Association Between Psychological Distress and Stress-Related Symptoms and Increased Risk of Type 2 Diabetes in Male Individuals: An Observational Study
Bibliographic record
Abstract
BACKGROUND: This study evaluated the association between psychological distress and development of type 2 diabetes (T2D) among male individuals in the workplace. METHODS: , hemoglobin A1c 5.5±0.3%), who received annual health checkups from April 2016 to March 2017. Those who had a hemoglobin A1c level ≥ 6.5%, previous history of diabetes, or used diabetes medication were excluded from the analysis. Psychological distress was measured using the Brief Job Stress Questionnaire. Multivariate Cox proportional hazard regression models were used to estimate the development of T2D in relation to psychological distress and stress-related symptoms. The calculated hazard ratio (aHR) was adjusted for age, body mass index, and hemoglobin A1c level. RESULTS: During a mean follow-up period of 1.9 years, the incidence rate of new-onset T2D was 2.0%. Baseline psychological parameters did not differ between participants with or without new-onset T2D. Depression was associated with an increased risk of T2D (aHR = 1.54, 95% confidence interval (CI): 1.07 - 2.22), whereas vigor, irritation, fatigue, and anxiety were not. Moreover, inability to handle work (aHR = 2.18, 95% CI: 1.14 - 4.19), sadness (aHR = 1.93, 95% CI: 1.14 - 3.26), headache (aHR = 1.98, 95% CI: 1.18 - 3.34), shoulder stiffness (aHR = 1.56, 95% CI: 1.10 - 2.23), and constipation/diarrhea (aHR = 1.71, 95% CI: 1.04 - 2.80) were associated with T2D incidence. CONCLUSIONS: Depression and stress-related symptoms were associated with an increased risk of T2D. Industrial physicians and health care providers should evaluate these factors during health checkups to lower the T2D incidence in this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".