Can genetic evidence help us to understand the fetal origins of type 2 diabetes?

Abstract

Lower birthweight is consistently associated with a higher risk of type 2 diabetes in observational studies, but the mechanisms underlying this association are not fully understood. Animal models and studies of famine-exposed populations have provided support for the developmental origins hypothesis, under which exposure to poor intrauterine nutrition results in reduced fetal growth and also contributes to the developmental programming of later type 2 diabetes risk. However, testing this hypothesis is difficult in human studies and studies aiming to do so are mostly observational and have limited scope for causal inference due to the presence of confounding factors. In this issue of Diabetologia, Wang et al (doi: 10.1007/s00125-016-4019-z ) have used genetic variation associated with birthweight in a Mendelian randomisation analysis to assess evidence of a causal link between fetal growth and type 2 diabetes. Mendelian randomisation offers the potential to examine associations between exposures and outcomes in the absence of factors that would normally confound observational studies. This commentary discusses the results of the Mendelian randomisation study carried out by Wang et al, in relation to the study design and its limitations. Challenges and opportunities for future studies are also outlined.

Keywords: Birthweight; Fetal growth; Genetics; Genome-wide association study; Instrumental variable; Mendelian randomisation; Pleiotropy; Type 2 diabetes.

Fig. 1

The principle of Mendelian randomisation and its similarity to a randomised trial. The diagrams, (a) and (b), are directed acyclic graphs (DAGs). Each arrow indicates a plausible causal relationship between the variables. The absence of an arrow between any two variables indicates that a causal relationship is not considered plausible. DAG (a) represents a randomised trial in which the effect of LDL cholesterol (LDL-c) levels on CHD risk is tested using randomisation to statin therapy or placebo. DAG (b) represents a Mendelian randomisation analysis in which the effect of LDL-c on CHD risk is tested using the PCSK9 genotype as a proxy for LDL-c levels [8]. In both examples, association between the IV (i.e. the statin in [a] and the genotype in [b]) and the outcome indicates a causal association between LDL-c and CHD if the following assumptions are upheld: (1) the IV is causally related to the exposure (LDL-c); (2) the IV is not associated with factors that confound the exposure outcome association; (3) the IV is only associated with the outcome via its effect on the exposure. In the Mendelian randomisation analysis, the size of the causal association between LDL-c and CHD may be calculated as the ratio of the genotype-CHD association over the genotype-LDL-c association

Fig. 2

Schematic diagram showing relationships between genetic variation, birthweight and type 2 diabetes that are relevant to Mendelian randomisation analysis. The thick, black arrows show the genotype-exposure and exposure-outcome associations, while arrow A illustrates pathways between genotype and outcome that are independent of the exposure and therefore violate a key assumption of IV analysis (e.g. variants at CDKAL1 and ADCY5, which are associated with both type 2 diabetes and birthweight). Arrow B illustrates that maternal genes may influence fetal growth and birthweight indirectly via the intrauterine environment, and could also be associated with the outcome via pathways independent of fetal growth. The maternal genotype is therefore important to account for when interpreting the results of a Mendelian randomisation analysis