Metabolic factors drive early increase in hepatic steatosis despite improvement in non-invasive fibrosis markers after hepatitis C eradication with direct-acting antivirals
Bibliographic record
Abstract
BACKGROUND: While direct-acting antivirals (DAAs) achieve high sustained virologic response (SVR) rates in people with hepatitis C virus (HCV), their impact on hepatic steatosis (HS) remains unclear. METHODS: We conducted a retrospective cohort study of 108 HCV patients from McGill University and the University of Milan who achieved SVR following DAAs. Controlled attenuation parameter (CAP) and liver stiffness measurement (LSM) were used to assess HS and liver fibrosis at baseline and 24 weeks post-SVR. HS was defined as CAP ≥248 dB/m, significant liver fibrosis as LSM ≥8 kPa, and metabolic dysfunction-associated steatotic liver disease (MASLD) as HS plus ≥1 cardiometabolic risk factors. Changes were evaluated using Wilcoxon signed-rank test and standardized mean difference (SMD). Multivariable logistic regression identified predictors of post-SVR HS. RESULTS: HS prevalence increased from 47 % to 61 % post-SVR (p = 0.0007, SMD = 0.30). Among patients with baseline HS, 88 % had persistent steatosis. New-onset steatosis developed in 37 % of patients without baseline HS, with a significant CAP increase (p < 0.0004, SMD=0.48). In patients without baseline HS, total cholesterol and triglycerides increased (p = 0.0084, SDM = 0.43 and p < 0.0001, SDM = 0.71, respectively), whereas in those with baseline HS, only total cholesterol rose (p = 0.0296, SDM = 0.50). MASLD remained the leading etiology at both time points (94 % at baseline, 92 % post-SVR). Significant fibrosis declined markedly from 49 % to 17 % (p < 0.0001, SMD = -0.80). Higher BMI at 24 weeks was independently associated with HS (adjusted odds ratio 1.92, 95 %CI 1.22-3.03). CONCLUSIONS: Despite improvement in liver fibrosis markers, HS often persists or emerges following DAAs therapy, particularly alongside metabolic dysfunctions marked by elevated cholesterol and triglycerides.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".