Abstract 2587: Investigating the impact of chemotherapy on gut microbiota and microbiota-derived metabolites and their link to inflammation and cardiometabolic disorders in children with cancer
Bibliographic record
Abstract
Abstract Children treated for acute lymphoblastic leukemia (ALL) often suffer serious side-effects, including vomiting, diarrhea, severe infections, and cardiometabolic disorders. We hypothesized that deregulation of the intestinal microbiota, and its metabolites, is related to these side effects, and promotes inflammation and cardiometabolic disorders in this population. To explore this new avenue, we are recruiting 38 children with ALL treated at Sainte-Justine UHC in Montreal. At each treatment cycle, we compile clinical, treatment, and diet data, cardiometabolic status (weight, height, blood lipids, glucose, insulin, blood pressure), and collect blood and stool samples. We use high throughput sequencing to analyze the intestinal microbiota. Bacterial metabolites (SCFA, acyl-carnitines, p-cresol and TMAO) in stool and plasma are measured by GS-MS and HPLC-MS/MS. Biomarkers of inflammation (CRP, leptin, TNF-α, IL-6) and oxidative stress (ox-LDL, MDA, 8-OHdG) are evaluated in plasma or red blood cells by ELISA/Bioplex. On admission, 83% of patients had at least one cardiometabolic risk factor (59% high CRP, 83% pre/hypertriglyceridemia, 51% hypertension, 32.5% overweight/obesity, 27.9% hyperglycaemia) and 49% had a family history. After 31 days of treatment, some of these disorders were stabilized while others persisted or worsened (+7% obesity/overweight, -34% hypertriglyceridemia, -4.5% hypertension, +16% high CRP, -16.6% hyperglycaemia) and were associated with changes in short-chain acyl-carnitines (x1.3 - x3.4) and TMAO (x18.3). The links between these disorders, their temporal variation and inflammation, treatments, and side effects are being investigated. This project will serve as a basis for mechanistic and interventional studies to prevent inflammation and cardiometabolic disorders in children with cancer. Citation Format: Abderrahim Benmoussa, Laurence Levasseur, Shuxia Coté-Sergerie, Véronique Bélanger, Emile Levy, Caroline Laverdière, Alain Stintzi, Daniel Sinnett, Valérie Marcil. Investigating the impact of chemotherapy on gut microbiota and microbiota-derived metabolites and their link to inflammation and cardiometabolic disorders in children with cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2587.
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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 | 0.000 |
| 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".