Role of Gut Microbiota in Cardiovascular Disease that Links to Host Genotype and Diet
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) are major outcomes of metabolic impairments in humans, which result from several genetic and environmental factors. In recent years, a ‘microbiome hypothesis’ has been proposed as a result of several studies that have attempted to understand underlying mechanisms of CVDs. Similar to CVDs, both genetic and environmental factors, especially diets, have a major impact on shaping gut microbiota and their functions. In the past decade, strong evidence has emerged to confirm the role of gut microbiota in contributing to the onset of CVDs. However, a comprehensive understanding of interactions among diet, host genotype, gut microbiota and CVDs is still facing challenges due to the complicated nature of CVDs. In this chapter, we review the present state of our knowledge about the contributory role of gut microbiota in CVDs and discuss the knowledge gaps that warrant further investigations. Moreover, we review the potential intervention strategies that may target the microbiota-driven pathology in CVDs and discuss the strength and weakness of animal models in studying the roles of gut microbiota in CVDs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".