Relationship of Metabolic Syndrome and Fibrinolytic Dysfunction to Cardiovascular Disease
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The clustering of impaired glucose metabolism, elevated triglycerides, low HDL cholesterol, and abdominal obesity is known as the metabolic syndrome. Individuals with this syndrome suffer an excess of cardiovascular disease (CVD) for reasons that are unclear. METHODS AND RESULTS: We randomly sampled 1276 adults of South Asian, Chinese, European, and Native Indian ancestry from 4 communities in Canada. Participants provided fasting blood samples for glucose, lipids, and fibrinolytic measurements; had an oral glucose tolerance test; and underwent a B-mode carotid ultrasound examination. CVD was determined by history and ECG. The prevalence of the metabolic syndrome was 25.8% (95% CI, 23.5 to 28.2) and varied substantially by ethnic group: 41.6% among Native Indians, 25.9% among South Asians, and 22.0% among Europeans, compared with 11.0% among the Chinese (overall, P=0.0001). People with the metabolic syndrome had more atherosclerosis (maximum intimal medial thickness, 0.78+/-0.18 versus 0.74+/-0.18 mm; P=0.0005), CVD (17.2% versus 7.0%; P=0.0001), and elevated plasminogen activator inhibitor-1 (24.2 versus 14.6 U/mL; P=0.001) compared with levels among people without the metabolic syndrome. For the same amount of atherosclerosis, people with the metabolic syndrome had a greater prevalence of CVD, even among nondiabetic individuals. This difference in CVD prevalence among the groups was attenuated after adjustment for plasminogen activator inhibitor-1 levels, suggesting that fibrinolytic dysfunction mediates the increased risk of CVD in individuals with the metabolic syndrome. CONCLUSIONS: CVD among people with the metabolic syndrome is explained by their excess of atherosclerosis and impaired fibrinolysis. Interventions to prevent atherosclerosis progression and improve fibrinolytic function require evaluation in this high-risk group.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it