Association between Leptin, Metabolic Factors and Liver Histology in Patients with Chronic Hepatitis C
Bibliographic record
Abstract
BACKGROUND: Steatosis is common in hepatitis C virus (HCV)-infected patients and likely accelerates fibrosis progression. Leptin, the peptide product of the obesity gene (ob), has been implicated in hepatic fibrogenesis; circulating levels of leptin correlate with body fat mass. The objective of the present study was to determine the clinical and histological correlates of serum leptin in HCV-infected patients, and to determine its utility in predicting liver histological lesions. PATIENTS AND METHODS: In 62 patients with chronic HCV, serum leptin was measured using a commercially available immunoassay. Associations between leptin, metabolic parameters, and severe hepatic fibrosis (stages 2 to 4) and steatosis (30% or greater) were determined. The utility of leptin in predicting liver histology was determined using receiver operating characteristic (ROC) curves. RESULTS: The median body mass index (BMI) was 23.2 kg/m2 (range 17.7 kg/m2 to 35.6 kg/m2); 16% of patients (n=10) had HCV genotype 3. Severe fibrosis and steatosis were present in 23% and 13% of patients, respectively. Leptin was strongly correlated with the BMI, and its levels were higher in women. BMI-corrected leptin levels were not independently associated with severe fibrosis but were significantly associated with steatosis (OR of 1.07; 95% CI 1.01 to 1.04). On it own, leptin was poorly predictive of severe steatosis (area under the ROC curve was 0.64; 95% CI 0.42 to 0.87). However, its accuracy improved with the addition of HCV genotype (area under the ROC curve was 0.86; 95% CI 0.72 to 1.00; P=0.07). CONCLUSIONS: As observed in the non-HCV setting, serum leptin correlates with BMI; higher leptin levels are found in women than men with chronic HCV. Serum leptin is a poor predictor of HCV-related fibrosis but may play a role in predicting steatosis when combined with HCV genotype.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".