The effect of weight gain and metabolic dysfunction-associated steatotic liver disease on liver fibrosis progression and regression in people with HIV
Bibliographic record
Abstract
OBJECTIVE: People with HIV (PWH) have high risk of liver fibrosis. We investigated the effect of weight gain and metabolic dysfunction-associated steatotic liver disease (MASLD) on liver fibrosis dynamics. DESIGN: Multicenter cohort study. METHODS: Fibrosis progression was defined as development of significant fibrosis [liver stiffness measurement (LSM) ≥8 kPa], or transition to cirrhosis (LSM ≥13 kPa), for those with significant fibrosis at baseline. Fibrosis regression was defined as transition to LSM less than 8 kPa, or to LSM less than 13 kPa for those with cirrhosis at baseline. MASLD was defined as hepatic steatosis (controlled attenuation parameter >248 dB/m) with at least one metabolic abnormality. A continuous-time multistate Markov model was used to describe transitions across fibrosis states. RESULTS: Among 1183 PWH included from three centers (25.2% with viral hepatitis coinfection), baseline prevalence of significant fibrosis and MASLD was 14.4 and 46.8%, respectively. During a median follow-up of 2.5 years (interquartile range 1.9-3.5), the incidence rate of fibrosis progression and regression was 2.8 [95% confidence interval (CI) 2.3-3.4] and 2.2 (95% CI 1.9-2.6) per 100 person-years, respectively. In Markov model, weight gain increased the odds of fibrosis progression [odds ratio (OR) 3.11, 95% CI 1.59-6.08], whereas weight gain (OR 0.30, 95% CI 0.10-0.84) and male sex (OR 0.32, 95% CI 0.14-0.75) decreased the odds of fibrosis regression. On multivariable Cox regression analysis, predictors of fibrosis progression were weight gain [adjusted hazard ratio (aHR) 3.12, 95% CI 1.41-6.90] and MASLD (aHR 2.72, 95% CI 1.05-7.02). CONCLUSION: Fibrosis transitions are driven by metabolic health variables in PWH, independently of viral hepatitis coinfection and antiretroviral class therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.000 | 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".