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.
GENERAL DISCUSSION
The primary aim of this thesis was to provide more insight on how pregnancy outcome is associated with both environmental factors in the preconception period, and placental morphometry after birth. To study the association between preconception dietary intake and pregnancy outcome, we created a linked birth cohort with detailed data on maternal dietary intake in the preconception period and pregnancy outcomes. To study the relationship between placental morphometry, ultrasonic measurements of utero-placental blood flow and fetal growth, and pregnancy outcome (e.g. neonatal and maternal morbidity), we used the Pregnancy Outcome Prediction Study 1, a large
prospective cohort study of nulliparous women.
The following paragraphs discuss how the main findings of this thesis fit with the existing literature. Challenges and opportunities of the preconception period as a window of interest for studies and potential interventions, linkage of health care databases, and the use of placental morphometry in detection of adverse pregnancy outcome will be described. Subsequently, implications for public health and clinical practice, and suggestions for future research will be presented. Finally, an overall conclusion will be given.
PRECONCEPTION ENVIRONMENTAL FACTORS AND
PREGNANCY OUTCOME
Reflection on results of preconception environmental factors
and pregnancy outcome
Within the environmental domain, there are two important questions related to healthy pregnancy. First, “do we get what we need?”, and second, “are we exposed to substances that are toxic?” 2,3. During pregnancy in particular, it appears that the
processes that direct development and growth of early human life are profoundly sensitive to nutritional requirements and vulnerable to environmental insults. Adverse exposures or insufficiency of required nutrients during critical phases of development may have serious and life-long consequences 4.
Air pollution
There is increasing concern over the adverse effects of air pollution on human health, and more specifically on pregnancy course and the offspring. Several studies have shown an association between pregnant women being exposed to air pollutants and an increased risk of fetal growth restriction (FGR) 5, low birth weight 5, preterm
9
birth and neonatal mortality 6. In addition, several studies have shown that maternalexposure to several air pollutants is possibly associated with congenital anomalies. However, the findings are inconsistent. Several studies found a significant association between NO2- exposure and congenital heart defects 7,8, while others found no
significant association between exposure to air pollutants (e.g. NO2) and congenital heart defects. This could be attributable to different assessments methods of the amount of air pollutants exposure and different classification of congenital anomalies among the studies. In chapter 2 we examined the association between exposure to several air pollutants in the peri-conception period with various congenital anomalies. Within our study, we found that exposure to air pollutants was associated with increased risk of genital anomalies, mainly driven by hypospadias. Hypospadias is one of the most common congenital anomalies in men 9. Due to incomplete closure
of the penile structures during embryogenesis, the urethral opening is displaced along the ventral side of the penis 9. The study was performed within EUROCAT NNL,
a large database of major structural malformations and chromosomal anomalies in all types of births. Most studies that did not find an increased risk for malformations only included live births. Excluding ‘terminations of pregnancy after prenatal diagnosis of a fetal anomaly’ (TOPFA) may lead to an underestimation of the association between maternal exposure to air pollutants and congenital anomalies.
As with any (potential) toxic exposure the most obvious solution is to remove the exposure or at least decrease it to a level on which it will cause less, and ideally not any, harm. However, as this is difficult to accomplish, it is important to improve the understanding of the impact of the toxic exposure, for instance air pollution, on biological systems. With an increased understanding on the mechanisms underlying the effects of air pollution, a more targeted approach would be to remove the most toxic components of air pollution.
Several studies concluded that there is substantial evidence for involvement of oxidative stress and inflammation in the mechanisms underlying the effects of air pollutants which can contribute to epigenetic changes, including alteration of DNA methylation 10,11. Such epigenetic modifications during pregnancy could impair normal
embryo development and lead to congenital anomalies.
However, the exact underlying mechanism on how air pollution affects pregnancy outcome, including embryo development and fetal growth, is not yet completely understood. Therefore, new insights into how for instance the fetal epigenome responds to cigarette smoking may provide clues as how air pollution may affect the developing fetus. Cigarette smoke, although somewhat different from air pollution,
shares several of the same components and is well known for its association with adverse health conditions, including fetal growth restriction 12. It is widely
acknowledged that exposure to cigarette smoke during pregnancy modifies important aspects of the placental function 13, as it mediates pathological placental hypoxia
and decreases cytotrophoblast proliferation with altered angiogenesis 14. In addition,
several studies in adulthood have demonstrated that cigarette smoking results in reproducible DNA methylation differences in blood, specifically demethylation in the promotor of AHRR, a gene involved in regulating detoxification processes 15.
Nutrition
Apart from the adverse exposures that need to be avoided, there are also absolute requirements for essential nutrient intake at critical stages of fetal development, in order for the embryo/fetus to develop successfully. While adults and grown up children may tolerate a temporary insufficiency of specific nutrients and recover when nutritional requirements are satisfied, the developing embryo/fetus may have disturbed development and may develop anomalies when essential nutrients are lacking in critical periods during pregnancy.
Alterations within one-carbon (1-C) metabolism have been appointed as having an important influence on fetal development 16. 1-C metabolic pathways utilize
substrates such as methionine, vitamin B12 and folate, and drive the synthesis of proteins, biogenic amines and lipids, which are important for early fetal growth. The pathways also drive the synthesis and methylation of DNA and histones essential for the regulation of gene expression. Deficiencies of periconceptional 1-C metabolism may affect both fertility and fetal development, as appointed by several animal studies 17. One of the best known relations is the link between preconception folate
deficiency and the risk of neural tube defects 18. Also the risk of hypospadias and
offspring adiposity is associated with dietary intake of nutrients related to one-carbon metabolism (folate, choline, vitamins B12 and B6, thiamine, riboflavin, methionine and zinc) 19,20.
An important, well-known, historical example on the possible consequence of maternal nutrient deficiencies on their offspring, are the observed effects of the Dutch hunger winter. A period of famine that took place in the German-occupied Netherlands, especially in the population in the western provinces, during the winter of 1944-45. It was shown that women exposed to famine during the first trimester of pregnancy, had an increased preterm birth risk and stillbirth risk, and obesity and cardiovascular diseases were more common in the surviving offspring later in life 21,22.
9
Nevertheless, offspring’s birth weight was within normal range whereby the placentaweight was increased 23. Contrary, the birth weight of the offspring of women exposed
to famine during mid- and late gestation was decreased compared to offspring of unexposed mothers, and they had an increased risk of impaired glucose tolerance as adults 24. The fact that placental weight increased in pregnancies of women exposed
to famine at the first trimester of pregnancy, while birth weight was within the normal range, can be interpreted as a compensatory mechanism by the placenta for the reduction in maternal nutrient intake. When famine occurred in mid or late gestation, it was possibly past this window of opportunity, and there was perhaps no time or opportunity to adapt to the relative short period of undernutrition, resulting in low birth weights offspring.
For the offspring from women exposed to famine at first trimester, birth weight may not be an appropriate proxy measure of undernutrition in pregnancy, as the placenta may have compensated for this. However, due to the widespread use of external standardized scales of measurement, birth weight (adjusted for gestational age) is generally considered as a very reliable measured indicator of physical development of a fetus or an infant. Infants with low birth weight, whereby generally a cutoff less than 2500 grams at term is used, are more likely to experience adverse pregnancy outcome, including stillbirth 25,26. Also increased birth weight, more specifically fetal
macrosomia (defined as birth weight ≥ 4,000 g), is related with adverse pregnancy outcome. Fetal macrosomia is a common adverse infant outcome of gestational diabetes mellitus if unrecognized and untreated in time. For the infant, macrosomia increases the risk of shoulder dystocia, clavicle fractures and brachial plexus injury and increases the rate of admissions to the neonatal intensive care unit 27.
Although birth weight can give a good first indication on the potential risk of adverse pregnancy outcomes for the infant, current literature, including results from the Dutch hunger winter, show that it is advisable to look at more variables than only birth weight. This mainly because of the fact that there are many cases too small according to their intrinsic growth potential, but are normal in birth weight according to the population based reference chart. These infants are, unjustified, not diagnosed as being growth restricted, while having increased risk for adverse outcomes (see ‘2.2
Abnormal fetal growth (the “SGA-FGR confusion”)’) 28–30. The birth weight to placental
weight (BWPW)-ratio, often described as indicator of placental efficiency, may be a more informative indicator of both supply and transfer of nutrients to the infant, and consequently of the potential adverse risks for the infant. In women exposed to famine during first trimester of pregnancy in the Dutch hunger winter, placental
weight was increased, but birth weight was not, leading to an increased BWPW-ratio
23. This is in line with our results described in chapter 7, where we showed that low
BWPW-ratio was associated with both neonatal and maternal morbidity. A decreased BWPW-ratio is also seen with maternal smoking 31,32 and maternal anemia 33. Maternal
smoking is an independent major risk factor for stillbirth, and there is a large body of evidence supporting associations between stillbirth and placental dysfunction
34. The association between decreased BWPW-ratio and maternal anemia is being
explained by increased placental weight due to compensatory placental hypertrophy since oxygen supply to the fetus is reduced. Maternal anemia may also be secondary to undernutrition during pregnancy.
Although nowadays severe undernutrition (starvation) and extreme low energy intake are not very common for pregnant women in the western world, however differences in nutrient contribution to total energy intake and diet quality (e.g. eating the right nutrients) intake do exist on a regular base. A study from Godfrey et al. showed that high carbohydrate intake in early pregnancy, especially combined with low dairy protein intake in late pregnancy, was associated with a low ponderal index in the offspring, meaning that these infants were thin at birth 35. Moreover, even
maternal dietary intake in the preconception period may affect pregnancy outcomes. Animal studies have suggested that diet may influence oocyte quality during the preconception period, as well as placenta and early embryonic development during the first trimester of pregnancy 36, thereby shifting the interest of research from dietary
intake during pregnancy, to the preconception phase.
In chapter 4 and chapter 5 we showed that in a large cohort of 1698 healthy women who delivered term infants, dietary intake in the preconception period was associated with birth weight (adjusted for gestational age) of their offspring. The women were relatively normal in terms of body mass index (BMI) when compared with the WHO classification, and their diet quality was representative for the complete LifeLines Cohort 37. In chapter 4 we demonstrated that the preconception
intake of polysaccharides was positively associated with birth weight (adjusted for gestational age). This association was independent of energy intake and maternal demographic covariates, such as maternal preconception BMI. Analyses in stratified groups of maternal preconception BMI, showed that increased intake of (animal) protein, fat, total carbohydrates, polysaccharides and mono-disaccharides were positively associated with birth weight only in the group of women with the 20% lowest BMI within our cohort. Although results showed that the interaction term between macronutrient intake and BMI quintiles was not significant, we do not think
9
this completely invalidates our findings, given the consistent significant results withmacronutrients intake in the lowest BMI quintile. Further research should examine the association of dietary intakes over groups of BMI, surely also in the group of women with low preconception BMI.
As it is not macronutrients we consume but food, we further investigated the association of preconception dietary intake with birth weight (adjusted for gestational age), by defining the dietary intake in food groups.
In chapter 5 we demonstrated that in particular preconception intake of ‘’artificially sweetened products” was also shown to be associated with increased birth weight. Artificial sweeteners were introduced into our diets with the intention to reduce caloric intake and normalize blood glucose levels, while people could have the sweet taste to their food. However, artificially sweetened products are increasingly subject to research as they may have adverse effects 38–40. A recent study from Suez et al.
found that these artificial sweeteners are altering the gut microbiota and thereby inducing glucose intolerance 39,40, which is associated with increased risk of diabetes
and obesity. In our study, we found an association between preconception intake of artificial sweetened products and increased birth weight, which may suggest that these sweeteners are inducing metabolic changes already from preconception onwards in the mother affecting the offspring’s birth weight. As these analyses were adjusted for maternal preconception BMI, these results cannot be explained by a potential effect of BMI. These results highlight the need to better understand how artificial sweeteners may affect the metabolism of the mother and her offspring already from preconception onwards.
Based on the results we speculate that the potential underlying mechanism for results found both in chapter 4 and chapter 5 is the (fetal) glucose metabolism. Theoretically, intake of polysaccharides may activate (fetal) gluconeogenesis, and “artificial sweetened products” may induce glucose intolerance; which can result in fetal hyperglycemia, causing fetal hyperinsulinemia and increased utilization of glucose, eventually increasing birth weight, and potentially resulting in macrosomia
27. This is the same underlying mechanism as with maternal (gestational) diabetes
mellitus and increased risk of macrosomia of the offspring 27. Unfortunately, we do
not have information on (gestational) diabetes, or glucose sensitivity, in our cohort, so we can therefore not adjust for the potential effect on the pregnancy outcomes examined. Although the incidence in gestational diabetes mellitus (GDM) may be slightly higher in the north of The Netherlands 41, the overall incidence of gestational
criteria used 42, and we therefore consider it as too low to have any major effect on
the association found in this study.
Focus on preconception period- challenges and opportunities
The preconception period has been recognized as one of the earliest sensitive windows for human development. It is an optimal period for potential intervention (lifestyle) changes and support of appropriate health care, as interventions that focus on this period have the potential to affect not only pregnancy but also later health outcomes. Increased emphasis on preconception care acknowledges that achieving substantial changes in lifestyle often requires substantial effort and involves making additive changes over time. Addressing behavioral changes before conception can allow a woman to identify constructive actions and to delay conception until she has achieved a healthier physical state. This will increase her chances for a successful pregnancy outcome. However, such preconception lifestyle changes may be difficult to accomplish, mainly when the eventual profit for the woman remains unclear. Studies have shown that multiparous women, women with unplanned pregnancies or lower socio-economic status are less likely to change their preconception lifestyle43. With any potential intervention these factors need to be taken into account, as this
may also be the population with the highest needs 44–46.
This preconception period can only be identified in retrospect after a woman has become pregnant, it is therefore important to consider the entire fertile period as potentially preconceptional. Some women, who have unplanned pregnancy, do not encounter a ‘conscious’ preconception period and therefore no possible influencing intervention factors can be installed. In other words, the preconception period may vary between one month to several years, and for some, pregnancy may never be achieved. The uncertainty of the duration of this preconception period may form a motivational barrier for future parents to act upon any potential advice given in this period. However, potential lifestyle changes may also benefit health in general. Therefore, providing nutrition assessment and intervention to encourage an optimal state of health benefits also the women who do not desire pregnancy at that point in time. For these women, the provision of nutritional care as part of a periodic health assessment can be a mechanism for promoting their health over the short term, with the potential for preventing problems in the event of an unplanned pregnancy and for preventing or delaying the development of chronic diseases in later life 47.
9
Who should we target for potential preconception intervention?
Adverse environmental exposure or insufficiency of required nutrients during preconception affects people of all regions, ages and social and economic groups. However, it is more likely to cause disease in certain individuals. The vulnerability to environmental aspects during preconception is dependent on several extrinsic and intrinsic factors. Extrinsic factors that contribute to vulnerability include poor housing, lack of stores and means (e.g. money) to purchase healthy food, poor access to health care and for exposure to air pollution: living in areas with greater traffic density 48,49.
Intrinsic factors that may increase vulnerability are age, pre-existing disease, genetic and epi-genetic variation and obesity 50,51.
Results from the Southampton Womens Study 52 showed that education had a
significant impact on the association between pregnancy status and vegetable intake prior to pregnancy 53. Their results suggest that more educated women may improve
their diet when they have the intention to become pregnant, while less educated women do not 53.
Women with obesity are often subject to lifestyle intervention studies and for studies of any potential beneficial effects on their weight 54–58. In chapter 3 we showed that
preconception macronutrient intake in women with low to normal BMI was associated with higher birth weight of their offspring. This suggests that any dietary assessment as well as advice should be tailored to the weight status of the mother.
Dekker et al. examined spatial clustering of dietary patterns in the Lifelines Cohort Study 59. They identified specific geographical areas with high and low dietary
pattern scores for four dietary patterns in the north of the Netherlands, irrespective of age and gender. This indicates that there is a difference in dietary intake among different geographical regions, even within the relatively small northern area of the Netherlands. This variable needs to be accounted for when performing analyses and it may be used as target for specific groups to tackle unhealthy dietary behavior and may possibly be used for the implementation of intervention strategies.
In short, vulnerability increases by inequality in health and environmental exposures (e.g. air pollution and maternal dietary intake). Reducing vulnerability across a population and increasing the chance of optimal pregnancy outcome requires appropriate intervention studies tailored to demographic variables. In addition, strategies to achieve healthy equality for vulnerable communities require societal and governmental commitment. Currently, there are multiple initiatives to improve preconception health. In the Netherlands, the minister of Health, Welfare and Sport endorses the importance of the first thousand days (from conception to second
birthday) for health in later life, and started the national program Kansrijke start (‘Promising start’), aiming to provide all children with an equally promising start in life 60.
Methodological challenges
Dietary assessment and complexity of diet
Diet consists of different nutrients of which some exert beneficial effects and others exert detrimental effects. Combinations present in food items, food groups and dietary patterns might have synergistic, antagonistic and food matrix effects. In this thesis, several aspects of diet, including macronutrients (chapter 4) and food groups (chapter 5), in relation to birth weight have been studied.
Relationships between dietary intake and any health (or pregnancy) outcome are difficult to define for both biologic and behavioral reasons. First, types and amounts of food intake may be related to important non-dietary determinants of health outcome, such as age, smoking, exercise and occupation. These determinants may confound and modify any potential relationship of pregnancy outcome with diet. In addition, the intake of specific nutrients tends to be intercorrelated 61, and therefore
associations with one nutrient may be confounded by other aspects of the diet. Furthermore, intake of one nutrient may modify the absorption, metabolism, or requirement of another nutrient, thus creating biological interaction. Due to these complexities the relationship between one single dietary factor and health outcome cannot be examined in isolation, in particular not for macronutrients providing energy. It is necessary to employ multivariate techniques, including both stratified analyses and statistical models, to adjust for any potential confounders and to examine interaction. Therefore, in chapter 4, several statistical models were performed, both in the complete cohort as in stratified analysis in BMI quintiles. As maternal (preconception) BMI is an important target for lifestyle intervention study related to pregnancy outcomes 56,57.
There are several options in obtaining information on dietary intake from study participants, including food frequency questionnaires (FFQ), 24-hour dietary recall and diet record method. Each method has its advantages and disadvantages, and it remains challenging to accurately measure dietary intake. The use of a FFQ has become the primary method for measuring dietary intake in epidemiological studies, as this report usual intake over longer periods of time and minimizes error of day-to-day consumption 62. Contrary to methods like 24-hour dietary recall and diet record
9
method, which are based on foods and amounts actually consumed on one or acouple of days. However, unfortunately the FFQ is prone to recall bias 63, misreporting
of dietary intake which is often affected by social desirability bias 64 and personal
characteristics 65 . To illustrate, obese people tend to underestimate their energy
intake 66,67, which was also shown in the ‘Perined-Lifelines linked birth cohort’ in
chapter 4. In contrast, advantages of the FFQ include relatively low administrative costs and time and the ability to assess regular and longer term intake 62. To study
possible associations between dietary intake retrieved from a FFQ in relation to health outcomes, larger datasets are needed.
Linkage of health care databases- challenges and opportunities
Linkage among multiple (health care) datasets can help to adequately study research questions that require large sample sizes or detailed data regarding populations that are potentially difficult to reach, or to assess the effectiveness of care delivery. Linking datasets is increasingly being performed and will allow for a better understanding of health outcomes for chronic diseases and identification of new risk factors or disease
68,69. Linkage can thus be performed without having to track individual data of study
participants. However, this linkage also raises privacy and technical issues and can be a time-consuming process.
In chapter 3 we described the methodology of linking a large population based cohort study in the Netherlands (The Lifelines Cohort Study) and the Dutch national birth registry (Perined), creating the ‘Perined-Lifelines linked birth cohort’. Within both these databases, data was pseudonymised, and thus all unique patient identifier were deleted. Therefore, the linkage was performed through a ‘trusted third party’, by creating corresponding pseudonyms in both datasets based on three personal linking variables. The Perined-Lifelines linked birth cohort demonstrated the feasibility of linking databases through a pseudonymised linking procedure. Our linkage method is generalizable to linkage of administrative data in other contexts where data is only provided anonymously to researchers. It is expected that there will be an increase in anonymously provided data with the adoption of the ‘General Data Protection Regulation (AVG)’ in the European Union. Linkage was done using three variables; ZIP code, birth date of the participant from Lifelines and birth date of their child, decreasing the likelihood of errors (in terms of missed-matches and false matches), and resulting in a high linkage rate of 86%. To facilitate such a linking process, strict access arrangements and secure data transfer procedures needed to be established. In addition, secure physical locations were created with restricted network and no
internet access. The full procedure of the development of the linked cohort was exhaustive as several parties were involved in this linking procedure, and agreements had to be made and signed between all the parties.
Another limitation of linking databases is the possible imperfect nature of data for research purpose, as often some important information for the specific research question may be missing and data has not be collected for research as its primary aim 70. However the large sample size and representativeness of linked data offer a
cost-effective alternative to traditional cohort studies for studying pregnancy and childhood outcomes, providing valuable maternal information prior to pregnancy and birth, and maternal risk-factors for adverse birth outcomes 71–74. Whilst existing
birth cohort studies across Europe and the United States have provided important information on short- and long-term outcomes 75, they are associated with major
costs, are subject to limited numbers for assessing rare conditions, and experienced difficulties in participant recruitment due to increasing participant burden, and suffer from recruitment bias and selectivity in follow-up 76. Mainly the latter has to be taken
into account when the representativeness of the specific cohort study is determined. These limitations show the need for exploring and finding alternative approaches using existing data sources 77.
PLACENTAL DEVELOPMENT AND PREGNANCY OUTCOME
Reflection on results of association of ultrasonic measurements,
placental morphometry and pregnancy outcome
The placenta is vital for fetal development and growth. With continuously changing fetal demands, the placenta is adapting its physiology, architecture and signaling throughout gestation. Reduced placental nutrient transport capacity and/or impairment in placental development, including reduced placental size, contribute to placental dysfunction 78. Placental dysfunction together with compromised maternal
nutrition status, appear to impair placental amino acid transport 79.
A large number of studies have investigated factors that may affect specific transport mechanisms in the placenta 80–82. To illustrate, reduced amino acid availability to the
fetus can result from low maternal amino acid concentrations, reduced placental transport and umbilical uptake of certain amino acids, or may also be reduced by high maternal concentrations because of competition of transporters 83. These
studies provide strong evidence that placental function (i.e. the ability to provide adequate nutrient and oxygen supply to the fetus) is modified by both maternal and fetal factors. This is in line with results from chapter 6 where we found that both the
9
uterine and umbilical artery Doppler flow velocity waveforms, the vascular functionon respectively the maternal and fetal side of the human placenta, are associated with both placental area and placental weight following birth. Both placental area and placental weight after birth were related to the rate of fetal growth between 20 and 36 weeks of gestation. This underlines the fact that normal fetal growth is dependent on both the maternal supply of nutrients, as well as fetal uptake of nutrients within the placenta. Higher values of UtA and UmA Doppler flow velocity waveforms by the pulsatility index (PI) are thought to reflect increased resistance in the vascular bed supplied 84. It is known that high resistance of UtA Doppler is associated with
defective trophoblast invasion of the placental bed arteries 85. An important finding in
chapter 6 is that the maternal surface area of the placenta was a major determinant of the UtA resistance. This suggests that it is not only the depth of the invasion of the trophoblast, but also to the total area of the placenta, as this will probably determine the total number of vessels that are invaded. Subsequently, the capacity for nutrient and oxygen transfer is reflected in the placental surface area for transport and hence the placental size/volume 86. As previously mentioned, this ‘placental
efficiency’, i.e. the capability of the placenta to maintain sufficient nutrient and oxygen supply, is described to be reflected by birth weight placenta weight (BWPW)-ratio 87.
In animal studies, a high BWPW-ratio (thus a relatively high birth weight compared to placental weight) was found to be positively correlated with placental uptake of ‘nutrient transport system A amino acid’, indicating that nutrient transfer per gram placenta must have increased compared to low or normal BWPW-ratio 88.
In chapter 7 we observed two key patterns between BWPW-ratio with fetal growth, utero-placental Doppler and neonatal and maternal morbidity. First, low BWPW-ratio was associated with both neonatal and maternal morbidity, including maternal obesity (BMI>30kg/m2), diabetes (pre-gestational and gestational) and preeclampsia.
Second, high BWPW-ratio was associated with both higher UmA PI (36 weeks of gestation) and higher UtA PI (20 weeks of gestation), but not with neonatal morbidity. Previous studies have shown that placentas of obese women have a significantly higher weight at birth 89,90, and that there is an association with fetal overgrowth,
which may be related to increased ‘placental system A amino acid transporters 1 and 2’ (SNAT1 and SNAT2) and insulin growth factor (IGF-1) signaling 91. Also, birth weight is
demonstrated to be higher in infants of obese women 89,92. A recent study in a cohort
of women with gestational diabetes showed a relation with diabetes related factors and both birth weight and placental weight, with a larger effect on placental weight
obesity is associated with a relatively larger placenta compared to the weight of the fetus. This could either be due to placentas failing to adapt placental nutrient transfer according to fetal demand, or because growth factors (e.g. IGF-1) are driving excessive placental growth in obese women 91. Our findings suggest that abnormal
growth and development of the placenta in obese women may explain some of the epidemiological associations of maternal obesity, such as increased risk of stillbirth and pre-eclampsia. This interpretation is supported by the observation that a low BWPW-ratio was associated with neonatal morbidity, even after adjusting for the associations with maternal diabetes and obesity, which supports the ‘efficiency’ theory of BWPW-ratio (less efficient placenta, less nutrient transfer to the fetus). High BWPW-ratio was associated with a higher UmA PI and there was a similar but weaker association with UtA PI. In chapter 6 we performed analysis within the same cohort, and found that low placenta weight was associated with higher UmA and higher UtA PI 94. This indicates that both absolute placenta size and relatively
reduced placental size (high BWPW-ratio) may be associated with higher resistance in the vascular bed, reflected by higher values of UtA and UmA Doppler flow velocity waveforms by the PI 84.
High resistance in the maternal (and/or fetal) vascular bed is a pathophysiological phenomenon often seen in FGR. During the course of normal, healthy, pregnancies, umbilical artery resistance decreases gradually throughout gestation, and increases with placental insufficiency 95. It has been proposed that impaired placental
perfusion causes FGR and morphologic changes of the placenta, by chances in the placental vascular bed in chronic fetal hypoxia (i.e. stimulation of vasculogenesis as a compensatory mechanism of the placenta due to the hypoxia, resulting in syncytial knots (Tenney-Parker changes) 96), which causes oxidative stress of the fetal
vasculature 97,98. Besides, FGR related hypoxia influences angiogenesis via various
growth receptors 99, which may result in altered placental morphometry. Thereafter
we wanted to investigate the possible role of placental morphometry in detection of FGR. In chapter 8 we gave an overview on studies that investigated antenatal or postnatal placental morphometry in relation to FGR. These studies found that FGR was associated with changes in placental morphometry such as decreased placental surface areas, decreased placental diameter, and decreased placental volume and weight 100–102.
Although several studies have looked at the relationship between placental morphometry and fetal growth and birth weight, only limited studies have used a definition that reflects truly growth restricted fetuses. Most of the studies use
9
“small” (population based) instead of “too small” (individually based) thus “small forgestational age” interchangeably with “fetal growth restriction”.
Abnormal fetal growth (the “SGA-FGR confusion”)
Fetal growth abnormalities were primarily based on birth weight below or above a reference cut-off on the population-based reference chart. For FGR, the most commonly used cut-off was birth weight below the 10th percentile 103. However, this
is in essence not FGR but small for gestational age (SGA). FGR is defined by the fetus not being able to reach its intrinsic growth potential. Similarly to SGA fetuses that may not be growth restricted (some may be constitutionally small), growth restricted fetuses may not be SGA. This means that FGR overlaps with, but is not synonymous to SGA 104, (“SGA-FGR confusion”). There are three important diagnostic
consequences what this “SGA-FGR confusion” elicits. First, about 75% of fetuses who are SGA (and therefore many who are FGR) remain unrecognized until they are born and the diagnosis is made on the baby scale, postnatally 103,105. Some of them are
severely compromised, as they are potentially exposed to long term sequelae, or even stillborn. Second, fetuses who are born too small according to the reference chart, may be physiologically small and have an appropriate growth according to their individual growth potential. They are therefore not at risk for any diseases related to FGR, but are exposed to unnecessary investigations. Finally, many cases are too small according to their intrinsic growth potential, though not small in the population based reference chart and fall within the normal variation of appropriate grown fetuses and having a normal birth weight as infant according to the reference chart.
However, there is not a single cutoff above which all infants have grown appropriately, or below which none have grown appropriately according to their individual growth potential. If SGA is used as the proxy for FGR in clinical practice, healthy fetuses without FGR will be subject to unnecessary monitoring interventions and those fetuses with unrecognized FGR don’t receive the potential interventions they should receive. In addition, if SGA is used as proxy for FGR in research, the study population is diluted by healthy small fetuses, making it difficult to adequately interpret results from association studies.
It is therefore fundamental to have easy and inexpensive diagnostic tools to detect only those fetuses who have an increased risk of adverse short- and long-term outcomes when not delivered in time 28–30. Placental morphology, including
BWPW-ratio, may play a relevant role after birth in the right identification of neonates who suffered from FGR during pregnancy.
As the major challenge of FGR is the diagnostic standard. An international Delphi procedure conducted in 2015 among 56 experts on FGR to establish a consensus definition on both early (<32 weeks of gestation) and late FGR (32 weeks of gestation)
106. In this definition parameters of placental function are included besides the
parameters of the fetus. Although the definition has been used widely since publication, the clinical applicability of this definition in predicting FGR is yet to be assessed.
With better identification of factors that are associated or causative for FGR, the infant who may be small or not too small, can be identified as ‘having an increased risk’. Several risk factors for FGR have been identified and can be classified into maternal, placental and fetal factors 107,108. However, recently more studies have been
conducted on the role of the gross examination of the placenta, with weight, shape, cord insertion.
The use of placental morphometry- challenges and possibilities
In chapter 8 we provided an overview on the possible role of placental morphometry in the detection of FGR. Although postnatal placental morphometry is cheap and easy to obtain, the postnatal placenta will only aid in the diagnosis of the adverse pregnancy conditions/outcome, including the growth restricted neonate, retrospectively. This makes birth a possible evaluation moment for FGR related follow-up monitoring. In addition, BWPW-ratio is an informative, relatively easy, measurement on “placental efficiency” and consequently placental function, and has been used in several studies investigating association between placental function/efficiency with (adverse) pregnancy outcome 109,110. However, it has to be acknowledged that thedeterminants of any given placental function are highly complex and either increased or decreased ratios may vary with these factors in multiple ways. For example, considering placental oxygen transfer several determinants can be included: uterine blood flow, maternal and fetal hemoglobin (concentration and affinity), matching and mismatching of maternal and fetal placental perfusion, the diffusion capacity of the placental surface (including the extent and thickness of the vasculo-syncytial membranes) and the oxygen utilization of the placenta itself (see Carter 2015 111 for
review). Given the underlying complexity of the function of the organ and the relatively crude nature of the BWPW-ratio it is not surprising that associations are complex 112,113.
Although we expect that aspects of placental morphometry can play a role in the diagnostic process of FGR, additional components as part of a multiparameter model might need to be taken into account. Within this model ultrasonic measurements
9
and biochemical biomarkers that reflect placental function and adverse pregnancyoutcome, including FGR, can be included. It has already been shown that when an elevated sFLIT:PIGF ratio (at 36 weeks of gestation) was combined with low ACGV, and a composite measure generated by the FGR definition from the Delphi procedure
114, they are indicators of FGR 115. The relation with placental morphometry has not
been examined yet.
IMPLICATIONS FOR PUBLIC HEALTH AND CLINICAL
PRACTICE
In the first part of this thesis, associative relationships between adverse environmental factors during preconception and pregnancy outcome have been shown. Our results showed that within a large representative cohort, preconception dietary intake is actually associated with birth weight, even within this relatively healthy cohort of term born infants in which there was minimal variation between gestational age adjusted birth weight, and whereby adjustment for important demographical variables was conducted. As these relationships described in these studies are associative and not based on causality, it is not possible to describe concrete dietary advices based on these results. However, the results in this part of the thesis can be of important value to inform further studies, which may hopefully eventually help in the development of potential dietary recommendations for women during preconception. Within these recommendations accurate measurements of maternal weight and gestational weight gain need to be taken into account, as they may contribute to the associations between dietary intake and pregnancy outcome.
In the second part of the thesis, we showed the potential of simple placental morphometry measures after birth for in relation to placental function (ultrasonic measurements of the umbilical and uterine artery) and fetal growth during pregnancy and pregnancy outcome. Currently in clinical practice, placental measures, including placental disk shape, diameter, surface area, disk thickness, weight, location of cord insertion relative to the edge of the placental disk, are not routinely measured and if measured, not in a standardized way despite previous advise 116. When the placenta
is investigated by a pathologist these measurements are performed, although some measure the placenta in formalin and other measure the placenta fresh. Despite the advice to perform measurements in a standardized way, and increasing evidence that features of placental morphometry are linked biologically to the functional capacity of the placenta 117, it has received little clinical interest. Based on our results we
birth in clinical practice. Using this assessment in a routine matter, important and valuable information can be given on the potential adverse effects of the infant, with the opportunity to increase the chance of a healthy life.
SUGGESTIONS FOR FUTURE RESEARCH AND OVERALL
CONCLUSION
For the studies described in chapter 3-5, information on pregnancy outcome was derived from Perined. As this is a healthcare database, valuable information for research purpose (e.g. important outcome measurements or possible confounders) may be missing. In addition, it has been described that in the Netherlands, information on perinatal data is often not recorded digitally, and that the collection of these data is poorly structured 118. To illustrate, information on several adverse pregnancy
outcomes (e.g. gestational diabetes, preeclampsia), but also demographical information like smoking, maternal BMI and gestational weight gain is not always recorded (correctly), and therefore for research purpose unusable. Standard recording of more data regarding pregnancy outcomes in health care databases such as Perined could be beneficial in the future. Instead of creating new large cohort studies, we recommend focusing on the data that is readily available, without having high costs and increase the participant burden. We suggest improving both the quality of the data of (healthcare) databases and the cooperation of the involved parties. In future, linkage with other databases, for instances the prenatal screening database Peridos, can be installed. Although the linkage of the Lifelines Cohort Study and Perined was a time consuming process, with our methodology we have proven the ability to create a reliable linked database and it allowed for exploration of specific associations between diet characteristics and pregnancy outcome. We expect that in the future such linking processes could be accelerated since this road has now already been taken before, thereby creating the possibility to also examine the association of preconception maternal dietary intake with other (adverse) pregnancy outcome (e.g. congenital anomalies, preeclampsia, neonatal morbidity).
Within this thesis we have investigated the relationship between environmental factors and pregnancy outcomes (e.g. congenital anomalies, birth weight) on one hand, and placental development and pregnancy outcome (e.g. neonatal morbidity, birth weight) on the other hand. It would be interesting to investigate the association between environmental factors during preconception and placental development, to determine the biological pathways that can be linked to placental dysfunction and corresponding development. Hereby the potential mechanism between
9
preconception dietary intake and pregnancy outcome can be further elucidated.Also the association with long-term adult cardiovascular and metabolic health risks needs to be investigated.
with health outcomes on the longer term can be investigated. For future studies we suggest examining whether associations found between preconception dietary intake and birth weight can also be confirmed in other large cohort studies with other distribution in terms of demographic representation (e.g. ethnicity, education level). To be able to draw meaningful conclusions in the future on potential adverse or beneficial effects of dietary intake, more details on maternal and perinatal outcome should be used. In addition, before evidence based dietary recommendations can be formulated, studies should focus on whether there are specific thresholds of dietary intake (e.g. of micro- and macronutrients) above or below which the specific nutrient has a potential adverse or beneficial effect on pregnancy outcome. In addition, the potential importance of intake of key essential trace elements (e.g. magnesium, iodine, iron, copper, zinc) in the preconception period should be further considered 119.
When FGR is used as outcome measure it is important that analyses will be performed in a group of optimally phenotyped cases of FGR to strengthen the association with any potential screening test, including placental morphometry. Further use of placenta morphometry in the diagnostic process of FGR needs to be explored in combination with for example BWPW-ratio, Doppler indices, ACGV, macroscopic placental surface area and volume, and placenta serum biomarkers. With this also paternal and maternal characteristics, e.g. height/stature, need to be taken into account, as literature has shown that these characteristics also influence the growth of the fetus 120.
Ideally for every fetus or infant all relevant factors associated with (adverse) health outcome are assessed to give a rational approach to answer the question whether the fetus has reached its full growth potential. This information can inform health care throughout childhood and can be of important value in any prediction of the growth, development and future health of both mother and child.
REFERENCES
1. Pasupathy, D. et al. Study protocol. A prospective cohort study of unselected primiparous women: the pregnancy outcome prediction study. BMC Pregnancy Childbirth 8, 51 (2008).
2. Genuis, S. J. What’s out there making us sick? J. Environ. Public Health 2012, 605137 (2012). 3. Genuis, S. J. & Genuis, R. A. Preconception
Care: A New Standard of Care within Maternal Health Services. Biomed Res. Int. 2016, 6150976 (2016).
4. 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).
5. Pedersen, M. et al. Ambient air pollution and low birthweight: a European cohort study (ESCAPE). Lancet. Respir. Med. 1, 695–704 (2013).
6. Effects of Air Pollution on Children’s Health and Development. (2005).
7. Dadvand, P., Rankin, J., Rushton, S. & Pless-Mulloli, T. Ambient air pollution and congenital heart disease: a register-based study. Environ. Res. 111, 435–41 (2011). 8. Schembari, A. et al. Traffic-related air pollution
and congenital anomalies in Barcelona. Environ. Health Perspect. 122, 317–23 (2014). 9. Bouty, A., Ayers, K. L., Pask, A., Heloury, Y. &
Sinclair, A. H. The Genetic and Environmental Factors Underlying Hypospadias. Sex. Dev. 9, 239–259 (2015).
10. Baccarelli, A. & Bollati, V. Epigenetics and environmental chemicals. Curr. Opin. Pediatr.
21, 243–51 (2009).
11. 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).
12. Salafia, C. & Shiverick, K. Cigarette Smoking and Pregnancy II: Vascular Effects. Placenta
20, 273–279 (1999).
13. Kawashima, A. et al. Effects of maternal smoking on the placental expression of genes related to angiogenesis and apoptosis during the first trimester. PLoS One 9, e106140 (2014). 14. Zdravkovic, T., Genbacev, O., McMaster, M. T.
& Fisher, S. J. The adverse effects of maternal smoking on the human placenta: A review. Placenta 26, S81–S86 (2005).
15. Philibert, R. A., Beach, S. R. H. & Brody, G. H. Demethylation of the aryl hydrocarbon receptor repressor as a biomarker for nascent smokers. Epigenetics 7, 1331–8 (2012). 16. Stabler, S. P. & Allen, R. H. VITAMIN B12
DEFICIENCY AS A WORLDWIDE PROBLEM. Annu. Rev. Nutr. 24, 299–326 (2004).
17. 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).
18. Peake, J. N., Copp, A. J. & Shawe, J. Knowledge and periconceptional use of folic acid for the prevention of neural tube defects in ethnic communities in the United Kingdom: Systematic review and meta-analysis. Birth Defects Res. Part A Clin. Mol. Teratol. 97, 444– 451 (2013).
19. Yajnik, C. S. et al. Vitamin B12 and folate concentrations during pregnancy and insulin resistance in the offspring: the Pune Maternal Nutrition Study. Diabetologia 51, 29–38 (2007). 20. Carmichael, S. L. et al. Hypospadias and
Intake of Nutrients Related to One-Carbon Metabolism. J. Urol. 181, 315–321 (2009).
9
21. Roseboom, T., de Rooij, S. & Painter, R. The Dutch famine and its long-term consequences for adult health. Early Hum. Dev. 82, 485–491 (2006).
22. Susser, M. & Stein, Z. Timing in Prenatal Nutrition: A Reprise of the Dutch Famine Study. Nutr. Rev. 52, 84–94 (2009).
23. Lumey, L. H. Compensatory placental growth after restricted maternal nutrition in early pregnancy. Placenta 19, 105–11 (1998). 24. 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).
25. Pilliod, R. A., Cheng, Y. W., Snowden, J. M., Doss, A. E. & Caughey, A. B. The risk of intrauterine fetal death in the small-for-gestational-age fetus. Am. J. Obstet. Gynecol.
207, 318.e1-6 (2012).
26. Bukowski, R. et al. Fetal growth and risk of stillbirth: a population-based case-control study. PLoS Med. 11, e1001633 (2014). 27. KC, K., Shakya, S. & Zhang, H. Gestational
Diabetes Mellitus and Macrosomia: A Literature Review. Ann. Nutr. Metab. 66, 14–20 (2015).
28. Jaddoe, V. W. V et al. First trimester fetal growth restriction and cardiovascular risk factors in school age children: population based cohort study. BMJ 348, g14 (2014). 29. Meher, S., Hernandez-Andrade, E., Basheer, S.
N. & Lees, C. Impact of cerebral redistribution on neurodevelopmental outcome in small-for-gestational-age or growth-restricted babies: a systematic review. Ultrasound Obstet. Gynecol. 46, 398–404 (2015).
30. Miller, S. L., Huppi, P. S. & Mallard, C. The consequences of fetal growth restriction on brain structure and neurodevelopmental outcome. J. Physiol. 594, 807–823 (2016).
31. Wang, N. et al. The effect of maternal prenatal smoking and alcohol consumption on the placenta-to-birth weight ratio. Placenta 35, 437–441 (2014).
32. Salavati, N. et al. Birth weight to placenta weight ratio and its relationship to ultrasonic measurements, maternal and neonatal morbidity: A prospective cohort study of nulliparous women. Placenta 63, 45–52 (2018). 33. GODFREY, K. M., REDMAN, C. W. G., BARKER,
D. J. P. & OSMOND, C. The effect of maternal anaemia and iron deficiency on the ratio of fetal weight to placental weight. BJOG An Int. J. Obstet. Gynaecol. 98, 886–891 (1991). 34. Smith, G. C. S. & Fretts, R. C. Stillbirth. Lancet
(London, England) 370, 1715–25 (2007). 35. Godfrey, K. M., Scientist, C., Professoddirector,
D. J. P. B., Nutritionist, S. R. & Statistician, C. O. Maternal birthweight and diet in pregnancy in relation to the infant’s thinness at birth. British Journal of Obstetrics and Gynaecology 104, (1997).
36. Kind, K. L., Moore, V. M. & Davies, M. J. Diet around conception and during pregnancy--effects on fetal and neonatal outcomes. Reprod. Biomed. Online 12, 532–41 (2006). 37. Vinke, P. C. et al. Development of the
food-based Lifelines Diet Score (LLDS) and its application in 129,369 Lifelines participants. Eur. J. Clin. Nutr. 72, 1111–1119 (2018).
38. Gardner, C. et al. Nonnutritive sweeteners: current use and health perspectives: a scientific statement from the American Heart Association and the American Diabetes Association. Diabetes Care 35, 1798–808 (2012).
39. Suez, J. et al. Artificial sweeteners induce glucose intolerance by altering the gut microbiota. Nature 514, 181–186 (2014).
40. Suez, J., Korem, T., Zilberman-Schapira, G., Segal, E. & Elinav, E. Non-caloric artificial sweeteners and the microbiome: findings and challenges. Gut Microbes 6, 149–155 (2015). 41. Goedegebure, E. A. R. et al. Pregnancy
outcomes in women with gestational diabetes mellitus diagnosed according to the WHO-2013 and WHO-1999 diagnostic criteria: a multicentre retrospective cohort study. BMC Pregnancy Childbirth 18, 152 (2018).
42. Koning, S. H., Hoogenberg, K., Lutgers, H. L., van den Berg, P. P. & Wolffenbuttel, B. H. R. Gestational Diabetes Mellitus:current knowledge and unmet needs. J. Diabetes 8, 770–781 (2016).
43. Goossens, J., Beeckman, D., Van Hecke, A., Delbaere, I. & Verhaeghe, S. Preconception lifestyle changes in women with planned pregnancies. Midwifery 56, 112–120 (2018). 44. Gipson, J. D., Koenig, M. A. & Hindin, M. J. The
effects of unintended pregnancy on infant, child, and parental health: a review of the literature. Stud. Fam. Plann. 39, 18–38 (2008). 45. DeFranco, E. A., Lian, M., Muglia, L. J. &
Schootman, M. Area-level poverty and preterm birth risk: A population-based multilevel analysis. BMC Public Health 8, 316 (2008).
46. Judge, K. Inequalities in infant mortality: Patterns, trends, policy responses and emerging issues in Canada, Chile, Sweden and the United Kingdom. Heal. Sociol. Rev.
18, 12–24 (2009).
47. Nutrition Services in Perinatal Care: Second Edition - PubMed - NCBI. at <http://www. ncbi.nlm.nih.gov/pubmed/25144096> 48. Sacks, J. D. et al. Particulate Matter–Induced
Health Effects: Who Is Susceptible? Environ. Health Perspect. 119, 446–454 (2011).
49. Schraufnagel, D. E. et al. Air Pollution and Noncommunicable Diseases: A Review by the Forum of International Respiratory Societies’ Environmental Committee, Part 1: The Damaging Effects of Air Pollution. Chest
155, 409–416 (2019).
50. Sood, A. et al. Wood smoke exposure and gene promoter methylation are associated with increased risk for COPD in smokers. Am. J. Respir. Crit. Care Med. 182, 1098–104 (2010). 51. Li, W. et al. Residential proximity to major
roadways, fine particulate matter, and adiposity: The framingham heart study. Obesity 24, 2593–2599 (2016).
52. Inskip, H. M. et al. Cohort profile: The Southampton Women’s Survey. Int. J. Epidemiol. 35, 42–8 (2006).
53. Crozier, S. R. et al. Do women change their health behaviours in pregnancy? Findings from the Southampton Women’s Survey. Paediatr. Perinat. Epidemiol. 23, 446–453 (2009).
54. van de Beek, C. et al. Women, their Offspring and iMproving lifestyle for Better cardiovascular health of both (WOMB project): a protocol of the follow-up of a multicentre randomised controlled trial. BMJ Open 8, e016579 (2018).
55. Karsten, M. D. A. et al. Determinants of successful lifestyle change during a 6-month preconception lifestyle intervention in women with obesity and infertility. Eur. J. Nutr. (2018). doi:10.1007/s00394-018-1798-7
56. van Elten, T. M. et al. Preconception lifestyle intervention reduces long term energy intake in women with obesity and infertility: a randomised controlled trial. Int. J. Behav. Nutr. Phys. Act. 16, 3 (2019).
9
57. Wekker, V. et al. Long-term effects of a preconception lifestyle intervention on cardiometabolic health of overweight and obese women. Eur. J. Public Health 29, 308– 314 (2019).
58. Hui, A. L. et al. Effects of lifestyle intervention on dietary intake, physical activity level, and gestational weight gain in pregnant women with different pre-pregnancy Body Mass Index in a randomized control trial. BMC Pregnancy Childbirth 14, 331 (2014).
59. Dekker, L. H., Rijnks, R. H., Strijker, D. & Navis, G. J. A spatial analysis of dietary patterns in a large representative population in the north of The Netherlands – the Lifelines cohort study. Int. J. Behav. Nutr. Phys. Act. 14, 166 (2017).
60. Rijksoverheid. Actieprogramma Kansrijke Start | Publicatie | Rijksoverheid.nl. (2018). at <https://www.rijksoverheid.nl/documenten/ publicaties/2018/09/12/actieprogramma-kansrijke-start>
61. Willett, W. C. in Nutritional Epidemiology (3rd edition) 1–17 (2013).
62. Willett, W. C. in Nutritional Epidemiology (3rd edition) 70–95 (2013).
63. Naska, A., Lagiou, A. & Lagiou, P. Dietary assessment methods in epidemiological research: current state of the art and future prospects. F1000Research 6, 926 (2017). 64. Hebert, J. R. et al. Social Desirability Trait
Influences on Self-Reported Dietary Measures among Diverse Participants in a Multicenter Multiple Risk Factor Trial. J. Nutr.
138, 226S–234S (2008).
65. Macdiarmid, J. & Blundell, J. Assessing dietary intake: Who, what and why of under-reporting. Nutr. Res. Rev. 11, 231–253 (1998). 66. Hill, R. J. & Davies, P. S. The validity of
self-reported energy intake as determined using the doubly labelled water technique. Br. J. Nutr. 85, 415–30 (2001).
67. Westerterp, K. R. & Goris, A. H. C. Validity of the assessment of dietary intake: problems of misreporting. Curr. Opin. Clin. Nutr. Metab. Care 5, 489–93 (2002).
68. Dokholyan, R. S. et al. Regulatory and ethical considerations for linking clinical and administrative databases. Am. Heart J. 157, 971–982 (2009).
69. Pasquali, S. K. et al. Differential Case Ascertainment in Clinical Registry Versus Administrative Data and Impact on Outcomes Assessment for Pediatric Cardiac Operations. Ann. Thorac. Surg. 95, 197–203 (2013). 70. Benchimol, E. I. et al. The REporting of studies
Conducted using Observational Routinely-collected health Data (RECORD) Statement. PLOS Med. 12, e1001885 (2015).
71. Bird, T. M. et al. Late Preterm Infants: Birth Outcomes and Health Care Utilization in the First Year. Pediatrics 126, e311–e319 (2010). 72. Johnson, K. E. et al. Methods of linking
mothers and infants using health plan data for studies of pregnancy outcomes. Pharmacoepidemiol. Drug Saf. 22, 776–782 (2013).
73. Lain, S. J., Nassar, N., Bowen, J. R. & Roberts, C. L. Risk Factors and Costs of Hospital Admissions in First Year of Life: A Population-Based Study. J. Pediatr. 163, 1014–1019 (2013). 74. Oliver-Williams, C., Fleming, M., Wood, A.
& Smith, G. Previous miscarriage and the subsequent risk of preterm birth in Scotland, 1980-2008: a historical cohort study. BJOG An Int. J. Obstet. Gynaecol. 122, 1525–1534 (2015). 75. Adams-Chapman, I. et al. Ten-year review of
major birth defects in VLBW infants. Pediatrics
132, 49–61 (2013).
76. Pearson, H. Massive UK baby study cancelled. Nature 526, 620–621 (2015).
77. Harron, K. et al. Linking Data for Mothers and Babies in De-Identified Electronic Health Data. PLoS One 11, 164667 (2016).
78. Zhang, S. et al. Placental Adaptations in Growth Restriction. Nutrients 7, 360–389 (2015).
79. Hayward, C. E. et al. Effect of maternal age and growth on placental nutrient transport: potential mechanisms for teenagers’ predisposition to small-for-gestational-age birth? Am. J. Physiol. Metab. 302, E233–E242 (2012).
80. Jansson, T. Amino Acid Transporters in the Human Placenta. Pediatr. Res. 49, 141–147 (2001).
81. Baumann, M. U., Deborde, S. & Illsley, N. P. Placental Glucose Transfer and Fetal Growth. Endocrine 19, 13–22 (2002).
82. Haggarty, P. Fatty Acid Supply to the Human Fetus. Annu. Rev. Nutr. 30, 237–255 (2010). 83. Regnault, T. R. H., de Vrijer, B. & Battaglia, F. C.
Transport and Metabolism of Amino Acids in Placenta. Endocrine 19, 23–42 (2002). 84. Bhide, A. et al. ISUOG practice guidelines:
use of Doppler ultrasonography in obstetrics. Ultrasound Obstet. Gynecol. 41, 233–39 (2013). 85. Mifsud, W. & Sebire, N. J. Placental Pathology
in Early-Onset and Late-Onset Fetal Growth Restriction. Fetal Diagn. Ther. 36, 117–128 (2014).
86. Fowden, A. L., Ward, J. W., Wooding, F. P. B., Forhead, A. J. & Constancia, M. Programming placental nutrient transport capacity. J. Physiol. 572, 5–15 (2006).
87. Wilson, M. E. & Ford, S. P. Comparative aspects of placental efficiency. Reprod. Suppl.
58, 223–32 (2001).
88. Hay ward, C. E. et al. Placental Adaptation: What Can We Learn from Birthweight:Placental Weight Ratio? Front. Physiol. 7, 28 (2016).
89. Catalano, P. M., Presley, L., Minium, J. & Hauguel-de Mouzon, S. Fetuses of Obese Mothers Develop Insulin Resistance in Utero. Diabetes Care 32, 1076–1080 (2009).
90. Leon-Garcia, S. M. et al. Maternal obesity and sex-specific differences in placental pathology. Placenta 38, 33–40 (2016). 91. Jansson, N. et al. Activation of placental
mTOR signaling and amino acid transporters in obese women giving birth to large babies. J. Clin. Endocrinol. Metab. 98, 105–13 (2013). 92. Catalano, P. M. & Shankar, K. Obesity and
pregnancy: mechanisms of short term and long term adverse consequences for mother and child. BMJ 356, j1 (2017).
93. Ramos, A. et al. In women with gestational diabetes mellitus factors influencing growth have a larger effect on placental weight than on birth weight. Eur. J. Obstet. Gynecol. Reprod. Biol. 202, 60–5 (2016).
94. Salavati, N., Sovio, U., Mayo, R. P., Charnock-Jones, D. S. & Smith, G. C. S. The relationship between human placental morphometry and ultrasonic measurements of utero-placental blood flow and fetal growth. Placenta 38, 41–8 (2016).
95. Unterscheider, J. et al. Optimizing the definition of intrauterine growth restriction: the multicenter prospective PORTO Study. Am. J. Obstet. Gynecol. 208, 290.e1-290.e6 (2013).
96. Heazell, A. E. P., Moll, S. J., Jones, C. J. P., Baker, P. N. & Crocker, I. P. Formation of Syncytial Knots is Increased by Hyperoxia, Hypoxia and Reactive Oxygen Species. Placenta 28, S33–S40 (2007).
97. Kingdom, J. C. & Kaufmann, P. Oxygen and placental villous development: origins of fetal hypoxia. Placenta 18, 613-21–6 (1997). 98. Kuzmina, I. Y., Hubina-Vakulik, G. I. & Burton, G.
J. Placental morphometry and Doppler flow velocimetry in cases of chronic human fetal hypoxia. Eur. J. Obstet. Gynecol. Reprod. Biol.
