Diabetes Drives Cardiovascular Risk in the Metabolic Triad: An Eight-Year Occupational Cohort Study of Ten Thousand Workers
Bibliographic record
Abstract
Background: Cardiovascular diseases remain the leading cause of death and maintain a high burden in Latin America. Obesity, hypertension, and type 2 diabetes are independent risk factors, but their coexistence could amplify cardiovascular damage beyond their individual sum. The aim of our study was to determine, in a Peruvian occupational cohort followed for 8 years, how the coexistence of obesity, hypertension, and diabetes is associated with the incidence of major cardiovascular events and to identify which combinations confer the highest risk. Methods: This was a retrospective observational cohort study of Peruvian workers without cardiovascular history, followed for 8 years. Obesity, hypertension, and diabetes were evaluated in eight mutually exclusive categories. The outcome was cardiovascular disease. Adjusted risk ratios were estimated using Cox regression, adjusted for age, sex, cholesterol, and triglycerides. Results: A progressive risk increase pattern was observed where the group without components had an incidence of 1.30 per 1,000 person-years, while the triad reached 34.94 per 1,000 person-years with adjusted hazard ratio (aHR) = 10.87 (2.81 - 42.06). Among isolated components, diabetes showed the highest risk (aHR = 4.24; 1.36 - 13.35) compared to obesity (1.45; 0.68 - 3.10) and hypertension (1.17; 0.32 - 4.24). In combinations, obesity + diabetes (aHR = 6.34; 1.73 - 23.27) and diabetes + hypertension (5.86; 1.47 - 23.39) concentrated the highest risks, above obesity + hypertension (2.01; 0.71 - 5.72). Conclusions: In the working population, the coexistence of obesity, hypertension, and diabetes progressively increases CVD risk, with diabetes as the main axis and the triad identifying a very high-risk subgroup. These findings support periodic screening and intensive multifactorial management in the occupational setting, prioritizing those presenting diabetes alone or in combination.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".