Potential therapeutic implications of histidine catabolism by the gut microbiota in NAFLD patients with morbid obesity
Bibliographic record
Abstract
The gut microbiota contributes to the pathophysiology of non-alcoholic fatty liver disease (NAFLD). Histidine is a key energy source for the microbiota, scavenging it from the host. Its role in NAFLD is poorly known. Plasma metabolomics, liver transcriptomics, and fecal metagenomics were performed in three human cohorts coupled with hepatocyte, rodent, and Drosophila models. Machine learning analyses identified plasma histidine as being strongly inversely associated with steatosis and linked to a hepatic transcriptomic signature involved in insulin signaling, inflammation, and trace amine-associated receptor 1. Circulating histidine was inversely associated with Proteobacteria and positively with bacteria lacking the histidine utilization (Hut) system. Histidine supplementation improved NAFLD in different animal models (diet-induced NAFLD in mouse and flies, ob/ob mouse, and ovariectomized rats) and reduced de novo lipogenesis. Fecal microbiota transplantation (FMT) from low-histidine donors and mono-colonization of germ-free flies with Enterobacter cloacae increased triglyceride accumulation and reduced histidine content. The interplay among microbiota, histidine catabolism, and NAFLD opens therapeutic opportunities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".