Metabolic Syndrome Components and Long-Term Incidence of Cardiovascular Disease in Eastern Mediterranean Region: A 13-Year Population-Based Cohort Study
Bibliographic record
Abstract
Background: The risk of cardiovascular events in individuals with metabolic syndrome (MetS) is higher than in general populations. We aimed at assessing the association between cardiovascular disease (CVD) and MetS and at identifying triple components that are the most predictive of future CVD events. Methods: Data on 1387 CVD-free individuals recruited in an ongoing cohort in Isfahan, Iran (ICS) were analyzed. This included serum tests and health and lifestyle questionnaires measured at baseline in 2001, 2007, and 2013. The association between CVD and MetS, irrespective of composing components, was evaluated by using logistic regression. The hazard ratio (HR) of CVD events after MetS diagnosis was calculated for different combinations by using Cox PH regression. Results: The prevalence of MetS was 34.4% at baseline, 19.5% of which was with diabetes. The prevalence of hypertension (blood pressure [BP]) and hyperglycemia (fasting plasma glucose [FPG]) increased over time. Irrespective of composing components, the odds of developing CVD in MetS individuals was higher than in those who did not develop MetS with adjusted odds ratio = 1.76; 95% confidence intervals (CI) = 1.22–2.55. Among the five most prevalent triple combinations, there was a significant association between CVD incidence and high-density lipoprotein + BP + waist circumference combination only with HR = 1.66; 95% CI = 1.04–2.67. Conclusion: Some MetS components are more likely to result in CVD. Identifying the most predictive components could help in the timely initiation of proper interventions rather than waiting for all MetS components or symptoms of CVD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".