Affect Regulation and Mortality Risk: The Role of Allostatic Load
Bibliographic record
Abstract
OBJECTIVE: Although growing evidence indicates that distinct affect regulation strategies (eg, positive reappraisal, anger suppression) predict mortality risk, the biological processes involved remain understudied. We investigated the association of various affect regulation exposures with mortality risk while examining the role of allostatic load. METHODS: From 2004 to 2006, 1941 participants from the Midlife in the United States longitudinal study completed validated scales assessing the use of 9 general and emotion-specific regulatory strategies (eg, denial, anger expression). An SD-based algorithm was also used to characterize how flexibly participants regulate their affect (lower, moderate, or greater variability). Participants further provided data on relevant covariates and 24 allostatic load biomarkers (eg, cortisol, glucose). Cox regressions modeled hazard ratios (HRs) and 95% CIs examined associations of affect regulation variables and all-cause mortality risk until 2022. The confounding, mediating, and moderating role of allostatic load was examined in subsequent models. RESULTS: In fully adjusted models, only greater versus lower affect regulation variability (HR=1.54; 95% CI=1.11-2.14) significantly predicted a higher mortality risk. Associations were relatively unchanged with further inclusion of allostatic load in models and allostatic load did not mediate affect regulation-mortality relationships. Yet, when evaluating moderation effects, greater versus lower and moderate variability, as well as denial, were marginally or significantly related to higher mortality risk among adults with lower allostatic load only. CONCLUSIONS: Allostatic load may modify rather than confound or mediate the association between some dimensions of affect regulation and mortality risk. Future work should evaluate the potential roles of allostatic load among diverse samples.
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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) | 0.001 | 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".