Approaches for drawing causal inferences from epidemiological birth cohorts: a review

Abstract

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.

Keywords: Birth cohort; Causal inference; Consortia; DOHaD; Epidemiology; Epigenetics; Life course; Metabolomics; Omics.

Figure 1

Schematic diagrams outlining the main causal inference methods MR = Mendelian randomization

Figure 2

Diagram outlining the interplay between genomics, other “omics” and environmental factors in relation to disease or health-related outcomes GWAS = Genome-wide association study