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Background: Interactions between the skin barrier, immune system, and microbiome underlie the development of atopic dermatitis (AD). Objective: To investigate the skin and nasal microbiome in relation to filaggrin gene (FLG) mutations. Methods: A cross-sectional study including 77 children with difficult-to-treat AD. The entire encoding region of FLG was screened for mutations using single molecule molecular inversion probes and next-generation sequencing. Bacterial swabs from the anterior nares, lesional and nonlesional skin were analyzed using 16S rRNA sequencing. For skin samples, additional qPCR was performed for Staphylococcus aureus and Staphylococcus epidermidis. Results: The prevalence of patients with a mutation in FLG was 40%, including 10 different mutations. Analyzing bacterial swabs from all three niches showed a significant effect for both niche and FLG mutation status on the overall microbiome composition. Using a subset analysis to test the effect of FLG mutation status per niche separately did not show a significant association to the microbiome. Shannon diversity and S. aureus abundance were significantly affected by the niche, but not by the presence of an FLG mutation. Conclusions: Our results suggest only a minor role for FLG mutation status on the overall microbiome, which is rather caused by differences in the present genera than by microbe richness and evenness.

Introduction A distinctive gut microbiome have been linked to type 2 diabetes mellitus (T2DM). We aimed to evaluate whether gut microbiota composition, in addition to clinical biomarkers, could improve the prediction of new incident cases of diabetes in patients with coronary heart disease. Methods All the patients from the CORDIOPREV (Clinical Trials.gov.Identifier: NCT00924937) study without T2DM at baseline were included (n = 462). Overall, 107 patients developed it after a median of 60 months. The gut microbiota composition was determined by 16S rRNA gene sequencing and predictive models were created using hold-out method. Results A gut microbiota profile associated with T2DM development was determined through a microbiome-based predictive model. The addition of microbiome data to clinical parameters (variables included in FINDRISC risk score and the diabetes risk score of the American Diabetes Association, HDL, triglycerides and HbA1c) improved the prediction increasing the area under the curve from 0.632 to 0.946. Furthermore, a microbiome-based risk score including the ten most discriminant genera, was associated with the probability of develop T2DM. Conclusion These results suggest that a microbiota profile is associated to the T2DM development. An integrate predictive model of microbiome and clinical data that can improve the prediction of T2DM is also proposed, if is validated in independent populations to prevent this disease.

Introduction A distinctive gut microbiome have been linked to type 2 diabetes mellitus (T2DM). We aimed to evaluate whether gut microbiota composition, in addition to clinical biomarkers, could improve the prediction of new incident cases of diabetes in patients with coronary heart disease. Methods All the patients from the CORDIOPREV (Clinical Trials.gov.Identifier: NCT00924937) study without T2DM at baseline were included (n = 462). Overall, 107 patients developed it after a median of 60 months. The gut microbiota composition was determined by 16S rRNA gene sequencing and predictive models were created using hold-out method. Results A gut microbiota profile associated with T2DM development was determined through a microbiome-based predictive model. The addition of microbiome data to clinical parameters (variables included in FINDRISC risk score and the diabetes risk score of the American Diabetes Association, HDL, triglycerides and HbA1c) improved the prediction increasing the area under the curve from 0.632 to 0.946. Furthermore, a microbiome-based risk score including the ten most discriminant genera, was associated with the probability of develop T2DM. Conclusion These results suggest that a microbiota profile is associated to the T2DM development. An integrate predictive model of microbiome and clinical data that can improve the prediction of T2DM is also proposed, if is validated in independent populations to prevent this disease.

Introduction A distinctive gut microbiome have been linked to type 2 diabetes mellitus (T2DM). We aimed to evaluate whether gut microbiota composition, in addition to clinical biomarkers, could improve the prediction of new incident cases of diabetes in patients with coronary heart disease. Methods All the patients from the CORDIOPREV (Clinical Trials.gov.Identifier: NCT00924937) study without T2DM at baseline were included (n = 462). Overall, 107 patients developed it after a median of 60 months. The gut microbiota composition was determined by 16S rRNA gene sequencing and predictive models were created using hold-out method. Results A gut microbiota profile associated with T2DM development was determined through a microbiome-based predictive model. The addition of microbiome data to clinical parameters (variables included in FINDRISC risk score and the diabetes risk score of the American Diabetes Association, HDL, triglycerides and HbA1c) improved the prediction increasing the area under the curve from 0.632 to 0.946. Furthermore, a microbiome-based risk score including the ten most discriminant genera, was associated with the probability of develop T2DM. Conclusion These results suggest that a microbiota profile is associated to the T2DM development. An integrate predictive model of microbiome and clinical data that can improve the prediction of T2DM is also proposed, if is validated in independent populations to prevent this disease.

Introduction A distinctive gut microbiome have been linked to type 2 diabetes mellitus (T2DM). We aimed to evaluate whether gut microbiota composition, in addition to clinical biomarkers, could improve the prediction of new incident cases of diabetes in patients with coronary heart disease. Methods All the patients from the CORDIOPREV (Clinical Trials.gov.Identifier: NCT00924937) study without T2DM at baseline were included (n = 462). Overall, 107 patients developed it after a median of 60 months. The gut microbiota composition was determined by 16S rRNA gene sequencing and predictive models were created using hold-out method. Results A gut microbiota profile associated with T2DM development was determined through a microbiome-based predictive model. The addition of microbiome data to clinical parameters (variables included in FINDRISC risk score and the diabetes risk score of the American Diabetes Association, HDL, triglycerides and HbA1c) improved the prediction increasing the area under the curve from 0.632 to 0.946. Furthermore, a microbiome-based risk score including the ten most discriminant genera, was associated with the probability of develop T2DM. Conclusion These results suggest that a microbiota profile is associated to the T2DM development. An integrate predictive model of microbiome and clinical data that can improve the prediction of T2DM is also proposed, if is validated in independent populations to prevent this disease.

Introduction A distinctive gut microbiome have been linked to type 2 diabetes mellitus (T2DM). We aimed to evaluate whether gut microbiota composition, in addition to clinical biomarkers, could improve the prediction of new incident cases of diabetes in patients with coronary heart disease. Methods All the patients from the CORDIOPREV (Clinical Trials.gov.Identifier: NCT00924937) study without T2DM at baseline were included (n = 462). Overall, 107 patients developed it after a median of 60 months. The gut microbiota composition was determined by 16S rRNA gene sequencing and predictive models were created using hold-out method. Results A gut microbiota profile associated with T2DM development was determined through a microbiome-based predictive model. The addition of microbiome data to clinical parameters (variables included in FINDRISC risk score and the diabetes risk score of the American Diabetes Association, HDL, triglycerides and HbA1c) improved the prediction increasing the area under the curve from 0.632 to 0.946. Furthermore, a microbiome-based risk score including the ten most discriminant genera, was associated with the probability of develop T2DM. Conclusion These results suggest that a microbiota profile is associated to the T2DM development. An integrate predictive model of microbiome and clinical data that can improve the prediction of T2DM is also proposed, if is validated in independent populations to prevent this disease.

Introduction A distinctive gut microbiome have been linked to type 2 diabetes mellitus (T2DM). We aimed to evaluate whether gut microbiota composition, in addition to clinical biomarkers, could improve the prediction of new incident cases of diabetes in patients with coronary heart disease. Methods All the patients from the CORDIOPREV (Clinical Trials.gov.Identifier: NCT00924937) study without T2DM at baseline were included (n = 462). Overall, 107 patients developed it after a median of 60 months. The gut microbiota composition was determined by 16S rRNA gene sequencing and predictive models were created using hold-out method. Results A gut microbiota profile associated with T2DM development was determined through a microbiome-based predictive model. The addition of microbiome data to clinical parameters (variables included in FINDRISC risk score and the diabetes risk score of the American Diabetes Association, HDL, triglycerides and HbA1c) improved the prediction increasing the area under the curve from 0.632 to 0.946. Furthermore, a microbiome-based risk score including the ten most discriminant genera, was associated with the probability of develop T2DM. Conclusion These results suggest that a microbiota profile is associated to the T2DM development. An integrate predictive model of microbiome and clinical data that can improve the prediction of T2DM is also proposed, if is validated in independent populations to prevent this disease.

Introduction A distinctive gut microbiome have been linked to type 2 diabetes mellitus (T2DM). We aimed to evaluate whether gut microbiota composition, in addition to clinical biomarkers, could improve the prediction of new incident cases of diabetes in patients with coronary heart disease. Methods All the patients from the CORDIOPREV (Clinical Trials.gov.Identifier: NCT00924937) study without T2DM at baseline were included (n = 462). Overall, 107 patients developed it after a median of 60 months. The gut microbiota composition was determined by 16S rRNA gene sequencing and predictive models were created using hold-out method. Results A gut microbiota profile associated with T2DM development was determined through a microbiome-based predictive model. The addition of microbiome data to clinical parameters (variables included in FINDRISC risk score and the diabetes risk score of the American Diabetes Association, HDL, triglycerides and HbA1c) improved the prediction increasing the area under the curve from 0.632 to 0.946. Furthermore, a microbiome-based risk score including the ten most discriminant genera, was associated with the probability of develop T2DM. Conclusion These results suggest that a microbiota profile is associated to the T2DM development. An integrate predictive model of microbiome and clinical data that can improve the prediction of T2DM is also proposed, if is validated in independent populations to prevent this disease.

Background: Atopic dermatitis (AD) is a chronic inflammatory skin disease that affects 10 to 20% of children and between 2 and 15% of the adults in Western Europe. Since 2000, therapeutic clothing or functional textiles based on silver or chitosan as antibacterial agents were introduced for AD. These agents aim to reduce skin colonization with Staphylococcus (S.) aureus. Increased colonization with S. aureus is correlated with increased AD severity. The antimicrobial effects of silver and chitosan have been demonstrated before. At this point, there is insufficient evidence for the effectiveness of antibacterial therapeutic clothing in patients with AD. Methods: This is a pragmatic randomized controlled double-blind multi-center trial comparing the effectiveness of antibacterial therapeutic clothing based on silver or chitosan as compared with non-antibacterial therapeutic clothing in patients with moderate to severe AD. A total of 165 participants, aged 0 to 80, diagnosed with moderate to severe AD are included. The study is performed in the Erasmus MC University Medical Center, University Medical Center Groningen, University Medical Center Utrecht, Amsterdam University Medical Centers, and St. Antonius Hospital Nieuwegein. Patients will be randomized 1:1:1 into one of the three intervention groups: group A will receive therapeutic clothing without antimicrobial agents, group B will receive microbial growth reducing therapeutic clothing based on chitosan, and group C will receive antimicrobial clothing based on silver. All therapeutic clothing is to be worn at night during the 12-month intervention period. Usual care is continued. The primary objective is to assess the effectiveness of antibacterial clothing (silver and chitosan group) as compared to non-antibacterial clothing assessed with the Eczema Area and Severity Index at 12 months compared to baseline. Secondary outcomes include between-group differences in physician- and patient-reported outcome measures

Human milk oligosaccharides (HMO) and prebiotic oligosaccharides are proposed to confer several health benefits to the infant. They shape the microbiota, have anti-inflammatory properties, and support epithelial barrier functioning. However, in order to select the best oligosaccharides for inclusion in infant formulas, there is a need to increase our understanding of the specific effects of HMO and prebiotics on the host immune system. Therefore, we investigated the effects of the HMO sialyllactose (SL), and galactooligosaccharides (GOS) on epithelial barrier functioning, microbiota composition, and SCFA production. The effect of GOS and SL on epithelial barrier functioning and microbiota composition was investigated using in vitro models. Epithelial barrier function was investigated by transcriptome analysis of fully polarized Caco-2 cells exposed for 6 h to SL or GOS. In addition, epithelial cell growth, alkaline phosphatase production, and re-epithelization was studied. Further, we investigated the effect of SL and GOS on microbiota composition and SCFA production using in vitro fecal batch cultures. Transcriptome analysis showed that SL and GOS both induced pathways that regulate cell cycle control. This gene-expression profile translated to a phenotype of halted proliferation and included the induction of alkaline phosphatase activity, a marker of epithelial cell differentiation. SL and GOS also promoted re-epithelialization in an in vitro epithelial wound repair assay. SL and GOS did show distinct modulation of microbiota composition, promoting the outgrowth of Bacteroides and bifidobacteria, respectively, which resulted in distinct changes in SCFA production profiles. Our results show that SL and GOS can both modulate epithelial barrier function by inducing differentiation and epithelial wound repair, but differentially promote the growth of specific genera in the microbiota, which is associated with differential changes in SCFA profilesIntroduction

[This corrects the article DOI: 10.3389/fimmu.2019.00094.].