Metabolic syndrome and risk of coronary heart disease in a Pakistani cohort
Bibliographic record
Abstract
OBJECTIVE: To assess the relation of the metabolic insulin resistance syndrome (M-IRS) with coronary heart disease (CHD) in Pakistani patients. SUBJECTS: 200 patients with angiographic disease (CHD(+)) matched with 200 patients with chest pain without occlusive disease (CHD(-)). DESIGN: Prospective case-control study. SETTING: Tertiary referral cardiology unit in Pakistan. RESULTS: M-IRS was present in 37% of CHD(+) versus 27% of CHD(-) patients by criteria for white patients or 47% versus 42%, respectively, by Asian criteria (p < 0.001). After adjustment for other risk factors, M-IRS was not a significant predictor for CHD or angiographic disease. Age (p = 0.03), smoking (p < 0.001), diabetes-years (p = 0.003), sialic acid (p = 0.01), and creatinine (p = 0.008) accounted for the excess risk of CHD. Similarly, age (p = 0.005), creatinine (p < 0.001), cigarette pack-years (p = 0.02), diabetes-years (p = 0.003), and sialic acid (p = 0.08) were predictors of greater angiographic disease. M-IRS differed between Pakistani and white patients, as waist circumference correlated weakly (r = -0.03-0.08, p = 0.45-0.52) with triglycerides, high density lipoprotein cholesterol, systolic blood pressure, or glucose. Sialic acid was the only inflammatory marker associated with M-IRS. CONCLUSIONS: Despite strong associations between individual risk factors associated with M-IRS and a univariate association between M-IRS and CHD in native Pakistanis, the principal discriminant risk factors in this group are age, smoking, inflammation, diabetes-years, and impaired renal function. The poor sensitivity of M-IRS for CHD reflects the high underlying prevalence of M-IRS, thus reducing sensitivity, confounding by other urban lifestyle traits, or a lack of association of waist circumference with M-IRS risk factors. The definition of M-IRS may have to be revised to increase its power as a discriminant risk factor for CHD in Pakistani populations.
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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 | 0.001 | 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".