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Area of Science:

  • Biostatistics
  • Genetic Epidemiology
  • Causal Inference

Background:

  • Mendelian randomization (MR) traditionally uses cross-sectional data, limiting its ability to analyze time-varying effects.
  • Estimating causal effects on the mean, slope, and within-individual variability of exposures over time requires advanced methodologies.

Purpose of the Study:

  • To develop and validate a multivariable Mendelian randomization (MR) approach using longitudinal summary statistics for time-varying exposures.
  • To assess the causal effects on the mean, slope, and within-individual variability of an exposure.

Main Methods:

  • Utilized longitudinal summary statistics within a multivariable MR framework.
  • Simulated 12 scenarios to evaluate power and type I error rates under varying conditions of shared instruments and regression models.
  • Applied the approach to two real-world datasets (POPS and UK Biobank).

Main Results:

  • Simulations demonstrated high power to detect causal effects on the mean and slope with strong instruments.
  • Causal effects on within-individual variability were detected with low power, particularly when instruments were shared with the mean.
  • Real data application identified significant causal estimates for the mean and slope, but weak instruments limited the detection of variability effects.

Conclusions:

  • The developed MR approach shows promise for analyzing time-varying exposures, especially with strong genetic instruments.
  • Accurate exposure regression model specification and sufficient genetic correlation are critical for reliable results.
  • The scarcity of strong instruments in real-world data necessitates cautious interpretation of findings, considering biological context and exposure trajectories.