Plasma Lipid Metabolites Differentiate Metabolic From Viral Chronic Liver Disease
Bibliographic record
Abstract
BACKGROUND: Lipid metabolism is altered in human immunodeficiency virus (HIV) infection and chronic liver diseases, but common and unique pathways have not been elucidated, limiting prevention and treatment strategies. The aim of this study was to discover lipid metabolite signatures for persons with HIV (PWH), PWH with HCV coinfection (PWH-HCV), and individuals with metabolic dysfunction-associated steatotic liver disease and steatohepatitis (MASLD, MASH). METHODS: Plasma metabolite profiling was performed in adult participants in 5 cohorts from a single center: PWH (n = 50), PWH-HCV (n = 50), HIV-negative biopsy-proven MASLD (n = 46), and MASH (n = 50), and controls without HIV or chronic liver disease (n = 29). Plasma metabolites were assessed using Biocrates Q500, bile acid, and oxylipin assays. Latent factor analysis along with unadjusted and adjusted logistic regression models were performed. Significance was defined as P value < .05 and false discovery rate < 0.10. RESULTS: Compared to controls, 457 of 816 measured metabolites were detected at different levels in PWH, 352 in PWH-HCV, 466 in MASLD, and 487 in MASH. Triglycerides and oxylipins were increased across disease states, but to a higher degree in PWH. PWH-HCV had a distinct metabolite signature with decreased ceramides and sphingomyelins. Levels of bile acid, amino acid, and fatty acid metabolites also differentiated cohorts. CONCLUSIONS: Lipid metabolites demonstrated pathways common to, and unique to, HIV, HCV, and MASLD. Further studies will hopefully reveal the pathogenic role of these metabolites in liver disease severity, particularly in PWH with steatotic liver disease.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".