Prevalence and risk factors of nonalcoholic fatty liver disease in HIV-monoinfection
Bibliographic record
Abstract
OBJECTIVE: To identify the prevalence and risk factors of nonalcoholic fatty liver disease (NAFLD), nonalcoholic steatohepatitis (NASH) and fibrosis in HIV-monoinfected patients. DESIGN: Systematic review and meta-analysis. METHODS: We searched Medline and Embase and included studies that enrolled HIV-monoinfected patients with NAFLD defined by imaging and/or liver histology. Data on prevalence and risk factors for NAFLD, NASH and fibrosis were collected for meta-analysis using random effects models. RESULTS: Ten studies were included from the United States of America (n = 4), Canada (n = 1), France (n = 2), Italy (n = 1), Japan (n = 1) and China (n = 1). The prevalence of NAFLD (Imaging studies), NASH and fibrosis (biopsied populations) were 35% [95% confidence interval (CI) 29-42], 42% (95% CI 22-64) and 22% (95% CI 13-34), respectively. Meta-analysis of risk factors showed that high BMI, waist circumference, type 2 diabetes, hypertension, triglycerides and high CD4 cell count were associated with NAFLD, whereas HIV viral load, duration of HIV infection, duration of antiretroviral therapy and CD4 cell count nadir were not. Patients with high BMI [mean difference (MD) 1.38, 95% CI 0.04-2.71 P = 0.04], fasting glucose (MD 0.80, 95% CI 0.47-1.13 P < 0.00001) and AST level (MD 13.00, 95% CI 4.34-21.65 P = 0.003) were at increased risk of significant liver fibrosis. CONCLUSION: NAFLD is frequently observed in HIV-monoinfected patients, and NASH is a common cause of unexplained abnormal liver function in patients selected for liver biopsy. Metabolic disorders are key risk factors independently of HIV parameters. Future trials on pharmacological interventions in NASH with fibrosis should include patients with HIV.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".