Early life stress and iron metabolism in developmental psychoneuroimmunology
Bibliographic record
Abstract
An estimated 250 million children face adverse health outcomes from early life exposure to severe or chronic social, economic, and nutritional adversity, highlighting/emphasizing the pressing concern about the link between ELS and long-term implications on mental and physical health. There is significant overlap between populations experiencing high levels of chronic stress and those experiencing iron deficiency, spotlighting the potential role of iron as a key mediator in this association. Iron, an essential micronutrient for brain development and immune function, is often depleted in stress conditions. Iron deficiency among the most common nutrient deficiencies in the world. Fetal and infant iron status may thus serve as a crucial intermediary between early chronic psychological stress and subsequent immune system changes to impact neurodevelopment. The review presents a hypothesized pathway between early life stress (ELS), iron deficiency, and neurodevelopment through the hypothalamic-pituitary-adrenocortical (HPA) axis and the IL-6-hepcidin axis. This hypothesis is derived from (1) evidence that stress impacts iron status (2) long-term neurodevelopmental outcomes that are shared by ELS and iron deficiency exposure, and (3) possible mechanisms for how iron may mediate the relation between ELS and iron deficiency through alterations in the developing immune system. The article concludes by proposing future research directions, emphasizing the need for rigorous studies to elucidate how stress and iron metabolism interact to modify the developing immune system. Understanding these mechanisms could open new avenues for improving human health and neurodevelopment for women and children globally, making it a timely and vital area of study in psychoneuroimmunology research.
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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 | 0.001 | 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".