Psychological stress in childhood and susceptibility to the chronic diseases of aging: Moving toward a model of behavioral and biological mechanisms.
Bibliographic record
Abstract
Among people exposed to major psychological stressors in early life, there are elevated rates of morbidity and mortality from chronic diseases of aging. The most compelling data come from studies of children raised in poverty or maltreated by their parents, who show heightened vulnerability to vascular disease, autoimmune disorders, and premature mortality. These findings raise challenging theoretical questions. How does childhood stress get under the skin, at the molecular level, to affect risk for later diseases? And how does it incubate there, giving rise to diseases several decades later? Here we present a biological embedding model, which attempts to address these questions by synthesizing knowledge across several behavioral and biomedical literatures. This model maintains that childhood stress gets "programmed" into macrophages through epigenetic markings, posttranslational modifications, and tissue remodeling. As a consequence these cells are endowed with proinflammatory tendencies, manifest in exaggerated cytokine responses to challenge and decreased sensitivity to inhibitory hormonal signals. The model goes on to propose that over the life course, these proinflammatory tendencies are exacerbated by behavioral proclivities and hormonal dysregulation, themselves the products of exposure to early stress. Behaviorally, the model posits that childhood stress gives rise to excessive threat vigilance, mistrust of others, poor social relationships, impaired self-regulation, and unhealthy lifestyle choices. Hormonally, early stress confers altered patterns of endocrine and autonomic discharge. This milieu amplifies the proinflammatory environment already instantiated by macrophages. Acting in concert with other exposures and genetic liabilities, the resulting inflammation drives forward pathogenic mechanisms that ultimately foster chronic disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".