Metabolic syndrome (MetS) and associated factors in middle-aged women: a cross-sectional study in Northeast Brazil
Bibliographic record
Abstract
We determined the prevalence of Metabolic Syndrome (MetS) and associated factors in 419 women (aged 40 to 65 years) in Northeast Brazil in a cross-sectional study conducted from April to November 2013. We defined MetS using the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) criteria. Socio-demographic variables, reproductive factors, lifestyle factors, anthropometrics, body composition, quality of life, and physical performance were assessed for their associations. We constructed multivariate Poisson regression models to estimate prevalence rate ratios (PRR) and 95% confidence intervals (CI). We identified 275 (65.6%) cases of MetS. The three most prevalent indicators were obesity (73.5%), reduced high-density lipoprotein level (63.0%), and elevated blood pressure (60.9%). In the final adjusted model, black race (PR 1.30, 95% CI: 1.07-1.57), lower grip strength/body mass index (PR 1.31, 95% CI: 1.15-1.50), and low estradiol levels (PR 1.17, 95% CI: 1.00-1.35) were associated with MetS. MetS is a long-term threat to the health of middle-aged women and a potential public health burden. These results may help in developing health promotion strategies to prevent morbidity and mortality associated with MetS in this vulnerable population.
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 | 0.002 |
| 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 | 0.001 |
| 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".