Testing the biological embedding hypothesis: Is early life adversity associated with a later proinflammatory phenotype?
Bibliographic record
Abstract
Accumulating evidence suggests that the experience of early life adversity is a risk factor for a range of poor outcomes across development, including poor physical health in adulthood. The biological embedding model of early adversity (Miller, Chen, & Parker, 2011) suggests that early adversity might become embedded within immune cells known as monocytes/macrophages, programming them to be overly aggressive to environmental stimuli and insensitive to inhibitory signals, creating a "proinflammatory phenotype" that increases vulnerability to chronic diseases across the life span. We tested this hypothesis in the present study. Adolescent girls (n = 147) had blood drawn every 6 months across a 2.5-year period. To assess inflammatory responses to challenge, their monocytes were stimulated in vitro with a bacterial product, and production of the cytokine interleukin-6 was quantified. Hydrocortisone was added to cultures to assess the cells' sensitivity to glucocorticoids' anti-inflammatory signal. Using cluster analyses, we found that early life adversity was associated with greater odds of displaying a proinflammatory phenotype characterized by relatively larger interleukin-6 responses and relatively less sensitivity to glucocorticoids. In contrast, ongoing social stress was not associated with increasing odds of being categorized in the proinflammatory cluster. These findings suggest that early life adversity increases the probability of developing a proinflammatory phenotype, which, if sustained, could forecast risk for health problems later in 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".