Socioeconomic disadvantage, chronic stress, and proinflammatory phenotype: an integrative data analysis across the lifecourse
Bibliographic record
Abstract
Socioeconomic disadvantage confers risk for many chronic illnesses, and theories have highlighted chronic psychological stress and alterations to inflammatory processes as key pathways. Specifically, disadvantage can heighten chronic stress, which may promote a proinflammatory phenotype characterized by immune cells mounting exaggerated cytokine responses to challenge and being less sensitive to inhibitory signals. Importantly, lifecourse perspectives emphasize that such immune alterations should be more potent earlier in life during a sensitive period when bodily tissues are highly plastic to environmental inputs. However, examining these propositions is resource intensive, as they require cell-culturing approaches to model functional inflammatory activities, a wide age range, and longitudinal data. Here, we integrated data from five independent studies to create a diverse sample of 1,607 individuals (960 with longitudinal data; 8 to 64 years old; 359 Asian, 205 Black, and 151 Latino/a). Leveraging the resulting lifecourse data, rich interview assessments of disadvantage and stress, and ex vivo assessments of inflammation, we examined two questions: (1) Does chronic stress account for the link between disadvantage and proinflammatory phenotype? (2) Is there a developmental period during which inflammatory responses are more sensitive to disadvantage and chronic stress? Disadvantage was associated with higher chronic stress, which was linked with a proinflammatory phenotype cross-sectionally, longitudinally, and in terms of prospective change across 1.5 to 2 years. Consistent with the sensitive period hypothesis, the magnitude of these indirect associations was strongest in earlier decades and declined across the lifecourse. These findings highlight the importance of taking a lifecourse perspective in examining health disparities.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".