Serum liver enzymes are associated with all‐cause mortality in an elderly population
Bibliographic record
Abstract
BACKGROUND & AIMS: Little is known about the association of serum liver enzymes with long-term outcome in the elderly. We sought to clarify the association of serum gamma-glutamyltransferase (GGT), alkaline phosphatase (ALP), alanine aminotransferase (ALT) and aspartate aminotransferase (AST) with all-cause and cause-specific mortality in an elderly population. METHODS: This study was embedded in the Rotterdam Study, a large population-based cohort of persons aged 55 years or older. Cox-regression analyses were performed to examine the association of baseline serum GGT, ALP, and aminotransferase levels with mortality, adjusted for age, sex, education, smoking status, alcohol intake, hypertension, diabetes mellitus, body mass index and total cholesterol levels. Liver enzyme levels were categorized according to sample percentiles; levels <25th percentile were taken as a reference. RESULTS: During a follow-up of up to 19.5 years, 2997 of 5186(57.8%) participants died: 672 participants died of causes related to cardiovascular diseases (CVD) and 703 participants died of cancer. All serum liver enzymes were associated with all-cause mortality (all P < 0.001). Moreover, GGT was associated with increased CVD mortality (P < 0.001), and ALP and AST with increased cancer-related mortality (P = 0.03 and P = 0.005 respectively). Participants with GGT and ALP in the top 5% had the highest risk for all-cause mortality (HR1.55; 95%CI 1.30-1.85 and HR1.49; 95%CI 1.25-1.78 respectively). AST and ALT <25th percentile were also associated with a higher risk of all-cause mortality. CONCLUSIONS: All serum liver enzymes were positively associated with long-term mortality in this elderly population. Why participants with low ALT and AST levels have higher risk of mortality remains to be elucidated.
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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 | 0.000 | 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".