Triangulating Causality Between Childhood Obesity and Neurobehavior: Twin and Longitudinal Evidence
Bibliographic record
Abstract
Abstract Objective Childhood obesity is a serious health concern that is not yet fully understood. Previous research has linked obesity with neurobehavioral factors such as behavior, cognition, and brain morphology. The causal directions of these relationships remain mostly untested. Methods We filled this gap by using the Adolescent Brain Cognitive Development study cohort comprising 11,875 children aged 9–10. First, correlations between body mass percentile and neurobehavioral measures were cross-sectionally analyzed. Effects were then aggregated by neurobehavioral domain for causal analyses. Direction of Causation twin modeling was used to test the direction of each relationship. Findings were validated by longitudinal cross-lagged panel modeling. Results Body mass percentile correlated with measures of impulsivity, motivation, psychopathology, eating behavior, and cognitive tests (executive functioning, language, memory, perception, working memory). Higher obesity was also associated with reduced cortical thickness in areas of the frontal and temporal lobe but with increased thickness in parietal and occipital brain areas. Similar although weaker patterns emerged for cortical surface area and volume. Twin modeling suggested causal effects of childhood obesity on eating behavior (β=.26), cognition (β=.05), cortical thickness (β=.15), and cortical surface area (β=.07). Personality/psychopathology (β=.09) and eating behavior (β=.16) appeared to causally influence childhood obesity. Longitudinal evidence broadly supported these findings. Results regarding cortical volume were inconsistent. Conclusions Results supported causal effects of obesity on brain functioning and morphology, consistent with effects of obesity-related brain inflammation on cognition. The present study highlights the importance of physical health for brain development during childhood and may inform interventions aimed at preventing or reducing pediatric obesity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".