Unaccounted risk of cardiovascular disease: the role of the microbiome in lipid metabolism
Bibliographic record
Abstract
PURPOSE OF REVIEW: Not all of the risk of cardiovascular disease can be explained by diet and genetics, and the human microbiome, which lies at the interface of these two factors, may help explain some of the unaccounted risk. This review examines some of the well established links between the microbiome and cardiovascular health, and proposes relatively unexplored associations. RECENT FINDINGS: Byproducts of microbial metabolism are associated with health and disease: Trimethylamine N oxide is associated with atherosclerosis; whereas short-chain fatty acids are associated with decreased inflammation and increased energy expenditure. More broadly, a large number of association studies have been conducted to explore the connections between bacterial taxa and metabolic syndrome. In contrast, the relationship between the microbiome and triglycerides levels remains poorly understood. SUMMARY: We suggest that deeper understanding of the molecular mechanisms that drive linkages between the microbiome and disease can be determined by replacing 16S rRNA gene sequencing with shotgun metagenomic sequencing or other functional approaches. Furthermore, to ensure translatability and reproducibility of research findings, a combination of multiple different complementary '-omic' approaches should be employed.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".