Gamma Glutamyl Transferase and Metabolic Syndrome, Cardiovascular Disease, and Mortality Risk
Bibliographic record
Abstract
OBJECTIVE: To determine whether serum gamma-glutamyl transferase (GGT) predicts cardiovascular disease (CVD) morbidity and mortality, accounting for temporal changes in known CVD risk factors and C-reactive protein (CRP). METHODS AND RESULTS: In 3451 Framingham Study participants (mean age 44 years, 52% women) we examined the relations of GGT with CVD risk factors, and prospectively determined the risk of new-onset metabolic syndrome, incident CVD, and death. GGT was positively associated with body mass index, blood pressure, LDL cholesterol, triglycerides, and blood glucose in cross-sectional analysis (P<0.005). On follow-up (mean 19 years), 968 participants developed metabolic syndrome, 535 developed incident CVD, and 362 died. The risk of metabolic syndrome increased with higher GGT (multivariable-adjusted hazard ratio [HR] per SD increment log-GGT, 1.26 [95%CI; 1.18 to 1.35]). Adjusting for established CVD risk factors (as time-dependent covariates updated quadriennially) and baseline CRP, a 1-SD increase in log-GGT conferred a 13% increase in CVD risk (P=0.007) and 26% increased risk of death (P<0.001). Individuals in the highest GGT quartile experienced a 67% increase in CVD incidence (multivariable-adjusted HR 1.67, 95%CI; 1.25 to 2.22). CONCLUSIONS: An increase in serum GGT predicts onset of metabolic syndrome, incident CVD, and death suggesting that GGT is a marker of metabolic and cardiovascular risk.
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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 | 0.003 |
| 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) | 0.001 | 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".