Connecting the Dots between Gut Microbiota Dysbiosis and Atherosclerosis: A Systematic Review Article Sidebar
Bibliographic record
Abstract
The prevalence of cardiovascular disease (CVD) is rising despite improvements in risk factor management. Numerous causes, including lifestyle modifications, environmental conditions, and gut microbiota dysbiosis, could be blamed for this. The type of bacteria involved and their primary role in atherosclerosis remain unknown even though the association between gut microbiota and atherosclerosis has been explored. Through a systematic review, this study sought to understand how dysbiosis of the gut microbiota contributes to atherosclerosis. PubMed, EBSCOhost, EMBASE, and Cochrane were searched for relevant literature. The Newcastle-Ottawa Scale for nonrandomized studies was used to measure the risk of bias, and study selection was conducted following PRISMA 2020 recommendations. Both subclinical and symptomatic atherosclerosis were taken into account. Seven of the 783 studies that included 566 patients with vascular disease associated with atherosclerosis met the inclusion criteria. The composition of the gut microbiota varied considerably between the healthy control group and the atherosclerotic group. Patients with coronary artery disease (CAD) have lower alpha diversity. Atherosclerosis was linked to an increase in potentially hazardous bacteria such as Escherichia sp., Shigella sp., Enterococcus sp., and Ruminococcus gnavus and a decrease in helpful bacteria like Subdoligranulum, Roseburia, Faecalibacterium, and Eubacterium rectale. Conclusively, changes in the makeup of the gut microbiota shown in the atherosclerotic group might have caused an imbalance in the synthesis of metabolites, indicating that dysbiosis of the gut microbiota may contribute to cardiovascular disease in several ways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".