Quantifying bias from reverse causation in observational studies of dementia risk factors: A simulation study informed by age-specific reverse Mendelian Randomization

BackgroundThe long preclinical phase of dementia can bias estimated effects of baseline exposures on dementia incidence. We demonstrate simulations informed by reverse Mendelian randomization (MR) findings to quantify the age-specific magnitude of reverse causation bias in analyses in observational studies of the effects of body mass index (BMI) on dementia. MethodsWe simulated longitudinal trajectories of BMI and dementia risk from ages 45 to 90 years, calibrating to published evidence on age-specific dementia incidence, BMI, and associations of dementia genetic risk with BMI. Under the null that BMI does not influence dementia and an alternative that BMI at any age increases subsequent dementia risk, we simulated hypothetical cohort studies (n=20,000, average 15 years of follow-up), varying age of entry from 45 to 80 years. In each hypothetical cohort, the association of z-standardized BMI at study entry and dementia incidence were estimated using Cox proportional hazards models. Bias was quantified using the ratio of observed to true hazard ratios (RHRs). All scenarios were replicated 500 times. ResultsIn the absence of a causal effect of BMI on dementia, when follow-up began at age 65 years, the RHR was 0.91 (95% CI: 0.90-0.92). When follow-up began at age 80 years, the RHR decreased to 0.68 (95% CI: 0.67-0.69), indicating substantial bias attributable to reverse causation. ConclusionReverse causation, presumably arising from preclinical dementia, can induce substantial bias in estimates of the association between baseline exposures and dementia incidence. Simulations provide a convenient tool to quantify this bias.