Amylose resistant starch (HAM‐RS2) supplementation increases the proportion of <i>Faecalibacterium</i> bacteria in end‐stage renal disease patients: Microbial analysis from a randomized placebo‐controlled trial
Bibliographic record
Abstract
INTRODUCTION: Many of the deleterious effects associated with chronic kidney disease (CKD) are secondary to the resultant systemic inflammation. The gut microbial changes caused by CKD are thought to perpetuate systemic inflammation. Therefore, strategies aimed at modulating the gut microbiota may be helpful in reducing complications associated with CKD. We hypothesized that supplementation with high-amylose maize resistant starch type 2 (HAM-RS2) would beneficially alter the gut microbiome and lead to lower levels of systemic inflammation. METHODS: A double-blind, parallel, randomized, placebo-controlled trial was performed comparing dietary supplementation of HAM-RS2 with placebo in patients with end-stage CKD. Fecal microbial data were obtained from a subset of patients after DNA extraction and 16s sequencing. FINDINGS: Supplementation of HAM-RS2 led to a decrease in serum urea, IL-6, TNFα, and malondialdehyde (P < 0.05). The Faecalibacterium genus was significantly increased in relative abundance following HAM-RS2 supplementation (HAM-RS2-Day 0: 0.40 ± 0.50 vs. HAM-RS2-Day 56: 3.21 ± 4.97 P = 0.03) and was unchanged by placebo (Control-Day 0: 0.72 ± 0.72 vs. Control-Day 56: 0.83 ± 1.57 P = 0.5). DISCUSSION: Supplementation of amylose resistant starch, HAM-RS2, in patients with CKD led to an elevation in Faecalibacterium and decrease in systemic inflammation. Microbial manipulation in CKD patients by using the prebiotic fiber may exert an anti-inflammatory effect through an elevation in the bacterial genera Faecalibacterium.
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) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".