Journal
EARLY HUMAN DEVELOPMENT
Volume 90, Issue 11, Pages 769-780Publisher
ELSEVIER IRELAND LTD
DOI: 10.1016/j.earlhumdev.2014.08.023
Keywords
Birth cohort; Causal inference; Consortia; DOHaD; Epidemiology; Epigenetics; Life course; Metabolomics; Omics
Categories
Funding
- UK Medical Research Council [MC_UU_12013]
- University of Bristol
- Wellcome Trust [WT083431MF]
- ESRC [RES-060-23-0011]
- European Research Council [DEVHEALTH 269874]
- BBSRC [BB/I025751/1, BB/I025263/1] Funding Source: UKRI
- ESRC [ES/L003023/1] Funding Source: UKRI
- MRC [MC_UU_12013/2, MC_UU_12013/3, MC_UU_12013/1, G0600705] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/I025263/1, BB/I025751/1] Funding Source: researchfish
- Economic and Social Research Council [ES/L003023/1] Funding Source: researchfish
- Medical Research Council [MC_UU_12013/3, MC_UU_12013/1, MC_UU_12013/2, G0600705] Funding Source: researchfish
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Large-scale population-based birth cohorts, which recruit women during pregnancy or at birth and follow up their offspring through infancy and into childhood and adolescence, provide the opportunity to monitor and model early life exposures in relation to developmental characteristics and later life outcomes. However, due to confounding and other limitations, identification of causal risk factors has proved challenging and published findings are often not reproducible. A suite of methods has been developed in recent years to minimise problems afflicting observational epidemiology, to strengthen causal inference and to provide greater insights into modifiable intra-uterine and early life risk factors. The aim of this review is to describe these causal inference methods and to suggest how they may be applied in the context of birth cohorts and extended along with the development of birth cohort consortia and expansion of omic technologies. (C) 2014 Elsevier Ireland Ltd. All rights reserved.
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