Association of <scp>MASLD</scp> Phenotypes With Liver Fibrosis in Hepatitis C: The Role of Cardiometabolic Risk Factors
Bibliographic record
Abstract
Steatotic liver disease is prevalent among people with hepatitis C virus (HCV). The new definition of metabolic dysfunction-associated steatotic liver disease (MASLD) emphasises the metabolic drivers of steatosis and recognises its frequent coexistence with other chronic liver diseases, including HCV. We aimed to evaluate the association of coexisting MASLD and HCV with liver fibrosis. Individuals with HCV who underwent transient elastography (TE) with associated controlled attenuation parameter (CAP) were included from two clinical centres. MASLD and significant liver fibrosis were defined as the presence of steatosis (CAP ≥ 275 dB/m) with at least one cardiometabolic risk factor, and liver stiffness measurement (LSM) ≥ 7.1 kPa measured by TE, respectively. Associated cofactors of significant liver fibrosis were determined using stepwise regression and cross-validation by LASSO models to select confounders. Among 590 participants, 31% were diagnosed with MASLD. The prevalence of significant liver fibrosis was the highest among people with MASLD (58%) followed by HCV-related steatosis (45%) and the non-steatosis group (39%). After adjusting for potential confounders, MASLD was associated with significant liver fibrosis (adjusted odds ratio [aOR] 2.29, 95% confidence interval [CI] 1.07-4.87). Furthermore, specific MASLD phenotypes including diabetes, hypertension and overweight were associated with significant liver fibrosis, with aORs of 4.76 (95% CI 2.16-10.49), 3.44 (95% CI 1.77-6.68) and 2.54 (95% CI 1.27-5.07), respectively. In conclusion, MASLD is associated with liver fibrosis in people with HCV, specifically the diabetes, overweight and hypertensive phenotypes. Beyond pursuing a virological cure, healthcare providers should prioritise managing metabolic conditions, particularly diabetes, hypertension and obesity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".