Distinct Gut Microbiota Signatures Are Associated with Severity of Metabolic Dysfunction-Associated Steatotic Liver Disease in People with HIV
Bibliographic record
Abstract
The progression of metabolic dysfunction-associated steatotic liver disease (MASLD) to severe forms, including metabolic dysfunction-associated steatohepatitis (MASH) and liver fibrosis, involves metabolic dysfunction, genetics, and gut dysbiosis. People with HIV (PWH) represent a high-risk group for MASLD, but the role of gut microbiota alterations in disease severity within this population remains poorly understood. We prospectively recruited PWH with MASLD, defined as the controlled attenuation parameter (CAP) ≥ 238 dB/m, and excluded those with viral hepatitis coinfection or alcohol abuse. Severe MASLD was defined as the presence of MASH (cytokeratin-18 ≥ 130.5 U/L) and/or significant liver fibrosis (liver stiffness ≥ 7.1 kPa). Stool samples were collected for 16S rRNA gene sequencing to characterize gut microbiota composition. Functional predictions were generated using PICRUSt. The differential abundance of bacterial taxa and predicted functions were analyzed using a generalized linear model with a negative binomial distribution. Among 34 PWH with MASLD, 18 (53%) met the criteria for severe MASLD. Microbiota profiling revealed significant differences in bacterial genera between the PWH with and without severe MASLD. Enrichment was observed in the Ruminococcus gnavus group, Negativibacillus, Holdemanella, Subdoligranulum, the Eubacterium hallii group, and Butyricicoccus, while depletion was seen in Prevotella, Alloprevotella, Dialister, Catenibacterium, the Christensenellaceae R 7 group, Clostridium sensu stricto, Olsenella, Oscillospiraceae UCG-005, Libanicoccus, and the Eubacterium siraeum group. Predicted functional pathways related to fatty acid degradation, folate biosynthesis, and amino acids metabolism did not differ between groups. MASLD severity in PWH is associated with a distinct gut microbiota signature, though not with functional pathway alterations. Microbial profiling may complement existing non-invasive biomarkers for risk stratification in this high-risk population.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".