Is anger, but not sadness, associated with chronic inflammation and illness in older adulthood?
Bibliographic record
Abstract
The discrete emotion theory of affective aging postulates that anger, but not sadness, becomes increasingly maladaptive during older adulthood in predicting health-relevant physiological processes and chronic disease (Kunzmann & Wrosch, 2018). However, it is largely unknown whether different negative emotions have distinct functional consequences in the development of older adults' physical disease. To start examining this possibility, we investigated whether older adults' daily experiences of anger and sadness were differentially associated with two biomarkers of chronic low-grade inflammation (interleukin-6 [IL-6] and C-reactive protein [CRP]) and the number of chronic illnesses (e.g., heart disease, cancer, etc.). In addition, we examined whether such divergent associations would become paramount in advanced, as compared with early, old age. A community-dwelling study of 226 older adults (age 59 to 93; M = 74.99, SD = 7.70) assessed participants' anger and sadness over 1 week, inflammatory processes, number of chronic illnesses, and relevant covariates. Regression analysis showed that anger predicted higher levels of IL-6 and chronic illness in advanced, but not in early, old age. The age effect of anger on chronic illness was mediated by increased IL-6 levels. Sadness exerted a reversed, but nonsignificant, association with IL-6 and chronic illness, independent of age. No emotion or age effects were obtained for CRP. The study's findings inform theories of health, emotion, and life span development by pointing to the age-related importance of discrete negative emotions in predicting a major physiological pathway to physical health across older adulthood. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".