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Statistical dependencies between two variables X and Y indicate that either X causes Y , or Y causes X, or there exists a latent variable Z which influences X and Y. In biology and medicine, an important problem is to find genetic or environmental unobserved causes of phenotypic difference between individuals. In this contribution, we introduce a novel approach to identify unobserved confounders in data. The proposed method is based on the state-of-the-art 3off2 causal network reconstruction algorithm, and on an evidence for a direct causal relation represented by purity of con-ditionals. The proposed method is implemented in Python, and it will be publicly available shortly. We discuss the results obtained on a real biomedical dataset.

International audience; Aims/hypothesisNot all people with type 2 diabetes who undergo bariatric surgery achieve diabetes remission. Thus it is critical to develop methods for predicting outcomes that are applicable for clinical practice. The DiaRem score is relevant for predicting diabetes remission post-Roux-en-Y gastric bypass (RYGB), but it is not accurate for all individuals across the entire spectrum of scores. We aimed to develop an improved scoring system for predicting diabetes remission following RYGB (the Advanced-DiaRem [Ad-DiaRem]).MethodsWe used a retrospective French cohort (n = 1866) that included 352 individuals with type 2 diabetes followed for 1 year post-RYGB. We developed the Ad-DiaRem in a test cohort (n = 213) and examined its accuracy in independent cohorts from France (n = 134) and Israel (n = 99).ResultsAdding two clinical variables (diabetes duration and number of glucose-lowering agents) to the original DiaRem and modifying the penalties for each category led to improved predictive performance for Ad-DiaRem. Ad-DiaRem displayed improved area under the receiver operating characteristic curve and predictive accuracy compared with DiaRem (0.911 vs 0.856 and 0.841 vs 0.789, respectively; p = 0.03); thus correcting classification for 8% of those initially misclassified with DiaRem. With Ad-DiaRem, there were also fewer misclassifications of individuals with mid-range scores. This improved predictive performance was confirmed in independent cohorts.Conclusions/interpretationWe propose the Ad-DiaRem, which includes two additional clinical variables, as an optimised tool with improved accuracy to predict diabetes remission 1 year post-RYGB. This tool might be helpful for personalised management of individuals with diabetes when considering bariatric surgery in routine care, ultimately contributing to precision medicine.

Aims/hypothesis Not all people with type 2 diabetes who undergo bariatric surgery achieve diabetes remission. Thus it is critical to develop methods for predicting outcomes that are applicable for clinical practice. The DiaRem score is relevant for predicting diabetes remission post-Roux-en-Y gastric bypass (RYGB), but it is not accurate for all individuals across the entire spectrum of scores. We aimed to develop an improved scoring system for predicting diabetes remission following RYGB (the Advanced-DiaRem [Ad-DiaRem]). Methods We used a retrospective French cohort (n = 1866) that included 352 individuals with type 2 diabetes followed for 1 year post-RYGB. We developed the Ad-DiaRemin a test cohort (n = 213) and examined its accuracy in independent cohorts from France (n = 134) and Israel (n = 99). Results Adding two clinical variables (diabetes duration and number of glucose-lowering agents) to the original DiaRem and modifying the penalties for each category led to improved predictive performance for Ad-DiaRem. Ad-DiaRem displayed improved area under the receiver operating characteristic curve and predictive accuracy compared with DiaRem (0.911 vs 0.856 and 0.841 vs 0.789, respectively; p = 0.03); thus correcting classification for 8% of those initially misclassified with DiaRem. With Ad-DiaRem, there were also fewer misclassifications of individuals with mid-range scores. This improved predictive performance was confirmed in independent cohorts. Conclusions/interpretation We propose the Ad-DiaRem, which includes two additional clinical variables, as an optimised tool with improved accuracy to predict diabetes remission 1 year post-RYGB. This tool might be helpful for personalised management of individuals with diabetes when considering bariatric surgery in routine care, ultimately contributing to precision medicine.

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.

MicroObese consortium; International audience; 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.

Data representation in a lower dimension is needed in applications, where information comes from multiple high dimensional sources. A final compact model has to be interpreted by human experts, and interpretation of a classifier whose weights are discrete is much more straightforward. In this contribution, we propose a novel approach, called Deep Kernel Dimensionality Reduction which is designed for learning layers of new compact data representations simultaneously. We show by experiments on standard and on real large-scale biomedical data sets that the proposed method embeds data in a new compact meaningful representation, and leads to a lower classification error compared to the state-of-the-art methods. We also consider some state-of-the art deep learners and their corresponding discrete classifiers. We illustrate by our experiments that although purely discrete models do not always perform better than real-valued classifiers, the trade-off between the model accuracy and the interpretability is quite reasonable.

International audience; L’étude des systèmes complexes tels que des systèmes écologiques ou urbains, nécessite sou- vent l’usage de simulateurs qui permettent de comprendre les dynamiques observées ou d’avoir une vision prospective de l’évolution du système. Cependant, le crédit donné aux résultats d’une simulation dépend fortement de la confiance qui est accordée au simulateur, et donc de la qualité de sa validation. Cette confiance ne s’obtient qu’au travers d’une étude avancée du modèle, d’une analyse de sensibilité aux paramètres et d’une confrontation des résultats de simulation et des données de terrain. Pour cela, pléthore de simulations est nécessaire, ce qui est coûteux du point de vue des ressources mobilisés (temps de calcul, processeurs et mé- moire) et est souvent impossible compte tenue de la taille de l’espace des paramètres à étudier. Il est donc important de réduire de manière significative et intelligente le domaine à explorer. L’une des particularités des simulateurs représentatifs de phénomènes réels est d’avoir un es- pace des paramètres dont la nature et la forme est fonction : (i) des objectifs scientifiques ; (ii) de la nature des paramètres manipulés ; et (iii) surtout du systèmes complexes étudiés. Ainsi, le choix d’une stratégie d’exploration est totalement dépendante du domaine de l’étude. Les algorithmes génériques de la littérature ne sont alors pas optimaux. Compte tenu de la singularité des simulateurs complexes et des difficultés rencontrées pour la mise en place d’algorithmes d’exploration, nous proposons une infrastructure, appelée GRA- DEA qui facilite (i) la combinaison d’algorithmes génériques d’exploration ; (ii) l’utilisation de moyens de calcul intensif. Pour cela nous proposons une architecture logicielle originale basée sur le paradigme agent, JEE et les systèmes de calcul haute performance tels que les clusters.

National audience

Background : Associations between dietary patterns, metabolic and inflammatory markers and gut microbiota are yet to be elucidated. Objectives : We aimed to characterize dietary patterns in overweight and obese subjects and evaluate the different dietary patterns in relation to metabolic and inflammatory variables as well as gut microbiota. Design : Dietary patterns, plasma and adipose tissue markers, and gut microbiota were evaluated in a group of 45 overweight and obese subjects (6 men and 39 women). A group of 14 lean subjects were also evaluated as a reference group. Results : Three clusters of dietary patterns were identified in overweight/obese subjects. Cluster 1 had the least healthy eating behavior (highest consumption of potatoes, confectionary and sugary drinks, and the lowest consumption of fruits that was associated also with low consumption of yogurt, and water). This dietary pattern was associated with the highest LDL cholesterol, plasma soluble CD14 (p = 0.01) a marker of systemic inflammation but the lowest accumulation of CD163+ macrophages with anti-inflammatory profile in adipose tissue (p = 0.05). Cluster 3 had the healthiest eating behavior (lower consumption of confectionary and sugary drinks, and highest consumption of fruits but also yogurts and soups). Subjects in this Cluster had the lowest inflammatory markers (sCD14) and the highest anti-inflammatory adipose tissue CD163+ macrophages. Dietary intakes, insulin sensitivity and some inflammatory markers (plasma IL6) in Cluster 3 were close to those of lean subjects. Cluster 2 was in-between clusters 1 and 3 in terms of healthfulness. The 7 gut microbiota groups measured by qPCR were similar across the clusters. However, the healthiest dietary cluster had the highest microbial gene richness, as evaluated by quantitative metagenomics. Conclusion: A healthier dietary pattern was associated with lower inflammatory markers as well as greater gut microbiota richness in overweight and obese subjects.

International audience

Our view of the microbial world and its impact on human health is changing radically with the ability to sequence uncultured or unculturable microbes sampled directly from their habitats, ability made possible by fast and cheap next generation sequencing technologies. Such recent developments represents a paradigmatic shift in the analysis of habitat biodiversity, be it the human, soil or ocean microbiome. We review here some research examples and results that indicate the importance of the microbiome in our lives and then discus some of the challenges faced by metagenomic experiments and the subsequent analysis of the generated data. We then analyze the economic and social impact on genomic-medicine and research in both developing and developed countries. We support the idea that there are significant benefits in building capacities for developing high-level scientific research in metagenomics in developing countries. Indeed, the notion that developing countries should wait for developed countries to make advances in science and technology that they later import at great cost has recently been challenged.

Learning discrete linear classifiers is known as a difficult challenge. In this paper, this learning task is cast as combinatorial optimization problem: given a training sample formed by positive and negative feature vectors in the Euclidean space, the goal is to find a discrete linear function that minimizes the cumulative hinge loss of the sample. Since this problem is NP-hard, we examine two simple rounding algorithms that discretize the fractional solution of the problem. Generalization bounds are derived for several classes of binary-weighted linear functions, by analyzing the Rademacher complexity of these classes and by establishing approximation bounds for our rounding algorithms. Our methods are evaluated on both synthetic and real-world data.