Prevalence and Heritability of Clusters for Diagnostic Components of Metabolic Syndrome: The Oman Family Study
Bibliographic record
Abstract
BACKGROUND: Prevalence and heritability of metabolic syndrome (MetS) vary between populations according to the currently used criteria. We examined combinations for joint probabilities and heritabilities of MetS criteria from the National Cholesterol Education Program Adult Treatment Panel III (NCEP), World Health Organization (WHO), and International Diabetes Federation (IDF) in a sample of Omani families. METHODS: We included 1277 subjects from 5 pedigrees. The likelihood ratio of diagnostic cluster dependence over clustering by chance was LDep = P(dependent)/P(independent). Heritabilities were adjusted by sex and age. RESULTS: The highest LDep were central obesity (CO) + high glucose level (HGl) + triglycerides (IDF, 3.08; NCEP, 4.38; WHO, 3.17; P < 0.001). Triglycerides combined with any other component were the most common cluster. The lowest LDep for IDF were high blood pressure (HBP) + CO + low HDL-C (1.21, P < 0.025); for NCEP were HBP + HGl + low HDL-C (1.21, P < 0.07). These components were gathered almost by chance alone. In contrast, the lowest LDep for WHO were HGl + CO + low HDL-C (2.01, P < 0.001). The WHO criteria yielded the highest heritability for a MetS diagnosis (h(2) = 0.9), followed by NCEP (0.48) and IDF (0.38). The rationale of the MetS diagnostics is based on insulin resistance. This base would be lost if we continue lowering cut-off points for diagnosis for increasing the sensitivity. The WHO showed the highest values for LDep for all components because they used the highest cut-off points.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".