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INTEGRATING MENDELIAN RANDOMIZATION WITH CAUSAL MEDIATION ANALYSES FOR CHARACTERIZING DIRECT AND INDIRECT

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Area of Science:

  • Genetics and Epidemiology
  • Causal Inference Methods

Background:

  • Mendelian randomization (MR) uses genome-wide association studies (GWAS) for causal inference but total effects can be misleading.
  • Causal mediation analysis requires individual data, limiting power for complex traits with multiple pathways.
  • Atrial fibrillation (AF) and Alzheimer's dementia (AD) have complex etiologies where mediation pathways are crucial.

Purpose of the Study:

  • To develop Integrative Mendelian randomization and Mediation Analysis (IMMA) for robust causal effect estimation.
  • To investigate the direct and indirect effects of atrial fibrillation (AF) on Alzheimer's dementia (AD) risk.
  • To improve power and estimation for mediation analysis using combined large-scale GWAS and individual-level data.

Main Methods:

  • Developed IMMA models integrating summary statistics from large GWASs with individual-level mediation data.
  • Applied IMMA to assess the relationship between atrial fibrillation (exposure), oral anticoagulant treatment (mediator), and Alzheimer's dementia (outcome).
  • Accounted for potential exposure-mediator interactions and study heterogeneity in IMMA models.

Main Results:

  • A significant positive direct effect of AF on Alzheimer's dementia risk, independent of anticoagulant treatment.
  • A significant indirect effect of AF-induced anticoagulant treatment, which reduces Alzheimer's dementia risk.
  • Sensitivity analysis confirmed the robustness of the IMMA approach and its conclusions.

Conclusions:

  • IMMA provides a powerful framework for dissecting complex causal pathways using diverse data sources.
  • Findings suggest AF directly elevates AD risk, but anticoagulant therapy mitigates this risk.
  • Results inform potential AD risk prediction and prevention strategies for AF patients and treatment guidelines.