, in chapter 9 overall conclusions of the thesis are discussed.
1
REFERENCES
1. Barker, D. J. P. The developmental origins of adult disease. J. Am. Coll. Nutr. 23, 588S–595S (2004).
2. Roseboom, T. J. et al. Effects of prenatal exposure to the Dutch famine on adult disease in later life: an overview. Mol. Cell. Endocrinol. 185, 93–8 (2001).
3. Stanner, S. A. et al. Does malnutrition in utero determine diabetes and coronary heart disease in adulthood? Results from the Leningrad siege study, a cross sectional study. BMJ 315, 1342–1348 (1997).
4. Barker, D. J. P., Osmond, C., Kajantie, E. & Eriksson, J. G. Growth and chronic disease: findings in the Helsinki Birth Cohort. Ann. Hum. Biol. 36, 445–458 (2009).
5. Valdez, R., Athens, M. A., Thompson, G. H., Bradshaw, B. S. & Stern, M. P. Birthweight and adult health outcomes in a biethnic population in the USA. Diabetologia 37, 624–31 (1994).
6. Bhargava, S. K. et al. Relation of Serial Changes in Childhood Body-Mass Index to Impaired Glucose Tolerance in Young Adulthood. N. Engl. J. Med. 350, 865–875 (2004).
7. Beune, I. M., Pels, A., Gordijn, S. J. & Ganzevoort, W. Definitions of fetal growth restriction in existing literature over time. Ultrasound Obstet. Gynecol. (2018). doi:10.1002/uog.19189
8. Painter, R. et al. Transgenerational effects of prenatal exposure to the Dutch famine on neonatal adiposity and health in later life. BJOG An Int. J. Obstet. Gynaecol. 115, 1243– 1249 (2008).
9. Das, U. (‘Shonu’) G. & Sysyn, G. D. Abnormal fetal growth: intrauterine growth retardation, small for gestational age, large for gestational age. Pediatr. Clin. North Am. 51, 639–654 (2004).
10. Maulik, D. Fetal growth restriction: the etiology. Clin. Obstet. Gynecol. 49, 228–35 (2006).
11. Sferruzzi-Perri, A. N., Vaughan, O. R., Forhead, A. J. & Fowden, A. L. Hormonal and nutritional drivers of intrauterine growth. Curr. Opin. Clin. Nutr. Metab. Care 16, 298–309 (2013). 12. Rappaport, S. M. & Smith, M. T. Environment
and Disease Risks. Science (80-. ). 330, 460– 461 (2010).
13. Wu, S., Powers, S., Zhu, W. & Hannun, Y. A. Substantial contribution of extrinsic risk factors to cancer development. Nature 529, 43–47 (2016).
14. Genuis, S. J. Our genes are not our destiny: incorporating molecular medicine into clinical practice. J. Eval. Clin. Pract. 14, 94–102 (2008). 15. Genuis, S. J. What’s out there making us sick?
J. Environ. Public Health 2012, 605137 (2012). 16. Genuis, S. J. The chemical erosion of human
health: adverse environmental exposure and in-utero pollution - determinants of congenital disorders and chronic disease. J. Perinat. Med. 34, 185–95 (2006).
17. De-Regil, L. M., Fernández-Gaxiola, A. C., Dowswell, T. & Peña-Rosas, J. P. Effects and safety of periconceptional folate supplementation for preventing birth defects. Cochrane database Syst. Rev. CD007950 (2010). doi:10.1002/14651858.CD007950.pub2 18. WHO | Periconceptional folic acid
supplementation to prevent neural tube defects. WHO (2019).
19. Pedersen, M. et al. Ambient air pollution and low birthweight: a European cohort study (ESCAPE). Lancet. Respir. Med. 1, 695–704 (2013).
20. Effects of Air Pollution on Children’s Health and Development. (2005).
21. Baccarelli, A. & Bollati, V. Epigenetics and environmental chemicals. Curr. Opin. Pediatr. 21, 243–51 (2009).
22. Mazzoli-Rocha, F., Fernandes, S., Einicker-Lamas, M. & Zin, W. A. Roles of oxidative stress in signaling and inflammation induced by particulate matter. Cell Biol. Toxicol. 26, 481–498 (2010).
23. King, J. C. A Summary of Pathways or Mechanisms Linking Preconception Maternal Nutrition with Birth Outcomes. J. Nutr. 146, 1437S–1444S (2016).
24. Susser, M. & Stein, Z. Timing in Prenatal Nutrition: A Reprise of the Dutch Famine Study. Nutr. Rev. 52, 84–94 (2009).
25. Lumey, L. H. Compensatory placental growth after restricted maternal nutrition in early pregnancy. Placenta 19, 105–11 (1998). 26. Painter, R. C., Roseboom, T. J. & Bleker, O. P.
Prenatal exposure to the Dutch famine and disease in later life: An overview. Reprod. Toxicol. 20, 345–352 (2005).
27. Knudsen, V. K., Orozova-Bekkevold, I. M., Mikkelsen, T. B., Wolff, S. & Olsen, S. F. Major dietary patterns in pregnancy and fetal growth. Eur. J. Clin. Nutr. 62, 463–470 (2008). 28. Grieger, J. & Clifton, V. A Review of the Impact
of Dietary Intakes in Human Pregnancy on Infant Birthweight. Nutrients 7, 153–178 (2014). 29. Godfrey, K., Robinson, S., Barker, D. J.,
Osmond, C. & Cox, V. Maternal nutrition in early and late pregnancy in relation to placental and fetal growth. BMJ 312, 410–4 (1996).
30. Williamson, C. S. Nutrition in pregnancy. Nutr. Bull. 31, 28–59 (2006).
31. Steegers-Theunissen, R. P. M., Twigt, J., Pestinger, V. & Sinclair, K. D. The periconceptional period, reproduction and long-term health of offspring: the importance of one-carbon metabolism. Hum. Reprod. Update 19, 640–655 (2013).
32. Griffin, J., Emery, B. R., Huang, I., Peterson, C. M. & Carrell, D. T. Comparative analysis of follicle morphology and oocyte diameter in four mammalian species (mouse, hamster, pig, and human). J. Exp. Clin. Assist. Reprod. 3, 2 (2006).
33. Singh, K. A. et al. Birth Weight and Body Composition of Neonates Born to Caucasian Compared With African-American Mothers. Obstet. Gynecol. 115, 998–1002 (2010). 34. Lewis, R. M. et al. The Placental Exposome:
Placental Determinants of Fetal Adiposity and Postnatal Body Composition. Ann. Nutr. Metab. 63, 208–215 (2013).
35. Linabery, A. M. et al. Stronger influence of maternal than paternal obesity on infant and early childhood body mass index: the Fels Longitudinal Study. Pediatr. Obes. 8, 159–169 (2013).
36. Pomeroy, E., Wells, J. C. K., Cole, T. J., O’Callaghan, M. & Stock, J. T. Relationships of maternal and paternal anthropometry with neonatal body size, proportions and adiposity in an Australian cohort. Am. J. Phys. Anthropol. 156, 625–636 (2015).
37. Starling, A. P. et al. Associations of maternal BMI and gestational weight gain with neonatal adiposity in the Healthy Start study. Am. J. Clin. Nutr. 101, 302–309 (2015).
38. Savitri, A. I. et al. Does pre-pregnancy BMI determine blood pressure during pregnancy? A prospective cohort study. BMJ Open 6, e011626 (2016).
39. Lewis, F. I. & McCormick, B. J. J. Revealing the Complexity of Health Determinants in Resource-poor Settings. Am. J. Epidemiol. 176, 1051–1059 (2012).
40. Burton, G. J., Barker, D. J. P., Moffett, A. & Thornburg, K. The Placenta and Human Developmental Programming. (Cambridge University Press, 2010). doi:10.1017/ CBO9780511933806
1
41. Proctor, L. K. et al. Placental size and the prediction of severe early-onset intrauterine growth restriction in women with low pregnancy-associated plasma protein-A. Ultrasound Obstet. Gynecol. 34, 274–82 (2009). 42. Ducray, J. F., Naicker, T. & Moodley, J.
Pilot study of comparative placental morphometry in pre-eclamptic and normotensive pregnancies suggests possible maladaptations of the fetal component of the placenta. Eur. J. Obstet. Gynecol. Reprod. Biol. 156, 29–34 (2011).
43. Odibo, A. O. et al. First-trimester serum analytes, biophysical tests and the association with pathological morphometry in the placenta of pregnancies with preeclampsia and fetal growth restriction. Placenta 32, 333–338 (2011).
44. Kajantie, E., Thornburg, K. L., Eriksson, J. G., Osmond, C. & Barker, D. J. P. In preeclampsia, the placenta grows slowly along its minor axis. Int. J. Dev. Biol. 54, 469–73 (2010). 45. Wilson, M. E. & Ford, S. P. Comparative
aspects of placental efficiency. Reprod. Suppl. 58, 223–32 (2001).
46. Fowden, A. L., Sferruzzi-Perri, A. N., Coan, P. M., Constancia, M. & Burton, G. J. Placental efficiency and adaptation: endocrine regulation. J. Physiol. 587, 3459–72 (2009). 47. Luque-Fernandez, M. A. et al. Is the
fetoplacental ratio a differential marker of fetal growth restriction in small for gestational age infants? Eur. J. Epidemiol. 30, 331–41 (2015). 48. Burton, G. J., Fowden, A. L. & Thornburg, K. L.
Placental Origins of Chronic Disease. Physiol. Rev. 96, 1509–65 (2016).
49. Benirschke, K., Burton, G. (Graham J. . & Baergen, R. N. Pathology of the human placenta. (Springer, 2012).
Preconception environmental factors and
pregnancy outcome
Nastaran Salavati, Maciek Strak, Johannes G.M.
Burgerhof, Hermien E.K. de Walle, Jan Jaap H.M.
Erwich, Marian K. Bakker
International Journal of Hygiene and Environmental Health, 2018; 1061-1067
The association of air pollution with
congenital anomalies: an exploratory
study in the Northern Netherlands
ABSTRACT
Background: There are a growing number of reports on the association between air pollution and the risk of congenital anomalies. However, the results are inconsistent and most studies have only focused on the association of air pollution with congenital heart defects and orofacial clefts.
Objectives: Using an exploratory study design, we aimed to identify congenital anomalies that may be sensitive to maternal exposure to specific air pollutants during the periconceptional period.
Methods: We conducted a case-control study of 7,426 subjects born in the 15 years between 1999 and 2014 and registered in the European Registration of Congenital Anomalies and Twins Northern Netherlands (EUROCAT NNL). Concentrations of various air pollutants (PM10, PM2.5, PM10-2.5, NO2, NOX, absorbance) were obtained using land use regression models from the European Study of Cohorts for Air Pollution Effects (ESCAPE). We linked these data to every subject in the EUROCAT NNL registry via their full postal code. Cases were classified as children or fetuses born in the 15-year period with a major congenital anomaly that was not associated with a known monogenic or chromosomal anomaly. Cases were divided into anomaly subgroups and compared with two different control groups: control group 1 comprised children or fetuses with a known monogenic or chromosomal anomaly, while control group 2 comprised all other non-monogenic and non-chromosomal registrations.
Results: Using control group 1 (n=1618) for analysis, we did not find any significant associations, but when we used control group 2 (ranges between n=4299 and n=5771) there were consistent positive associations between several air pollutants (NO2, PM2.5, PM10-2.5, absorbance) and the genital anomalies subgroup.
Conclusion: We examined various congenital anomalies and their possible associations with a number of air pollutants in order to generate hypotheses for future research. We found that air pollution exposure was positively associated with genital anomalies, mainly driven by hypospadias. These results broaden the evidence of associations between air pollution exposure during gestation and congenital anomalies in the child. They warrant further research, which should also focus on possible underlying mechanisms.
2
INTRODUCTION
Congenital anomalies are one of the main causes of perinatal mortality 1. Worldwide, an estimated 10% of under five-year-olds die due to congenital anomalies 2. Therefore, congenital anomalies are a major public health issue, especially because of the lack of information on prevention. There is growing evidence that fetal development is particularly vulnerable to air pollution. Several studies have shown an association between pregnant women being exposed to air pollutants and an increased risk of fetal growth restriction 3, low birth weight 3, preterm birth and neonatal mortality 4. In addition, several studies have shown that maternal exposure to several air pollutants is possibly associated with congenital anomalies. Farhi et al. described the increased risk for congenital anomalies, specifically in the circulatory system and genital organs, when mothers were exposed to higher levels of particulate matter (PM10) and nitrogen oxide (NOX) 5. Liang et al. showed an association between maternal exposure to PM
10 and the risk of congenital anomalies 6.
There is substantial evidence that oxidative stress and inflammation are involved in the mechanisms underlying the effects of air pollutants which can contribute to epigenetic changes, including alteration of DNA methylation 7,8. Such epigenetic modifications during pregnancy could impair normal embryo development and lead to congenital anomalies.
Despite this evidence, there remain inconsistencies and uncertainties about the effects of specific air pollutants. Most studies have focused on congenital heart defects or orofacial clefts. We hypothesize that other anomalies may also be sensitive to air pollution. Therefore, using an exploratory study design, we set out to identify congenital anomalies that may be sensitive to maternal exposure to specific air pollutants during the periconceptional period.
MATERIAL AND METHODS
Study design and population
We performed an exploratory case-malformed control study on congenital anomalies and air pollution using data from EUROCAT (European Registration of Congenital Anomalies and Twins) Northern Netherlands (NNL). The air pollution data was obtained from ESCAPE (European Study of Cohorts for Air Pollution Effects).
EUROCAT NNL is a population-based registry of children and fetuses with congenital malformations in the three northern provinces of the Netherlands. The methods of case ascertainment have been described elsewhere (http://www.eurocat-network. eu/content/Reg-Des-North-Netherlands.pdf). The registry is based on multiple sources of information such as hospital records, and post mortem examinations, and includes information about live births (LB), spontaneous abortions, fetal deaths (FD) with a gestational age greater than 24 weeks, and terminations of pregnancy after prenatal diagnosis of a fetal anomaly (TOPFA). All major structural malformations are registered and coded according to ICD9 or ICD10 with BPA (British Pediatric Association) extension and the EUROCAT guidelines (www.eurocat-network.eu). Approximately 15,000 children born between 1981 and 2014 have been registered in the database of EUROCAT NNL. Registration is voluntary and requires parental consent. Information on associated risk factors, such as maternal medication use, parents’ professions, family history of congenital anomalies, use of alcohol and cigarettes, prenatal screening and diagnostic procedures performed during pregnancy is collected through a parental questionnaire and supplemented with information from medical files and local pharmacies. The EUROCAT NNL registry records a full postal code for the maternal residence at time of birth. EUROCAT NNL does not collect data on non-malformed children.
Definition of cases and controls
In this study, we classified cases as children or fetuses born between 1999 and 2014 with a major congenital anomaly that was not associated with a known monogenic or chromosomal anomaly. The congenital anomalies were divided into anomaly subgroups, according to organ system.
Anomaly subgroups with 30 cases or more were the primary outcome of the analysis (30 cases was set as a cutoff to perform meaningful analyses). These subgroups included anomalies of the nervous system, eye, heart, respiratory tract, digestive system, urinary tract, limb, genital tract, abdominal wall defects, and orofacial clefts. The cases in these anomaly groups all had isolated birth defects, i.e. they had an
2
isolated anomaly or only anomalies in one organ system. A separate subgroup was created consisting of multiple congenital anomalies (cases diagnosed with multiple, unrelated anomalies in more than one organ system).
We excluded any subjects without a full postal code (needed to link EUROCAT NNL data with air pollution data from ESCAPE), or if no data was available on air pollution for their specific postal code.
In absence of a non-malformed control group, we used two malformed control groups in the exploratory analyses to identify anomaly groups sensitive to air pollution 9: Control group 1 comprised children or fetuses born or with an end–of-pregnancy date between 1999 and 2014 with a known monogenic or chromosomal anomaly (including microdeletions). This control group was used since a relationship between the genetic disorder and air pollution was not expected.
Control group 2 differed per anomaly subgroup and comprised all the other non-monogenic and non-chromosomal cases. For example, when the orofacial clefts subgroup was analyzed, control group 2 consisted of all the other non-monogenic and non-chromosomal cases that did not have an orofacial cleft.
Maternal characteristics
Maternal BMI was calculated using self-reported pre-pregnancy weight and height, and grouped using the WHO classification: underweight (BMI < 18.5 kg/m2), normal weight (BMI 18.5– 25.0 kg/m2), overweight (BMI > 25.0 kg/m2). Maternal education was assigned in three categories: 1. lower education (including lower general secondary education and lower vocational education); 2. medium education (including higher general secondary education and intermediate vocational education); and 3. higher education (defined as higher vocational education, university and further tertiary college). Maternal age was divided into seven categories: 15-19 years, 20-24 years, 25-29 years, 30-34 years, 35-39 years, 40-44 years and > 44 years. Use of folic acid was divided into two categories: ‘use’ (400 or 500 µg per day in the periconceptional period of four weeks prior to conception to two months after conception) and ‘no use or incorrect ‘use’ (use in wrong period or wrong dose (<400 µg)). Maternal smoking was divided into ‘smoking’ or ‘non-smoking’ during pregnancy. ‘Smoking during pregnancy’ was defined by ‘mother smoked during pregnancy or stopped smoking when she knew she was pregnant’. Maternal alcohol use was divided into ‘alcohol use’ (defined as ‘mother drank alcohol during all or a part of pregnancy’) or ‘no alcohol use’ during pregnancy (defined as ‘mother stopped drinking alcohol before conception or did not drink alcohol at all’). Pregnancy outcome was divided
into live birth, stillbirth (after 24 weeks of gestation), spontaneous abortion (until 24 weeks of gestation), and termination of pregnancy after prenatal diagnosis of a fetal anomaly (TOPFA, up to 24 weeks of gestation). Season of conception was calculated by subtracting the gestation period (in days) from the child’s date of birth, which gave a date of conception. Then season of conception was divided into winter (December–February), spring (March–May), summer (June–August) and fall (September–November). For all subjects, their area-level socio-economic status (SES) score was based on the social status of their neighborhood retrieved from the Netherlands Institute of Social Research (Sociaal Cultureel Planbureau). This was determined for the postal code areas (first four digits) based on educational level, income and labor market position of the residents in the area 10 (https://www.scp. nl/Onderzoek/Lopend_onderzoek/A_Z_alle_lopende_onderzoeken/Statusscores). The area-level SES-score was divided into three groups based on the rankings: low, intermediate, and high.
Exposure assessment
The maternal exposure to nitrogen dioxide and nitrogen oxides (NO2, NOX), particulate matter with aerodynamic diameter £ 10 mm (PM10), £ 2.5 mm (PM2.5), the coarse fraction of particulate matter (PM10-2.5), and absorbance (soot) were obtained from land use regression (LUR) models developed in the European Study of Cohorts for Air Pollution Effects (ESCAPE) 11,12. Briefly, all the air pollutants included in the study were measured in three two-week periods in the cold, warm and intermediate seasons. The annual average concentration was calculated for each measurement, with adjustment for temporal variation using year-round measurement data from central reference sites. The air pollution concentration obtained from the measurements were then used as outcome variables for a LUR model. Variables derived from geographic information systems (e.g. distance to nearest road, traffic intensity, built-up land, population density, altitude) were used as predictor variables to explain the concentrations measured. The concentrations of all the air pollutants for all addresses in the Netherlands were modeled by using LUR models in the PCRaster environmental software, using 5×5 m grids 13. In our analysis, we used the median concentration for each specific full postal code of the mother’s address at time of birth. On average, there are 19.4 addresses per full postal code in the Netherlands. The LUR models are based on 2009 measurement campaigns. Since spatial distribution of air pollution is generally stable over periods of 10-15 years 14,15, our study population contains cases and controls over a 15-year period (birth years 1999 – 2014).
2
Statistical analyses
Since the air pollution data in our study population was skewed, we compared the distribution of values for specific air pollutants between cases and controls using the Wilcoxon ranksum test. The association between maternal characteristics and outcome (cases vs. controls) was examined using the Pearson chi square test for categorical variables or the Student’s T-test for independent groups for continuous and reasonably normally distributed maternal characteristics.
Univariable logistic regression was used to determine association between exposure to specific air pollutants and different congenital anomaly subgroups. Multivariable logistic regression complete case analysis was performed to estimate the adjusted odds ratio (OR) and the 95% confidence interval (CI). The explanatory variables were the median concentrations of specific air pollutants and the outcome variables were the congenital anomaly subgroups.
Maternal smoking, level of education, age of mother, sex of child, season of conception, folic acid use, and area-level SES-score were included as covariates in multivariable analysis, based on information from the literature 16–18.
The adjusted OR, 95% CI and p-values were reported. A p-value < 0.05 was considered statistically significant. Analyses were performed using SPSS 23.0 for Windows (IBM SPSS Statistics, Armonk, New York, USA).
RESULTS
During the 15-year study period 294,421 births were monitored in the northern Netherlands and 7,787 children or fetuses were registered in the EUROCAT NNL database with a major congenital anomaly and a full postal code that could be linked to air pollution data. This resulted in a total prevalence of 2.6%. After excluding two anomaly subgroups ‘ear, face & neck’ (n = 15, 0.2%) and ‘endocrine organs’ (n = 8, 0.1%), since they had fewer than 30 cases, and excluding those cases that could not be attributed to one specific anomaly subgroup (n = 338, 4.3%), we had 7,426 (95.4%) subjects eligible for analysis. Of these, 5,808 (78.2%) were cases with a major congenital anomaly attributed to one of the subgroups and 1,618 (21.8%) were controls (control group 1) diagnosed with a known monogenic or chromosomal anomaly (including microdeletions). Limb anomalies were the most common subgroup (1,509 cases), followed by congenital heart defects (1,360 cases), and urinary anomalies (550 cases). Control group 1 was comprised mainly of chromosomal anomalies (n = 952): the main groups were Down Syndrome (trisomy 21, n = 464), Edwards syndrome
(trisomy 18, n = 175), Turner syndrome (n = 77) and Patau syndrome (trisomy 13, n = 49). There were 538 malformed controls diagnosed with a monogenic anomaly and 128 with microdeletions. Maternal and infant characteristics of the cases and control group 1 are shown in Table 1.
Table 1. Maternal and infant characteristics of cases and controls.
Characteristic Cases1 Control group 12 p-value
N=5808 (100%) N=1618 (100%)
Age at delivery (years) (mean (sd)) 30.4 (4.7) 32.3 (5.2) <0.001
Missing 77 9 Age at delivery <0.001 15-19 58 (1.0) 14 (0.9) 20-24 563 (9.8) 102 (6.3) 25-29 1809 (31.6) 396 (24.6) 30-34 2220 (38.7) 540 (33.6) 35-39 940 (16.4) 406 (25.2) 40-44 135 (2.4) 141 (8.8) > 44 6 (0.1) 10 (0.6) BMI (kg/m2) 0.34 low (<18.5) 133 (2.8) 35 (2.7) medium (18.5-25) 2910 (62.2) 840 (64.4) high (>25) 1639 (35.0) 430 (33.0) Missing 1126 313 Level of education 0.21 Low 678 14.4) 192 (14.7) Medium 2302 (49.0) 605 (46.4) High 1715 (36.5) 508 (38.9) Missing 1113 313 Sex <0.001 Male 3218 (55.4) 802 (49.6) Female 2590 (44.6) 816 (50.4) Missing 0 0 Season of conception 0.38 Winter 1380 (25.8) 385 (24.6) Spring 1372 (25.6) 434 (27.7) Summer 1283 (23.9) 362 (23.1)
2
Table 1. Continued
Characteristic Cases1 Control group 12 p-value
N=5808 (100%) N=1618 (100%)
Autumn 1324 (24.7) 383 (24.5)
Missing 449 54
Folic acid use 0.30
Use 3653 (80.2) 994 (78.9)
No use or incorrect use 901 (19.8) 266 (21.1)
Missing 1254 358
Smoking during pregnancy 0.02
Yes 1123 (22.8) 274 (19.8)
No 3796 (77.2) 1108 (80.2)
Missing 889 236
Alcohol consumption during pregnancy
0.08
Yes 1051 (21.6) 326 (23.8)
No 3823 (78.4) 1044 (76.2)
Missing 934 248
Area-level SES4-score 0.71
Low 1514 (26.8) 418 (26.4) Intermediate 3870 (68.6) 1098 (69.4) High 259 (4.6) 66 (4.2) Missing 165 36 Pregnancy outcome <0.001 Live birth 5467 (94.1) 1052 (65.0) Stillbirth 63 (1.1) 67 (4.1) Spontaneous abortion 30 (0.5) 59 (3.6) TOPFA3 248 (4.3) 440 (27.2)
Gestation period (weeks) (mean (sd))
37.6 (5.0) 31.3 (10.3) <0.001
Missing 449 54
1Infants or fetuses born between 1997 and 2014 with a non-chromosomal and non-monogenic birth defect
including anomalies of the nervous system, eye, heart, respiratory tract, digestive system, urinary tract, limb, genital tract, abdominal wall defects and oro-facial clefts.
2Infants or fetuses born between 1997 and 2014 diagnosed with a known monogenic anomaly or a chromosomal
anomaly (including microdeletions).
3TOPFA denotes termination of pregnancy for fetal anomaly 4SES denotes socio-economic status
The distribution of median air pollution concentrations and their range in the congenital anomaly subgroups and control group 1 are shown in Table 2.
Both univariable logistic regression analyses (Supplementary Tables 1 and 2) and multivariable logistic regression analyses were performed (both with control group 1 and control group 2). These were adjusted for age of mother, sex of child, level of education, season of conception, smoking, folic acid use, and area-level SES-score (Tables 3 and 4).
Univariable logistic regression analyses, using control group 1, showed mostly inconsistent significant negative associations, apart from the significant negative association of cases with anomalies of the digestive system with several air pollutants (NO2, NOx, PM10, Absorbance) (Supplementary Table 1). In the multivariable logistic regression analyses, using control group 1, we found no more consistent associations (Table 3).
With univariable logistic regression analyses, using control group 2 (all other non-monogenic and non-chromosomal malformations), we found that cases with a genital anomaly had consistent significant positive associations with all the air pollutants compared to the controls (Supplementary Table 2). In the multivariable logistic regression analyses, using control group 2, the significant positive association of cases with a genital anomaly with several air pollutants (NO2, PM2.5, PM10-2.5, Absorbance) remained (Table 4).
Since the genital anomalies subgroup mainly consists of hypospadias, we performed extra analyses in which we only included cases with hypospadias1 and male controls. As shown in Table 4, in the analysis for the complete subgroup of genital anomalies, the association between air pollution and cases with hypospadias remained significant for NO2 and PM10-2.5.
2
Ta b le 2 . D is tr ib u ti o n o f a ir p o llu ti o n c o n ce n tr at io n ( in m g /m 3) p e r Z IP c o d e b y c o n g e n it al a n o m al y s u b g ro u p s a n d c o n tr o l g ro u p 1 a n d 2 . N N O2 N Ox PM 10 PM 2. 5 PM 10 -2 .5 A bs o rb an ce A ll c ases 1 5808 15 .6 6 ( 9 .2 2; 47 .37 ) 21 .8 0 ( 16 .7 9 ;7 1. 9 9 ) 23 .8 9 (23 .7 3; 29 .0 2) 15 .4 5 ( 15 .1 2; 18 .3 9 ) 7. 74 (7. 6 0 ;9 .5 5) 0 .9 3 ( 0 .8 5; 2.1 4) C o n tr o l g ro u p 1 2 161 8 15 .84 (8 .9 3; 29 .7 3) 22 .0 1 ( 16 .7 9 ;5 7. 8 7) 23 .9 3 ( 23 .7 3; 27 .7 1) 15 .4 5 ( 15 .1 6; 18 .01 ) 7. 75 (7. 6 0 ;9 .6 2) 0. 9 5 ( 0. 8 5; 1. 6 1) A no m al ie s o f t he ner vo u s sy ste m 28 2 15 .7 3 ( 10 .6 9 ;2 8 .4 5) 21 .8 3 ( 17 .5 6 ;4 9 .87) 23 .9 5 ( 23 .7 3; 26 .8 8 ) 15 .4 6 (1 5 .1 4; 16 .9 2) 7. 74 (7. 6 0 ;9 .3 4) 0. 9 5 ( 0. 8 5; 1. 47 ) C o nt ro l g ro u p 2 3 55 26 15 .6 6 (9 .2 2; 47. 37 ) 21 .7 9 ( 16 .7 9 ;7 1. 9 9 ) 23 .8 9 ( 23 .7 3; 29 .02 ) 15 .4 5 ( 15 .12 ;1 8 .3 9 ) 7. 74 (7. 6 0 ;9 .5 5) 0 .9 3 ( 0 .8 5; 2. 14 ) E ye a n oma lie s 107 15 .3 2 ( 11 .3 8 ;2 6 .7 4) 21 .7 8 (1 7. 75; 42 .9 9) 23 .91 (23 .7 3; 26 .9 0 ) 15 .4 5 ( 15 .2 6 ;1 6 .7 2) 7. 76 (7. 6 0 ;8 .9 2) 0 .9 3 ( 0 .8 5; 1. 38) C o nt ro l g ro u p 2 3 57 0 1 15 .6 6 (9 .2 2; 47. 37 ) 21 .8 0 (1 6. 79 ;7 1. 99 ) 23 .8 9 ( 23 .7 3; 29 .02 ) 15 .4 5 ( 15 .12 ;1 8 .3 9 ) 7. 74 (7. 6 0 ;9 .5 5) 0 .9 3 ( 0 .8 5; 2. 14 ) H ear t d e fe ct s 13 6 0 15 .6 4 ( 10 .0 2; 47 .37 ) 21 .8 1 ( 16 .8 3; 71 .9 9 ) 23 .91 (23 .7 3; 29 .0 2) 15 .4 5 ( 15 .15 ;1 8 .3 9 ) 7. 74 (7. 6 0 ;9 .4 1) 0 .9 4 ( 0 .8 5; 2.1 4) C o nt ro l g ro u p 2 3 444 8 15 .6 7 ( 9 .2 2; 32 .32) 21 .7 9 ( 16 .7 9 ;5 4. 49 ) 23 .8 8 ( 23 .7 3; 27 .9 0 ) 15 .4 5 ( 15 .12 ;1 8 .2 6 ) 7. 74 (7. 6 0 ;9 .5 5) 0 .9 3 ( 0 .8 5; 1. 8 2) A n o m al ie s o f t h e res p ir at o ry tr ac t 37 15 .08 (11 .9 3; 27 .60 ) 20 .7 6 (1 7. 8 8 ;4 9 .7 0 ) 23 .8 4 ( 23 .7 3; 26 .6 6 ) 15 .4 4 ( 15 .16 ;16 .5 6 ) 7. 73 (7. 6 0 ;9 .1 7) 0. 9 2 ( 0. 8 5; 1. 35 ) C o nt ro l g ro u p 2 3 57 71 15 .6 6 (9 .2 2; 47. 37 ) 21 .8 1 ( 16. 79 ;7 1. 99 ) 23 .8 9 ( 23 .7 3; 29 .02 ) 15 .4 5 ( 15 .12 ;1 8 .3 9 ) 7. 74 (7. 6 0 ;9 .5 5) 0 .9 3 ( 0 .8 5; 2. 14 ) O ro -f aci al c le ft s 427 15 .3 4 ( 9 .4 1; 27 .37 ) 21 .6 3 ( 16 .8 5; 46 .1 0 ) 23 .87 (23 .7 3; 26 .8 0 ) 15 .4 4 ( 15 .1 2; 16 .87) 7. 73 (7. 6 0 ;9 .3 6 ) 0 .9 3 ( 0 .8 5; 1. 6 8) C o nt ro l g ro u p 2 3 53 81 15 .6 9 ( 9 .2 2; 47. 37 ) 21 .8 2 ( 16. 79 ;7 1. 99 ) 23 .8 9 ( 23 .7 3; 29 .02 ) 15 .45 (1 5. 13; 18 .3 9) 7. 74 (7. 6 0 ;9 .5 5) 0 .9 3 ( 0 .8 5; 2. 14 ) A no m al ie s o f t he d ig e st iv e sy ste m 53 5 15 .4 1 ( 9 .3 5; 2 8 .3 4) 21 .5 8 (1 6 .8 5; 52 .0 5) 23 .87 (23 .7 3; 27 .0 0 ) 15 .4 5 ( 15 .1 4; 17 .0 8 ) 7. 73 (7. 6 0 ;9 .4 5) 0. 9 3 ( 0. 8 5; 1. 53 )Ta b le 2 . C o nt in u ed N N O2 N Ox PM 10 PM 2. 5 PM 10 -2 .5 A bs o rb an ce C o nt ro l g ro u p 2 3 527 3 15 .6 8 ( 9 .2 2; 47. 37 ) 21 .8 2 ( 16. 79 ;7 1. 99 ) 23 .8 9 ( 23 .7 3; 29 .02 ) 15 .4 5 ( 15 .12 ;1 8 .3 9 ) 7. 74 (7. 6 0 ;9 .5 5) 0 .9 3 ( 0 .8 5; 2. 14 ) A n o m al ie s o f t h e u ri n ar y tr ac t 550 15 .6 9 (9 .64 ;32 .32 ) 21 .9 5 ( 16 .9 0 ;4 5 .4 6 ) 23 .91 (23 .7 3; 26 .87) 15 .4 5 ( 15 .17 ;17 .3 9 ) 7. 74 (7. 6 0 ;9 .4 1) 0. 9 4 ( 0. 8 5; 1. 6 7) C o nt ro l g ro u p 2 3 52 58 15 .6 6 (9 .2 2; 47. 37 ) 21 .7 8 ( 16 .7 9 ;7 1. 9 9 ) 23 .8 9 ( 23 .72; 29 .02 ) 15 .4 5 ( 15 .12 ;1 8 .3 9 ) 7. 74 (7. 6 0 ;9 .5 5) 0 .9 3 ( 0 .8 5; 2. 14 ) L imb a n om al ie s 150 9 15 .7 7 ( 9 .2 2;3 2.1 0 ) 21 .8 4 ( 16 .7 9 ;5 4 .4 9 ) 23 .87 (23 .7 3; 27 .9 0 ) 15 .4 4 ( 15 .1 3; 18 .2 6 ) 7. 73 (7. 6 0 ;9 .5 5) 0. 9 2 ( 0. 8 5; 1. 76 ) C o nt ro l g ro u p 2 3 42 9 9 15 .6 2 ( 9 .3 5; 47 .3 7) 21 .7 7 ( 16.8 3; 71 .99 ) 23 .9 0 (2 3. 73 ;2 9 .02 ) 15 .4 5 ( 15 .12 ;1 8 .3 9 ) 7. 74 (7. 6 0 ;9 .5 4) 0. 9 4 ( 0. 8 5; 2. 14 ) A b d o m in al w all d e fe ct s 52 17 .3 0 (1 1. 27; 24 .6 1) 23 .4 2 ( 17 .6 8; 34 .7 7) 24 .1 0 (2 3 .73 ;2 5 .3 8 ) 15 .51 (1 5 .2 2; 16 .72 ) 7. 8 4 ( 7. 6 0 ;8 .3 3) 0 .9 8 (0 .8 7; 1.1 7) C o nt ro l g ro u p 2 3 57 56 15 .6 5 ( 9 .2 2; 47. 37 ) 21 .7 7 ( 16 .7 9 ;7 1. 9 9 ) 23 .8 9 ( 23 .7 3; 29 .02 ) 15 .4 5 ( 15 .12 ;1 8 .3 9 ) 7. 74 (7. 6 0 ;9 .5 5) 0 .9 3 ( 0 .8 5; 2. 14 ) G e n it al a n oma lie s 482 16 .2 0 (10 .10 ;2 7. 23 ) 22 .0 5 ( 17. 29 ;4 7. 6 1) 23 .9 3 ( 23 .7 3; 27 .3 8) 15 .4 6 (1 5 .17 ;17 .8 3) 7. 77 (7. 6 0 ;9 .5 4) 0. 9 4 ( 0. 8 5; 1. 8 2) C o nt ro l g ro u p 2 3 53 26 15 .6 2 ( 9 .2 2; 47. 37 ) 21 .7 7 ( 16 .7 9 ;7 1. 9 9 ) 23 .8 9 ( 23 .7 3; 29 .02 ) 15 .4 5 ( 15 .12 ;1 8 .3 9 ) 7. 74 (7. 6 0 ;9 .5 5) 0 .9 3 ( 0 .8 5; 2. 14 ) Mu lt ip le c on g e n it al an oma lie s 46 7 15 .3 5 ( 9 .9 6 ;3 1.9 9 ) 21 .5 0 (1 7. 14 ;4 5 .6 1) 23 .8 8 (23 .7 3; 26 .6 6 ) 15 .4 6 (1 5 .1 7; 16 .9 8) 7. 74 (7. 6 0 ;9 .4 9 ) 0 .9 3 ( 0 .85 ;1 .65 ) C o nt ro l g ro u p 2 3 53 41 15 .6 7 ( 9 .2 2; 47. 37 ) 21 .8 3 ( 16. 79 ;7 1. 99 ) 23 .8 9 ( 23 .7 3; 29 .02 ) 15 .4 5 ( 15 .12 ;1 8 .3 9 ) 7. 74 (7. 6 0 ;9 .5 5) 0 .9 3 ( 0 .8 5; 2. 14 ) V a lu es ar e m ed ian (r an g e) . 1In fa nt s o r f et u se s b o rn b et w ee n 1 9 97 a n d 2 0 14 w ith a n o n-ch ro m o so m a l a n d n o n-m o no g en ic b ir th d ef ec t i n cl u d in g a no m a lie s o f t he n er vo u s s ys te m , e ye , h ea rt , r es p ira to ry tr a ct , d ig es tiv e s ys te m , u ri n a ry t ra ct , l im b , g en ita l t ra ct , a b d o m in a l w a ll d ef ec ts a n d o ro -f a ci a l c le ft s. 2In fa nt s o r f et u se s b o rn b et w ee n 1 9 97 a n d 2 0 14 d ia g no se d w ith a k no w n c hr o m o so m a l a no m a ly o r m o no g en ic a no m a ly . 3 In fa nt s o r f et u se s b o rn b et w ee n 1 9 97 a n d 2 0 14 w ith a n o n-ch ro m o so m a l a n d n o n-m o no g en ic a no m a ly , e xc lu d in g t he a no m a ly o f i nt er es t i n t he s u b g ro u p .
