Representing Diet in a Tree-Based Format for Interactive and Exploratory Assessment of Dietary Intake Data

International audienceAbstract Objectives We assessed the utility of representing dietary intake data in hierarchical tree structures that consider relationships among foods. Methods Dietary intake was collected from 1909 adults (≥18 years) using a food frequency questionnaire (FFQ; VioScreen) from the American Gut Project. FFQ food items were formatted into hierarchical tree structures based on 1) USDA's Food Nutrient and Database for Dietary Studies (FNDDS) classifications, 2) nutrient content, and 3) molecular compound information detected via mass spectrometry to capture the non-nutrient composition of foods. Next, we compared how well representing dissimilarities (or distances) between individuals based on their diet corresponded with indices such as the Healthy Eating Index (HEI-2015), when those distances are calculated using tree-based versus non-tree-based metrics. We performed an Adonis test (PERMANOVA) to measure the amount of variation explained (R2) in these diet-based distances by HEI-2015. Results We observed that dietary ordinations generated using tree-based relationships between foods have better agreement with HEI than ordinations generated without considering relatedness between foods. The variation explained by HEI-2015 increased by 35% when using the FNDDS tree compared to using a non-tree based quantitative metric (Bray-Curtis (not tree-based) R2 = 0.02931 vs. Weighted UniFrac (tree-based) R2 = 0.03969), by >20% when using the nutrient tree (vs. Weighted UniFrac R2 = 0.03627), and only marginally (6%) when using the molecular compound tree (vs. Weighted UniFrac R2 = 0.03116). Conclusions We show that tree-based measurements of dietary similarity lead to better agreement with diet indices (e.g., HEI) than when relationships among foods are not considered. We also show that representing dietary intake in a tree-like structure can offer interactive visualizations of data that can be used to inform hypotheses regarding dietary characteristics. Funding Sources Danone Nutricia Research

Basophils from allergic patients are neither hyperresponsive to activation signals nor hyporesponsive to inhibition signals

International audienceBACKGROUND:Basophil activation contributes to inflammatory reactions, especially in allergy. It is controlled, both positively and negatively, by several mechanisms. High-affinity IgE receptors (FcεRI) generate a mixture of activation and inhibition signals when aggregated, the ratio of which depends on the concentration of allergen recognized by receptor-bound IgE. Low-affinity IgG receptors (FcγRIIA/B) generate inhibition signals when coengaged with FcεRI by allergen-antibody immune complexes. Commensal and probiotic bacteria, such as Lactobacillus paracasei, generate inhibition signals through still unclear mechanisms.OBJECTIVE:We sought to investigate whether mechanisms that control, both positively and negatively, basophil activation, which were unraveled and studied in basophils from healthy donors, are functional in allergic patients.METHODS:FcεRI and FcγRIIA/B expression, FcεRI-dependent activation, FcεRI-dependent inhibition, and FcγRIIB-dependent inhibition were examined in blood basophils incubated overnight with or without L paracasei and challenged under 10 experimental conditions. Basophils from healthy donors were compared with basophils from patients who consulted an allergology outpatient clinic over a period of 3 months with respiratory allergy, anaphylaxis antecedents, chronic urticaria, and/or atopic dermatitis.RESULTS:Patients' basophils expressed neither more FcεRI nor less FcγRIIB than basophils from healthy donors. They were neither hyperreactive to positive regulation nor hyporeactive to negative regulation, irrespective of the receptors or mechanisms involved and the allergic manifestations of the patients.CONCLUSION:Regulatory mechanisms that control basophil activation are fully functional in allergic patients. Intrinsic defects in these mechanisms do not explain allergic manifestations. Based on these mechanisms, immune checkpoint modifiers can be developed as novel therapeutic tools for allergy

Dietary patterns differently associate with inflammation and gut microbiota in overweight and obese subjects

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

Mean daily food consumption (grams) for lean, overweight/obese subjects and the 3 dietary clusters.

<p>Data are presented as means ± SEM.</p><p>*Kruskal-Wallis rank sum test with Bonferroni correction, <i>P</i> value significant at ≤0.002 is shown in bold italics. Wilcoxon rank sum test stands for variance between overweight and obese individual clusters.</p>†<p>significant difference between Clusters 1 and 3.</p>‡<p>significant difference between Clusters 1 and 2;</p>§<p>significant difference between Clusters 2 and 3. <i>P</i> values testing variance between lean subjects and all overweight and obese subjects and between lean subjects and the individual clusters are shown in Table S2 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0109434#pone.0109434.s003" target="_blank">Supporting Information S1</a>.</p><p>**This group contains other fermented dairy products, e.g. fromage blanc.</p><p>Mean daily food consumption (grams) for lean, overweight/obese subjects and the 3 dietary clusters.</p

Differences of metabolic and inflammatory markers after stratified Kruskal-Wallis tests in the 3 dietary pattern clusters.

<p>Black, grey and white columns represent the median values of the parameters in Cluster 1, Cluster 2 and Cluster 3, respectively, after age adjustment (see Methods S1 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0109434#pone.0109434.s003" target="_blank">Supporting Information S1</a>). *: significant differences (p≤0.05) between the 3 clusters after stratified Kruskal-Wallis tests, #: a tendency of differences (0.05</p

Canonical analysis: graphical representation of the food categories by cluster.

<p>A graphical representation of the food categories that created the distinction between the clusters i.e. those which were strongly correlated with canonical axis (Can) or significantly different between clusters (KW test with Bonferroni correction). The can 1 axis separates Cluster1 from 2 or 3, the can 2 axis separates Cluster 2 from 1 or 3. If the food category is strongly correlated with the two canonical axes it separates the three clusters at the same time. Food categories shown in black characterise Cluster 1, in green characterise Cluster 2, and in red characterise Cluster 3. The projection of each food or drink category on each canonical axis represents the contribution of this category to the building of this canonical axis. Therefore, if a category has a high contribution to the first axis (e.g. fruit), it discriminates Cluster 1 from Cluster 2 or Cluster 3. The food categories with weak contribution (below 0.5 in the inner circle) are shown in blue. These categories do not contribute to the discrimination/characterization of the three clusters. The distance between the centre point of the figure and the food category represents the correlation to the canonical axis and therefore the contribution to the separation of clusters. Food categories close to the axis between Clusters 2 and 3 indicate that intakes are similar, as for yogurt. Food category names have been shorted in this figure for readability.</p

Canonical correlation analysis for significant food categories and selected clinical parameters (all subjects).

<p>Visualization of the association between the food categories that significantly distinguish one pattern from another and selected clinical parameters. Pairs of canonical axes were determined to maximize the covariance between the food categories and the clinical parameters. The canonical coefficients were used to assess the contributions of each food category and each clinical parameter to the correlation by evaluating their signs and magnitude. The healthy foods (yogurt, soups, fruits, vegetables) are in the area of CD163+ macrophages indicating the higher the consumption of these healthy foods, the higher the value for the alternatively (M2)-activated macrophages. The less healthy foods (potatoes, sweetened soft drinks, sweets) are in the area of LDL cholesterol, inflammatory parameters CD14, total fat mass and adipocyte diameter indicating that the higher the consumption of these foods, the higher the value of these clinical parameters; Food and clinical parameter arrows pointing in the same direction indicate positive correlation between them. The closer the food is to the clinical parameter, the greater the link (but in some cases this link is not strong, and the value for the correlation is less than 0.05).</p

Subject Characteristics in the lean and the overweight/obese groups and in the 3 dietary clusters.

<p>Data are presented as means ± SEM; n = 45 subjects. BMI: Body Mass Index; *Kruskal-Wallis rank sum test stratified by age groups. **tests for trend stratified by age. <i>P</i> value ≤0.05 is shown in bold italics. 0.05<<i>P</i> value<0.15 is shown in italics. Stratified post-hoc Nemenyi tests stands for variance between each set of 2 clusters: ^†Significant difference between Clusters 1 and 3;.^‡Significant difference between Clusters 1 and 2.</p><p>Subject Characteristics in the lean and the overweight/obese groups and in the 3 dietary clusters.</p

Consort Flowchart.

<p>Consort Flowchart.</p