Adverse Childhood Experiences and Weight Loss in Overweight and Obese Children in a 9-Year Study: A Prospective Cohort Study with Structural Equation Modeling
Bibliographic record
Abstract
Abstract Background Exposure to adverse childhood experiences (ACEs) is associated with a 30 to 50% increased risk of obesity in adolescence. The role of ACEs as a determinant of weight loss among overweight and obese children remains unclear. Methods Among 8568 nine-year-old children randomly sampled in 2007/2008 for the Growing up in Ireland cohort, 2210 were overweight or obese at 9 years and provided complete follow-up data at age 13 and 18 years. Structural equation and natural effects mediation models tested for a direct causal relationship between ACEs before 9 years and remission risk at 18 years, and indirect effects mediated via daily activity, diet quality, self-image and behavioural difficulties and BMI at 9 years. Results Among the 1676 adolescents that were overweight or obese at age 9, 46% achieved healthy weight status by age 18; 56% (n=618) of overweight children and 27% (n=153) of obese children), 13% experienced an ACE, and 41% were female. Exposure to an ACE was associated with a higher BMI Z at ages 9 (0.47 vs 0.36, p < 0.05) and 13 years (0.39 vs 0.29, p < 0.05) and reduced the odds of achieving healthy weight status at age 18 by 27% (OR: 0.73, 95% CI:0.54- 0.99). Overweight and obese children exposed to an ACE had lower household income, higher behavioural difficulties, and lower self-concept at ages 9, 13 and 18. Children exposed to an ACE were also 2 to 3-fold more likely to have started smoking before 12 years old and 50% more likely to smoke more than 10 cigarettes a week regularly vape. Behavioural difficulties, self-concept and baseline weight status, but not smoking or dietary habits, mediated the association between ACE exposure and achieving healthy weight at age 18. Conclusion Among overweight and obese children, exposure to ACEs indirectly reduces the likelihood of achieving healthy weight status at age 18, mediated by its effects on weight in childhood, and behavioural difficulties and self-concept in mid-adolescence. These findings highlight the complex factors that influence weight loss among overweight and obese children exposed to adverse experiences early in life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".