Prevalence of metabolic syndrome in pre- and postmenopausal Iranian women
Bibliographic record
Abstract
BACKGROUND AND AIMS: The metabolic syndrome (MetS) is a constellation of risk factors increasing the risk of developing cardiovascular disease and diabetes. Little information is available on the association between MetS and menopausal status in Iranian women. Therefore, the purpose of the current study was to examine the prevalence and severity of MetS in pre- and postmenopausal women based on two commonly employed assessment criteria. METHODS: A total of 490 premenopausal and 434 postmenopausal women from the Shiraz Women's Health Cohort Study were included in the study. MetS was defined according to the criteria of the National Cholesterol Education Program-Adult Panel Treatment III (NCEP-ATPIII) and the International Diabetes Federation (IDF). Clinical, biochemical and anthropometric measures were collected from all study participants for determination of MetS. RESULTS: The majority of participants had at least two components of MetS. Postmenopausal women, compared to premenopausal women, had a higher prevalence of MetS. The prevalences of MetS were 60.2% and 59.4% based on the NCEP-ATPIII and IDF definitions, respectively. Waist circumference, waist-to-hip ratio, blood pressure, and levels of fasting plasma glucose, total cholesterol, low density lipoprotein cholesterol, and triglycerides were higher in postmenopausal women compared to premenopausal women. CONCLUSIONS: MetS is a major threat to women's health and an aging population, and longitudinal studies to determine the mechanism of postmenopausal MetS are required.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".