Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease.

One in ten severe acute respiratory syndrome coronavirus 2 infections result in prolonged symptoms termed long coronavirus disease (COVID), yet disease phenotypes and mechanisms are poorly understood1. Here we profiled 368 plasma proteins in 657 participants ≥3 months following hospitalization. Of these, 426 had at least one long COVID symptom and 233 had fully recovered. Elevated markers of myeloid inflammation and complement activation were associated with long COVID. IL-1R2, MATN2 and COLEC12 were associated with cardiorespiratory symptoms, fatigue and anxiety/depression; MATN2, CSF3 and C1QA were elevated in gastrointestinal symptoms and C1QA was elevated in cognitive impairment. Additional markers of alterations in nerve tissue repair (SPON-1 and NFASC) were elevated in those with cognitive impairment and SCG3, suggestive of brain-gut axis disturbance, was elevated in gastrointestinal symptoms. Severe acute respiratory syndrome coronavirus 2-specific immunoglobulin G (IgG) was persistently elevated in some individuals with long COVID, but virus was not detected in sputum. Analysis of inflammatory markers in nasal fluids showed no association with symptoms. Our study aimed to understand inflammatory processes that underlie long COVID and was not designed for biomarker discovery. Our findings suggest that specific inflammatory pathways related to tissue damage are implicated in subtypes of long COVID, which might be targeted in future therapeutic trials.

Association between polymorphisms on chromosome 17q12-q21 and rhinovirus-induced interferon responses.

BACKGROUND: Single nucleotide polymorphisms (SNPs) in genes on chromosome 17q12-q21 are associated with childhood-onset asthma and rhinovirus-induced wheeze. There are few mechanistic data linking chromosome 17q12-q21 to wheezing illness. OBJECTIVE: We investigated whether 17q12-q21 risk alleles were associated with impaired interferon responses to rhinovirus. METHODS: In a population-based birth cohort of European ancestry, we stimulated peripheral blood mononuclear cells with rhinovirus A1 (RV-A1) and rhinovirus A16 (RV-A16) and measured IFN and IFN-induced C-X-C motif chemokine ligand 10 (aka IP10) responses in supernatants. We investigated associations between virus-induced cytokines and 6 SNPs in 17q12-q21. Bayesian profile regression was applied to identify clusters of individuals with different immune response profiles and genetic variants. RESULTS: Five SNPs (in high linkage disequilibrium, r2 ≥ 0.8) were significantly associated with RV-A1-induced IFN-ß (rs9303277, P = .010; rs11557467, P = .012; rs2290400, P = .006; rs7216389, P = .008; rs8079416, P = .005). A reduction in RV-A1-induced IFN-ß was observed among individuals with asthma risk alleles. There were no significant associations for RV-A1-induced IFN-α or CXCL10, or for any RV-A16-induced IFN/CXCL10. Bayesian profile regression analysis identified 3 clusters that differed in IFN-ß induction to RV-A1 (low, medium, high). The typical genetic profile of the cluster associated with low RV-A1-induced IFN-ß responses was characterized by a very high probability of being homozygous for the asthma risk allele for all SNPs. Children with persistent wheeze were almost 3 times more likely to be in clusters with reduced/average RV-A1-induced IFN-ß responses than in the high immune response cluster. CONCLUSIONS: Polymorphisms on chromosome 17q12-q21 are associated with rhinovirus-induced IFN-ß, suggesting a novel mechanism-impaired IFN-ß induction-links 17q12-q21 risk alleles with asthma/wheeze.

Lung function trajectories from school age to adulthood and their relationship with markers of cardiovascular disease risk.

RATIONALE: Lung function in early adulthood is associated with subsequent adverse health outcomes. OBJECTIVES: To ascertain whether stable and reproducible lung function trajectories can be derived in different populations and investigate their association with objective measures of cardiovascular structure and function. METHODS: Using latent profile modelling, we studied three population-based birth cohorts with repeat spirometry data from childhood into early adulthood to identify trajectories of forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC). We used multinomial logistic regression models to investigate early-life predictors of the derived trajectories. We then ascertained the extent of the association between the derived FEV1/FVC trajectories and blood pressure and echocardiographic markers of increased cardiovascular risk and stroke in ~3200 participants at age 24 years in one of our cohorts. RESULTS: We identified four FEV1/FVC trajectories with strikingly similar latent profiles across cohorts (pooled N=6377): above average (49.5%); average (38.3%); below average (10.6%); and persistently low (1.7%). Male sex, wheeze, asthma diagnosis/medication and allergic sensitisation were associated with trajectories with diminished lung function in all cohorts. We found evidence of an increase in cardiovascular risk markers ascertained by echocardiography (including left ventricular mass indexed to height and carotid intima-media thickness) with decreasing FEV1/FVC (with p values for the mean crude effects per-trajectory ranging from 0.10 to p<0.001). In this analysis, we considered trajectories as a pseudo-continuous variable; we confirmed the assumption of linearity in all the regression models. CONCLUSIONS: Childhood lung function trajectories may serve as predictors in the development of not only future lung disease, but also the cardiovascular disease and multimorbidity in adulthood.

Component-specific clusters for diagnosis and prediction of allergic airway diseases.

BACKGROUND: Previous studies which applied machine learning on multiplex component-resolved diagnostics arrays identified clusters of allergen components which are biologically plausible and reflect the sources of allergenic proteins and their structural homogeneity. Sensitization to different clusters is associated with different clinical outcomes. OBJECTIVE: To investigate whether within different allergen component sensitization clusters, the internal within-cluster sensitization structure, including the number of c-sIgE responses and their distinct patterns, alters the risk of clinical expression of symptoms. METHODS: In a previous analysis in a population-based birth cohort, by clustering component-specific (c-s)IgEs, we derived allergen component clusters from infancy to adolescence. In the current analysis, we defined each subject's within-cluster sensitization structure which captured the total number of c-sIgE responses in each cluster and intra-cluster sensitization patterns. Associations between within-cluster sensitization patterns and clinical outcomes (asthma and rhinitis) in early-school age and adolescence were examined using logistic regression and binomial generalized additive models. RESULTS: Intra-cluster sensitization patterns revealed specific associations with asthma and rhinitis (both contemporaneously and longitudinally) that were previously unseen using binary sensitization to clusters. A more detailed description of the subjects' within-cluster c-sIgE responses in terms of the number of positive c-sIgEs and unique sensitization patterns added new information relevant to allergic diseases, both for diagnostic and prognostic purposes. For example, the increase in the number of within-cluster positive c-sIgEs at age 5 years was correlated with the increase in prevalence of asthma at ages 5 and 16 years, with the correlations being stronger in the prediction context (e.g. for the largest 'Broad' component cluster, contemporaneous: r = .28, p = .012; r = .22, p = .043; longitudinal: r = .36, p = .004; r = .27, p = .04). CONCLUSION: Among sensitized individuals, a more detailed description of within-cluster c-sIgE responses in terms of the number of positive c-sIgE responses and distinct sensitization patterns, adds potentially important information relevant to allergic diseases.

Dog ownership in infancy is protective for persistent wheeze in 17q21 asthma-risk carriers.

BACKGROUND: Asthma-associated single nucleotide polymorphisms from large genome-wide association studies only explain a fraction of genetic heritability. Likely causes of the missing heritability include broad phenotype definitions and gene-environment interactions (GxE). The mechanisms underlying GxE in asthma are poorly understood. Previous GxE studies on pet ownership showed discordant results. OBJECTIVES: We sought to study the GxE between the 17q12-21 locus and pet ownership in infancy in relation to wheeze. METHODS: Wheezing classes derived from 5 UK-based birth cohorts (latent class analysis) were used to study GxE between the 17q12-21 asthma-risk variant rs2305480 and dog and cat ownership in infancy, using multinomial logistic regression. A total of 9149 children had both pet ownership and genotype data available. Summary statistics from individual analyses were meta-analyzed. RESULTS: rs2305480 G allele was associated with increased risk of persistent wheeze (additive model odds ratio, 1.37; 95% CI, 1.25-1.51). There was no evidence of an association between dog or cat ownership and wheeze. We found significant evidence of a GxE interaction between rs2305480 and dog ownership (P = 8.3 × 10-4) on persistent wheeze; among dog owners, the G allele was no longer associated with an increased risk of persistent wheeze (additive model odds ratio, 0.95; 95% CI, 0.73-1.24). For those without pets, G allele was associated with increased risk of persistent wheeze (odds ratio, 1.61; 95% CI, 1.40-1.86). Among cat owners, no such dampening of the genetic effect was observed. CONCLUSIONS: Among dog owners, rs2305480 G was no longer associated with an increased risk of persistent wheeze (or asthma). Early-life environmental exposures may therefore attenuate likelihood of asthma in those carrying 17q12-21 risk alleles.

Optimal dose of adrenaline auto-injector for children and young people at risk of anaphylaxis: A phase IV randomized controlled crossover study.

BACKGROUND: Guidelines recommend intramuscular injection of 500 µg adrenaline (epinephrine) for anaphylaxis in teenagers and adults; however, most autoinjectors deliver a maximum 300 µg dose. We evaluated plasma adrenaline levels and cardiovascular parameters (including cardiac output) following self-injection with 300 µg or 500 µg adrenaline in teenagers at risk of anaphylaxis. METHODS: Subjects were recruited to a randomized, single-blind two period crossover trial. Participants received all 3 injections (Emerade® 500 µg, Emerade® 300 µg, Epipen® 0.3 mg) on 2 separate visits (allocated in a randomized block design), at least 28 days apart. Intramuscular injection was confirmed by ultrasound, and heart rate/stroke volume assessed using continuous monitoring. The trial was registered at Clinicaltrials.gov (NCT03366298). RESULTS: Twelve participants (58% male, median 15.4 years) participated; all completed the study. 500 µg injection resulted in a higher and more prolonged peak concentration (p = 0.01) and greater Area-Under-Curve for plasma adrenaline (p < 0.05) compared to 300 µg, with no difference in adverse events. Adrenaline caused a significant increase in heart rate irrespective of dose and device. Unexpectedly, 300 µg adrenaline resulted in a significant increase in stroke volume when delivered with Emerade®, but a negative inotropic effect with Epipen® (p < 0.05). CONCLUSIONS: These data support a 500 µg dose of adrenaline to treat anaphylaxis in individuals >40 kg in the community. The contrasting effects on stroke volume between Epipen® and Emerade®, despite similar peak plasma adrenaline levels, are unexpected. There is an urgent need to better understand differences in pharmacodynamics following adrenaline administration by autoinjector. In the meantime, we recommend adrenaline injection by needle/syringe in the healthcare setting in individuals with anaphylaxis refractory to initial treatment.

Current state and prospects of artificial intelligence in allergy.

The field of medicine is witnessing an exponential growth of interest in artificial intelligence (AI), which enables new research questions and the analysis of larger and new types of data. Nevertheless, applications that go beyond proof of concepts and deliver clinical value remain rare, especially in the field of allergy. This narrative review provides a fundamental understanding of the core concepts of AI and critically discusses its limitations and open challenges, such as data availability and bias, along with potential directions to surmount them. We provide a conceptual framework to structure AI applications within this field and discuss forefront case examples. Most of these applications of AI and machine learning in allergy concern supervised learning and unsupervised clustering, with a strong emphasis on diagnosis and subtyping. A perspective is shared on guidelines for good AI practice to guide readers in applying it effectively and safely, along with prospects of field advancement and initiatives to increase clinical impact. We anticipate that AI can further deepen our knowledge of disease mechanisms and contribute to precision medicine in allergy.

EAACI guidelines on environmental science in allergic diseases and asthma - Leveraging artificial intelligence and machine learning to develop a causality model in exposomics.

Allergic diseases and asthma are intrinsically linked to the environment we live in and to patterns of exposure. The integrated approach to understanding the effects of exposures on the immune system includes the ongoing collection of large-scale and complex data. This requires sophisticated methods to take full advantage of what this data can offer. Here we discuss the progress and further promise of applying artificial intelligence and machine-learning approaches to help unlock the power of complex environmental data sets toward providing causality models of exposure and intervention. We discuss a range of relevant machine-learning paradigms and models including the way such models are trained and validated together with examples of machine learning applied to allergic disease in the context of specific environmental exposures as well as attempts to tie these environmental data streams to the full representative exposome. We also discuss the promise of artificial intelligence in personalized medicine and the methodological approaches to healthcare with the final AI to improve public health.

Identification of eczema clusters and their association with filaggrin and atopic comorbidities: analysis of five birth cohorts.

BACKGROUND: Longitudinal modelling of the presence/absence of current eczema through childhood has identified similar phenotypes, but their characteristics often differ between studies. OBJECTIVES: To demonstrate that a more comprehensive description of longitudinal pattern of symptoms may better describe trajectories than binary information on eczema presence. METHODS: We derived six multidimensional variables of eczema spells from birth to 18 years of age (including duration, temporal sequencing and the extent of persistence/recurrence). Spells were defined as consecutive observations of eczema separated by no eczema across 5 epochs in five birth cohorts: infancy (first year); early childhood (age 2-3 years); preschool/early school age (4-5 years); middle childhood (8-10 years); adolescence (14-18 years). We applied Partitioning Around Medoids clustering on these variables to derive clusters of the temporal patterns of eczema. We then investigated the stability of the clusters, within-cluster homogeneity and associated risk factors, including FLG mutations. RESULTS: Analysis of 7464 participants with complete data identified five clusters: (i) no eczema (51.0%); (ii) early transient eczema (21.6%); (iii) late-onset eczema (LOE; 8.1%); (iv) intermittent eczema (INT; 7.5%); and (v) persistent eczema (PE; 11.8%). There was very-high agreement between the assignment of individual children into clusters when using complete or imputed (n = 15 848) data (adjusted Rand index = 0.99; i.e. the clusters were very stable). Within-individual symptom patterns across clusters confirmed within-cluster homogeneity, with consistent patterns of symptoms among participants within each cluster and no overlap between the clusters. Clusters were characterized by differences in associations with risk factors (e.g. parental eczema was associated with all clusters apart from LOE; sensitization to inhalant allergens was associated with all clusters, with the highest risk in the PE cluster). All clusters apart from LOE were associated with FLG mutations. Of note, the strongest association was for PE [relative risk ratio (RRR) 2.70, 95% confidence interval (CI) 2.24-3.26; P < 0.001] followed by INT (RRR 2.29, 95% CI 1.82-2.88; P < 0.001). CONCLUSIONS: Clustering of multidimensional variables identified stable clusters with different genetic architectures. Using multidimensional variables may capture eczema development and derive stable and internally homogeneous clusters. However, deriving homogeneous symptom clusters does not necessarily mean that these are underpinned by completely unique mechanisms.

Understanding progression from pre-school wheezing to school-age asthma: Can modern data approaches help?

Preschool wheezing and childhood asthma create a heavy disease burden which is only exacerbated by the complexity of the conditions. Preschool wheezing exhibits both "curricular" and "aetiological" heterogeneity: that is, heterogeneity across patients both in the time-course of its development and in its underpinning pathological mechanisms. Since these are not fully understood, but clinical presentations across patients may nonetheless be similar, current diagnostic labels are imprecise-not mapping cleanly onto underlying disease mechanisms-and prognoses uncertain. These uncertainties also make a identifying new targets for therapeutic intervention difficult. In the past few decades, carefully designed birth cohort studies have collected "big data" on a large scale, incorporating not only a wealth of longitudinal clinical data, but also detailed information from modalities as varied as imaging, multiomics, and blood biomarkers. The profusion of big data has seen the proliferation of what we term "modern data approaches" (MDAs)-grouping together machine learning, artificial intelligence, and data science-to make sense and make use of this data. In this review, we survey applications of MDAs (with an emphasis on machine learning) in childhood wheeze and asthma, highlighting the extent of their successes in providing tools for prognosis, unpicking the curricular heterogeneity of these conditions, clarifying the limitations of current diagnostic criteria, and indicating directions of research for uncovering the etiology of the diseases underlying these conditions. Specifically, we focus on the trajectories of childhood wheeze phenotypes. Further, we provide an explainer of the nature and potential use of MDAs and emphasize the scope of what we can hope to achieve with them.

Evolution of Eczema, Wheeze, and Rhinitis from Infancy to Early Adulthood: Four Birth Cohort Studies.

Rationale: The relationship between eczema, wheeze or asthma, and rhinitis is complex, and epidemiology and mechanisms of their comorbidities is unclear. Objectives: To investigate within-individual patterns of morbidity of eczema, wheeze, and rhinitis from birth to adolescence/early adulthood. Methods: We investigated onset, progression, and resolution of eczema, wheeze, and rhinitis using descriptive statistics, sequence mining, and latent Markov modeling in four population-based birth cohorts. We used logistic regression to ascertain if early-life eczema or wheeze, or genetic factors (filaggrin [FLG] mutations and 17q21 variants), increase the risk of multimorbidity. Measurements and Main Results: Single conditions, although the most prevalent, were observed significantly less frequently than by chance. There was considerable variation in the timing of onset/remission/persistence/intermittence. Multimorbidity of eczema+wheeze+rhinitis was rare but significantly overrepresented (three to six times more often than by chance). Although infantile eczema was associated with subsequent multimorbidity, most children with eczema (75.4%) did not progress to any multimorbidity pattern. FLG mutations and rs7216389 were not associated with persistence of eczema/wheeze as single conditions, but both increased the risk of multimorbidity (FLG by 2- to 3-fold, rs7216389 risk variant by 1.4- to 1.7-fold). Latent Markov modeling revealed five latent states (no disease/low risk, mainly eczema, mainly wheeze, mainly rhinitis, multimorbidity). The most likely transition to multimorbidity was from eczema state (0.21). However, although this was one of the highest transition probabilities, only one-fifth of those with eczema transitioned to multimorbidity. Conclusions: Atopic diseases fit a multimorbidity framework, with no evidence for sequential atopic march progression. The highest transition to multimorbidity was from eczema, but most children with eczema (more than three-quarters) had no comorbidities.

Modeling Wheezing Spells Identifies Phenotypes with Different Outcomes and Genetic Associates.

Rationale: Longitudinal modeling of current wheezing identified similar phenotypes, but their characteristics often differ between studies. Objectives: We propose that a more comprehensive description of wheeze may better describe trajectories than binary information on the presence/absence of wheezing. Methods: We derived six multidimensional variables of wheezing spells from birth to adolescence (including duration, temporal sequencing, and the extent of persistence/recurrence). We applied partition-around-medoids clustering on these variables to derive phenotypes in five birth cohorts. We investigated within- and between-phenotype differences compared with binary latent class analysis models and ascertained associations of these phenotypes with asthma and lung function and with polymorphisms in asthma loci 17q12-21 and CDHR3 (cadherin-related family member 3). Measurements and Main Results: Analysis among 7,719 participants with complete data identified five spell-based wheeze phenotypes with a high degree of certainty: never (54.1%), early-transient (ETW) (23.7%), late-onset (LOW) (6.9%), persistent (PEW) (8.3%), and a novel phenotype, intermittent wheeze (INT) (6.9%). FEV1/FVC was lower in PEW and INT compared with ETW and LOW and declined from age 8 years to adulthood in INT. 17q12-21 and CDHR3 polymorphisms were associated with higher odds of PEW and INT, but not ETW or LOW. Latent class analysis- and spell-based phenotypes appeared similar, but within-phenotype individual trajectories and phenotype allocation differed substantially. The spell-based approach was much more robust in dealing with missing data, and the derived clusters were more stable and internally homogeneous. Conclusions: Modeling of spell variables identified a novel intermittent wheeze phenotype associated with lung function decline to early adulthood. Using multidimensional spell variables may better capture wheeze development and provide a more robust input for phenotype derivation.

Dopamine-related polymorphisms and Affective Working Memory in aging.

The neurotransmitter dopamine (DA) is a key regulatory component of executive functioning and dysfunction in dopaminergic circuity has been shown to result in impaired working memory. Studies have identified multiple common genetic variants suggested to functionally impact the DA system and behaviorally alter working memory performance. Here, we aimed to develop a predictive model of affective working memory and to examine whether specific combinations of polymorphisms differently influence later encoding processes in affective working memory. Specifically, we examined the effects of the dopamine D2 and D1 receptors and Catechol-O-methyltransferase (COMT), on affective working memory in 155 older adults. Our model identified genotype variants, and scores on the Mini-Mental State exam and Geriatric depression scales as significant influencers in the predictive model whereas behavioral results showed specific patterns of performance linked to valence and string length but not to specific genetic variants. That is, all participants remembered a more positive words compared to negative and neutral words when remembering short strings of 3 or 4 words whereas performance on long strings, 5 or 6 words, revealed a more general affective enhancement independent of genotype. These findings are some of the first to investigate the effects dopaminergic enzyme and receptor interactions on affective working memory.

Recurrent Severe Preschool Wheeze: From Prespecified Diagnostic Labels to Underlying Endotypes.

Rationale: Preschool wheezing is heterogeneous, but the underlying mechanisms are poorly understood.Objectives: To investigate lower airway inflammation and infection in preschool children with different clinical diagnoses undergoing elective bronchoscopy and BAL.Methods: We recruited 136 children aged 1-5 years (105 with recurrent severe wheeze [RSW]; 31 with nonwheezing respiratory disease [NWRD]). Children with RSW were assigned as having episodic viral wheeze (EVW) or multiple-trigger wheeze (MTW). We compared lower airway inflammation and infection in different clinical diagnoses and undertook data-driven analyses to determine clusters of pathophysiological features, and we investigated their relationships with prespecified diagnostic labels.Measurements and Main Results: Blood eosinophil counts and percentages and allergic sensitization were significantly higher in children with RSW than in children with a NWRD. Blood neutrophil counts and percentages, BAL eosinophil and neutrophil percentages, and positive bacterial culture and virus detection rates were similar between groups. However, pathogen distribution differed significantly, with higher detection of rhinovirus in children with RSW and higher detection of Moraxella in sensitized children with RSW. Children with EVW and children with MTW did not differ in terms of blood or BAL-sample inflammation, or bacteria or virus detection. The Partition around Medoids algorithm revealed four clusters of pathophysiological features: 1) atopic (17.9%), 2) nonatopic with a low infection rate and high use of inhaled corticosteroids (31.3%), 3) nonatopic with a high infection rate (23.1%), and 4) nonatopic with a low infection rate and no use of inhaled corticosteroids (27.6%). Cluster allocation differed significantly between the RSW and NWRD groups (RSW was evenly distributed across clusters, and 60% of the NWRD group was assigned to cluster 4; P < 0.001). There was no difference in cluster membership between the EVW and MTW groups. Cluster 1 was dominated by Moraxella detection (P = 0.04), and cluster 3 was dominated by Haemophilus or Staphylococcus or Streptococcus detection (P = 0.02).Conclusions: We identified four clusters of severe preschool wheeze, which were distinguished by using sensitization, peripheral eosinophilia, lower airway neutrophilia, and bacteriology.

The Montelukast Therapy in Asthmatic Children with and without Food Allergy: Does It Make Any Difference?

INTRODUCTION: Children with food allergy are at increased risk for asthma and asthma morbidity. Since leukotrienes are implicated in the pathogenesis of both asthma and probably in food allergies, we hypothesized that asthmatic children with concomitant food allergy may have a favorable response to antileukotriene treatment. METHODS: Asthmatic children aged 6-18 years with and without food allergy were treated with montelukast and placebo in a double-blind, placebo-controlled cross-over parallel-group study. The primary outcome of the study was improvement in FEV1%. Asthma control tests, spirometry and methacholine challenges were performed as well as Fractional Exhaled Nitric Oxide (FeNO) levels. PGD2, CystLT, and lipoxin levels were measured in exhaled breath condensate (EBC). RESULTS: A total of 113 children were enrolled and 87 completed the study in accordance with the protocol. At baseline, children with food allergy and asthma (FAA) had higher levels of PGD2 and CysLT levels in the EBC than children with asthma alone (AA) (p < 0.001 for each). In the montelukast arm, although FEV1% was significantly higher in the FAA group compared to AA (p = 0.005), this effect was linked to the baseline difference of FEV1% between both arms. Montelukast treatment failed to improve FEV1% in both groups compared to the placebo. No effect of montelukast was observed in the remaining study parameters. CONCLUSION: Although children with FAA do not show a more favorable response to montelukast treatment compared to AA, a significant difference between baseline PGD2 and CystLT levels between FAA and AA groups may point to a different endotype of childhood asthma.

Connectivity patterns between multiple allergen specific IgE antibodies and their association with severe asthma.

BACKGROUND: Allergic sensitization is associated with severe asthma, but assessment of sensitization is not recommended by most guidelines. OBJECTIVE: We hypothesized that patterns of IgE responses to multiple allergenic proteins differ between sensitized participants with mild/moderate and severe asthma. METHODS: IgE to 112 allergenic molecules (components, c-sIgE) was measured using multiplex array among 509 adults and 140 school-age and 131 preschool children with asthma/wheeze from the Unbiased BIOmarkers for the PREDiction of respiratory diseases outcomes cohort, of whom 595 had severe disease. We applied clustering methods to identify co-occurrence patterns of components (component clusters) and patterns of sensitization among participants (sensitization clusters). Network analysis techniques explored the connectivity structure of c-sIgE, and differential network analysis looked for differences in c-sIgE interactions between severe and mild/moderate asthma. RESULTS: Four sensitization clusters were identified, but with no difference between disease severity groups. Similarly, component clusters were not associated with asthma severity. None of the c-sIgE were identified as associates of severe asthma. The key difference between school children and adults with mild/moderate compared with those with severe asthma was in the network of connections between c-sIgE. Participants with severe asthma had higher connectivity among components, but these connections were weaker. The mild/moderate network had fewer connections, but the connections were stronger. Connectivity between components with no structural homology tended to co-occur among participants with severe asthma. Results were independent from the different sample sizes of mild/moderate and severe groups. CONCLUSIONS: The patterns of interactions between IgE to multiple allergenic proteins are predictors of asthma severity among school children and adults with allergic asthma.

Longitudinal trajectories of severe wheeze exacerbations from infancy to school age and their association with early-life risk factors and late asthma outcomes.

INTRODUCTION: Exacerbation-prone asthma subtype has been reported in studies using data-driven methodologies. However, patterns of severe exacerbations have not been studied. OBJECTIVE: To investigate longitudinal trajectories of severe wheeze exacerbations from infancy to school age. METHODS: We applied longitudinal k-means clustering to derive exacerbation trajectories among 887 participants from a population-based birth cohort with severe wheeze exacerbations confirmed in healthcare records. We examined early-life risk factors of the derived trajectories, and their asthma-related outcomes and lung function in adolescence. RESULTS: 498/887 children (56%) had physician-confirmed wheeze by age 8 years, of whom 160 had at least one severe exacerbation. A two-cluster model provided the optimal solution for severe exacerbation trajectories among these 160 children: "Infrequent exacerbations (IE)" (n = 150, 93.7%) and "Early-onset frequent exacerbations (FE)" (n = 10, 6.3%). Shorter duration of breastfeeding was the strongest early-life risk factor for FE (weeks, median [IQR]: FE, 0 [0-1.75] vs. IE, 6 [0-20], P < .001). Specific airway resistance (sRaw ) was significantly higher in FE compared with IE trajectory throughout childhood. We then compared children in the two exacerbation trajectories with those who have never wheezed (NW, n = 389) or have wheezed but had no severe exacerbations (WNE, n = 338). At age 8 years, FEV1 /FVC was significantly lower and FeNO significantly higher among FE children compared with all other groups. By adolescence (age 16), subjects in FE trajectory were significantly more likely to have current asthma (67% FE vs. 30% IE vs. 13% WNE, P < .001) and use inhaled corticosteroids (77% FE vs. 15% IE vs. 18% WNE, P < .001). Lung function was significantly diminished in the FE trajectory (FEV1 /FVC, mean [95%CI]: 89.9% [89.3-90.5] vs. 88.1% [87.3-88.8] vs. 85.1% [83.4-86.7] vs. 74.7% [61.5-87.8], NW, WNE, IE, FE respectively, P < .001). CONCLUSION: We have identified two distinct trajectories of severe exacerbations during childhood with different early-life risk factors and asthma-related outcomes in adolescence.

Simple Structure Detection Through Bayesian Exploratory Multidimensional IRT Models.

In modern validity theory, a major concern is the construct validity of a test, which is commonly assessed through confirmatory or exploratory factor analysis. In the framework of Bayesian exploratory Multidimensional Item Response Theory (MIRT) models, we discuss two methods aimed at investigating the underlying structure of a test, in order to verify if the latent model adheres to a chosen simple factorial structure. This purpose is achieved without imposing hard constraints on the discrimination parameter matrix to address the rotational indeterminacy. The first approach prescribes a 2-step procedure. The parameter estimates are obtained through an unconstrained MCMC sampler. The simple structure is, then, inspected with a post-processing step based on the Consensus Simple Target Rotation technique. In the second approach, both rotational invariance and simple structure retrieval are addressed within the MCMC sampling scheme, by introducing a sparsity-inducing prior on the discrimination parameters. Through simulation as well as real-world studies, we demonstrate that the proposed methods are able to correctly infer the underlying sparse structure and to retrieve interpretable solutions.