Biological embedding from adverse childhood experiences in the development of an altered inflammatory state and poor health outcomes in young adults
Bibliographic record
Abstract
Abstract Exposure to emotional, physical, and/or sexual abuse as a child, cumulatively referred to as Adverse Childhood Experiences (ACEs), has been linked with poor health outcomes in early adulthood. Toward a mechanistic explanation for this link, dysregulated inflammation has garnered attention as a potential ‘social-immunological’ axis. Altered immune profiles are associated with ACEs and may contribute to mental health status, cardiovascular disease, and early mortality. Increases in circulating interleukin-6 (IL-6), C-reactive protein (CRP), and tumor necrosis factor α (TNFα) have been detected in individuals exposed to ACEs and are proposed to signal an immunological link to these outcomes. While this sets the stage in characterizing the inflammatory profile, gaps remain in the cast of mediators involved, preventing a more refined mechanistic view. We thus aim to expand the associated immunological cast. A sample of young adults from the Niagara Longitudinal Heart Study self-reported early life trauma (ACEs), and serum levels of a panel of immune biomarkers were quantified. We confirm that levels of IL-6, CRP and TNFα are altered, but also further identify new immune biomarkers that are significantly correlated (p<0.05) with ACEs, including pentraxin, chitinase, and interferons. This further refines the nature of ACE-dependent immunological dysregulation and offers new insights into contributing mechanisms, potential early risk-factor detection, and/or therapeutic targets in those exposed to ACEs. This evidence encourages promotion of public health initiatives and psychosocial preventative interventions aimed at limiting early adversity and its sequalae as an investment in later health status/quality of life.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".