Trait Anger, Hostility, and the Risk of Type 2 Diabetes and Diabetes-Related Complications: A Systematic Review of Longitudinal Studies
Bibliographic record
Abstract
BACKGROUND: Research suggests associations between trait anger, hostility, and type 2 diabetes and diabetes-related complications, though evidence from longitudinal studies has not yet been synthesized. OBJECTIVE: The present systematic review examined findings from longitudinal research on trait anger or hostility and the risk of incident type 2 diabetes or diabetes-related complications. The review protocol was pre-registered in PROSPERO (CRD42020216356). METHODS: Electronic databases (MEDLINE, PsychINFO, Web of Science, and CINAHL) were searched for articles and abstracts published up to December 15, 2020. Peer-reviewed longitudinal studies with adult samples, with effect estimates reported for trait anger/hostility and incident diabetes or diabetes-related complications, were included. Title and abstract screening, full-text screening, data extraction, and quality assessment using the Newcastle-Ottawa Scale were conducted by two independent reviewers. A narrative synthesis of the extracted data was conducted according to the Synthesis Without Meta-Analysis guidelines. RESULTS: Five studies (N = 155,146 participants) met the inclusion criteria. While results were mixed, our synthesis suggested an overall positive association between high trait-anger/hostility and an increased risk of incident diabetes. Only one study met the criteria for the diabetes-related complications outcome, which demonstrated a positive association between hostility and incident coronary heart disease but no significant association between hostility and incident stroke. CONCLUSION: Based on the available longitudinal evidence, trait anger and hostility are associated with an increased risk of diabetes. Longitudinal studies are needed to investigate the association between trait-anger or hostility and the risk of diabetes-related complications.
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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 | 0.020 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".