Cholesterol lowering with bile salt hydrolase-active probiotic bacteria, mechanism of action, clinical evidence, and future direction for heart health applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Cardiovascular diseases (CVD) are the leading cause of global mortality and morbidity. Current CVD treatment methods include dietary intervention, statins, fibrates, niacin, cholesterol absorption inhibitors, and bile acid sequestrants. These formulations have limitations and, thus, additional treatment modalities are needed. Probiotic bacteria, especially bile salt hydrolase (BSH)-active probiotic bacteria, have demonstrated cholesterol-lowering efficacy in randomized controlled trials. AREAS COVERED: This review describes the current treatments for CVD and the need for additional therapeutics. Gut microbiota etiology of CVD, cholesterol metabolism, and the role of probiotic formulations as therapeutics for the treatment and prevention of CVD are described. Specifically, we review studies using BSH-active bacteria as cholesterol-lowering agents with emphasis on their cholesterol-lowering mechanisms of action. Potential limitations and future directions are also highlighted. EXPERT OPINION: Numerous clinical studies have concluded that BSH-active probiotic bacteria, or products containing them, are efficient in lowering total and low-density lipoprotein cholesterol. However, the mechanisms of action of BSH-active probiotic bacteria need to be further supported. There is also the need for a meta-analysis to provide better information regarding the therapeutic use of BSH-active probiotic bacteria. The future of BSH-active probiotic bacteria most likely lies as a combination therapy with already existing treatment options.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it