Nasal and skin microbiomes are associated with disease severity in paediatric atopic dermatitis.

BACKGROUND: Alterations of the skin microbiome have been associated with atopic dermatitis (AD) and its severity. The nasal microbiome in relation to AD severity is less well studied. OBJECTIVES: We aimed to characterize the nasal and skin microbiomes in children with AD in relation to disease severity. In addition, we explored the differences and correlations between the nasal and skin communities. METHODS: We characterized the microbial composition of 90 nasal and 108 lesional skin samples cross-sectionally from patients with AD, using 16S-rRNA sequencing. In addition, a quantitative polymerase chain reaction was performed for Staphylococcus aureus and Staphylococcus epidermidis on the skin samples, and AD severity was estimated using the self-administered Eczema Area and Severity Index. RESULTS: We found an association between the microbial composition and AD severity in both the nose and skin samples (R2  = 2·6%; P = 0·017 and R2  = 7·0%; P = 0·004), strongly driven by staphylococci. However, other species also contributed, such as Moraxella in the nose. Skin lesions were positive for S. aureus in 50% of the children, and the presence and the load of S. aureus were not associated with AD severity. Although the nose and skin harbour distinct microbial communities (n = 48 paired samples; P < 0·001), we found that correlations exist between species in the nose and (other) species on the skin. CONCLUSIONS: Our results indicate that both the nasal and the skin microbiomes are associated with AD severity in children and that, next to staphylococci, other species contribute to this association.

Ranges of phenotypic flexibility in healthy subjects.

BACKGROUND: A key feature of metabolic health is the ability to adapt upon dietary perturbations. A systemic review defined an optimal nutritional challenge test, the "PhenFlex test" (PFT). Recently, it has been shown that the PFT enables the quantification of all relevant metabolic processes involved in maintaining or regaining homeostasis of metabolic health. Furthermore, it was demonstrated that quantification of PFT response was more sensitive as compared to fasting markers in demonstrating reduced phenotypic flexibility in metabolically impaired type 2 diabetes subjects. METHODS: This study aims to demonstrate that quantification of PFT response can discriminate between different states of health within the healthy range of the population. Therefore, 100 healthy subjects were enrolled (50 males, 50 females) ranging in age (young, middle, old) and body fat percentage (low, medium, high), assuming variation in phenotypic flexibility. Biomarkers were selected to quantify main processes which characterize phenotypic flexibility in response to PFT: flexibility in glucose, lipid, amino acid and vitamin metabolism, and metabolic stress. Individual phenotypic flexibility was visualized using the "health space" by representing the four processes on the health space axes. By quantifying and presenting the study subjects in this space, individual phenotypic flexibility was visualized. RESULTS: Using the "health space" visualization, differences between groups as well as within groups from the healthy range of the population can be easily and intuitively assessed. The health space showed a different adaptation to the metabolic PhenFlex test in the extremes of the recruited population; persons of young age with low to normal fat percentage had a markedly different position in the health space as compared to persons from old age with normal to high fat percentage. CONCLUSION: The results of the metabolic PhenFlex test in conjunction with the health space reliably assessed health on an individual basis. This quantification can be used in the future for personalized health quantification and advice.