Reproductive and Cardio-metabolic health: Debbie Lawlor
The reproductive and cardio-metabolic health programme is concerned with identifying the causes and consequences of variation in women’s reproductive health, including how this affects their and their children’s cardio-metabolic health.
Our research provides the evidence base for identifying those at risk (or benefit) of: (i) healthy pregnancy, live-birth and adverse perinatal outcomes in women treated with in vitro fertilisation (IVF) and those who have become pregnant naturally; (ii) which women and their children might have increased risk of adverse cardio-metabolic health as a result of IVF, pregnancy complications, risk factors in pregnancy, or adverse perinatal health; and (iii) which women may have adverse cardio-metabolic health as a result of variation in life-course reproductive health exposures (for example, variation in age at menarche and menopause, use of hormonal contraception and replacement, and parity). We use novel methods to distinguish causal from non-causal associations in these areas, so that as well as being able to risk stratify, we can develop targets for effective treatment and preventive interventions (lifestyle or pharmaceutical).
Aims and Objectives
Our central aim in the programme currently is to accurately predict (risk stratification), and identify causal paths for: (i) response to IVF; (ii) healthy pregnancy and perinatal outcomes in IVF and natural conception; and (iii) offspring cardio-metabolic health. Our focus is on maternal smoking, physical activity, adiposity, pregnancy metabolic profiles and fetal (cord-blood) DNA methylation as potential predictors or risk factors.
IVF – pregnancy, perinatal, and offspring cardio-metabolic outcomes.
- Determine the impact of different ovarian stimulation approaches, and of separating ovarian stimulation from embryo transfer, on live-birth and infant rates, and adverse pregnancy and perinatal outcomes (of gestational diabetes (GDM), hypertensive disorders of pregnancy (HDP), small for gestational age (SGA), large for gestational age (LGA), and preterm birth).
- Determine the effect of IVF on gestational metabolic disruption, fetal epigenome (cord-blood DNA methylation) and future offspring cardio-metabolic health.
- Develop prediction tools for live-birth, infant birth, perinatal, pregnancy and offspring cardio-metabolic outcomes.
Natural conception – pregnancy and perinatal outcomes.
- Determine the value of novel biomarkers (including epigenomic and metabolomic) for accurate risk prediction of GDM, HDP, SGA, LGA, and preterm birth.
- Determine the causal effects of maternal smoking, physical activity, adiposity, and their related metabolic disruption on repeat USS determined fetal growth, GDM, HDP, SGA, LGA and preterm birth.
Natural conception – offspring cardio-metabolic outcomes.
- Determine the extent to which maternal gestational metabolic profiles, and the extent to which the change in pregnancy, causally influence, via intrauterine mechanisms, future offspring cardio-metabolic health.
- Explore whether maternal pregnancy characteristics, including metabolic profiles, and fetal growth, birth size and cord-blood biomarkers, can accurately predict future offspring cardio-metabolic health.
Development of Causal Methods
- Explore how to appropriately account for the impact of causal effects of duration and timing of exposure on outcomes in a triangulation framework.
- Develop methods for dealing with 1-sample and weak instrument Mendelian Randomization, and the potential for combining non-transmitted haplotype analyses in trios with 2-sample MR, in research concerning the effects of exposures in pregnant women on offspring outcomes.
- Determine the age at which individual level confounders are likely to invalidate within sibship comparisons and develop methods for dealing with order of risk factor bias in these analyses.
Current Postdoctoral Researchers and Fellows
Dr Abigail Fraser 2013-
Dr Laura Howe 2013-
Dr Hannah Elliott (Joint with Caroline Relton) 2013-
Dr Emma Anderson (Supervised by Laura Howe) 2014-
Dr Loubaba Mamluk (Supervised by Abi Fraser) 2014-
Dr Diana Santos Ferreira 2014-
Dr Maria Magnus (Supervised by Abi Fraser) 2015-
Dr Maria Carolina Borge 2016-
Dr Judith Brand (Joint with Kate Tilling) 2016-
Dr Leanne Kupers (Joint with Caroline Relton) 2016-
Dr Harriet Mills (Joint with Kate Tilling) 2016-
Dr Maria Clara Restrepo-Mendez 2016-
Dr Ana Luiza Goncalvez Soares 2016
Dr James Stayley (Joint with Kate Tilling) 2016-
Dr Michelle Taylor 2016-
Current PhD students
2014- James Jungius, PhD “Prenatal exposures, offspring health and mediation by DNA methylation”
2016- Will Thompson, PhD “Using genetics to understand how the maternal intrauterine environment influences fetal growth”
Predicting outcomes and optimising outcomes with IVF
Using clinical infertility data for all couples treated in the UK we have developed, and externally validated, an accurate tool for predicting livebirth (IVFpredict.com), and demonstrated that transfer of one embryo in women under 40 years has similar live-birth success, but lower adverse perinatal outcomes in comparison to transfer of two- or more embryos, but that in women 40 years or older transfer of two embryos is preferable. Transfer of three embryos does not improve livebirth, but increases adverse outcomes, at all ages. Using data from 156,947 women receiving 257,398 IVF ovarian stimulation cycles, we have quantified the value of persisting with IVF on cumulative live-birth rates, showing that women less than 40-years using their own oocytes (or those using donor oocytes at any age) can achieve live-birth rates, after six cycles, that are similar to those seen in couples who conceive naturally. This takes ~24 months with repeat IVF compared with ~12 months with spontaneous conception. Our work in this area has contributed to health policy and practice.
Smith ADAC, Tilling K, Nelson S, Lawlor DA. Live-birth rate associated with repeat in vitro fertilization treatment cycles. JAMA 2015; 314: 2654-2662
Pregnancy, maternal adiposity and offspring cardio-metabolic health
Using data from 3 independent cohorts, we have shown higher concentrations in all lipoprotein subclasses and lipids, many fatty acids, several amino acids, and inflammatory markers in women who were pregnant compared with those who were not. In a sub-samples of women who were not pregnant at baseline but pregnant 4 to 10-years later (or vice versa) changes in metabolic concentrations matched (or were mirror images of) the cross-sectional association patterns. These longitudinal analyses showed that the marked atherogenic changes seen with becoming pregnant returned to normal following pregnancy. We have shown that increases across pregnancy (assessed using repeat assessments) of lipoprotein subclasses and lipids, fatty acids, amino acids and glucose are considerably greater in magnitude than the differences between pregnant and non-pregnant women. Using Mendelian randomization, we have shown causal positive effects of greater maternal pre-/early-pregnancy BMI and fasting glucose on birthweight and ponderal index. Thus, there is widespread metabolic disruption on becoming pregnant, which then appears to return to normal. This disruption might be more extreme in obese women, and having higher levels of adiposity (fatness) and fasting glucose in pregnancy causes larger babies at birth.
Tyrrell J, et al., Frayling T*, Lawlor DA*, Freathy RM*, for the Early Growth Genetics (EGG) Consortium [*Joint senior & corresponding authors]. Genetic evidence for causal relationships between maternal obesity-related traits and birth weight. JAMA 2016; 315: 1129-1240
Development of novel causal methods
A key feature of our research is concerned with the potential causal effects of intrauterine exposures on future risk of offspring health and well-being. Conventional multivariable regression and randomised controlled trial approaches are limited in their ability to address these questions. There are also difficulties with using novel methods, including Mendelian Randomization, to address these questions, in part because these questions are about the effect of a risk factor that occurs in a pregnant woman on an outcome that occurs in their offspring (often several years or even decades later). A strand of this programme, has been to develop approaches that can be used in this situation. Our methodological work is an important example of how applied research in IEU influences wider methodological developments, as well as vice versa. We have pioneered the use of Triangulation, the explicit integration of evidence from several different approaches, each of which has different and unrelated key sources of bias, to improve causal inference. The idea behind triangulation is that confidence that a causal answer is correct is increased if each of the approaches point to the same answer, as it would be unlikely for the different sources of bias all to produce the same incorrect (biased) answer. Where the different approaches point to different answers clear understanding of the assumptions underlying each approach, and the likelihood of these being violated when answering a particular question, can identify what further research is required to address the question. We have developed a set of criteria for using triangulation, and demonstrated it with three examples of how to apply it in practice, which are beginning to be used in applied research.
Lawlor DA, Tilling K, Davey Smith G. Triangulation in aetiological epidemiology. International Journal of Epidemiology 2017; doi:10.1093/ije/dyw314