It is highly likely that there is a broad and reproducible footprint of variation in body composition (lean and fat mass) on the circulating metabolome. Technological developments are now such to allow a broad characterisation of circulating metabolome using an approach called mass spectrometry which can allow for an analysis of the cell derived products of metabolic processes by exposures of interest. Here the exposure of interest is body mass index (BMI) - or weight given height.

The problem with looking at the direct relationship between BMI and measures of the metabolome is that BMI is related to many other biological and non-biological things. Therefore, when one analyses the relationship between BMI measured directly and health outcomes (like metabolites), then it is the case that BMI and those other things are jointly assessed as they are correlated. To avoid this, it is possible to conduct randomised controlled trials which shuffle up these correlations before assessing the impact of a risk factor - however you cannot randomise to BMI. Therefore, by taking genetically predicted BMI, it is possible to avoid this problem and to assess the impact of BMI variation on metabolite measures in ALSPAC.

We will do this by using the ALSPAC genetic data to generate a score for the burden of genetic contributions to higher and lower BMI, then select samples from storage for metabolite measurement at a company called Metabolon.

The main analysis of the data generated by this experiment will be focused on characterising the impact of BMI on the human metabolome. In principle, the two groups generated in this sample (high and low BMI as prescribed by being at the ends of a distribution of BMI genetic risk) will be compared.