3.8 Article

Intergenerational Associations Between Maternal Diet and Childhood Adiposity: A Bayesian Regularized Mediation Analysis

Journal

STATISTICS IN BIOSCIENCES
Volume 13, Issue 3, Pages 524-542

Publisher

SPRINGER
DOI: 10.1007/s12561-021-09305-7

Keywords

Childhood adiposity; Mediation analysis; Potential outcome; Regularization; Lasso

Funding

  1. Intramural Research Program of Eunice Kennedy Shriver National Institute of Child Health and Human Development

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Research indicates a positive association between childhood obesity and chronic diseases later in life, possibly linked to maternal metabolic disorders during pregnancy. Mediation analysis identifies how multiple maternal risk factors can directly or indirectly impact a child's body mass index. The use of shrinkage priors in parameter estimation shows good performance in selecting relevant prevention pathways.
Growing evidence supports a positive association between childhood obesity and chronic diseases in later life. It is also suggested that childhood obesity is more prevalent for children born from pregnancies complicated by metabolic disorders such as gestational diabetes, and can be related to maternal dietary factors during gestation. Extending conventional analyses that report only the marginal associations within non-causal mediation frameworks, we present mediation analysis in the case of multiple exposures and multiple mediators using a regularized two-stage approach. By placing shrinkage priors on each parameter relating to direct and indirect effects, a parsimonious model can be obtained, and consequently, the most relevant pathways will be selected to inform the development of efficient prevention programs. We apply this method to data from the Danish site of the Diabetes & Women's Health Study, Danish National Birth Cohort (DNBC), and find 6 significant maternal risk factors either directly or indirectly affecting childhood body mass index z score at age 7. Simulations with data-generating mechanisms similar to the DNBC data demonstrate good performance of the proposed model.

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