Age and Daily Positive Events Moderate the Link Between Depressive Symptoms and Inflammation
Bibliographic record
Abstract
Abstract Inflammation is a pathway underlying numerous aging-related conditions. Depression is related to elevated inflammation, whereas daily positive events have been linked to lower inflammation; these psychological experiences may interact with age to predict inflammation. The purpose of this study was to examine whether daily positive events moderate the association between depressive symptoms and inflammation in an adult lifespan sample. A sample of 343 adults ages 25-75 (55% women, 83% white) in the Midlife in the United States Refresher Study completed daily diary interviews for 8 evenings about their daily positive events. Depressive symptoms were assessed with the 20-item Center for Epidemiological Studies Depression scale, and blood samples were assayed for inflammatory markers interleukin-6 (IL-6) and C-reactive protein (CRP). On average, depression scores ranged from 0 to 44 (mean = 9.31, SD = 7.80), and participants reported 1.25 (SD = .70) positive events per day (range = 0–5). Depressive symptoms and daily positive events were separately associated with higher and lower log IL-6 and CRP, respectively. Depressive symptoms, daily positive events, and age interacted such that daily positive events predicted lower IL-6 (but not CRP) among midlife and older adults who reported lower depressive symptoms, whereas positive events were not related to inflammation among younger adults. Thus, these findings suggest that the protective association between daily positive events and inflammation was blunted when depressive symptoms were elevated and for younger adults. This work has implications for understanding age variations in the role of positive experiences in depression and inflammation.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".