Nonalcoholic fatty liver disease is associated with dysbiosis independent of body mass index and insulin resistance
Bibliographic record
Abstract
This study aimed to determine if there is an association between dysbiosis and nonalcoholic fatty liver disease (NAFLD) independent of obesity and insulin resistance (IR). This is a prospective cross-sectional study assessing the intestinal microbiome (IM) of 39 adults with biopsy-proven NAFLD (15 simple steatosis [SS]; 24 nonalcoholic steatohepatitis [NASH]) and 28 healthy controls (HC). IM composition (llumina MiSeq Platform) in NAFLD patients compared to HC were identified by two statistical methods (Metastats, Wilcoxon). Selected taxa was validated using quantitative PCR (qPCR). Metabolites in feces and serum were also analyzed. In NAFLD, 8 operational taxonomic units, 6 genera, 6 families and 2 phyla (Bacteroidetes, Firmicutes) were less abundant and; 1 genus (Lactobacillus) and 1 family (Lactobacillaceae) were more abundant compared to HC. Lower abundance in both NASH and SS patients compared to HC were confirmed by qPCR for Ruminococcus, Faecalibacterium prausnitzii and Coprococcus. No difference was found between NASH and SS. This lower abundance in NAFLD (NASH+SS) was independent of BMI and IR. NAFLD patients had higher concentrations of fecal propionate and isobutyric acid and serum 2-hydroxybutyrate and L-lactic acid. These findings suggest a potential role for a specific IM community and functional profile in the pathogenesis of NAFLD.
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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) | 0.002 | 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".