Increased Body Mass Index and Type 2 Diabetes Are the Main Predictors of Nonalcoholic Fatty Liver Disease and Advanced Fibrosis in Liver Biopsies of Patients With Human Immunodeficiency Virus Monoinfection
Bibliographic record
Abstract
BACKGROUND: Liver disease is an important cause of morbidity and mortality in people living with human immunodeficiency virus (PLWH), of which nonalcoholic fatty liver disease (NAFLD) is an increasingly recognized cause. There are limited data investigating NAFLD in HIV monoinfection and histologically defined disease. We aimed to identify who is at risk of fibrosis, NAFLD, and nonalcoholic steatohepatitis (NASH) among PLWH and explore the diagnostic accuracy of noninvasive markers of fibrosis. METHODS: This was a retrospective, cross-sectional, international, multicenter study including patients with HIV monoinfection, without chronic viral hepatitis or other known causes of chronic liver disease, who underwent liver biopsy for abnormal liver biochemistry and/or clinical suspicion of liver fibrosis. RESULTS: A total of 116 patients from 5 centers were included. Sixty-three (54%) had NAFLD, of whom 57 (92%) had NASH. Overall, 36 (31%) had advanced fibrosis (≥F3) and 3 (3%) had cirrhosis. Of the 53 cases without NAFLD, 15 (28%) had advanced fibrosis. Collagen proportionate area was similar between cases with and without NAFLD (3% vs 2%). Body mass index was independently associated with NAFLD (aOR, 1.2; 95% CI, 1.08-1.34), and type 2 diabetes was independently associated with advanced fibrosis (aOR, 3.42; 95% CI, 1.00-11.71). The area under the curve for advanced fibrosis was 0.65 and 0.66 for both NAFLD Fibrosis Score (NFS) and FIB-4. Cutoff values of -1.455 (NFS) and 1.3 (FIB-4) have negative-predictive values of 0.80 and 0.82, respectively. CONCLUSIONS: Advanced fibrosis is strongly associated with type 2 diabetes in PLWH. Serological markers require further optimization.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".