Gut microbiota alterations induced by intensive chemotherapy in acute myeloid leukaemia patients are associated with gut barrier dysfunction and body weight loss
Bibliographic record
Abstract
BACKGROUND & AIMS: Acute myeloid leukaemia (AML) chemotherapy has been reported to impact gut microbiota composition. In this study, we investigated using a multi -omics strategy the changes in the gut microbiome induced by AML intense therapy and their association with gut barrier function and cachectic hallmarks. METHODS: 10 AML patients, allocated to standard induction chemotherapy (SIC), were recruited. Samples and data were collected before any therapeutic intervention (T0), at the end of the SIC (T1) and at discharge (T4). Gut microbiota composition and function, markers of inflammation, metabolism, gut barrier function and cachexia, as well as faecal, blood and urine metabolomes were assessed. RESULTS: AML patients demonstrated decreased appetite, weight loss and muscle wasting during hospitalization, with an incidence of cachexia of 50%. AML intensive treatment transiently impaired the gut barrier function and led to a long-lasting change of gut microbiota composition characterized by an important loss of diversity. Lactobacillaceae and Campylobacter concisus were increased at T1 while Enterococcus faecium and Staphylococcus were increased at T4. Metabolomics analyses revealed a reduction in urinary hippurate and faecal bacterial amino acid metabolites (bAAm) (2-methylbutyrate, isovalerate, phenylacetate). Integration using DIABLO revealed a deep interconnection between all the datasets. Importantly, we identified bacteria which disappearance was associated with impaired gut barrier function (Odoribacter splanchnicus) and body weight loss (Gemmiger formicilis), suggesting these bacteria as actionable targets. CONCLUSION: AML intensive therapy transiently impairs the gut barrier function while inducing enduring alterations in the composition and metabolic activity of the gut microbiota that associate with body weight loss. TRIAL REGISTRATION: NCT03881826, https://clinicaltrials.gov/ct2/show/NCT03881826.
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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 | 0.000 | 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 | 0.001 |
| 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".