Effects of a Microbiome Restoration Strategy on Metabolic Markers in Healthy Adults
Bibliographic record
Abstract
Industrialization has increased chronic disease prevalence, potentially due to lifestyle-induced disruptions of the gut microbiome. Decreased intake of dietary fibers is likely a key factor as they play an important role in chronic disease prevention and are growth substrates for the gut microbiota. Strategies that restore microbiome diversity, such as reintroducing health-promoting bacterial species and microbiota accessible carbohydrates (MACs), have been proposed to improve health but have not yet been systematically tested. The objective of this study was to determine the effects of a microbiome restoration strategy on metabolic markers in healthy adults. Using a randomized controlled pilot study, 30 subjects consumed either a MAC-rich diet or their usual diet for three weeks each in a crossover fashion, with a three-week washout following each diet period. Participants were further divided into three groups and consumed either a single dose of one of two Limosilactobacillus reuteri strains, a rare species in industrialized microbiomes, or a placebo on day four of each diet period. Metabolic markers (standard lipid panel, glucose, insulin, C-reactive protein (CRP)) were assessed in blood collected at the start and end of each diet period. Data were analyzed using repeated measures ANOVA. Compared to baseline, the MAC-rich diet induced substantial metabolic changes, as it reduced total cholesterol (P < 0.0001), low density lipoprotein cholesterol (P < 0.0001), high density lipoprotein (HDL) cholesterol (P < 0.0001), non-HDL cholesterol (P < 0.0001), and glucose (P < 0.01). Other metabolic markers, such as insulin and CRP, were not significantly affected. Though the MAC-rich diet increased L. reuteri persistence in the gut for eight days (P < 0.05), the metabolic effects were independent of L. reuteri supplementation. Our results show that a MAC-rich diet significantly benefited metabolic markers and transiently enhanced the persistence of a lost bacterial species in the gut. Ongoing analyses are exploring how the gut microbiome specifically contributes to the observed health effects of the MAC-rich diet. This work was supported by the Weston Family Microbiome Initiative, CIHR, Alberta Innovates Postgraduate Fellowship, and Science Foundation Ireland.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".