Epigenetics – the interaction between biology and the environment

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Customised birthweight centiles

Customised birthweight centiles have been created in an attempt to better account for normal physiological influences on birthweight. Population references by definition include all births, which also means pregnancies with pathologic conditions that affect fetal growth are included resulting in a flawed assessment of ‘normal’ growth (Bukowski et al., 2008). In addition, although based on large populations of births, most birthweight references in use in Western countries were created in the 1990’s or earlier using predominantly European births, when obesity was not as common (G. R. Alexander, Himes, Kaufman, Mor, & Kogan, 1996; Beeby, Bhutap, & Taylor, 1996; Thompson, Mitchell, & Borman, 1994). The well-established association between preterm birth and fetal growth restriction also means that birthweight references are systematically biased towards lower weights at preterm gestations (Gardosi, 2005; Zeitlin, Ancel, Saurel-Cubizolles, & Papiernik, 2000).

Screening for SGA

Early pregnancy prediction and screening for SGA is the subject of much research although to date no methods perform well enough for clinical use. Screening for SGA includes testing of biomarkers as well as ultrasound assessments of fetal size and uterine circulation through performing uterine artery Doppler studies. Early pregnancy biomarkers that have been shown to be associated with later SGA include low levels of β-hCG and pregnancy-associated plasma protein A (PAPP-A), high inhibin A or serum α-feto-protein (Figueras & Gardosi, 2011) as well as disturbances in angiogenic factors such as vascular endothelial growth factor (VEGF, placental growth factor (PlGF) and soluble vascular endothelial growth factor receptor-1 (sFlt-1) (see also Figure 3.11and section 3.4.2 above) (Poon, Syngelaki, Akolekar, Lai, & Nicolaides, 2012; J. Zhang et al., 2010). These biomarkers alone have low sensitivity and specificity for SGA. Additionally, abnormal uterine artery Doppler studies in early to mid gestation are also poor predictors of SGA (Chien, Arnott, Gordon, Owen, & Khan, 2000), although biomarker and Doppler studies both perform better in high risk pregnancies and for predicting early-onset disease (Figueras & Gardosi, 2011). A clinically useful screening test to predict SGA will likely involve a combination of clinical history, biomarkers and ultrasound assessments.

Methods

The NWH clinical database of births from 2006 to 2009 was used for the present cohort study. NWH is a tertiary referral hospital in Auckland, New Zealand, with a diverse ethnic population and approximately 7500 maternities per year. The NWH database of births consists of deidentified, prospectively collected maternity data for all births occurring at greater than or equal to 20 weeks of gestation, and includes demographic data, antenatal complications, and detailed delivery and newborn data. Data are routinely checked for completeness, out-of-range values and inconsistency (National Women’s Health, 2010). Ethical approval for this study was gained from the Northern X Regional Ethics Committee (NTX/09/179/EXP). Included in the study were women booked to deliver at NWH from January 2006 to December 2009 with singleton pregnancies n = 29 573. Consistent with previous methodology (Gardosi, Mongelli, Wilcox, & Chang, 1995; L.M.E. McCowan et al., 2004), the population used to calculate birthweight customisation coefficients excluded pregnancies with major congenital abnormalities, preterm birth (<37 weeks of gestation) and stillbirth, Figure 6.1. The eligible study population consisted of 26 611 women. Of these, 2429 (9.1%) had incomplete or missing data on one or more variable required to generate centile coefficients: 2033 (7.6%) were missing height, 1599 (6.0%) were missing weight and 352 (1.3%) were missing smoking status.

Discussion

The new updated coefficients for the NZ customised birthweight model identified 603 (2.3%) additional infants as SGA and reclassified 45 (0.2%) as non-SGA compared with the previous model. As this new SGA group had a three-fold increase in risk of perinatal death, the updated calculator appears to have identified a small additional group of at-risk SGA infants. The cost of identifying these at-risk infants is an increase in the overall number of infants classified as SGA therefore, contrary to our hypothesis, the overall performance of the two calculators in terms of SGA-associated preterm birth and perinatal mortality is similar. Despite the data limitations of the previous model (see Discussion of section 6.2 above), infants who are SGA using the old model coefficients have been shown to have an increased risk of perinatal morbidity and mortality (L. M. E. McCowan et al., 2005). The new model uses data that has more robust collection and cleaning, meaning the observed changes in ethnicity coefficients are more likely to reflect the characteristics of our current obstetric population. Although true changes in demographic characteristics may have occurred between the two study periods (Table 6.4) with the present study population also being older and more likely to be nulliparous, these differences may also represent differences in data quality between the two studies.

Manuscript: Independent risk factors for customised small for gestational age infants

Abstract Background: Infants born small for gestational age (SGA) by customised birthweight centiles are at increased risk of adverse outcomes compared with those SGA by population centiles. Risk factors for customised SGA have not previously been described in a general obstetric population. Aim: To determine independent risk factors for customised SGA in a multi-ethnic New Zealand population. Methods: We performed a retrospective cohort analysis of prospectively recorded maternity data from 2006 to 2009 at National Women’s Health, Auckland, New Zealand. After exclusion of infants with congenital anomalies and missing data, our final study population was 26 254 singleton pregnancies. Multivariable logistic regression analysis adjusted for ethnicity, body mass index, maternal age, parity, smoking status, social deprivation, hypertensive disease, antepartum haemorrhage (APH), diabetes and relevant pre-existing medical conditions.

Table of Contents :

  • Title Page
  • Abstract
  • Dedication
  • Acknowledgements
  • Table of contents
  • List of
  • List of figures 
  • Abbreviations 
  • Co-Authorship forms
  • Chapter 1. | Introduction
    • 1.1. Background
    • 1.2. Aims of this research
    • 1.3. Structure of thesis
  • Chapter 2. | Overweight and obesity
    • 2.1. Introduction
    • 2.2. Definitions
    • 2.3. Epidemiology
    • 2.4. Aetiology
    • 2.4.1. Environmental factors – diet and physical activity
    • 2.4.2. Biological factors
    • 2.4.3. Epigenetics – the interaction between biology and the environment
    • 2.4.4. Behaviour
    • 2.4.5. Physiology
    • 2.5. Consequences of obesity
    • 2.5.1. Insulin resistance
    • 2.5.2. Inflammation/ immunity
    • 2.5.3. Oxidative stress and endothelial dysfunction
    • 2.5.4. Dyslipidaemia
    • 2.5.5. Hypertension
    • 2.5.6. Visceral and ectopic adipose tissue
    • 2.5.7. Obesity-related disease
    • 2.6. Summary
  • Chapter 3. | Overweight and obesity in pregnancy
    • 3.1. Introduction
    • 3.2. Epidemiology of maternal obesity
    • 3.3. The physiology of pregnancy and obesity
    • 3.3.1. Hormonal and metabolic changes
    • 3.3.2. Cardiovascular changes
    • 3.4. Specific pregnancy outcomes and obesity
    • 3.4.1. Infant birthweight and obesity
    • 3.4.2. Pre-eclampsia and obesity
    • 3.4.3. Caesarean section and obesity
    • 3.4.4. Minimising obesity-related adverse pregnancy outcomes
    • 3.5. Financial and resource implications of obesity in pregnancy
  • Chapter 4. | Ethnicity and pregnancy
    • 4.1. Introduction
    • 4.2. Definition of ethnicity
    • 4.3. Health and ethnicity
    • 4.4. Ethnicity and pregnancy outcomes
    • 4.4.1. Ethnicity and birthweight
    • 4.4.2. Ethnicity and pre-eclampsia
    • 4.4.3. Ethnicity and Caesarean section
    • 4.5. The interaction between ethnicity, BMI and adverse pregnancy outcomes
    • 4.6. Summary
  • Chapter 5. | Infant birthweight
  • Chapter 6. | Maternal characteristics in customised birthweight centiles
    • 6.1. Preamble
    • 6.2. Manuscript: Maternal characteristics in customised birthweight centiles
    • 6.3. Comparisons between the previous and new (2012) NZ customised birthweight models
  • Chapter 7. | Independent risk factors for customised small for gestational age infants
    • 7.1. Preamble
    • 7.2. Manuscript: Independent risk factors for customised small for gestational age infants
  • Chapter 8. | The phenotype of pre-eclampsia in overweight and obese women
    • 8.1. Preamble
    • 8.2. Manuscript: The phenotype of pre-eclampsia in overweight and obese women
  • Chapter 9. | Ethnicity, body mass index and pre-eclampsia
    • 9.1. Preamble
    • 9.2. Manuscript: Ethnicity, body mass index and pre-eclampsia
  • Chapter 10. | Ethnicity and Caesarean section

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The impact of body mass index and ethnicity on adverse pregnancy outcomes

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