Non-Alcoholic Fatty Liver Disease Modifies Serum Gamma-Glutamyl Transferase in Cigarette Smokers
Bibliographic record
Abstract
BACKGROUND: Serum gamma-glutamyl transferase (GGT) is a marker of oxidative stress, associated with increased cardiovascular (CV) risk. The impact of smoking on oxidative stress may be aggravated in individuals with non-alcoholic fatty liver disease (NAFLD). We aimed to ascertain the association of smoking on GGT levels in the presence or absence of NAFLD. METHODS: We evaluated 6,354 healthy subjects (43 ± 10 years, 79% males) without clinical cardiovascular disease (CVD) undergoing an employer-sponsored physical between December 2008 and December 2010. NAFLD was diagnosed by ultrasound and participants were categorized as current or non-smokers by self report. A multivariate linear regression of the cross-sectional association between smoking and GGT was conducted based on NAFLD status. RESULTS: The prevalence of NAFLD was 36% (n = 2,299) and 564 (9%) were current smokers. Smokers had significantly higher GGT levels in the presence of NAFLD (P < 0.001). After multivariable adjustment, current smoking was associated with 4.65 IU/L higher GGT level, P < 0.001, compared to non-smokers. When stratified by NAFLD, the magnitude of this association was higher in subjects with NAFLD (β-coefficient: 11.12; 95% confidence interval (CI): 5.76 - 16.48; P < 0.001); however, no such relationship was observed in those without NAFLD (β: -0.02; 95% CI: -3.59, 3.56; P = 0.992). Overall the interaction of NAFLD and smoking with GGT levels as markers of oxidative stress was statistically significant. CONCLUSIONS: Smoking is independently associated with significantly increased oxidative stress as measured by GGT level. This association demonstrates effect modification by NAFLD status, suggesting that smoking may intensify CV risk in individuals with NAFLD.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".