Prevalence of Metabolic Syndrome by different definitions, and its association with type 2 diabetes, pre-diabetes, and cardiovascular disease risk in Brazil
Bibliographic record
Abstract
BACKGROUND AND AIMS: Metabolic Syndrome (MS) is increasing in developing countries. Different definitions of MS lead to discrepancies in prevalence estimates and applicability. We assessed the prevalence of MS as defined by the International Diabetes Federation (IDF), modified National Cholesterol Education Program Adult Treatment Plan III (Modified NCEP) and Joint Interim Statement (JIS); compared the diagnostic performance and association of these definitions of MS with pre-diabetes, type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) risk. METHODS: A total of 714 randomly selected subjects from Northeastern Brazil were investigated in a cross-sectional study. Sociodemographic, anthropometric, and clinical data were recorded. Diagnostic test performance measures assessed the ability of the different MS definitions to identify those with pre-diabetes, T2DM and increased CVD risk. RESULTS: The adjusted prevalence of MS was 36.1% applying the JIS criteria, 35.1% the IDF and 29.5% Modified NCEP. Women were more affected by MS according to all definitions. MS was significantly associated with pre-diabetes, T2DM and CVD risk following the three definitions. However, the JIS and IDF definitions showed higher sensitivity than the Modified NCEP to identify pre-diabetes, T2DM and CVD risk. The odds ratios for those conditions were not significantly different when comparing the definitions. CONCLUSIONS: MS is highly prevalent in Brazil, particularly among those with pre-diabetes, T2DM, and high CVD risk. The IDF and JIS criteria may be better suited in the Brazilian population to identify pre-diabetes, T2DM and CVD risk. This may also signify the importance of the assessment of MS in clinical practice.
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 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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".