Childhood Adversities and the ATTACHTM Program’s Influence on Immune Cell Gene Expression
Bibliographic record
Abstract
Objective: To determine whether maternal Adverse Childhood Experiences (ACEs) are (a) associated with increased inflammatory gene expression in mother–child dyads and (b) whether a parenting intervention (ATTACH™) moderates the association between maternal ACEs and mother and/or child inflammatory gene expression. Methods: Twenty mother–child dyads, recruited from a domestic violence shelter in Calgary, AB, Canada, were randomized into an ATTACH™ parenting intervention group (n = 9) or a wait-list control group (n = 11). Maternal ACEs were assessed. The mothers and children each provided one non-fasting blood sample after the intervention group completed the ATTACH™ program, which was assayed to quantify the Conserved Transcriptional Response to Adversity (CTRA) score, indicating inflammatory gene expression profile. Mixed-effect linear models were used, separately in mothers and children, to examine the associations between CTRA score, maternal ACEs, and the ACEs-by-intervention group interaction term. The covariates were age, sex, ethnicity, and maternal medication use. Results: Higher maternal ACEs were associated with higher child CTRA scores (b = 0.123 ± SE 0.044, p = 0.005), indicating an increased pro-inflammatory gene expression profile. The ATTACH™ parenting intervention moderated this association between maternal ACEs and child CTRA scores (b = 0.328 ± SE 0.133, p = 0.014). In mothers, the ACEs-by-intervention interaction terms were insignificant (p = 0.305). Conclusions: Maternal ACEs could exert an intergenerational impact on child inflammatory activity, and this association could be moderated by participating in the ATTACH™ parenting intervention.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".