Dataset Information

Mendelian Randomization With Repeated Measures of a Time-varying Exposure: An Application of Structural Mean Models.

ABSTRACT: Mendelian randomization (MR) is often used to estimate effects of time-varying exposures on health outcomes using observational data. However, MR studies typically use a single measurement of exposure and apply conventional instrumental variable (IV) methods designed to handle time-fixed exposures. As such, MR effect estimates for time-varying exposures are often biased, and interpretations are unclear. We describe the instrumental conditions required for IV estimation with a time-varying exposure, and the additional conditions required to causally interpret MR estimates as a point effect, a period effect or a lifetime effect depending on whether researchers have measurements at a single or multiple time points. We propose methods to incorporate time-varying exposures in MR analyses based on g-estimation of structural mean models, and demonstrate its application by estimating the period effect of alcohol intake, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol on intermediate coronary heart disease outcomes using data from the Framingham Heart Study. We use this data example to highlight the challenges of interpreting MR estimates as causal effects, and describe other extensions of structural mean models for more complex data scenarios.

SUBMITTER: Shi J

PROVIDER: S-EPMC9067358 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Mendelian Randomization With Repeated Measures of a Time-varying Exposure: An Application of Structural Mean Models.

Shi Joy J Swanson Sonja A SA Kraft Peter P Rosner Bernard B De Vivo Immaculata I Hernán Miguel A MA

Epidemiology (Cambridge, Mass.) 20220101 1

Mendelian randomization (MR) is often used to estimate effects of time-varying exposures on health outcomes using observational data. However, MR studies typically use a single measurement of exposure and apply conventional instrumental variable (IV) methods designed to handle time-fixed exposures. As such, MR effect estimates for time-varying exposures are often biased, and interpretations are unclear. We describe the instrumental conditions required for IV estimation with a time-varying exposu ...[more]

PMID: 34847085

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Project description:BackgroundFindings from Mendelian randomization (MR) studies are conventionally interpreted as lifelong effects, which typically do not provide insight into the molecular mechanisms underlying the effect of an exposure on an outcome. In this study, we apply two recently developed MR approaches (known as 'lifecourse' and 'tissue-partitioned' MR) to investigate lifestage-specific effects and tissues of action in the relationship between adiposity and circulating leptin levels.MethodsGenetic instruments for childhood and adult adiposity were incorporated into a multivariable MR (MVMR) framework to estimate lifestage-specific effects on leptin levels measured during early life (mean age: 10 y) in the Avon Longitudinal Study of Parents and Children and in adulthood (mean age: 55 y) using summary-level data from the deCODE Health study. This was followed by partitioning body mass index (BMI) instruments into those whose effects are putatively mediated by gene expression in either subcutaneous adipose or brain tissues, followed by using MVMR to simultaneously estimate their separate effects on childhood and adult leptin levels.ResultsThere was strong evidence that childhood adiposity has a direct effect on leptin levels at age 10 y in the lifecourse (β = 1.10 SD change in leptin levels, 95% CI = 0.90-1.30, p=6 × 10-28), whereas evidence of an indirect effect was found on adulthood leptin along the causal pathway involving adulthood body size (β = 0.74, 95% CI = 0.62-0.86, p=1 × 10-33). Tissue-partitioned MR analyses provided evidence to suggest that BMI exerts its effect on leptin levels during both childhood and adulthood via brain tissue-mediated pathways (β = 0.79, 95% CI = 0.22-1.36, p=6 × 10-3 and β = 0.51, 95% CI = 0.32-0.69, p=1 × 10-7, respectively).ConclusionsOur findings demonstrate the use of lifecourse MR to disentangle direct and indirect effects of early-life exposures on time-varying complex outcomes. Furthermore, by integrating tissue-specific data, we highlight the etiological importance of appetite regulation in the effect of adiposity on leptin levels.FundingThis work was supported by the Integrative Epidemiology Unit, which receives funding from the UK Medical Research Council and the University of Bristol (MC_UU_00011/1).

Project description:We present an approach to analyzing physiologic timetrends recorded during a stimulus by comparing means at each time point using repeated measures analysis of variance (RMANOVA). The approach allows temporal patterns to be examined without an a priori model of expected timing or pattern of response. The approach was originally applied to signals recorded from functional magnetic resonance imaging (fMRI) volumes-of-interest (VOI) during a physiologic challenge, but we have used the same technique to analyze continuous recordings of other physiological signals such as heart rate, breathing rate, and pulse oximetry. For fMRI, the method serves as a complement to whole-brain voxel-based analyses, and is useful for detecting complex responses within pre-determined brain regions, or as a post-hoc analysis of regions of interest identified by whole-brain assessments. We illustrate an implementation of the technique in the statistical software packages R and SAS. VOI timetrends are extracted from conventionally preprocessed fMRI images. A timetrend of average signal intensity across the VOI during the scanning period is calculated for each subject. The values are scaled relative to baseline periods, and time points are binned. In SAS, the procedure PROC MIXED implements the RMANOVA in a single step. In R, we present one option for implementing RMANOVA with the mixed model function "lme". Model diagnostics, and predicted means and differences are best performed with additional libraries and commands in R; we present one example. The ensuing results allow determination of significant overall effects, and time-point specific within- and between-group responses relative to baseline. We illustrate the technique using fMRI data from two groups of subjects who underwent a respiratory challenge. RMANOVA allows insight into the timing of responses and response differences between groups, and so is suited to physiologic testing paradigms eliciting complex response patterns.

Dataset Information

Mendelian Randomization With Repeated Measures of a Time-varying Exposure: An Application of Structural Mean Models.

Publications

Mendelian Randomization With Repeated Measures of a Time-varying Exposure: An Application of Structural Mean Models.

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