Strengthening causal inference in cardiovascular epidemiology through Mendelian randomization

Abstract

Observational studies have contributed in a major way to understanding modifiable determinants of cardiovascular disease risk, but several examples exist of factors that were identified in observational studies as potentially protecting against coronary heart disease, that in randomized controlled trials had no such effect. The likely reason for misleading findings from observational epidemiological studies is that associations are influenced by confounding, bias, and reverse causation--where disease influences a risk factor, rather than vice versa. Mendelian randomization utilizes genetic variants that serve as proxy measures for modifiable risk factors to allow estimation of the causal influence of the modifiable risk factor in question. We present examples of the use of the Mendelian randomization approach and discuss both the limitations and potentials of this strategy.