Potential effects of natural dietary compounds on trimethylamine <i>N</i> -oxide (TMAO) formation and TMAO-induced atherosclerosis
Bibliographic record
Abstract
Cardiovascular disease (CVD) is the leading cause of death worldwide. Recently, trimethylamine N-oxide (TMAO) is identified to be highly associated with CVD development and exacerbates atherosclerosis by several mechanisms. TMAO is a gut microbiota-dependent metabolite formed from dietary quaternary amines, mainly choline and carnitine. These trimethylamine (TMA)-containing compounds are first converted to TMA by enzymes in gut microbiota and subsequently metabolized by the host hepatic enzymes to TMAO. As the microbiome is the source of TMAO, administration of broad spectrum antibiotics shows marked decrease in TMAO levels. However, antibiotics may possess many possible undesirable side effects and chronic treatment consideration effects of antibiotic resistance in bacteria. Thus, studies have focused on the alternative strategies, including use of natural dietary compounds to reduce elevated TMAO levels and prevent atherogenesis. Natural dietary compounds have been studied for their beneficial health effects for decades. Diet and nutritional interventions based on the use of natural bioactive compounds is an effective strategy for remodeling gut microbiota composition and improving human health. This review focuses on the mechanisms by which TMAO promote atherosclerosis, the microbes that contribute to TMA formation, the enzymes involved, and the potential of natural dietary compounds that contribute to TMAO reduction and attenuate TMAO-induced atherosclerosis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".