A large number of studies have shown associations between birth weight and later adult disease, and these studies have ignited an interest in the developmental origins of disease and health. A paper in this volume of Epidemiology finds an overall U-shaped association between birth weight and all-cause mortality in a large Danish cohort. In this commentary, I discuss some of the issues that are important to epidemiologic studies concerned with the developmental origins of disease and health. These include considerations of causality and the public health/clinical relevance of the developmental origins of disease. I suggest that this area of research needs to move away from simply describing the association of birth weight with disease/health outcomes. Instead, we must aim to understand whether there are modifiable risk factors during the developmental period that are importantly causally related to later disease outcomes in ways that mean public health interventions should be aimed at the developmental period.
From the MRC Centre of Causal Analyses in Translational Epidemiology, Department of Social Medicine, University of Bristol, Bristol, United Kingdom.
The views expressed in this commentary are those of the author and not necessarily any individual or funding body acknowledged here.
Editors' note: A related article appears on page 204.
Correspondence: Debbie A. Lawlor, MRC Centre of Causal Analyses in Translational Epidemiology, Department of Social Medicine, University of Bristol, Canynge Hall, Whiteladies Rd, Bristol, BS8 2PR, UK. E-mail: firstname.lastname@example.org.
The paper by Baker et al1 in this issue of Epidemiology adds to a large number of publications relating birth weight to adult disease outcomes by examining the relationship of birth weight with all-cause, cardiovascular and cancer mortality in a very large cohort covering a wide-range of ages. Although studies relating birth weight to later disease outcomes have been important in highlighting a role for the development origins of adult disease, birth weight per se is unlikely to be causally related to cancer and cardiovascular disease. So how do we take the developmental origins of health and disease agenda forward in a meaningful way so that it contributes appropriately to improving public health?
For developmental origins epidemiology to contribute meaningfully to improvements in public health, it is necessary to distinguish causal associations, with the possibility of intervention and disease prevention, from noncausal associations. A number of methods within observational epidemiologic studies, which test the likelihood of causality, have been developed and are increasingly used.2 These include the use of parental-offspring comparisons, within- and between-sibling/twin comparisons, and the use of instrumental variable analyses (including the use of genetic variants as instrumental variables).2 Use of these approaches has revealed some interesting findings. Parental-offspring comparisons suggest that the association of maternal smoking during pregnancy with increased offspring adiposity may not be caused by intrauterine mechanisms, but rather explained by shared familial factors that result in a similar magnitude of association between paternal smoking and offspring adiposity to that between maternal smoking and offspring adiposity.3 Within- and between-sibling comparisons suggest that the inverse associations of birth weight and gestational age with later blood pressure are not explained by confounding due to family socioeconomic position, maternal skeletal size, or factors from across the earlier part of a mother's life course.4 Instead the most likely explanations for these associations involve differences between pregnancies in maternal metabolic or vascular health during pregnancy or differences in placental implantation.4 Intergenerational studies,5 and the largest twin-study to date,6 also support an intrauterine mechanism for the association of birth weight with cardiovascular end points. The use of maternal FTO genotype as an instrumental variable for developmental overnutrition suggests that this is unlikely to have been a major driver of the recent obesity epidemic, although the one study to assess this has imprecise estimates and demonstrates the need for very large sample sizes when using this approach to assess causality.7
A particularly robust method for testing causality is the long-term follow-up of randomized controlled trials (RCT) of interventions during developmental periods. An obvious difficulty here is that such trials are rarely sufficiently resourced or powered to support follow-up many decades later. For example, long-term follow-up of a RCT that randomized infants to different levels of dietary sodium in formulae feeds provides tantalizing evidence that infancy is a particularly sensitive period with respect to the effect of dietary sodium on later blood pressure, but this finding is hampered by the marked loss to follow-up of the original participants.8 One exemplary long-term follow-up of a RCT of a developmental intervention is the Republic of Belarus-based Promotion of Breast-feeding Intervention Trial,9 which has demonstrated that breast-feeding is causally related to greater intelligence in later childhood, but does not seem to have important protective effects against asthma or allergy,10 greater adiposity or greater blood pressure.11
Although there are obvious concerns about randomizing individuals to interventions during the developmental period, trials of interventions aimed at controlling maternal glycaemic and lipid levels during pregnancy have been conducted with respect to perinatal outcomes. The challenge for researchers interested in the potential long-term effects of such interventions is to persuade funders of the value of providing sufficient funds for the sample sizes and the long-term follow-up required to examine associations with outcomes in offspring adulthood.
Developmental and Degenerative Models of Disease Causation in Relation to Public Health Interventions
Much of the epidemiology conducted between the 1950s and 1980s was concerned with the effect of modifiable risk factors assessed in middle age on chronic complex diseases such as cardiovascular disease and cancer (the degenerative model). Life course epidemiology attempts to integrate developmental and degenerative models of disease causation, and a framework of models have been described.12 The importance of applying these to our epidemiologic work is that even if an exposure during the developmental period is robustly shown to be causally related to important later life health outcomes, it doesn't necessarily mean that public health interventions should target this exposure. If the developmental exposure sets off a chain of causal events, then it may be more cost effective (taking account of side effects and financial costs) to intervene later in life targeting the events in the causal chain that are more proximal to disease outcomes.
Realizing Not All populations Are the Same
The conclusions that one might draw from work conducted in high income populations might not apply to low or middle income populations.13 Similarly, findings from healthy term infants may not reflect pathways to disease in preterm infants or other infants requiring intensive care in early life. There are marked differences in the distributions of birth weight among populations from different countries, although surprisingly little difference in skeletal measurements at birth (crown heel length, crown rump length, leg length, or head circumference) among populations.14 Infants born in south Asia have particularly low birth weight,15 and this difference in birth weight distribution when compared with European populations appears to persist through at least 2 generations of South Asian immigrants to the UK.16–18 However, pioneering work by Chittaranjan Yajnik and his colleagues suggests that these differences might mask greater levels of adiposity and insulin resistance at birth in south Asian infants when compared with European origin infants.19,20 Recently, using very detailed MRI scans at birth, Yajnik and colleagues have shown that despite lower birth weights and smaller amounts of peripheral adiposity, south Asian infants have considerably greater visceral adiposity at birth than European infants and that location of adiposity at birth relates to cord levels of lipids and insulin.21 These findings illustrate not only the importance of recognizing population differences but just how crude a measure birth weight may turn out to be as a relevant developmental risk factor.
The Public Health Importance of Infant Survival and the Balance With Later Disease
In our keenness to understand the developmental origins of later disease and health outcomes, we should never lose sight of the fact that these become irrelevant if one does not survive the developmental period. Although recent evidence on the long-term detrimental effects of “catch-up” growth might be interpreted as implying that those born small-for-gestational-age or with relative weight loss in early infancy are best kept thin, as Victora and Barros point out “in developing countries, it seems reasonable to continue to promote growth for small infants and young children.”22 To fail to do so is likely to have serious consequences for their survival that clearly outweigh any possible long-term adverse effects on future cardiovascular health.
Baker et al1 provide further evidence of a developmental origin of cancer and cardiovascular disease. The challenge now is to work together across disciplines to understand causal mechanisms and determine the best interventions to optimize development and prevent degeneration in all populations of the world.
ABOUT THE AUTHORS
DEBBIE LAWLOR is a Professor of Epidemiology and Deputy Director of the MRC Centre for Causal Analyses in Translational Epidemiology, University of Bristol. Her work is concerned with determining causal and modifiable risk factors from across the life course for obesity, insulin resistance, diabetes, and cardiovascular disease.
I am very grateful to C.S. Yajnik, Diabetes Unit, King Edward Memorial Hospital Research Centre, Pune, India, and to C. Victora, Universidade Federal de Pelotas, Brazil, for stimulating discussions that have informed the ideas discussed here. The UK Department of Health contributes to my salary through a Career Scientist Award and the UK Medical Research Council (MRC) provides funds for the Centre for Causal Analyses in Translational Epidemiology.
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