Global and Partial Effect Assessment in Metabolic Syndrome Explored by Metabolomics.

In nutrition and health research, untargeted metabolomics is actually analyzed simultaneously with clinical data to improve prediction and better understand pathological status. This can be modeled using a multiblock supervised model with several input data blocks (metabolomics, clinical data) being potential predictors of the outcome to be explained. Alternatively, this configuration can be represented with a path diagram where the input blocks are each connected by links directed to the outcome-as in multiblock supervised modeling-and are also related to each other, thus allowing one to account for block effects. On the basis of a path model, we show herein how to estimate the effect of an input block, either on its own or conditionally to other(s), on the output response, respectively called "global" and "partial" effects, by percentages of explained variance in dedicated PLS regression models. These effects have been computed in two different path diagrams in a case study relative to metabolic syndrome, involving metabolomics and clinical data from an older men's cohort (NuAge). From the two effects associated with each path, the results highlighted the complementary information provided by metabolomics to clinical data and, reciprocally, in the metabolic syndrome exploration.

Associations between persistent organic pollutants and endometriosis: A multiblock approach integrating metabolic and cytokine profiling.

Humans are exposed daily to complex mixtures of chemical pollutants through their environment and diet, some of which have the potential to disrupt the bodies' natural endocrine functions and contribute to reproductive diseases like endometriosis. Increasing epidemiological and experimental evidence supports the association between endometriosis and certain persistent organic pollutants (POPs) like dioxins; however, little is known about the underlying linking mechanisms. The main objective of this study is to proof the methodological applicability and discovery potential of integrating ultra-trace mass spectrometry (MS) profiling of POP biomarkers and endogenous biomarker profiling (MS metabolomics and cytokines) in a case-control study for the etiological research of endometriosis. The approach is applied in a pilot clinical-based study conducted in France where women with and without surgically confirmed endometriosis were recruited. Serum samples were analysed with high-resolution MS for about 30 polychlorinated biphenyls (PCBs), organochlorinated pesticides and perfluoroalkyl substances (PFAS). About 600 serum metabolites and lipids were identified with targeted metabolomics using tandem MS with the Biocrates MxP® Quant 500 Kit. A panel of 4 pro-inflammatory cytokines were analysed using ELISA-based 4-PLEX analyser. Statistical analysis included a battery of variable selection approaches, multivariate logistic regression for single-chemical associations, Bayesian kernel machine regressions (BKMR) to identify mixture effects of POPs and a multiblock approach to identify shared biomarker signatures among high risk clusters. The results showed the positive associations between some POPs and endometriosis risk, including the pesticide trans-nonachlor Odds Ratio (95% Confidence Interval) 3.38 (2.06-5.98), p < 0.0001 and PCB 114 OR (95% CI) 1.83 (1.17-2.93), p = 0.009. The BKMR approach showed a tendency of a positive cumulative effect of the mixture, however trans-nonachlor exhibited significant associations within the mixture and interacted with other PCBs, strengthening the effects at highest concentrations. Finally, the multiblock analysis, relating the various blocks of data, revealed a latent cluster of women with higher risk of endometrioma presenting higher concentrations of trans-nonachlor, PCB 114 and dioxin-like toxic equivalents from PCBs, together with an increased inflammatory profile (i.e. elevated interleukin-8 and monocyte chemoattractant protein-1). It was also highlighted a specific metabolic pattern characterized by dysregulation of bile acid homeostasis and lipase activity. Further research will be required with larger sample size to confirm these findings and gain insight on the underlying mechanisms between POPs and endometriosis.

Prebiotic Supplementation During Pregnancy Modifies the Gut Microbiota and Increases Metabolites in Amniotic Fluid, Driving a Tolerogenic Environment In Utero.

The gut microbiota is influenced by environmental factors such as food. Maternal diet during pregnancy modifies the gut microbiota composition and function, leading to the production of specific compounds that are transferred to the fetus and enhance the ontogeny and maturation of the immune system. Prebiotics are fermented by gut bacteria, leading to the release of short-chain fatty acids that can specifically interact with the immune system, inducing a switch toward tolerogenic populations and therefore conferring health benefits. In this study, pregnant BALB/cJRj mice were fed either a control diet or a diet enriched in prebiotics (Galacto-oligosaccharides/Inulin). We hypothesized that galacto-oligosaccharides/inulin supplementation during gestation could modify the maternal microbiota, favoring healthy immune imprinting in the fetus. Galacto-oligosaccharides/inulin supplementation during gestation increases the abundance of Bacteroidetes and decreases that of Firmicutes in the gut microbiota, leading to increased production of fecal acetate, which was found for the first time in amniotic fluid. Prebiotic supplementation increased the abundance of regulatory B and T cells in gestational tissues and in the fetus. Interestingly, these regulatory cells remained later in life. In conclusion, prebiotic supplementation during pregnancy leads to the transmission of specific microbial and immune factors from mother to child, allowing the establishment of tolerogenic immune imprinting in the fetus that may be beneficial for infant health outcomes.

Application of a path-modelling approach for deciphering causality relationships between microbiota, volatile organic compounds and off-odour profiles during meat spoilage.

Microbiological spoilage of meat is considered as a process which involves mainly bacterial metabolism leading to degradation of meat sensory qualities. Studying spoilage requires the collection of different types of experimental data encompassing microbiological, physicochemical and sensorial measurements. Within this framework, the objective herein was to carry out a multiblock path modelling workflow to decipher causality relationships between different types of spoilage-related responses: composition of microbiota, volatilome and off-odour profiles. Analyses were performed with the Path-ComDim approach on a large-scale dataset collected on fresh turkey sausages. This approach enabled to quantify the importance of causality relationships determined a priori between each type of responses as well as to identify important responses involved in spoilage, then to validate causality assumptions. Results were very promising: the data integration confirmed and quantified the causality between data blocks, exhibiting the dynamical nature of spoilage, mainly characterized by the evolution of off-odour profiles caused by the production of volatile organic compounds such as ethanol or ethyl acetate. This production was possibly associated with several bacterial species like Lactococcus piscium, Leuconostoc gelidum, Psychrobacter sp. or Latilactobacillus fuchuensis. Likewise, the production of acetoin and diacetyl in meat spoilage was highlighted. The Path-ComDim approach illustrated here with meat spoilage can be applied to other large-scale and heterogeneous datasets associated with pathway scenarios and represents a promising key tool for deciphering causality in complex biological phenomena.

Food perception, lifestyle, nutritional and health status in the older people: Typologies and factors associated with aging well.

The aging process is associated with physiological, sensory, psychological, and sociological changes likely to have an impact on food intake and the nutritional status. The present study aimed to explore the heterogeneity of the French older population (>65 years old) using a multidisciplinary approach. More specifically, the study aimed to highlight different typologies (i.e. clusters of individuals with similar characteristics) within the older population. We conducted face-to-face interviews and tests with 559 French older people, recruited from different categories of dependency (at home without help, at home with help, in nursing homes). Clustering analysis highlighted seven clusters. Clusters 1-3 contained 'young' older people (<80) with a good nutritional status; these clusters differed according to food preferences, the desire to have a healthy diet, or interest in food. Clusters 4-7 mainly contained 'old' older people (80+), with an increase in the nutritional risk from cluster 4 to cluster 7. Two of these clusters grouped healthy and active people with a good level of appetite, while the two other clusters were associated with a clear decline in nutritional status, with people suffering from eating difficulties or depression. The results raise the need to develop targeted interventions to tackle malnutrition and implement health promotion strategies among the seniors.

Prebiotic Supplementation During Gestation Induces a Tolerogenic Environment and a Protective Microbiota in Offspring Mitigating Food Allergy.

Food allergy is associated with alterations in the gut microbiota, epithelial barrier, and immune tolerance. These dysfunctions are observed within the first months of life, indicating that early intervention is crucial for disease prevention. Preventive nutritional strategies with prebiotics are an attractive option, as prebiotics such as galacto-oligosaccharides and inulin can promote tolerance, epithelial barrier reinforcement, and gut microbiota modulation. Nonetheless, the ideal period for intervention remains unknown. Here, we investigated whether galacto-oligosaccharide/inulin supplementation during gestation could protect offspring from wheat allergy development in BALB/cJRj mice. We demonstrated that gestational prebiotic supplementation promoted the presence of beneficial strains in the fecal microbiota of dams during gestation and partially during mid-lactation. This specific microbiota was transferred to their offspring and maintained to adulthood. The presence of B and T regulatory immune cell subsets was also increased in the lymph nodes of offspring born from supplemented mothers, suggestive of a more tolerogenic immune environment. Indeed, antenatal prebiotic supplementation reduced the development of wheat allergy symptoms in offspring. Our study thus demonstrates that prebiotic supplementation during pregnancy induces, in the offspring, a tolerogenic environment and a microbial imprint that mitigates food allergy development.

Associations between persistent organic pollutants and endometriosis: A multipollutant assessment using machine learning algorithms.

Endometriosis is a gynaecological disease characterised by the presence of endometriotic tissue outside of the uterus impacting a significant fraction of women of childbearing age. Evidence from epidemiological studies suggests a relationship between risk of endometriosis and exposure to some organochlorine persistent organic pollutants (POPs). However, these chemicals are numerous and occur in complex and highly correlated mixtures, and to date, most studies have not accounted for this simultaneous exposure. Linear and logistic regression models are constrained to adjusting for multiple exposures when variables are highly intercorrelated, resulting in unstable coefficients and arbitrary findings. Advanced machine learning models, of emerging use in epidemiology, today appear as a promising option to address these limitations. In this study, different machine learning techniques were compared on a dataset from a case-control study conducted in France to explore associations between mixtures of POPs and deep endometriosis. The battery of models encompassed regularised logistic regression, artificial neural network, support vector machine, adaptive boosting, and partial least-squares discriminant analysis with some additional sparsity constraints. These techniques were applied to identify the biomarkers of internal exposure in adipose tissue most associated with endometriosis and to compare model classification performance. The five tested models revealed a consistent selection of most associated POPs with deep endometriosis, including octachlorodibenzofuran, cis-heptachlor epoxide, polychlorinated biphenyl 77 or trans-nonachlor, among others. The high classification performance of all five models confirmed that machine learning may be a promising complementary approach in modelling highly correlated exposure biomarkers and their associations with health outcomes. Regularised logistic regression provided a good compromise between the interpretability of traditional statistical approaches and the classification capacity of machine learning approaches. Applying a battery of complementary algorithms may be a strategic approach to decipher complex exposome-health associations when the underlying structure is unknown.

Effect of Salt Reduction on Children's Acceptance of Bread.

Salt reduction is becoming a major concern for public authorities, especially in cereal products. As childhood is important for the development of healthy eating habits, this study aimed to formulate salt-reduced breads with satisfying sensory properties for children. Sourdough and an artisanal bread-making process were used to compensate the flavor loss due to salt reduction. French breads (FBs) made with sourdough and artisanal processing were compared with white breads (WBs). Two salt levels were applied (1.2 and 1.8 g /100 g flour). To determine their acceptability and characterization, the four breads were assessed (i) by an adult panel (n = 39) according to cohesiveness, overall odor intensity, overall aroma in the mouth and saltiness intensity and (ii) a panel of children (n = 100, aged 6 to 11 years) according to overall liking and saltiness intensity. Finally, consumption by children (n = 89, aged 6 to 11 years) was measured during school lunch to evaluate the acceptability of salt reduction in a real consumption context. Both formulation and salt level induced physical and sensory changes in breads perceived by adults. They described WB as less dense, cohesive, and aromatic but more odorant than FB. Saltiness differences were perceived by adults but not by children. Children showed a preference for the saltiest breads and the FB but these drivers of preference were not confirmed during consumption measurements. These results shed new light on how natural solutions to enhance the flavor of bread can reduce its salt level while maintaining acceptability. PRACTICAL APPLICATION: Salt reduction in bread could be compensated by the use of sourdough and an artisanal bread-making process. These methods allow an improvement of the nutritional quality of breads while maintaining their acceptance by young consumers by favoring the development of appealing organoleptic characteristics (aroma, texture). These methods are natural, easy to implement, and could be adapted to other fermented products in order to improve their nutritional quality.

Breast Milk Lipidome Is Associated with Early Growth Trajectory in Preterm Infants.

Human milk is recommended for feeding preterm infants. The current pilot study aims to determine whether breast-milk lipidome had any impact on the early growth-pattern of preterm infants fed their own mother's milk. A prospective-monocentric-observational birth-cohort was established, enrolling 138 preterm infants, who received their own mother's breast-milk throughout hospital stay. All infants were ranked according to the change in weight Z-score between birth and hospital discharge. Then, we selected infants who experienced "slower" (n = 15, -1.54 ± 0.42 Z-score) or "faster" (n = 11, -0.48 ± 0.19 Z-score) growth; as expected, although groups did not differ regarding gestational age, birth weight Z-score was lower in the "faster-growth" group (0.56 ± 0.72 vs. -1.59 ± 0.96). Liquid chromatography-mass spectrometry lipidomic signatures combined with multivariate analyses made it possible to identify breast-milk lipid species that allowed clear-cut discrimination between groups. Validation of the selected biomarkers was performed using multidimensional statistical, false-discovery-rate and ROC (Receiver Operating Characteristic) tools. Breast-milk associated with faster growth contained more medium-chain saturated fatty acid and sphingomyelin, dihomo-γ-linolenic acid (DGLA)-containing phosphethanolamine, and less oleic acid-containing triglyceride and DGLA-oxylipin. The ability of such biomarkers to predict early-growth was validated in presence of confounding clinical factors but remains to be ascertained in larger cohort studies.

Hierarchical classification of microorganisms based on high-dimensional phenotypic data.

The classification of microorganisms by high-dimensional phenotyping methods such as FTIR spectroscopy is often a complicated process due to the complexity of microbial phylogenetic taxonomy. A hierarchical structure developed for such data can often facilitate the classification analysis. The hierarchical tree structure can either be imposed to a given set of phenotypic data by integrating the phylogenetic taxonomic structure or set up by revealing the inherent clusters in the phenotypic data. In this study, we wanted to compare different approaches to hierarchical classification of microorganisms based on high-dimensional phenotypic data. A set of 19 different species of molds (filamentous fungi) obtained from the mycological strain collection of the Norwegian Veterinary Institute (Oslo, Norway) is used for the study. Hierarchical cluster analysis is performed for setting up the classification trees. Classification algorithms such as artificial neural networks (ANN), partial least-squared discriminant analysis and random forest (RF) are used and compared. The 2 methods ANN and RF outperformed all the other approaches even though they did not utilize predefined hierarchical structure. To our knowledge, the RF approach is used here for the first time to classify microorganisms by FTIR spectroscopy.