A Longitudinal Causal Graph Analysis Investigating Modifiable Risk Factors and Obesity in a European Cohort of Children and Adolescents

Ronja Foraita*, Janine Witte, Claudia Börnhorst, Wencke Gwozdz, Valeria Pala, Lauren Lissner, Fabio Lauria, Lucia A. Reisch, Dénes Molnar, Stefaan De Henauw, Luis Alberto Moreno, Toomas Veidebaum, Michael Tornaritis, Iris Pigeot, Vanessa Didelez

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

15 Downloads (Pure)

Abstract

Childhood obesity is a complex disorder that appears to be influenced by an interacting system of many factors. Taking this complexity into account, we aim to investigate the causal structure underlying childhood obesity. Our focus is on identifying potential early, direct or indirect, causes of obesity which may be promising targets for prevention strategies. Using a causal discovery algorithm, we estimate a cohort causal graph (CCG) over the life course from childhood to adolescence. We adapt a popular method, the so-called PC-algorithm, to deal with missing values by multiple imputation, with mixed discrete and continuous variables, and that takes background knowledge such as the time-structure of cohort data into account. The algorithm is then applied to learn the causal structure among 51 variables including obesity, early life factors, diet, lifestyle, insulin resistance, puberty stage and cultural background of 5112 children from the European IDEFICS/I.Family cohort across three waves (2007–2014). The robustness of the learned causal structure is addressed in a series of alternative and sensitivity analyses; in particular, we use bootstrap resamples to assess the stability of aspects of the learned CCG. Our results suggest some but only indirect possible causal paths from early modifiable risk factors, such as audio-visual media consumption and physical activity, to obesity (measured by age- and sex-adjusted BMI z-scores) 6 years later.
Original languageEnglish
Article number6822
JournalScientific Reports
Volume14
Issue number1
Number of pages14
ISSN2045-2322
DOIs
Publication statusPublished - Mar 2024

Keywords

  • Audio-visual media consumption
  • Causal structure learning
  • DAG
  • Healthy diet
  • IDEFICS/I. Family cohort
  • Multiple imputations
  • PC-algorithm
  • Physical activity
  • Sleep
  • Well-being

Cite this