Plasma levels of trimethylamine-N-oxide can be increased with 'healthy' and 'unhealthy' diets and do not correlate with the extent of atherosclerosis but with plaque instability

AIMS

The microbiome-derived metabolite trimethylamine-N-oxide (TMAO) has attracted major interest and controversy both as a diagnostic biomarker and therapeutic target in atherothrombosis.

METHODS AND RESULTS

Plasma TMAO increased in mice on 'unhealthy' high-choline diets and notably also on 'healthy' high-fibre diets. Interestingly, TMAO was found to be generated by direct oxidation in the gut in addition to oxidation by hepatic flavin-monooxygenases. Unexpectedly, two well-accepted mouse models of atherosclerosis, ApoE-/- and Ldlr-/- mice, which reflect the development of stable atherosclerosis, showed no association of TMAO with the extent of atherosclerosis. This finding was validated in the Framingham Heart Study showing no correlation between plasma TMAO and coronary artery calcium score or carotid intima-media thickness (IMT), as measures of atherosclerosis in human subjects. However, in the tandem-stenosis mouse model, which reflects plaque instability as typically seen in patients, TMAO levels correlated with several characteristics of plaque instability, such as markers of inflammation, platelet activation, and intraplaque haemorrhage.

CONCLUSIONS

Dietary-induced changes in the microbiome, of both 'healthy' and 'unhealthy' diets, can cause an increase in the plasma level of TMAO. The gut itself is a site of significant oxidative production of TMAO. Most importantly, our findings reconcile contradictory data on TMAO. There was no direct association of plasma TMAO and the extent of atherosclerosis, both in mice and humans. However, using a mouse model of plaque instability we demonstrated an association of TMAO plasma levels with atherosclerotic plaque instability. The latter confirms TMAO as being a marker of cardiovascular risk.

Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data-model integration

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data-model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model-data benchmarking; and data assimilation and ecological forecasting. This community-driven approach is a key to meeting the pressing needs of science and society in the 21st century.