Interpretation of mendelian randomization using one measure of an exposure that varies over time

Abstract

BackgroundMendelian randomization (MR) is a powerful tool through which the causal effects of modifiable exposures on outcomes can be estimated from observational data. Most exposures vary throughout the life course, but MR is commonly applied to one measurement of an exposure (e.g., weight measured once between ages 40 and 60). It has been argued that MR provides biased causal effect estimates when applied to one measure of an exposure that varies over time.MethodsWe propose an approach that emphasises the liability that causes the entire exposure trajectory. We demonstrate this approach using simulations and an applied example.ResultsWe show that rather than estimating the direct or total causal effect of changing the exposure value at a given time, MR estimates the causal effect of changing the liability as induced by a specific genotype that gives rise to the exposure at that time. As such, results from MR conducted at different time points are expected to differ (unless the liability of exposure is constant over time), as we demonstrate by estimating the effect of BMI measured at different ages on systolic blood pressure.ConclusionsPractitioners should not interpret MR results as timepoint-specific direct or total causal effects, but as the effect of changing the liability that causes the entire exposure trajectory. Estimates of how the effects of a genetic variant on an exposure vary over time are needed to interpret timepoint-specific causal effects.

Identifying molecular mediators of the relationship between body mass index and endometrial cancer risk: a Mendelian randomization analysis

“…the World Cancer Research Fund (WCRF) pooled analysis of 26 prospective studies: RR 1.50, 95% CI 1.42 to 1.59 per 5.0 kg/m 2 increase), consistent with previous comparisons of observational and MR estimates across other cancer sites [57, 58]. Smaller magnitudes of effect in observational analyses may reflect regression dilution bias from single time-point measurements of BMI and/or reverse causation from cancer-induced weight loss, whereas MR estimates reflect accumulated exposure across the life-course and are unlikely to be influenced by reverse causation [59].…”

“…69 [57,58]. Smaller magnitudes of effect in observational analyses may reflect regression dilution bias from single time-point measurements of BMI and/or reverse causation from cancerinduced weight loss, whereas MR estimates reflect accumulated exposure across the life-course and are unlikely to be influenced by reverse causation [59].…”

“…For the endometrioid histological subtype, using the same approach, the effect of fasting insulin on endometrioid endometrial cancer did not markedly change (~14% logOR reduction) when SHBG was included in the model (OR per increase in natural log transformed pmol/L fasting insulin: unlikely to be influenced by reverse causation [59].…”

Applying Mendelian randomization to appraise causality in relationships between nutrition and cancer

“…Under this definition, if effects are time-varying, however, an MR estimate cannot reflect a “lifetime” effect, as it cannot be summarised by measuring it at any one point in time and it is advised that MVMR be employed to examine potential time-varying exposures. More recently, Morris et al have proposed that a “lifetime” causal effect can be estimated in MR analyses using one measure of a time-varying exposure if conceptualised as the “causal effect of changing the liability [to the exposure] such that the exposure would be one unit higher at a given time” [ 122 ]. In this framework, the estimated “lifetime effect” would differ in magnitude if measured at a different point in time, but MR estimates would nonetheless be consistent with the underlying trajectory of an exposure induced by a SNP.…”

Systematic comparison of Mendelian randomization studies and randomized controlled trials using electronic databases

“…The direct comparison of MR and RCT findings is facilitated by the use of a precisely-defined estimand 18 , for example, the effect on incident coronary heart disease risk of lowering LDL cholesterol by 1 mmol/l for 5 years. Whilst RCTs will estimate something close to this, and be scalable to it, with MR studies the exposure difference associated with the genetic instruments will often exist from birth (or before) and may change in magnitude over time 19 . This is discussed further in the Supplementary Box.…”