Editorial |
Rising to the challenges and opportunities of life course epidemiology
Professor of Clinical Epidemiology, Department of Social Medicine, Canynge Hall, Whiteladies Road, Bristol BS8 2PR.
E-mail: y.ben-shlomo{at}bristol.ac.uk
Accepted 4 February 2007
This edition of the International Journal of Epidemiology has four papers1–4 and accompanying commentaries5–7 that can be conveniently clustered under the heading of life course epidemiology. In the concluding chapter of ‘A life course approach to chronic disease epidemiology’,8 Diana Kuh and I raised several emerging and common themes that we felt needed to be addressed by future research. These were (i) understanding heterogeneity, (ii) going beyond repeat measures to understand trajectories, (iii) the role of accelerated postnatal weight and height gain and (iv) the use of life cohort cohorts and less conventional designs. All of these topics are addressed to some degree by these publications.
The papers by Strachan1 and Power2 are some of the first to come out from the recent follow-up of the 1958 Birth cohort and these researchers and their colleagues are to be congratulated on the valuable addition these new data will make to other existing studies such as the 1946 National Survey of Health and Development.9 Their work re-examines the relative importance of exposures in early and later life by comparing the influence of geography, region of birth or examination amongst migrants, and personal characteristics, childhood and adult socioeconomic position. Each paper alone presents fairly clear conclusions though reconciling their findings across both papers remains more of a challenge. Both childhood and adult socioeconomic position were in general associated with cardiovascular risk factors, in some cases the earlier measure had if anything a bigger effect. There was no evidence of any critical or sensitive period or any interaction and the most parsimonious model was a simple accumulation of effect model.10,11 This is consistent with previous studies and the observation that childhood socioeconomic position still predicts adult cardiovascular disease, though with a moderately attenuated effect after adjustment for adult socioeconomic position.12 This is also consistent with an ‘early origins’ hypothesis though it does not restrict exposures to just the pre-natal period as post-natal growth, childhood and adolescent development may also be relevant, as highlighted by the Guatemalan paper.4 However, the accompanying 1958 Birth cohort paper appears to contradict this conclusion. It identifies only current adult region of examination as a predictor of cardiovascular risk factors, other than height, which was predicted by region of birth and claims to negate the early life hypothesis in terms of its importance in explaining regional differences in risk factors. Clearly, as Geoffrey Rose highlighted over 20 years ago,13 what determines an individual's risk may not be the same as what determines the risk between populations. Hence within each population, poorer individuals both in childhood and adulthood have a worse risk profile, but this may have little effect on the mean values between areas. However, given the known ecological associations between area-deprivation and cardiovascular risk, this requires more careful consideration.14 One possible explanation is that the effects of early environment are simply less important than that of later environment and hence after mutual adjustment they cease to add any explanatory power. This would not refute the early origins per se, but simply argue that the relative importance of early environment has been overstated. However, as Nybo Anderson points out5 what does region of birth actually mean? Let us consider body mass index as our outcome of interest. Firstly there is marked regional heterogeneity in the non-migrant group. This could be explained by both variations in genetic15 and/or environmental risk factors. The former, however would not explain the marked effect of region of examination for migrants, whose genetic predisposition is more likely to be reflected by their region of origin. However, how have dietary habits and/or physical activity levels altered over time between areas? Recent data from the ALSPAC cohort16 show the large effects of physical activity on fat mass. Physical levels in children are likely to have reduced over the last 50 years, but what is less clear is whether hetereogeneity at a regional level has changed. If activity levels were not only higher in the past, but more similar across regions than they are today, the effects of current region would naturally be stronger even though the relative effects of activity on obesity for individuals may be the same at both time periods.
Also of interest is the failure to show an association between socioeconomic position at either time period and migration on total IgE levels. At first glance this seems counter-intuitive as external allergens should play a role in sensitising susceptible individuals especially for allergen-specific IgE responses. However, total IgE is a crude measure of atopic sensitization and may be driven solely by endogenous genetic factors. This is consistent with twin studies, where the correlation for monozygotic twins reared apart were similar to those reared together implying that the familial environment may not be of great importance, though this does not preclude the intrauterine environment.17 The marked geographical heterogeneity in total IgE levels seen amongst the non-migrant population may therefore be better explained by geographical variations in genetics factors and future studies from the 1958 Birth cohort could test such a hypothesis.
One of the advantages or disadvantages of data sets that are suited to life course analyses is that they often have repeat measures of exposure and/or outcomes. This presents analytical challenges,18 which are not always welcome, but it does allow one to examine dynamic processes and tease out if any critical or sensitive periods exist. Most interest in this area has tended to focus on changing exposures, particularly developmental variables such as height and weight, which change rapidly with growth and may have specific periods (e.g. post-natal catch-up growth, adiposity rebound, pubertal growth spurt) where endogenous changes, triggered or modified by environmental factors play a role and may have life long consequences on endocrine systems.19 The findings from the Guatemalan study are important as they build on a body of work from developing countries that highlight that weight gain in early childhood may have longer term negative consequences in relation to adult obesity. This has also been seen in a more detailed study from New Delhi.20 As Stettler highlights in his commentary,7 we remain uncertain as to the short term benefits of weight gain vs long term adverse effects. In addition, it also remains unclear as to whether dietary or physical interventions instituted in early life are more cost effective than those occurring in adulthood.
Kaplan and colleagues take on the task of modelling trajectories in self-rated health and death. This is often avoided in epidemiological analyses as either non-recurrent events are studied, e.g. mortality or survival analyses are undertaken, where one models the time to first event. Their work highlights the need to consider the cumulative person-years of good health and is highly relevant to the concept of ‘compression of morbidity’. It could be similarly applied to psychological well being which will fluctuate over time or other continuous measures which show developmental and age-related changes such as cognitive function, lung function and muscle strength.8 Their tree structure approach, highlighting various potential trajectories, has the virtue of transparency and appear similar to those with some knowledge of Markov models in clinical decision making. Pickles highlights6 various other analytical options, such as latent trajectory classes, and there is clearly need for further methodological work to help refine which approach may be more suitable given different types of outcomes. Those interested in understanding the life course influences on disease will logically also wish to understand the natural history of risk factors for disease and changes in the underlying traits (cognitive function) that will eventually present as a clinical end-point (e.g. dementia). I predict that we will see more publications on outcome trajectories as data becomes available from long-term longitudinal studies.
The methodological and analytical challenges that need to be faced by life course epidemiology often seen overwhelming. Given the inherent limitations of all observational studies, it will always be necessary to synthesise evidence from basic molecular biology, genetic epidemiology, Mendelian Randomization approaches,21 natural experiments and where feasible randomized controlled trials. Let's hope we can rise to this challenge.
References
1 Strachan DP, Rudnicka AR, Power C, et al. Lifecourse influences on health among British adults: effects of region of residence in childhood and adulthood. Int J Epidemiol (2007) 36::532–539.
2 Power C, Atherton K, Strachan DP, et al. Life-course influences on health in British adults: effects of socio-economic position in childhood and adulthood. Int J Epidemiol (2007) 36:532–539.
3 Kaplan GA, Baltrus PT, Raghunathan TE. The shape of health to come: prospective study of the determinants of 30-year health trajectories in the Alameda County Study. Int J Epidemiol (2007) 36:542–548.
4 Corvalán C, Gregory CO, Ramirez-Zea M, Martorell R, Stein AD. Size at birth, infant, early and later childhood growth and adult body composition: a prospective study in a stunted population. Int J Epidemiol (2007) 36::551–558.
5 Nybo Anderson A-M. Commentary: life-course and social epidemiology, the biological fig leaf, and Bob Dylan. Int J Epidemiol (2007) 36:540–541.
6 Pickles A. Commentary: trajectories, selection and cumulative causation. Int J Epidemiol (2007) 36:549–550.
7 Stettler N. Commentary: growing up optimally in societies undergoing the nutritional- transition, public health and research challenges. Int J Epidemiol (2007) 36:559–560.
8 Ben-Shlomo Y, Kuh D. Conclusions. In: A Life Course Approach to Chronic Disease Epidemiology—Kuh D, Ben-Shlomo Y, eds. (2004) 2nd. Oxford: Oxford University Press. 446–64.
9 Wadsworth MEJ. The Imprint of Time: Childhood, History and Adult Life (1991) Oxford: Oxford University Press.
10 Ben-Shlomo Y, Kuh D. A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives. Int J Epidemiol (2002) 31:285–93.
11 Kuh D, Ben-Shlomo Y, Lynch J, Hallqvist J, Power C. Life course epidemiology. J Epidemiol Community Health (2003) 57:778–83.
12 Davey Smith G, Hart C, Blane D, Hole D. Adverse socioeconomic conditions in childhood and cause-specific adult mortality: prospective observational study. BMJ (1998) 316:1631–35.
13 Rose G. Sick individuals and sick populations. Int J Epidemiol (1985) 14:32–38.
14 Elford J, Ben-Shlomo Y. Geography and migration with special reference to cardiovascular disease. In: A Life Course Approach to Chronic Disease Epidemiology—Kuh D, Ben-Shlomo Y, eds. (2004) 2nd. Oxford: Oxford University Press. 144–64.
15 Frayling TM, Timpson NJ, Weedon MN, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. In: Science (2007) (April 12, 2007: 1141634v1) DOI: 10.1126/science.1141634.
16 Ness AR, Leary SD, Mattocks C, et al. Objectively measured physical activity and fat mass in a large cohort of children. PLoS Med (2007) 4:476–84.[Web of Science]
17 Hanson B, McGue M, Roitman-Johnson B, Segal NL, Bouchard TJ Jr., Blumenthal MN. Atopic diserase and immunoglobulin E in twins reared apart and together. Am J Hum Genet (1991) 48:873–79.[Web of Science][Medline]
18 de Stavola BL, Nitsch D, dos Santos Silva I, et al. Statistical issues in life course epidemiology. Am J Epidemiol (2006) 163:84–96.
19 Sandhu J, Davey Smith G, Holly J, Cole TJ, Ben-Shlomo Y. Timing of puberty determines serum insulin-like growth factor-I in late adulthood. J Clin Endocrinol Metab (2006) 91:3150–57.
20 Sachdev HS, Fall CH, Osmond C, et al. Anthropometric indicators of body composition in young adults: relation to size at birth and serial measurements of body mass index in childhood in the New Delhi Birth cohort. Am J Clin Nutr (2005) 82:456–66.
21 Davey Smith G, Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol (2003) 32:1–22.
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