Air Pollution, Stress, and Allostatic Load: Linking Systemic and Central Nervous System Impacts
Bibliographic record
Abstract
Air pollution is a risk factor for cardiovascular and respiratory morbidity and mortality. A growing literature also links exposure to diverse air pollutants (e.g., nanoparticles, particulate matter, ozone, traffic-related air pollution) with brain health, including increased incidence of neurological and psychiatric disorders such as cognitive decline, dementia (including Alzheimer’s disease), anxiety, depression, and suicide. A critical gap in our understanding of adverse impacts of pollutants on the central nervous system (CNS) is the early initiating events triggered by pollutant inhalation that contribute to disease progression. Recent experimental evidence has shown that particulate matter and ozone, two common pollutants with differing characteristics and reactivity, can activate the hypothalamic-pituitary-adrenal (HPA) axis and release glucocorticoid stress hormones (cortisol in humans, corticosterone in rodents) as part of a neuroendocrine stress response. The brain is highly sensitive to stress: stress hormones affect cognition and mental health, and chronic stress can produce profound biochemical and structural changes in the brain. Chronic activation and/or dysfunction of the HPA axis also increases the burden on physiological stress response systems, conceptualized as allostatic load, and is a common pathway implicated in many diseases. The present paper provides an overview of how systemic stress-dependent biological responses common to particulate matter and ozone may provide insight into early CNS effects of pollutants, including links with oxidative, inflammatory, and metabolic processes. Evidence of pollutant effect modification by non-chemical stressors (e.g., socioeconomic position, psychosocial, noise), age (prenatal to elderly), and sex will also be reviewed in the context of susceptibility across the lifespan.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".