Anger Expression Styles, Cynical Hostility, and the Risk for the Development of Type 2 Diabetes or Diabetes-Related Heart Complications: Secondary Analysis of the Health and Retirement Study
Bibliographic record
Abstract
OBJECTIVE: Limited research has examined associations between trait anger and hostility and incident type 2 diabetes (T2D) and diabetes-related heart complications. However, anger expression styles (i.e., anger-in, anger-out) have not been examined. The present study used secondary data to examine the associations between anger expression styles, cynical hostility, and the risk of developing T2D (objective 1) or diabetes-related heart complications (objective 2). METHODS: Self-report data came from participants aged 50 to 75 years in the Health and Retirement Study. Anger-in (anger that is suppressed and directed toward oneself, anger-out (anger directed toward other people or the environment), and cynical hostility were measured at baseline (2006 or 2008). Follow-up data (i.e., diabetes status or diabetes-related heart complications status) were collected every 2 years thereafter until 2020. The objective 1 sample included 7898 participants without T2D at baseline, whereas the objective 2 sample included 1340 participants with T2D but without heart complications at baseline. RESULTS: Only anger-in was significantly associated with incident T2D after controlling for sociodemographic characteristics (hazard ratio = 1.08, 95% confidence interval = 1.01-1.16), but the association did not hold after further adjustment for depressive symptoms. Only anger-out was significantly associated with incident diabetes-related heart complications after adjusting for sociodemographic characteristics, health-related covariates, and depressive symptoms (hazard ratio = 1.21, 95% confidence interval = 1.02-1.39). CONCLUSIONS: Anger expression styles were differentially related to diabetes outcomes. These findings demonstrate the value of expanding the operationalization of anger beyond trait anger in this literature and encourage further investigation of anger expression styles.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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".