Nataliya Sokolovska

Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology

Objective Individuals with obesity and type 2 diabetes differ from lean and healthy individuals in their abundance of certain gut microbial species and microbial gene richness. Abundance of Akkermansia muciniphila, a mucin-degrading bacterium, has been inversely associated with body fat mass and glucose intolerance in mice, but more evidence is needed in humans. The impact of diet and weight loss on this bacterial species is unknown. Our objective was to evaluate the association between faecal A. muciniphila abundance, faecal microbiome gene richness, diet, host characteristics, and their changes after calorie restriction (CR). Design The intervention consisted of a 6-week CR period followed by a 6-week weight stabilisation diet in overweight and obese adults (N=49, including 41 women). Faecal A. muciniphila abundance, faecal microbial gene richness, diet and bioclinical parameters were measured at baseline and after CR and weight stabilisation. Results At baseline A. muciniphila was inversely related to fasting glucose, waist-to-hip ratio and subcutaneous adipocyte diameter. Subjects with higher gene richness and A. muciniphila abundance exhibited the healthiest metabolic status, particularly in fasting plasma glucose, plasma triglycerides and body fat distribution. Individuals with higher baseline A. muciniphila displayed greater improvement in insulin sensitivity markers and other clinical parameters after CR. These participants also experienced a reduction in A. muciniphila abundance, but it remained significantly higher than in individuals with lower baseline abundance. A. muciniphila was associated with microbial species known to be related to health. Conclusions A. muciniphila is associated with a healthier metabolic status and better clinical outcomes after CR in overweight/obese adults. The interaction between gut microbiota ecology and A. muciniphila warrants further investigation. Trial registration number NCT01314690.

Circulating phospholipid profiling identifies portal contribution to NASH signature in obesity

BACKGROUND & AIMS Non-alcoholic steatohepatitis (NASH) is characterized by steatosis, lobular inflammation, hepatocyte ballooning with fibrosis in severe cases, and high prevalence in obesity. We aimed at defining NASH signature in morbid obesity by mass spectrometry-based lipidomic analysis. METHODS We analyzed systemic blood before and 12 months after bariatric surgery, along with portal blood and adipose tissue lipid efflux collected from obese women at the time of surgery (9 structural classes, 150 species). RESULTS Increased concentrations of several glycerophosphocholines (PC), glycerophosphoethanolamines (PE), glycerophosphoinositols (PI), glycerophosphoglycerols (PG), lyso-glycerophosphocholines (LPC), and ceramides (Cer) were detected in systemic circulation of NASH subjects. Post-surgery weight loss (12 months) improved the levels of liver enzymes, as well as several lipids, but most PG and Cer species remained elevated. Analysis of lipids from hepatic portal system at the time of surgery revealed limited lipid alterations compared to systemic circulation, but PG and PE classes were found significantly increased in NASH subjects. We evaluated the contribution of visceral adipose tissue to lipid alterations in portal circulation by measuring adipose tissue lipid efflux ex vivo, and observed only minor alterations in NASH subjects. Interestingly, integration of clinical and lipidomic data (portal and systemic) led us to define a NASH signature in which lipids and clinical parameters are equal contributors. CONCLUSIONS Circulatory (portal and systemic) phospholipid profiling and clinical data defines NASH signature in morbid obesity. We report weak contribution of visceral adipose tissue to NASH-related portal lipid alterations, suggesting possible contribution from other organs draining into hepatic portal system.

Insulin resistance and inflammation predict kinetic body weight changes in response to dietary weight loss and maintenance in overweight and obese subjects by using a Bayesian network approach

BACKGROUND The ability to identify obese subjects who will lose weight in response to energy restriction is an important strategy in obesity treatment. OBJECTIVE We aimed to identify obese subjects who would lose weight and maintain weight loss through 6 wk of energy restriction and 6 wk of weight maintenance. DESIGN Fifty obese or overweight subjects underwent a 6-wk energy-restricted, high-protein diet followed by another 6 wk of weight maintenance. Network modeling by using combined biological, gut microbiota, and environmental factors was performed to identify predictors of weight trajectories. RESULTS On the basis of body weight trajectories, 3 subject clusters were identified. Clusters A and B lost more weight during energy restriction. During the stabilization phase, cluster A continued to lose weight, whereas cluster B remained stable. Cluster C lost less and rapidly regained weight during the stabilization period. At baseline, cluster C had the highest plasma insulin, interleukin (IL)-6, adipose tissue inflammation (HAM56+ cells), and Lactobacillus/Leuconostoc/Pediococcus numbers in fecal samples. Weight regain after energy restriction correlated positively with insulin resistance (homeostasis model assessment of insulin resistance: r = 0.5, P = 0.0002) and inflammatory markers (IL-6; r = 0.43, P = 0.002) at baseline. The Bayesian network identified plasma insulin, IL-6, leukocyte number, and adipose tissue (HAM56) at baseline as predictors that were sufficient to characterize the 3 clusters. The prediction accuracy reached 75.5%. CONCLUSION The resistance to weight loss and proneness to weight regain could be predicted by the combination of high plasma insulin and inflammatory markers before dietary intervention.

The FAT Score, a Fibrosis Score of Adipose Tissue: Predicting Weight-Loss Outcome After Gastric Bypass

Context Bariatric surgery (BS) induces major and sustainable weight loss in many patients. Factors predicting poor weight-loss response (PR) need to be identified to improve patient care. Quantification of subcutaneous adipose tissue (scAT) fibrosis is negatively associated with post-BS weight loss, but whether it could constitute a predictor applicable in clinical routine remains to be demonstrated. Objective To create a semiquantitative score evaluating scAT fibrosis and test its predictive value on weight-loss response after Roux-en-Y gastric bypass (RYGB). Methods We created a fibrosis score of adipose tissue (FAT score) integrating perilobular and pericellular fibrosis. Using this score, we characterized 183 perioperative scAT biopsy specimens from severely obese patients who underwent RYGB (n = 85 from a training cohort; n = 98 from a confirmation cohort). PR to RYGB was defined as <28% of total weight loss at 1 year (lowest tertile). The link between FAT score and PR was tested in univariate and multivariate models. Results FAT score was directly associated with increasing scAT fibrosis measured by a standard quantification method (P for trend <0.001). FAT score interobserver agreement was good (κ = 0.76). FAT score ≥2 was significantly associated with PR. The association remained significant after adjustment for age, diabetes status, hypertension, percent fat mass, and interleukin-6 level (adjusted odds ratio, 3.6; 95% confidence interval, 1.8 to 7.2; P = 0.003). Conclusion The FAT score is a new, simple, semiquantitative evaluation of human scAT fibrosis that may help identify patients with a potential limited weight-loss response to RYGB.

Spectral consensus strategy for accurate reconstruction of large biological networks

BackgroundThe last decades witnessed an explosion of large-scale biological datasets whose analyses require the continuous development of innovative algorithms. Many of these high-dimensional datasets are related to large biological networks with few or no experimentally proven interactions. A striking example lies in the recent gut bacterial studies that provided researchers with a plethora of information sources. Despite a deeper knowledge of microbiome composition, inferring bacterial interactions remains a critical step that encounters significant issues, due in particular to high-dimensional settings, unknown gut bacterial taxa and unavoidable noise in sparse datasets. Such data type make any a priori choice of a learning method particularly difficult and urge the need for the development of new scalable approaches.ResultsWe propose a consensus method based on spectral decomposition, named Spectral Consensus Strategy, to reconstruct large networks from high-dimensional datasets. This novel unsupervised approach can be applied to a broad range of biological networks and the associated spectral framework provides scalability to diverse reconstruction methods. The results obtained on benchmark datasets demonstrate the interest of our approach for high-dimensional cases. As a suitable example, we considered the human gut microbiome co-presence network. For this application, our method successfully retrieves biologically relevant relationships and gives new insights into the topology of this complex ecosystem.ConclusionsThe Spectral Consensus Strategy improves prediction precision and allows scalability of various reconstruction methods to large networks. The integration of multiple reconstruction algorithms turns our approach into a robust learning method. All together, this strategy increases the confidence of predicted interactions from high-dimensional datasets without demanding computations.

Long-term Relapse of Type 2 Diabetes After Roux-en-Y Gastric Bypass: Prediction and Clinical Relevance

OBJECTIVE Roux-en-Y gastric bypass (RYGB) induces type 2 diabetes remission (DR) in 60% of patients at 1 year, yet long-term relapse occurs in half of these patients. Scoring methods to predict DR outcomes 1 year after surgery that include only baseline parameters cannot accurately predict 5-year DR (5y-DR). We aimed to develop a new score to better predict 5y-DR. RESEARCH DESIGN AND METHODS We retrospectively included 175 RYGB patients with type 2 diabetes with 5-year follow-up. Using machine learning algorithms, we developed a scoring method, 5-year Advanced-Diabetes Remission (5y-Ad-DiaRem), predicting longer-term DR postsurgery by integrating medical history, bioclinical data, and antidiabetic treatments. The scoring method was based on odds ratios and variables significantly different between groups. This score was further validated in three independent RYGB cohorts from three European countries. RESULTS Compared with 5y-DR patients, patients who had relapsed after 5 years exhibited more severe type 2 diabetes at baseline, lost significantly less weight during the 1st year after RYGB, and regained more weight afterward. The 5y-Ad-DiaRem includes baseline (diabetes duration, number of antidiabetic treatments, and HbA1c) and 1-year follow-up parameters (glycemia, number of antidiabetic treatments, remission status, 1st-year weight loss). The 5y-Ad-DiaRem was accurate (area under the receiver operating characteristic curve [AUROC], 90%; accuracy, 85%) at predicting 5y-DR, performed better than the Diabetes Remission score (DiaRem) and the Advanced-DiaRem (AUROC, 81% and 84%; accuracy, 79% and 78%, respectively), and correctly reclassified 13 of 39 patients misclassified with the DiaRem. The 5y-Ad-DiaRem robustness was confirmed in the independent cohorts. CONCLUSIONS The 5y-Ad-DiaRem accurately predicts 5y-DR and appears relevant to identify patients at risk for relapse. Using this score could help personalize patient care after the 1st year post-RYGB to maximize weight loss, limit weight regains, and prevent relapse.

A Semi-supervised Approach to Discover Bivariate Causality in Large Biological Data

An important question in microbiology is whether treatment causes changes in gut flora, and whether it also affects metabolism. The reconstruction of causal relations purely from non-temporal observational data is challenging. We address the problem of causal inference in a bivariate case, where the joint distribution of two variables is observed. The state-of-the-art causality inference methods for continuous data suffer from high computational complexity. Some modern approaches are not suitable for categorical data, and others need to estimate and fix multiple hyper-parameters.

Sparse Zero-Sum Games as Stable Functional Feature Selection.

In large-scale systems biology applications, features are structured in hidden functional categories whose predictive power is identical. Feature selection, therefore, can lead not only to a problem with a reduced dimensionality, but also reveal some knowledge on functional classes of variables. In this contribution, we propose a framework based on a sparse zero-sum game which performs a stable functional feature selection. In particular, the approach is based on feature subsets ranking by a thresholding stochastic bandit. We provide a theoretical analysis of the introduced algorithm. We illustrate by experiments on both synthetic and real complex data that the proposed method is competitive from the predictive and stability viewpoints.