Clinical Scorecard: A Structural Equation Modeling Approach to Mendelian Randomization for Examining the Lifespan Impact of Adiposity: Practical and Methodological Insights

At a Glance

Key Highlights

• Higher adulthood adiposity has a persistent causal effect increasing risk of cardiovascular disease and type 2 diabetes.
• Higher childhood adiposity has a protective causal effect on breast cancer risk.
• Structural mean models (SMM) with g-estimation and inverse variance weighted multivariable MR (IVW-MVMR) provide complementary approaches to assess time-varying effects of adiposity.

Guideline-Based Recommendations

Diagnosis

• Use genetic instruments associated with adiposity to infer causal effects at different life stages.
• Employ MR methods that consider time-varying exposures to disentangle early and later life effects.

Management

• Interpret MR findings considering the assumptions of each method (SMM-MR and IVW-MVMR) regarding genetic instrument validity and exposure timing.
• Consider adiposity reduction strategies in adulthood to mitigate cardiovascular and diabetes risk.

Monitoring & Follow-up

• Monitor adiposity changes across life stages to understand their differential impact on disease risk.
• Use genetic epidemiology tools to refine causal inference in lifecourse studies.

Risks

• Potential misinterpretation if MR methods do not account for time-varying genetic effects.
• Violation of instrumental variable assumptions may bias causal estimates.

Patient & Prescribing Data

Individuals with varying adiposity levels measured in childhood and adulthood

Adiposity interventions may have differential effects depending on life stage; adulthood adiposity reduction is critical for cardiovascular and diabetes risk reduction, while childhood adiposity may have complex effects including protective influence on breast cancer.

Clinical Best Practices

• Apply MR methods that account for time-varying exposures to accurately estimate causal effects across the lifecourse.
• Validate instrumental variable assumptions specific to the lifecourse period under study.
• Use complementary MR approaches (SMM-MR and IVW-MVMR) to triangulate findings and understand methodological limitations.
• Interpret MR results within the context of genetic epidemiology and lifecourse research frameworks.

References

• Mendelian randomization and instrumental variable assumptions
• Structural mean models and g-estimation in MR
• Inverse variance weighted multivariable Mendelian randomization