Changes in bile acids, FGF-19 and sterol absorption in response to bile salt hydrolase active<i>L. reuteri</i>NCIMB 30242
Bibliographic record
Abstract
The size and composition of the circulating bile acid (BA) pool are important factors in regulating the human gut microbiota. Disrupted regulation of BA metabolism is implicated in several chronic diseases. Bile salt hydrolase (BSH)-active Lactobacillus reuteri NCIMB 30242, previously shown to decrease LDL-cholesterol and increase circulating BA, was investigated for its dose response effect on BA profile in a pilot clinical study. Ten otherwise healthy hypercholesterolemic adults, recruited from a clinical trial site in London, ON, were randomized to consume delayed release or standard release capsules containing L. reuteri NCIMB 30242 in escalating dose over 4 weeks. In another aspect, 4 healthy normocholesterolemic subjects with LDL-C below 3.4 mmol/l received delayed release L. reuteri NCIMB 30242 at a constant dose over 4 weeks. The primary outcome measure was the change in plasma BA profile over the intervention period. Additional outcomes included circulating fibroblast growth factor (FGF)-19, plant sterols and LDL-cholesterol as well as fecal microbiota and bsh gene presence. After one week of intervention subjects receiving delayed release L. reuteri NCIMB 30242 increased total BA by 1.13 ± 0.67 μmol/l (P = 0.02), conjugated BA by 0.67 ± 0.39 μmol/l (P = 0.02) and unconjugated BA by 0.46 ± 0.43 μmol/l (P = 0.07), which represented a greater than 2-fold change relative to baseline. Increases in BA were largely maintained post-week 1 and were generally correlated with FGF-19 and inversely correlated with plant sterols. This is the first clinical support showing that a BSH-active probiotic can significantly and rapidly influence BA metabolism and may prove useful in chronic diseases beyond hypercholesterolemia.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".