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Host and environment early-life risk factors are associated with progression of wheezing symptoms over time; however, their individual contribution is relatively small. We hypothesised that the dynamic interactions of these factors with an infant's developing respiratory system are the dominant factor for subsequent wheeze and asthma.

IMPORTANCE: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease and is driven by a complex pathophysiology underlying highly heterogeneous phenotypes. Current advances in precision medicine emphasize the need for stratification. OBJECTIVE: To perform deep phenotyping and identification of severity-associated factors in adolescent and adult patients with AD. DESIGN, SETTING, AND PARTICIPANTS: Cross-sectional data from the baseline visit of a prospective longitudinal study investigating the phenotype among inpatients and outpatients with AD from the Department of Dermatology and Allergy of the University Hospital Bonn enrolled between November 2016 and February 2020. MAIN OUTCOMES AND MEASURES: Patients were stratified by severity groups using the Eczema Area and Severity Index (EASI). The associations of 130 factors with AD severity were analyzed applying a machine learning–gradient boosting approach with cross-validation–based tuning as well as multinomial logistic regression. RESULTS: A total of 367 patients (157 male [42.8%]; mean [SD] age, 39 [17] years; 94% adults) were analyzed. Among the participants, 177 (48.2%) had mild disease (EASI ≤7), 120 (32.7%) had moderate disease (EASI >7 and ≤ 21), and 70 (19.1%) had severe disease (EASI >21). Atopic stigmata (cheilitis: odds ratio [OR], 8.10; 95% CI, 3.35-10.59; white dermographism: OR, 4.42; 95% CI, 1.68-11.64; Hertoghe sign: OR, 2.75; 95% CI, 1.27-5.93; nipple eczema: OR, 4.97; 95% CI, 1.56-15.78) was associated with increased probability of severe AD, while female sex was associated with reduced probability (OR, 0.30; 95% CI, 0.13-0.66). The probability of severe AD was associated with total serum immunoglobulin E levels greater than 1708 IU/mL and eosinophil values greater than 6.8%. Patients aged 12 to 21 years or older than 52 years had an elevated probability of severe AD; patients aged 22 to 51 years had an elevated probability of mild AD. Age at AD onset older than 12 years was associated with increased probability of severe AD up to a peak at 30 years; age at onset older than 33 years was associated with moderate to severe AD; and childhood onset was associated with mild AD (peak, 7 years). Lifestyle factors associated with severe AD were physical activity less than once per week and (former) smoking. Alopecia areata was associated with moderate (OR, 5.23; 95% CI, 1.53-17.88) and severe (OR, 4.67; 95% CI, 1.01-21.56) AD. Predictive performance of machine learning–gradient boosting vs multinomial logistic regression differed only slightly (mean multiclass area under the curve value: 0.71 [95% CI, 0.69-0.72] vs 0.68 [0.66-0.70], respectively). CONCLUSIONS AND RELEVANCE: The associations found in this cross-sectional study among patients with AD might contribute to a deeper disease understanding, closer monitoring of predisposed patients, and personalized prevention and therapy.

Growing up on a farm is associated with an asthma-protective effect, but the mechanisms underlying this effect are largely unknown. In the Protection against Allergy: Study in Rural Environments (PASTURE) birth cohort, we modeled maturation using 16S rRNA sequence data of the human gut microbiome in infants from 2 to 12 months of age. The estimated microbiome age (EMA) in 12-month-old infants was associated with previous farm exposure (ß?=?0.27 (0.12–0.43), P?=?0.001, n?=?618) and reduced risk of asthma at school age (odds ratio (OR)?=?0.72 (0.56–0.93), P?=?0.011). EMA mediated the protective farm effect by 19%. In a nested case–control sample (n?=?138), we found inverse associations of asthma with the measured level of fecal butyrate (OR?=?0.28 (0.09–0.91), P?=?0.034), bacterial taxa that predict butyrate production (OR?=?0.38 (0.17–0.84), P?=?0.017) and the relative abundance of the gene encoding butyryl–coenzyme A (CoA):acetate–CoA-transferase, a major enzyme in butyrate metabolism (OR?=?0.43 (0.19–0.97), P?=?0.042). The gut microbiome may contribute to asthma protection through metabolites, supporting the concept of a gut–lung axis in humans.

ABSTRACTPrevious studies indicated an intrinsic relationship between infant diet, intestinal microbiota composition and fermentation activity with a strong focus on the role of breastfeeding on microbiota composition. Yet, microbially formed short-chain fatty acids acetate, propionate and butyrate and other fermentation metabolites such as lactate not only act as substrate for bacterial cross-feeding and as mediators in microbe–host interactions but also confer antimicrobial activity, which has received considerably less attention in the past research. It was the aim of this study to investigate the nutritional–microbial interactions that contribute to the development of infant gut microbiota with a focus on human milk oligosaccharide (HMO) fermentation. Infant fecal microbiota composition, fermentation metabolites and milk composition were analyzed from 69 mother-infant pairs of the Swiss birth cohort Childhood AlleRgy nutrition and Environment (CARE) at three time points depending on breastfeeding status defined at the age of 4 months, using quantitative microbiota profiling, HPLC-RI and 1H-NMR. We conducted in vitrofermentations in the presence of HMO fermentation metabolites and determined the antimicrobial activity of lactate and acetate against major Clostridiaceaeand Peptostreptococcaceaerepresentatives. Our data show that fucosyllactose represented 90% of the HMOs present in breast milk at 1- and 3-months post-partum with fecal accumulation of fucose, 1,2-propanediol and lactate indicating fermentation of HMOs that is likely driven by Bifidobacterium. Concurrently, there was a significantly lower absolute abundance of Peptostreptococcaceaein feces of exclusively breastfed infants at 3 months. In vitro, lactate inhibited strains of Peptostreptococcaceae. Taken together, this study not only identified breastfeeding dependent fecal microbiota and metabolite profiles but suggests that HMO-derived fermentation metabolites might exert an inhibitory effect against selected gut microbes.

RationaleAsthma is characterised by inflammation and reversible airway obstruction. However, these features are not always closely related. Fluctuations of daily lung function contain information on asthma phenotypes, exacerbation risk and response to long-acting β-agonists.ObjectivesIn search of subgroups of asthmatic participants with specific lung functional features, we developed and validated a novel clustering approach to asthma phenotyping, which exploits the information contained within the fluctuating behaviour of twice-daily lung function measurements.MethodsForced expiratory volume during the first second (FEV1) and peak expiratory flow (PEF) were prospectively measured over 4 weeks in 696 healthy and asthmatic school children (Protection Against Allergy – Study in Rural Environments (PASTURE)/EFRAIM cohort), and over 1 year in 138 asthmatic adults with mild-to-moderate or severe asthma (Pan-European Longitudinal Assessment of Clinical Course and BIOmarkers in Severe Chronic AIRway Disease (BIOAIR) cohort). Using enrichment analysis, we explored whether the method identifies clinically meaningful, distinct clusters of participants with different lung functional fluctuation patterns.Measurements and main resultsIn the PASTURE/EFRAIM dataset, we found four distinct clusters. Two clusters were enriched in children with well-known clinical characteristics of asthma. In cluster 3, children from a farming environment predominated, whereas cluster 4 mainly consisted of healthy controls. About 79% of cluster 3 carried the asthma-risk allele rs7216389 of the 17q21locus. In the BIOAIR dataset, we found two distinct clusters clearly discriminating between individuals with mild-to-moderate and severe asthma.ConclusionsOur method identified dynamic functional asthma and healthy phenotypes, partly independent of atopy and inflammation but related to genetic markers on the 17q21locus. The method can be used for disease phenotyping and possibly endotyping. It may identify participants with specific functional abnormalities, potentially needing a different therapeutic approach.

IMPORTANCE: Atopic dermatitis is an inflammatory, pruritic skin disease that often occurs in early infancy with a chronic course. However, a specific description of subtypes of atopic dermatitis depending on the timing of onset and progression of the disease in childhood is lacking. OBJECTIVE: To identify different phenotypes of atopic dermatitis using a definition based on symptoms before age 6 years and to determine whether some subtypes are more at risk for developing other allergic diseases. DESIGN, SETTING, AND PARTICIPANTS: The Protection Against Allergy Study in Rural Environments (PASTURE) is a European birth cohort where pregnant women were recruited between August 2002 and March 2005 and divided in 2 groups dependent on whether they lived on a farm. Children from this cohort with data on atopic dermatitis from birth to 6 years of age were included. EXPOSURES: Atopic dermatitis, defined as an itchy rash on typical locations from birth to 6 years. MAIN OUTCOMES AND MEASURES: The latent class analysis was used to identify subtypes of atopic dermatitis in childhood based on the course of symptoms. Multivariable logistic regressions were used to analyze the association between atopic dermatitis phenotypes and other allergic diseases. RESULTS: We included 1038 children; of these, 506 were girls. The latent class analysis model with the best fit to PASTURE data separated 4 phenotypes of atopic dermatitis in childhood: 2 early phenotypes with onset before age 2 years (early transient [n = 96; 9.2%] and early persistent [n = 67; 6.5%]), the late phenotype with onset at age 2 years or older (n = 50; 4.8%), and the never/infrequent phenotype (n = 825; 79.5%), defined as children with no atopic dermatitis. Children with both parents with history of allergies were 5 times more at risk to develop atopic dermatitis with an early-persistent phenotype compared with children with parents with no history of allergies. Both early phenotypes were strongly associated with food allergy. The risk of developing asthma was significantly increased among the early-persistent phenotype (adjusted odds ratio, 2.87; 95% CI, 1.31-6.31). The late phenotype was only positively associated with allergic rhinitis. CONCLUSIONS AND RELEVANCE: Using latent class analysis, 4 phenotypes of atopic dermatitis were identified depending on the onset and course of the disease. The prevalence of asthma and food allergy by 6 years of age was strongly increased among children with early phenotypes (within age 2 years), especially with persistent symptoms. These findings are important for the development of strategies in allergy prevention.