A Bayesian life-course linear structural equations model (Bayesian Linear Structural Equations Model (BLSEM)) to explore the development of body mass index (BMI) from the prenatal stage until middle age
Authors' conclusion
The Bayesian approach we present and the BLSEM software developed advances methodologies for the analysis of complex, multifaceted life-course data prior to the estimation of potential causal pathways. Our results, although exploratory in nature, suggest that the effective interventions to tackle adverse BMI development could be designed throughout childhood, though the period by AgeAR may be paramount. We feature the importance of integrated life-course analyses that help to understand the contribution of life-stage factors of development.
Study context
- Population
- Offspring of a Finnish population-based birth cohort followed from the pre-pregnancy period to age 46 yearsbirth cohort
- Participants
- 4,119 offspring
- Outcome
- BMI at 46 y (BMI46)
- Follow-up
- 46 y
Methodological detailsBayesian linear structural equations model, variable selection priors, directed acyclic graphs, exploratory
- Bayesian linear structural equations model
- Variable selection priors
- Directed acyclic graphs
- Exploratory
Main finding
As reported in the abstract
Paths linking maternal prepregnancy BMI, smoking and socioeconomic position with BMI46
35, 31 and 26 paths
Observational data: associations, not effects.
Original publication
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