Background And Aims: The pathways and metabolites that contribute to residual cardiovascular disease risks are unclear. Low-calorie sweeteners are widely used sugar substitutes in processed foods with presumed health benefits. Many low-calorie sweeteners are sugar alcohols that also are produced endogenously, albeit at levels over 1000-fold lower than observed following consumption as a sugar substitute.
View Article and Find Full Text PDFDespite intensive preventive cardiovascular disease (CVD) efforts, substantial residual CVD risk remains even for individuals receiving all guideline-recommended interventions. Niacin is an essential micronutrient fortified in food staples, but its role in CVD is not well understood. In this study, untargeted metabolomics analysis of fasting plasma from stable cardiac patients in a prospective discovery cohort (n = 1,162 total, n = 422 females) suggested that niacin metabolism was associated with incident major adverse cardiovascular events (MACE).
View Article and Find Full Text PDFThe heightened cardiovascular disease (CVD) risk observed among omnivores is thought to be linked, in part, to gut microbiota-dependent generation of trimethylamine-N-oxide (TMAO) from L-carnitine, a nutrient abundant in red meat. Gut microbial transformation of L-carnitine into trimethylamine (TMA), the precursor of TMAO, occurs via the intermediate γ-butyrobetaine (γBB). However, the interrelationship of γBB, red meat ingestion and CVD risks, as well as the gut microbial genes responsible for the transformation of γBB to TMA, are unclear.
View Article and Find Full Text PDFBackground Trimethylamine--oxide (TMAO) is a small molecule derived from the metabolism of dietary nutrients by gut microbes and contributes to cardiovascular disease. Plasma TMAO increases following consumption of red meat. This metabolic change is thought to be partly because of the expansion of gut microbes able to use nutrients abundant in red meat.
View Article and Find Full Text PDFHigh-throughput sequencing of full-length transcripts using long reads has paved the way for the discovery of thousands of novel transcripts, even in well-annotated mammalian species. The advances in sequencing technology have created a need for studies and tools that can characterize these novel variants. Here, we present SQANTI, an automated pipeline for the classification of long-read transcripts that can assess the quality of data and the preprocessing pipeline using 47 unique descriptors.
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