Gut microbiota and cardiovascular disease: opportunities and challenges
Bibliographic record
Abstract
Coronary artery disease (CAD) is the most common health problem worldwide and remains the leading cause of morbidity and mortality. Over the past decade, it has become clear that the inhabitants of our gut, the gut microbiota, play a vital role in human metabolism, immunity, and reactions to diseases, including CAD. Although correlations have been shown between CAD and the gut microbiota, demonstration of potential causal relationships is much more complex and challenging. In this review, we will discuss the potential direct and indirect causal roots between gut microbiota and CAD development via microbial metabolites and interaction with the immune system. Uncovering the causal relationship of gut microbiota and CAD development can lead to novel microbiome-based preventative and therapeutic interventions. However, an interdisciplinary approach is required to shed light on gut bacterial-mediated mechanisms (e.g., using advanced nanomedicine technologies and incorporation of demographic factors such as age, sex, and ethnicity) to enable efficacious and high-precision preventative and therapeutic strategies for CAD.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".