Hepatic enzyme ALT as a marker of glucose abnormality in men with cystic fibrosis
Bibliographic record
Abstract
AIM: Cystic fibrosis (CF) patients are at high risk of developing CF-related diabetes (CFRD). In non-CF patients, liver disease, specifically steatosis and non-alcoholic fatty liver disease (NAFLD), is strongly associated with type 2 diabetes. We compared glycemic status and metabolic profiles in CF patients according to a biomarker of hepatic injury, alanine aminotransferase (ALT). METHODS: We conducted a cross-sectional study among 273 adult CF patients recruited from the Montreal CF Cohort. A 2-hour oral glucose tolerance test (OGTT) was performed to collect glucose and insulin measures every 30 minutes. Fasting ALT levels and anthropometric measures were also obtained. Patients were categorized into 2 groups based on ALT cut-off of 25 U/L. RESULTS: Patients in the high ALT group were mostly men (83%), had higher mean weight and BMI (p<0.001) and showed elevated glucose levels throughout OGTT (p≤0.01). When stratified by sex, only men with high ALT showed significantly higher weight (p<0.001), higher glycemic values at 60, 90 and 120 minutes of OGTT (p≤0.01), higher frequency of de novo CFRD (20.5% vs 8.2%, p = 0.04) as well as lower insulin sensitivity than men with normal ALT (p = 0.03). ALT levels were strongly associated with HOMA-IR in CFRD patients (p = 0.001, r2 = 0.28). CONCLUSIONS: Adult CF men with higher ALT show an increased frequency of dysglycemia and de novo CFRD, lower insulin sensitivity and higher eight. Our data suggests that ALT levels could be an interesting tool to guide targeted diabetes screening, particularly among CF men. Prospective studies are needed to confirm these observations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".