Proposed Physiological Mechanisms Underlying the Association between Adverse Childhood Experiences and Mental Health Conditions: A Narrative Review
Bibliographic record
Abstract
Adverse childhood experiences (ACEs; e.g., physical abuse) can impact lifelong mental health both directly and intergenerationally, with effects transmitted from the parent to the child. Several physiological mechanisms have been proposed to explain the impacts of ACEs on mental health. The purpose of this narrative review was to synthesize and critique the peer-reviewed literature on physiological mechanisms proposed to underlie the impacts of ACEs on mental health, specifically: (1) hypothalamic-pituitary-adrenal axis functioning, (2) inflammation, (3) genetic inheritance and differential susceptibility, (4) epigenetics, (5) brain structure and function, (6) oxidative stress, and (7) metabolic profiles. We searched Google Scholar using variations of the terms "adverse childhood experiences", "mechanisms", and "mental health" to locate relevant peer-reviewed literature. We also mined citations of the identified literature to find additional important sources. The role of inflammation in the etiology of mental health conditions among those exposed to ACEs appeared promising, followed by hypothalamic-pituitary-adrenal axis functioning, brain structure and function, genetics, epigenetics, metabolism, and lastly, oxidative stress. Replication studies that examine the associations among ACEs, genetic inheritance and differential susceptibility, epigenetics, oxidative stress, and metabolism are required to better define links with mental health.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".