Breast milk immune composition varies during the transition stage of lactation: characterization of immunotypes in the MAMI cohort.

Background: Breast milk is a complex and dynamic fluid needed for infant development and protection due to its content of bioactive factors such as immunoglobulins (Igs). Most studies focus primarily on IgA, but other types of Ig and even other immune components (cytokines and adipokines) may also play significant roles in neonatal health. As a first step, we aimed to characterize the Ig profile, many cytokines, and two adipokines (leptin and adiponectin) at two sampling time points within the transitional stage, which is the least studied phase in terms of these components. The secondary objective was to identify different breast milk immunotypes in the MAMI cohort substudy, and finally, we further aimed at analyzing maternal and infant characteristics to identify influencing factors of breast milk immune composition. Methods: Breast milk samples from 75 mothers were studied between days 7 and 15 postpartum. The Igs, cytokines, and adipokine levels were determined by a multiplex approach, except for the IgA, IgM, and leptin that were evaluated by ELISA. Results: IgA, IgM, IgE, IgG2, IL-1ß, IL-5, IL-6, IL-10, and IL-17 were significantly higher on day 7 with respect to day 15. The multiple factor analysis (MFA) allowed us to identify two maternal clusters (immunotypes) depending on the breast milk immune profile evolution from day 7 to day 15, mainly due to the IgE and IgG subtypes, but not for IgA and IgM, which always presented higher levels early in time. Conclusion: All these results demonstrated the importance of the dynamics of the breast milk composition in terms of immune factors because even in the same lactation stage, a difference of 1 week has induced changes in the breast milk immune profile. Moreover, this immune profile does not evolve in the same way for all women. The dynamic compositional changes may be maternal-specific, as we observed differences in parity and exclusive breastfeeding between the two BM immunotype groups, which could potentially impact infant health.

Discovery and classification of complex multimorbidity patterns: unravelling chronicity networks and their social profiles.

Multimorbidity can be defined as the presence of two or more chronic diseases in an individual. This condition is associated with reduced quality of life, increased disability, greater functional impairment, increased health care utilisation, greater fragmentation of care and complexity of treatment, and increased mortality. Thus, understanding its epidemiology and inherent complexity is essential to improve the quality of life of patients and to reduce the costs associated with multi-pathology. In this paper, using data from the European Health Survey, we explore the application of Mixed Graphical Models and its combination with social network analysis techniques for the discovery and classification of complex multimorbidity patterns. The results obtained show the usefulness and versatility of this approach for the study of multimorbidity based on the use of graphs, which offer the researcher a holistic view of the relational structure of data with variables of different types and high dimensionality.

The relevance of urolithins-based metabotyping for assessing the effects of a polyphenol-rich dietary intervention on intestinal permeability: A post-hoc analysis of the MaPLE trial.

A polyphenol-rich diet reduced intestinal permeability (IP) in older adults. Our aim was to evaluate if participants categorized according to urolithin metabotypes (UMs) exhibited different responses in the MaPLE trial. Fifty-one older adults (mean age: 78 years) completed an 8-week randomized-controlled-crossover trial comparing the effects of a polyphenol-rich vs. a control diet on IP, assessed through zonulin levels. Plasma and urinary metabolomics were evaluated with a semi-targeted UHPLC-MS/MS method. Gut microbiota was characterized by 16S rRNA gene profiling. UMs were determined according to urolithin excretion in 24 h urine samples. Multivariate statistics were used to characterize the differences in metabolomic and metataxonomic responses across UMs. Thirty-three participants were classified as urolithin metabotype A (UMA), 13 as urolithin metabotype B (UMB), and 5 as urolithin metabotype 0 (UM0) according to their urinary excretion of urolithins. Clinical, dietary, and biochemical characteristics at baseline were similar between UMs (all p > 0.05). After the polyphenol-rich diet, UMB vs. UMA participants showed a 2-fold higher improvement of zonulin levels (p for interaction = 0.033). Moreover, UMB vs. UMA participants were characterized for alterations in fatty acid metabolism, kynurenine pathway of tryptophan catabolism, and microbial metabolization of phenolic acids. These changes were correlated with the reduction of zonulin levels and modifications of gut microbes (increased Clostridiales, including, R. lactaris, and G. formicilis). In conclusion, urolithin-based metabotyping identified older adults with a higher improvement of IP after a polyphenol-rich diet. Our results reinforce the concept that UMs may contribute to tailor personalized nutrition interventions.

Machine Learning Representation of Loss of Eye Regularity in a Drosophila Neurodegenerative Model.

The fruit fly compound eye is a premier experimental system for modeling human neurodegenerative diseases. The disruption of the retinal geometry has been historically assessed using time-consuming and poorly reliable techniques such as histology or pseudopupil manual counting. Recent semiautomated quantification approaches rely either on manual region-of-interest delimitation or engineered features to estimate the extent of degeneration. This work presents a fully automated classification pipeline of bright-field images based on orientated gradient descriptors and machine learning techniques. An initial region-of-interest extraction is performed, applying morphological kernels and Euclidean distance-to-centroid thresholding. Image classification algorithms are trained on these regions (support vector machine, decision trees, random forest, and convolutional neural network), and their performance is evaluated on independent, unseen datasets. The combinations of oriented gradient + gaussian kernel Support Vector Machine [0.97 accuracy and 0.98 area under the curve (AUC)] and fine-tuned pre-trained convolutional neural network (0.98 accuracy and 0.99 AUC) yielded the best results overall. The proposed method provides a robust quantification framework that can be generalized to address the loss of regularity in biological patterns similar to the Drosophila eye surface and speeds up the processing of large sample batches.

Metabolic Signature of a Functional High-Catechin Tea after Acute and Sustained Consumption in Healthy Volunteers through 1H NMR Based Metabolomics Analysis of Urine.

Functional tea beverages have emerged as a novel approach to achieving health benefits associated with tea. The use of metabolomics may improve the evaluation of their consumption and their effects. The current study aimed at exploring the urinary signature of exposure to a functional high-catechin tea (HCT) using untargeted NMR-based metabolomics. Ten volunteers participated in a crossover intervention study. Individuals consumed an HCT or a control beverage over a period of 28 days. Multilevel partial least-squares discriminant analysis (ML-PLS-DA) was used for paired comparisons. A further crossover model was performed to assess the significant changes. The consumption of the HCT resulted in the excretion of theanine, epicatechin, pyrogallol sulfate, and higher levels of 3-methyl-2-oxovalerate and succinate, as well as unknown compounds. In conclusion, the present work established novel urinary signatures of a functional drink. Such signatures may be potential biomarkers and/or reflect certain benefits of functional tea beverages.

Cannabis-based medicine reduces multiple pathological processes in AßPP/PS1 mice.

Several recent findings suggest that targeting the endogenous cannabinoid system can be considered as a potential therapeutic approach to treat Alzheimer's disease (AD). The present study supports this hypothesis demonstrating that delta-9-tetrahydrocannabinol (THC) or cannabidiol (CBD) botanical extracts, as well as the combination of both natural cannabinoids, which are the components of an already approved cannabis-based medicine, preserved memory in AßPP/PS1 transgenic mice when chronically administered during the early symptomatic stage. Moreover, THC + CBD reduced learning impairment in AßPP/PS1 mice. A significant decrease in soluble Aß42 peptide levels and a change in plaques composition were also observed in THC + CBD-treated AßPP/PS1 mice, suggesting a cannabinoid-induced reduction in the harmful effect of the most toxic form of the Aß peptide. Among the mechanisms related with these positive cognitive effects, the anti-inflammatory properties of cannabinoids may also play a relevant role. Here we observed reduced astrogliosis, microgliosis, and inflammatory-related molecules in treated AßPP/PS1 mice, which were more marked after treatment with THC + CBD than with either THC or CBD. Moreover, other cannabinoid-induced effects were uncovered by a genome-wide gene expression study. Thus, we have identified the redox protein thioredoxin 2 and the signaling protein Wnt16 as significant substrates for the THC + CBD-induced effects in our AD model. In summary, the present findings show that the combination of THC and CBD exhibits a better therapeutic profile than each cannabis component alone and support the consideration of a cannabis-based medicine as potential therapy against AD.