Early-life Stress, Depressive Symptoms, and Inflammation: The Role of Social Factors
Bibliographic record
Abstract
Abstract Early-life stress (ELS) is associated with elevated risk of adverse psychological (e.g., depression) and physical health outcomes (chronic diseases driven by inflammation) in older adulthood. We evaluated whether four social factors buffered the ELS-depressive symptoms and ELS-inflammation associations. Data were from 3,416 adults (58.28% female; Mage=68.41; SDage=10.24) who participated in the 2006 wave of the Health and Retirement Study, a nationally representative sample of older adults in the United States. We used hierarchical regression analyses to first test the main effects of ELS on depressive symptoms and inflammation (high-sensitivity C-reactive protein). We then assessed whether four social factors (perceived support, frequency of social contact, network size, and volunteer activity) moderated the ELS-depressive symptoms and ELS-inflammation relationships. We found a small positive association between ELS and depressive symptoms (B=0.17, SE=0.05, p=.002), which was moderated by social contact and perceived support. Specifically, ELS was only associated with elevated depressive symptoms for participants with limited social contact (B=0.24, SE=0.07, p<.001) and low perceived support (B=0.24, SE=0.07, p<.001). These associations remained after accounting for potential confounders (age, body-mass index, adulthood stress, and marital status). ELS was not associated with inflammation, and no social factors moderated the ELS-inflammation link. Increased social contact and perceived support may be protective for individuals at an elevated risk of developing depressive symptoms as a result of ELS. Future interventions may benefit from leveraging these social factors to improve quality of life in adults with ELS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".