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.

Dynamics of the normal gut microbiota: A longitudinal one-year population study in Sweden.

Temporal dynamics of the gut microbiota potentially limit the identification of microbial features associated with health status. Here, we used whole-genome metagenomic and 16S rRNA gene sequencing to characterize the intra- and inter-individual variations of gut microbiota composition and functional potential of a disease-free Swedish population (n = 75) over one year. We found that 23% of the total compositional variance was explained by intra-individual variation. The degree of intra-individual compositional variability was negatively associated with the abundance of Faecalibacterium prausnitzii (a butyrate producer) and two Bifidobacterium species. By contrast, the abundance of facultative anaerobes and aerotolerant bacteria such as Escherichia coli and Lactobacillus acidophilus varied extensively, independent of compositional stability. The contribution of intra-individual variance to the total variance was greater for functional pathways than for microbial species. Thus, reliable quantification of microbial features requires repeated samples to address the issue of intra-individual variations of the gut microbiota.

Developmental trajectory of the healthy human gut microbiota during the first 5 years of life.

The gut is inhabited by a densely populated ecosystem, the gut microbiota, that is established at birth. However, the succession by which different bacteria are incorporated into the gut microbiota is still relatively unknown. Here, we analyze the microbiota from 471 Swedish children followed from birth to 5 years of age, collecting samples after 4 and 12 months and at 3 and 5 years of age as well as from their mothers at birth using 16S rRNA gene profiling. We also compare their microbiota to an adult Swedish population. Genera follow 4 different colonization patterns during establishment where Methanobrevibacter and Christensenellaceae colonize late and do not reached adult levels at 5 years. These late colonizers correlate with increased alpha diversity in both children and adults. By following the children through age-specific community types, we observe that children have individual dynamics in the gut microbiota development trajectory.

Integration of molecular profiles in a longitudinal wellness profiling cohort.

An important aspect of precision medicine is to probe the stability in molecular profiles among healthy individuals over time. Here, we sample a longitudinal wellness cohort with 100 healthy individuals and analyze blood molecular profiles including proteomics, transcriptomics, lipidomics, metabolomics, autoantibodies and immune cell profiling, complemented with gut microbiota composition and routine clinical chemistry. Overall, our results show high variation between individuals across different molecular readouts, while the intra-individual baseline variation is low. The analyses show that each individual has a unique and stable plasma protein profile throughout the study period and that many individuals also show distinct profiles with regards to the other omics datasets, with strong underlying connections between the blood proteome and the clinical chemistry parameters. In conclusion, the results support an individual-based definition of health and show that comprehensive omics profiling in a longitudinal manner is a path forward for precision medicine.

The Gut Microbiota in Prediabetes and Diabetes: A Population-Based Cross-Sectional Study.

The link between the gut microbiota and type 2 diabetes (T2D) warrants further investigation because of known confounding effects from antidiabetic treatment. Here, we profiled the gut microbiota in a discovery (n = 1,011) and validation (n = 484) cohort comprising Swedish subjects naive for diabetes treatment and grouped by glycemic status. We observed that overall gut microbiota composition was altered in groups with impaired glucose tolerance, combined glucose intolerance and T2D, but not in those with impaired fasting glucose. In addition, the abundance of several butyrate producers and functional potential for butyrate production were decreased both in prediabetes and T2D groups. Multivariate analyses and machine learning microbiome models indicated that insulin resistance was strongly associated with microbial variations. Therefore, our study indicates that the gut microbiota represents an important modifiable factor to consider when developing precision medicine approaches for the prevention and/or delay of T2D.

Gut microbiota of obese subjects with Prader-Willi syndrome is linked to metabolic health.

OBJECTIVE: The gut microbiota has been implicated in the aetiology of obesity and associated comorbidities. Patients with Prader-Willi syndrome (PWS) are obese but partly protected against insulin resistance. We hypothesised that the gut microbiota of PWS patients differs from that of non-genetically obese controls and correlate to metabolic health. Therefore, here we used PWS as a model to study the role of gut microbiota in the prevention of metabolic complications linked to obesity. DESIGN: We conducted a case-control study with 17 adult PWS patients and 17 obese subjects matched for body fat mass index, gender and age. The subjects were metabolically characterised and faecal microbiota was profiled by 16S ribosomal RNA gene sequencing. The patients' parents were used as a non-obese control group. Stool samples from two PWS patients and two obese controls were used for faecal microbiota transplantations in germ-free mice to examine the impact of the microbiota on glucose metabolism. RESULTS: The composition of the faecal microbiota in patients with PWS differed from that of obese controls, and was characterised by higher phylogenetic diversity and increased abundance of several taxa such as Akkermansia, Desulfovibrio and Archaea, and decreased abundance of Dorea. Microbial taxa prevalent in the PWS microbiota were associated with markers of insulin sensitivity. Improved insulin resistance of PWS was partly transmitted by faecal microbiota transplantations into germ-free mice. CONCLUSION: The gut microbiota of PWS patients is similar to that of their non-obese parents and might play a role for the protection of PWS patients from metabolic complications.

Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug.

Metformin is widely used in the treatment of type 2 diabetes (T2D), but its mechanism of action is poorly defined. Recent evidence implicates the gut microbiota as a site of metformin action. In a double-blind study, we randomized individuals with treatment-naive T2D to placebo or metformin for 4 months and showed that metformin had strong effects on the gut microbiome. These results were verified in a subset of the placebo group that switched to metformin 6 months after the start of the trial. Transfer of fecal samples (obtained before and 4 months after treatment) from metformin-treated donors to germ-free mice showed that glucose tolerance was improved in mice that received metformin-altered microbiota. By directly investigating metformin-microbiota interactions in a gut simulator, we showed that metformin affected pathways with common biological functions in species from two different phyla, and many of the metformin-regulated genes in these species encoded metalloproteins or metal transporters. Our findings provide support for the notion that altered gut microbiota mediates some of metformin's antidiabetic effects.