Pea hull fiber supplementation does not modulate uremic metabolites in adults receiving hemodialysis: a randomized, double-blind, controlled trial
Bibliographic record
Abstract
Background Fiber is a potential therapeutic to suppress microbiota-generated uremic molecules. This study aimed to determine if fiber supplementation decreased serum levels of uremic molecules through the modulation of gut microbiota in adults undergoing hemodialysis. Methods A randomized, double-blinded, controlled crossover study was conducted. Following a 1-week baseline, participants consumed muffins with added pea hull fiber (PHF) (15 g/d) and control muffins daily, each for 4 weeks, separated by a 4-week washout. Blood and stool samples were collected per period. Serum p-cresyl sulfate (PCS), indoxyl sulfate (IS), phenylacetylglutamine (PAG), and trimethylamine N-oxide (TMAO) were quantified by LC–MS/MS, and fecal microbiota profiled by 16S rRNA gene amplicon sequencing and specific taxa of interest by qPCR. QIIME 2 sample-classifier was used to discover unique microbiota profiles due to the consumption of PHF. Results Intake of PHF contributed an additional 9 g/d of dietary fiber to the subjects’ diet due to compliance. No significant changes from baseline were observed in serum PCS, IS, PAG, or TMAO, or for the relative quantification of Akkermansia muciniphila, Faecalibacterium prausnitzii, Bifidobacterium, or Roseburia, taxa considered health-enhancing. Dietary protein intake and IS (r = −0.5, p = 0.05) and slow transit stool form and PCS (r = 0.7, p < 0.01) were significantly correlated at baseline. PHF and control periods were not differentiated; however, using machine learning, taxa most distinguishing the microbiota composition during the PHF periods compared to usual diet alone were enriched Gemmiger, Collinsella, and depleted Lactobacillus, Ruminococcus, Coprococcus, and Mogibacteriaceae. Conclusion PHF supplementation did not mitigate serum levels of targeted microbial-generated uremic molecules. Given the high cellulose content, which may be resistant to fermentation, PHF may not exert sufficient effects on microbiota composition to modulate its activity at the dose consumed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".