Cardiovascular benefits of probiotics: a review of experimental and clinical studies
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
The microbiota inhabiting the human gastro-intestinal tract is reported to have a significant impact on the health of an individual. Recent findings suggest that the microbial imbalance of the gut may play a role in pathogenesis of cardiovascular diseases (CVD). Therefore, several studies have delved into the aspect of altering gut microbiota with probiotics as an approach to prevent and/or treat CVD. The World Health Organization defines probiotics as live microorganisms that, when consumed in adequate amounts, have a positive influence on the individual's health. The present review focuses on strategies of human dietary intervention with probiotic strains and their impact on cardiovascular risk factors like hypercholesterolemia, hypertension, obesity and type-2 diabetes. Accumulating evidence shows probiotics to lower low density lipoproteins (LDL)-cholesterol and improve the LDL/high density lipoproteins (HDL) ratio, as well as lower blood pressure, inflammatory mediators, blood glucose levels and body mass index. Thus, probiotics have the scope to be developed as dietary supplements with potential cardiovascular health benefits. However, there is not only ambiguity regarding the exact strains and dosages of the probiotics that will bring about positive health effects, but also factors like immunity and genetics of the individual that might influence the efficacy of probiotics. Therefore, further studies are required not only to understand the mechanisms by which probiotics may beneficially affect the cardiovascular system, but also to rule out any of their probable negative effects on health. The present review aims to critically appraise the complexity of the available data with regard to the cardiovascular benefits of probiotics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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