Bridging the Gap between Gut Microbial Dysbiosis and Cardiovascular Diseases
Bibliographic record
Abstract
The human gut is heavily colonized by a community of microbiota, primarily bacteria, that exists in a symbiotic relationship with the host and plays a critical role in maintaining host homeostasis. The consumption of a high-fat (HF) diet has been shown to induce gut dysbiosis and reduce intestinal integrity. Recent studies have revealed that dysbiosis contributes to the progression of cardiovascular diseases (CVDs) by promoting two major CVD risk factors-atherosclerosis and hypertension. Imbalances in host-microbial interaction impair homeostatic mechanisms that regulate health and can activate multiple pathways leading to CVD risk factor progression. Dysbiosis has been implicated in the development of atherosclerosis through metabolism-independent and metabolite-dependent pathways. This review will illustrate how these pathways contribute to the various stages of atherosclerotic plaque progression. In addition, dysbiosis can promote hypertension through vascular fibrosis and an alteration of vascular tone. As CVD is the number one cause of death globally, investigating the gut microbiota as a locus of intervention presents a novel and clinically relevant avenue for future research, with vast therapeutic potential.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".