Multiomics profiling and parenteral nutrition weaning in pediatric patients with intestinal failure: A longitudinal cohort study
Bibliographic record
Abstract
BACKGROUND: Intestinal failure (IF) is a life-limiting condition that includes a variety of intestinal pathologies. Currently, there are few clinical biomarkers that reflect intestinal function or a patient's potential to wean off parenteral nutrition (PN), making it difficult to predict the clinical trajectory. By associating gut microbiome taxonomic and functional features and blood analytes with the proportion of daily energy delivered via PN-a proxy for intestinal function-our study aimed to discover potential predictors of intestinal function and PN weaning potential. METHODS: In this longitudinal multiomics cohort study, we followed 18 pediatric patients with IF and PN support for ≤1.5 years. Fecal and stoma samples were analyzed using metagenomic shotgun sequencing to assess bacterial taxonomy and function and internal transcribed spacer 2 ribosomal RNA sequencing to characterize the fungal community. Targeted metabolomics was used to quantify 257 blood analytes. Linear mixed models were used to analyze the associations of PN dependence with microbiome features and blood analytes. RESULTS: The bacterial and fungal taxonomic composition exhibited substantial interpatient and intrapatient variability, with no link to PN dependence. In contrast, bacterial functional analysis revealed 63 MetaCyc pathways significantly associated with PN dependence. Additionally, 32 blood analytes were associated with PN dependence. CONCLUSION: In this exploratory study, we found that functional microbiome features and blood metabolomic profiles-particularly urea cycle metabolites, creatinine, asparagine, and tryptophan-derived metabolites-show promise for predicting intestinal function. Furthermore, they may have therapeutic implications for promoting intestinal adaptation. Confirmatory trials with larger sample sizes are needed to validate these findings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".