Mendelian Randomization Studies in the Elderly

To the Editor: Mendelian randomization studies use genetically determined variation in exposure levels to study causal effects. Unmeasured confounding and reverse causation, which hamper the estimation of causal effects of exposures in conventional analysis, can thereby be circumvented.1 The genetic variant used as an instrument should fulfill the following conditions: (1) it is associated with the exposure; (2) it affects the outcome only through the exposure; and (3) it is not related to other factors that affect the outcome.1–3 If these main assumptions are fulfilled, the effect of the genetic variant on the outcome can be attributed to the effect of the exposure on the outcome. An overview of numerous scenarios in which these assumptions are violated was published recently, with a discussion of the consequences and suggestions for alternative approaches to Mendelian randomization studies.3 Another scenario in which the assumptions could be violated is if a Mendelian randomization study is performed in an elderly population. Let us think of a Mendelian randomization study in which the effect of exposure X on outcome Y is studied in a population aged > 80 years, using genetic variant G as the genetic instrument to circumvent the confounding by unmeasured factors U1 of the X–Y relation. If the genetic variant G, through its effect on the exposure X has affected survival up to age 80, collider stratification bias (selection bias) may occur. This is illustrated in the Figure: both genotype G (through exposure X) and other risk factors U2 affect survival up to age 80. Survival S is therefore a collider, and restriction of the population to those who have survived up to age 80 results in collider stratification bias by inducing an association between G and U2. The intuitive interpretation of this phenomenon is as follows. We assume that genotype G increases mortality rates. If a person with a genotype G is still alive at age 80, this person will be less likely to have other risk factors for mortality (high blood pressure, smoking, etc.) compared with people without genotype G. This means that in the population aged over 80 the genetic variant is associated with other factors that affect the outcome, violating assumption 3. The effect of the genetic variant G on the outcome Y can therefore no longer be solely attributed to the effect of the exposure X on outcome Y.FIGURE: Genetic variant G is an instrument for the effect of exposure X on outcome Y, with unmeasured factors U1 confounding the X–Y relation. Both X and unmeasured risk factors U2 affect survival S, and U2 also affects Y. Because of selection on S, there is an association between G and U2.An example in which this collider stratification bias might occur is a Mendelian randomization study in subjects aged over 80 using APOE variants (G) as an instrument for cholesterol level (X), with (for example) myocardial infarction as the outcome (Y). APOE variants are known to cause variation in cholesterol levels. Because cholesterol levels will have influenced survival up to age 80 (S) and hence selection into the study population, an association of APOE variants with other risk factors that influenced survival up to age 80 (U2, eg, smoking) is introduced. For example, among those with a cholesterol increasing APOE-variant who have survived up to age 80, there will be fewer smokers than among those without the variant. If these other factors also affect the risk of myocardial infarction, the genotype–outcome relation will be biased; in this example, the bias in the estimated effect of genetically increased cholesterol will be towards a lower risk of myocardial infarction due to the inverse relation with smoking. This also applies if the outcome is survival (from age 80). The bias introduced by selection on survival will, of course, be most prominent for Mendelian randomization studies investigating exposures that strongly affect survival. Anna G. C. Boef Saskia le Cessie Olaf M. Dekkers Department of Clinical Epidemiology Leiden University Medical Centre The Netherlands [email protected]