Comparative study on prevalence of metabolic syndrome based on three criteria among adults in Zhejiang province, China: an observational study
Bibliographic record
Abstract
OBJECTIVES: In this study, we aimed to estimate the prevalence of metabolic syndrome (MetS) among Chinese adults, describe the disease components and compare utility of the existing international criteria and Chinese diagnostic criteria. DESIGN, SETTING AND PARTICIPANTS: A retrospective database analysis was conducted for one hospital in Zhejiang province, China. We analysed data (collected in 2017) from a total of 64 902 participants (37 500 males and 27 402 females), aged between 18 and 97 years, and who met the eligibility criteria. MAIN OUTCOME MEASURES: We employed three criteria for MetS proposed by the International Diabetes Federation (IDF) in 2005, the 2009 Joint Scientific Statement (harmonising criteria) and the China Diabetes Society (CDS) in 2013 to detect prevalence of MetS. Specifically, we analysed waist circumference, blood pressure, fasting plasma glucose, plasma triglycerides and plasma high-density lipoprotein cholesterol. RESULTS: We found an estimated age-adjusted MetS prevalence of 20.4% using IDF 2005, 30.0% based on harmonising criteria 2009 and 16.3% under the CDS 2013. This prevalence was higher in males, older adults and increased body mass index. Analysis of agreements among the criteria were 87.2% (IDF and CDS), 87.1% (IDF and harmonising criteria) and 81.6% (CDS and harmonising criteria), while their kappa coefficients were 0.641, 0.708 and 0.572 for IDF versus CDS, IDF versus harmonising criteria and CDS versus harmonising criteria, respectively. The most prevalent MetS component was abdominal obesity (50.1%), followed by dyslipidaemia (49.5%) and hypertension (46.8%) using harmonising criteria. CONCLUSION: These findings revealed moderate agreement among the three criteria with utility in Chinese clinical settings. The harmonising criteria 2009 performed better in early identification of MetS in the Chinese population.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".