Abstract FR543: Gut Microbiota and Blood Pressure Regulation: A Systematic Review of Microbial Influences on Hypertension
Bibliographic record
Abstract
Introduction: New research shows that the group of microbes in the intestine can influence blood pressure. Researchers believe that gut microbes may partly influence blood pressure through molecules called metabolites and by causing inflammation. We gathered and analyzed data to link the gut microbiota with blood pressure and to understand the outcome of efforts to treat hypertension using microbiome targets. Hypothesis: Activating G-protein-coupled receptors (GPR41/43) in renal epithelial cells, butyrate stimulates sodium excretion and modulates the activity of endothelial nitric oxide synthase (eNOS) by affecting its histone deacetylase activity (HDAC). Methods: Using a PRISMA method, we researched people in whom gut microbiota or probiotic effects were measured for blood pressure. The approach found sixteen studies, and fifteen were included after using the chosen criteria (6 randomized controlled trials and 9 observational studies). We looked at the gut microbiome of hypertensive and normotensive people and tracked how probiotic supplements or a certain diet changed blood pressure. To check the quality of the studies, researchers applied the Cochrane Risk of Bias for RCTs and the Newcastle-Ottawa Scale. Results: The gut microbiome of people with hypertension remained much lower in diversity and reduced in some health-promoting genera, especially Lactobacillus and Bifidobacterium. Researchers discovered that high amounts of microbiome-related metabolites in the body are linked to raised blood pressure, suggesting these play a role in hypertension. Probiotics or higher fiber intake in RCTs helped decrease blood pressure. The individual outcomes differed, but overall, treating the microbiome with probiotics reduced both blood pressure and markers of inflammation. Even though most research was moderate in strength, it kept showing that the gut plays a role in managing blood pressure. Conclusions: The review gathers existing information on the relationship between gut microbiota and hypertension. We have found that blood pressure is related to gut bacteria and that small improvements in blood pressure may result from changing the gut microbiome (with probiotics or by diet). Even so, this new knowledge suggests that manipulating gut microbes could be used to treat hypertension. However, further and longer-lasting research is necessary to determine causality and how best to approach treatment strategy.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".