Self-organized metabotyping of obese individuals identifies clusters responding differently to bariatric surgery.

Weight loss through bariatric surgery is efficient for treatment or prevention of obesity related diseases such as type 2 diabetes and cardiovascular disease. Long term weight loss response does, however, vary among patients undergoing surgery. Thus, it is difficult to identify predictive markers while most obese individuals have one or more comorbidities. To overcome such challenges, an in-depth multiple omics analyses including fasting peripheral plasma metabolome, fecal metagenome as well as liver, jejunum, and adipose tissue transcriptome were performed for 106 individuals undergoing bariatric surgery. Machine leaning was applied to explore the metabolic differences in individuals and evaluate if metabolism-based patients' stratification is related to their weight loss responses to bariatric surgery. Using Self-Organizing Maps (SOMs) to analyze the plasma metabolome, we identified five distinct metabotypes, which were differentially enriched for KEGG pathways related to immune functions, fatty acid metabolism, protein-signaling, and obesity pathogenesis. The gut metagenome of the most heavily medicated metabotypes, treated simultaneously for multiple cardiometabolic comorbidities, was significantly enriched in Prevotella and Lactobacillus species. This unbiased stratification into SOM-defined metabotypes identified signatures for each metabolic phenotype and we found that the different metabotypes respond differently to bariatric surgery in terms of weight loss after 12 months. An integrative framework that utilizes SOMs and omics integration was developed for stratifying a heterogeneous bariatric surgery cohort. The multiple omics datasets described in this study reveal that the metabotypes are characterized by a concrete metabolic status and different responses in weight loss and adipose tissue reduction over time. Our study thus opens a path to enable patient stratification and hereby allow for improved clinical treatments.

The potential of tailoring the gut microbiome to prevent and treat cardiometabolic disease.

Despite milestones in preventive measures and treatment, cardiovascular disease (CVD) remains associated with a high burden of morbidity and mortality. The protracted nature of the development and progression of CVD motivates the identification of early and complementary targets that might explain and alleviate any residual risk in treated patients. The gut microbiota has emerged as a sentinel between our inner milieu and outer environment and relays a modified risk associated with these factors to the host. Accordingly, numerous mechanistic studies in animal models support a causal role of the gut microbiome in CVD via specific microbial or shared microbiota-host metabolites and have identified converging mammalian targets for these signals. Similarly, large-scale cohort studies have repeatedly reported perturbations of the gut microbial community in CVD, supporting the translational potential of targeting this ecological niche, but the move from bench to bedside has not been smooth. In this Review, we provide an overview of the current evidence on the interconnectedness of the gut microbiome and CVD against the noisy backdrop of highly prevalent confounders in advanced CVD, such as increased metabolic burden and polypharmacy. We further aim to conceptualize the molecular mechanisms at the centre of these associations and identify actionable gut microbiome-based targets, while contextualizing the current knowledge within the clinical scenario and emphasizing the limitations of the field that need to be overcome.

Microbiome and metabolome features of the cardiometabolic disease spectrum.

Previous microbiome and metabolome analyses exploring non-communicable diseases have paid scant attention to major confounders of study outcomes, such as common, pre-morbid and co-morbid conditions, or polypharmacy. Here, in the context of ischemic heart disease (IHD), we used a study design that recapitulates disease initiation, escalation and response to treatment over time, mirroring a longitudinal study that would otherwise be difficult to perform given the protracted nature of IHD pathogenesis. We recruited 1,241 middle-aged Europeans, including healthy individuals, individuals with dysmetabolic morbidities (obesity and type 2 diabetes) but lacking overt IHD diagnosis and individuals with IHD at three distinct clinical stages-acute coronary syndrome, chronic IHD and IHD with heart failure-and characterized their phenome, gut metagenome and serum and urine metabolome. We found that about 75% of microbiome and metabolome features that distinguish individuals with IHD from healthy individuals after adjustment for effects of medication and lifestyle are present in individuals exhibiting dysmetabolism, suggesting that major alterations of the gut microbiome and metabolome might begin long before clinical onset of IHD. We further categorized microbiome and metabolome signatures related to prodromal dysmetabolism, specific to IHD in general or to each of its three subtypes or related to escalation or de-escalation of IHD. Discriminant analysis based on specific IHD microbiome and metabolome features could better differentiate individuals with IHD from healthy individuals or metabolically matched individuals as compared to the conventional risk markers, pointing to a pathophysiological relevance of these features.

Increased NKCC1 expression in arachnoid cysts supports secretory basis for cyst formation.

Arachnoid cysts (AC) are filled with liquid very similar to cerebrospinal fluid (CSF). The mechanisms of fluid accumulation have remained unknown; previous studies have however indicated both fluid secretion and a one-way valve as a mechanism. If the filling was caused by fluid secretion, mechanisms similar to those underlying CSF production would be anticipated. We have investigated the expression levels of all genes known to be involved in mammalian CSF production in surgically removed AC. Based on mRNA microarray analysis of AC and normal arachnoid tissue, we extracted the RNA expression profiles of all genes known to code for proteins involved in CSF production. A selection of genes was further investigated with quantitative real-time polymerase chain reaction (qRT-PCR). For selected CSF production proteins, electron microscopic immunogold techniques (EM) and Western blots were performed. Seven genes were expressed in both cysts and controls. The gene encoding the Na(+)-K(+)-2Cl(-) cotransporter NKCC1 was significantly up-regulated in AC. Gene expression data were supported by Western blot. EM demonstrated NKCC1 expressed at the plasma membranes of the cyst-lining cells. This result points at secretion as the main mechanism of cyst filling, and NKCC1 as the key candidate of fluid transport. Based on these findings, we hypothesize that selective NKCC1 inhibitors could be used in preventing expansion of temporal AC.