Trait-anger, hostility, and the risk of incident type 2 diabetes and diabetes-related complications: a systematic review of longitudinal studies
Bibliographic record
Abstract
Introduction There is a well-established association between anger, hostility, and an increased risk of cardiovascular disease. Emerging evidence also suggests associations between anger/hostility and type 2 diabetes (T2D), though evidence from longitudinal studies has not yet been synthesized. Objectives To systematically review findings from existing prospective cohort studies on trait anger/hostility and the risk of T2D and diabetes-related complications. Methods Electronic searches of MEDLINE (PubMed), PsychINFO, Web of Science, and CINAHL were performed for articles/abstracts published up to December 15, 2020. Peer-reviewed longitudinal studies conducted with adult samples, with effect estimates reported for trait anger or hostility and incident T2D or diabetes-related complications, were eligible for inclusion. Risk of bias/study quality was assessed. The review protocol was published a priori in PROSPERO (CRD42020216356) and was in keeping with PRISMA guidelines. Screening for eligibility, data extraction, and quality assessment was conducted by two independent reviewers. Results Four studies with a total of 155,146 participants met the inclusion criteria. A narrative synthesis of extracted data was conducted according to the Synthesis Without Meta-Analysis guidelines. While results were mixed, our synthesis suggested a positive association between high trait-anger/hostility and increased risk of incident T2D. No longitudinal studies were identified relating to anger/hostility and incident diabetes-related complications. Geographical locations of the study samples were limited to the USA and Japan. Conclusions Further research is needed to investigate whether trait-anger/hostility predicts incident type 2 diabetes after adjustments for potential confounding factors. Longitudinal studies are needed to investigate trait-anger/hostility and the risk of diabetes-related vascular complications. Disclosure No significant relationships.
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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 | 0.021 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.012 | 0.011 |
| 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".