Metabolic Activation of Flavin Monooxygenase-mediated Trimethylamine-N-Oxide Formation in Experimental Kidney Disease
Bibliographic record
Abstract
Cardiovascular disease (CVD) remains the leading cause of death in chronic kidney disease (CKD) patients despite treatment of traditional risk factors, suggesting that non-traditional CVD risk factors are involved. Trimethylamine-N-oxide (TMAO) correlates with atherosclerosis burden in CKD patients and may be a non-traditional CVD risk factor. Serum TMAO concentrations are significantly increased in CKD patients, which may be due in part to increased hepatic flavin monooxygenase (FMO)-mediated TMAO formation. The objective of this work was to elucidate the mechanism of increased FMO activity in CKD. In this study, FMO enzyme activity experiments were conducted in vitro with liver microsomes isolated from experimental CKD and control rats. Trimethylamine was used as a probe substrate to assess FMO activity. The FMO activator octylamine and human uremic serum were evaluated. FMO gene and protein expression were also determined. FMO-mediated TMAO formation was increased in CKD versus control. Although gene and protein expression of FMO were not changed, metabolic activation elicited by octylamine and human uremic serum increased FMO-mediated TMAO formation. The findings suggest that metabolic activation of FMO-mediated TMAO formation is a novel mechanism that contributes to increased TMAO formation in CKD and represents a therapeutic target to reduce TMAO exposure and CVD.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".