A randomized trial to determine the impact of a digestion resistant starch composition on the gut microbiome in older and mid-age adults
Bibliographic record
Abstract
Background: The elderly often have a diet lacking resistant starch (RS) which is thought to lead to gut microbiome dysbiosis that may result in deterioration of gut colonocytes.Objective: The primary objective was to assess if elderly (ELD; !70 years age) had microbiome dysbiosis compared to mid-age (MID; 30e50 years age) adults and then determine the impact of daily consumption of MSPrebiotic ® (a RS) or placebo over 3 months on gut microbiome composition.Secondary objectives included assessment of stool short-chain fatty acids (SCFA) and inflammatory markers in ELD and MID Canadian adults.Design: This was a prospective, placebo controlled, randomized, double-blinded study.Stool was collected at enrollment and 6, 10 and 14 weeks after randomization to placebo or MSPrebiotic ® .Microbiome analysis was done using 16S rRNA sequencing of DNA extracted from stool.SCFA analysis of stool was performed using gas chromatography.Results: There were 42 ELD and 42 MID participants randomized to either placebo or MSPrebiotic ® who completed the study.There was significantly higher abundance of Proteobacteria (Escherichia coli/ Shigella) in ELD compared to MID at enrollment (p < 0.001) that was not observed after 12 weeks of MSPrebiotic ® consumption.There was a significant increase in Bifidobacterium in both ELD and MID compared to placebo (p ¼ 0.047 and 0.006, respectively).There was a small but significant increase in the stool SCFA butyrate levels in the ELD on MSPrebiotic ® versus placebo. Conclusions:The study data demonstrated that MSPrebiotic ® meets the criteria of a prebiotic and can stimulate an increased abundance of endogenous Bifidobacteria in both ELD and MID without additional probiotic supplementation.MSPrebiotic ® consumption also eliminated the dysbiosis of gut Proteobacteria observed in ELD at baseline.Clinical Trial Registry Number: NCT01977183 listed on NIH website: ClinicalTrials.gov.The full trial protocol is available on request from the corresponding author.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".