Mendelian randomisation analysis of clustered causal effects of body mass on cardiometabolic biomarkers

Abstract

BackgroundRecent advances in data analysis methods based on principles of Mendelian Randomisation, such as Egger regression and the weighted median estimator, add to the researcher’s ability to infer cause-effect links from observational data. Now is the time to gauge the potential of these methods within specific areas of biomedical research. In this paper, we choose a study in metabolomics as an illustrative testbed. We apply Mendelian Randomisation methods in the analysis of data from the DILGOM (Dietary, Lifestyle and Genetic determinants of Obesity and Metabolic syndrome) study, in the context of an effort to identify molecular pathways of cardiovascular disease. In particular, our illustrative analysis addresses the question whether body mass, as measured by body mass index (BMI), exerts a causal effect on the concentrations of a collection of 137 cardiometabolic markers with different degrees of atherogenic power, such as the (highly atherogenic) lipoprotein metabolites with very low density (VLDLs) and the (protective) high density lipoprotein metabolites.ResultsWe found strongest evidence of a positive BMI effect (that is, evidence that an increase in BMI causes an increase in the metabolite concentration) on those metabolites known to represent strong risk factors for coronary artery disease, such as the VLDLs, and evidence of a negative effect on protective biomarkers.ConclusionsThe methods discussed represent a useful scientific tool, although they assume the validity of conditions that are (at best) only partially verifiable. This paper provides a rigorous account of such conditions. The results of our analysis provide a proof-of-concept illustration of the potential usefulness of Mendelian Randomisation in genomic biobank studies aiming to dissect the molecular causes of disease, and to identify candidate pharmacological targets.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2178-2) contains supplementary material, which is available to authorized users.

Mendelian Randomization with Incomplete Exposure Data: a Bayesian Approach

“…Once the appropriate assumptions are entertained, MR analysis can be used to assess the effect of an "exposure" variable on the medical outcome of interest, even when the former is not experimentally controlled. In most MR studies so far, the exposure is a late actor in the biological cascade, for example obesity [13]. Many such studies exploit the wealth of data gathered from follow-up of a large cohort of (initially) healthy population subjects.…”