Cardiovascular effects of TMAO and other toxic metabolites of the intestinal microbiome
Notice bibliographique
Résumé
In this issue of the Journal of Internal Medicine, Liu et al. [1] report that plasma levels of trimethylamine N-oxide (TMAO)—but not plasma levels of its metabolic precursors choline, carnitine, betaine, and trimethyl lysine—were associated with the risk of stroke. From a community-based cohort of 16,113 persons recruited from 2013 to 2018, the authors identified 412 cases of first stroke (321 ischemic and 91 non-ischemic) over a median follow-up of 5.3 years and compared them with 412 controls matched for age and sex. The odds ratio for ischemic stroke, for the highest versus lowest quartile of TMAO, was 1.81 (95% CI: 1.14–2.86, p = 0.020). The role of the intestinal microbiome in human disease has been of increasing interest in recent years. The best known of the toxic metabolites of the intestinal microbiome is TMAO, but there are a number of other toxic metabolites, including p-cresyl sulfate, p-cresyl glucuronide, and phenylacetylglutamine. Plasma levels of TMAO and these three other metabolites were significantly higher among patients with severe atherosclerosis not explained by traditional risk factors (“unexplained atherosclerosis”), compared to patients with a “protected” phenotype (little or no carotid plaque burden despite high levels of coronary risk factors) [2]. In linear regression, both TMAO and p-cresylsulfate were independent predictors of carotid plaque burden [2]. Trimethylamine is formed by intestinal bacteria from precursors such as carnitine (mainly from red meat) and phosphatidylcholine (from egg yolk and other sources) by the action of bacterial trimethylamine lyase (TMA lyase). Trimethylamine is then oxidized in the liver by flavin-containing monooxygenase 3 (FMO3). The role of the intestinal microbiome in the findings of Liu et al. [1] is revealed by their finding that high levels of plasma TMAO, but not of its metabolic precursors, were associated with stroke risk. TMAO causes atherosclerosis in a murine model [3]. Among more than 4000 patients referred for coronary angiography, TMAO levels in the top quartile were associated with a 2.5-fold increase in the risk of myocardial infarction, stroke, or vascular death over 3 years [4]. In patients with kidney disease, TMAO accelerates the decline of renal function and increases mortality [5]. Thus, it is no surprise that it would be associated with an increased risk of stroke. It should be noted, however, that high levels of TMAO are probably a marker for high plasma levels of other toxic metabolites of the intestinal microbiome, as even modest impairment of renal function—to an estimated glomerular filtration rate < 66 ml/min/1.73 m2, a normal level for people aged ≥70 years—is associated with significantly increased plasma levels of all seven metabolites assayed in our study of unexplained atherosclerosis [6]. TMAO levels can be reduced by diet—particularly by avoiding red meat [7] and egg yolk—and there are treatments in development to block both TMA lyase and FMO3. However, blocking the effects of TMAO will not prevent the adverse effects of the other toxic intestinal metabolites. Most of them are produced from amino acids found mainly in meat, so a vegetarian diet would reduce levels of most of those metabolites. Among patients with renal failure, renal transplantation or more intensive dialysis could reduce plasma levels of all the toxic intestinal metabolites [8]. Another approach that is being tested is fecal microbial transplantation; in a mouse model, this was shown to ameliorate atherosclerosis [9]. One lesson from the study of Liu et al. [1] is that people with renal impairment—including the elderly—should avoid red meat and egg yolk, and limit their intake of animal flesh [10]. The author declares no conflict of interest.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».