Determinants of Incident Metabolic Syndrome in a Middle Eastern Population: Isfahan Cohort Study
Bibliographic record
Abstract
BACKGROUND: To identify the associated risk factors with development of metabolic syndrome (MetS) in a longitudinal prospective cohort study in an Iranian population. METHODS: A total of 1994 participants, aged ≥35 years, free of MetS, diabetes, and cardiovascular disease at baseline were followed up for 7 years. Physical examination, laboratory studies, and interview about lifestyle factors were performed, and MetS was defined based on harmonized definition at both time points. Logistic regression was used to calculate odds ratio (OR) and corresponding 95% confidence interval (CI). RESULTS: MetS occurred in 27% of subjects with an incidence rate of 39.2 and 46.6 per 1000 person-year in men and women, respectively (P = 0.04). Among the components of MetS, triglyceride (TG) alone (OR 2.59, 95% CI 1.78-3.78) or in combination with waist circumference (WC; OR 5.01, 95% CI 3.59-7.01) was the strongest predictor of incident MetS compared to those free of components. In multivariable analysis, all components were associated with higher risk except fasting plasma glucose in both genders and high-density lipoprotein cholesterol in men. Impaired glucose tolerance was associated with two (95% CI 1.11-3.65) times increased risk in women. The multivariable adjusted OR (95% CI) of overweight and obesity was 1.68 (1.13-2.50) and 2.88 (1.73-4.78) in women and 2.46 (1.74-3.46) and 2.47 (1.38-4.43) in men, respectively. Unhealthy diet [1.57 (1.02-2.41)] and weekly Cola consumption [1.50 (1.05, 2.14)] increased the risk in women only. CONCLUSIONS: TG and WC components showed the highest predictive values for MetS incidence, while general obesity was independently associated with it.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".