Self‐reported child maltreatment and cardiometabolic risk in 30‐year‐old adults
Bibliographic record
Abstract
BACKGROUND: Childhood maltreatment (CM) is associated with both dietary fat intake and obesity in later life. There is less information on associations with metabolic risk factors and specific types of CM such as physical, sexual and emotional abuse, as well as neglect. AIMS: To assess the association between five types of self-reported CM and a range of obesity and metabolic indicators in a subsample of a birth cohort. METHODS: This was a study of 1689 adults born in a major metropolitan maternity hospital in Australia and followed up 30 years later. Body mass index, bioimpedance and fasting lipid levels/insulin resistance were measured. Details on self-reported CM were collected using the Child Trauma Questionnaire. We adjusted for birth weight, parental income and relationship at participants' birth, as well as maternal age and alcohol or tobacco use. We also adjusted for participants' smoking, depression, educational level, marital and employment status at follow up. RESULTS: One-fifth reported maltreatment (n = 362), most commonly emotional neglect (n = 175), followed by emotional abuse (n = 128), physical neglect (n = 123), sexual (n = 121) and physical abuse (n = 116). On adjusted analyses, there were significant associations for CM, particularly neglect or emotional abuse, and one or more of the following outcomes: obesity, the total cholesterol/high-density lipoprotein cholesterol (TC/HDL-C) ratio and HDL levels. Results for other outcomes were more equivocal. CONCLUSIONS: Of child maltreatment types, emotional abuse and neglect show the strongest associations with obesity and several cardiometabolic risk factors, therefore highlighting the public health importance of early intervention to reduce childhood adversity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".