Gamma-glutamyltransferase predicts increased risk of mortality: A systematic review and meta-analysis of prospective observational studies
Bibliographic record
Abstract
The aim of this study was to evaluate the association between gamma-glutamyltransferase (GGT) and mortality through a comprehensive analysis of existing evidence. PubMed, Embase, Chinese Biomedical Literature, and Science Citation Index databases were electronically searched. Studies were included if the study design was prospective and included reference and at-risk levels of GGT at baseline and mortality as a separate outcome. The quality of the studies included was assessed on the basis of Newcastle-Ottawa scale. Data from selected qualified studies were systematically reviewed, pooled, and analyzed according to the MOOSE guidelines and PRISMA statement. The results included the following: 1. 35 studies including 571,511 participants and 72,196 cases of mortality; 2. GGT, even at physiologic levels, was associated with increased all-cause mortality and cardiovascular mortality, and might also be associated with cancer-related mortality in the general population; and 3. GGT was very likely to be associated with all-cause mortality and cardiovascular mortality in patients with coronary artery disease and type 2 diabetes mellitus. Many of the studies included did not specifically exclude subjects with hepatic diseases or alcohol abuse, which may have obscured the results. Moderate heterogeneity was observed in the meta-analysis of GGT and all-cause mortality. Different compositions of cause-specific mortality might be the reason. However, subgroup analysis could only be performed on cardiovascular death because of insufficient information. GGT, even at physiologic high levels, predicted mortality, especially cardiovascular mortality and cancer mortality. The underlining mechanism and potential effects of GGT-targeted intervention on health warrant further investigation.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".