Prevalences of metabolic syndrome and its sex-specific association with socioeconomic status in rural China: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Few studies have reported the prevalence of metabolic syndrome (MS) and examined relationships between socioeconomic status and MS in rural China. This study aimed to evaluate the prevalence of MS and MS components as well as their associations with socioeconomic status among rural Chinese adults. METHODS: A cross-sectional study of 26,836 participants aged 20 years and older was conducted from June to December 2012 in Yuhuan City, Zhejiang Province, China, which is located on Yuhuan Island. A multivariable logistic regression model was used to identify risk factors for MS and their possible interactions. RESULTS: Among 26,836 subjects with an average age of 53.4 ± 14.0 years, 59% were female. The overall prevalence of MS was 20.5%, and there was a significant sex difference in the prevalence (15.1% for males vs. 24.2% for females, P < 0.001). Compared with males, females also showed a significantly higher proportion of most MS components. A significantly higher prevalence of MS was found among subjects who were elderly, had a lower income level, had a lower level of education, or were unemployed. Multiple significant interactions were observed between the prevalence of MS and sex, age or socioeconomic status (P < 0.001). The risk of MS increased significantly with age in females but not in males. Additionally, a lower income level and a lower level of education were significantly related to an increased risk only in females, and unemployed males had a higher risk of MS than unemployed females. CONCLUSIONS: The prevalence of MS and its components was relatively high in a rural island Chinese population with rapid urbanization, and sex-specific associations between socioeconomic factors and MS were found. Targeted preventive interventions should be developed and implemented to prevent and control MS among those with low socioeconomic status, especially females.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".