Abstract P539: Association of Metabolic Syndrome With Angina: Results From NHANES 2009 - 2020
Bibliographic record
Abstract
Background: Metabolic syndrome (MetS) is a known risk factor for cardiovascular disease. However, there are limited data on its association with angina. We assessed the association of MetS with angina in a nationally representative sample of noninstitutionalized Americans. Methods: We combined data from five National Health and Nutrition Examination Survey (NHANES) data cycles from 2009 to 2020. We included participants aged 40 years or older. MetS was defined using the AHA/NHLBI criteria. Angina was ascertained based on the ROSE angina questionnaire. Multivariable logistic regression models adjusting for age, race, and sex were used to assess the association between MetS and angina. Results: Among the 21752 participants included in the analysis, 11381 (52.3%) met the criteria for MetS. Nine hundred and fifty-nine participants (4.4%) had angina. Angina was more prevalent among those with MetS than those without MetS (5.9% vs. 2.8%; p = <.0001). Compared with participants without MetS, Those with MetS were more than two-fold more likely to suffer from angina (adjusted Odds Ratio, 2.06; 95% CI 1.76-2.40; p = <.0001). Conclusion: From this nationally representative sample, participants aged 40 years or older with MetS were more likely to suffer from angina compared with those without MetS. Suggesting MetS as an independent risk factor of coronary artery disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".