BACKGROUND:Sensitisation to Alternaria is clinically important in asthma and rhinitis, but its life-course patterns and optimal diagnostic markers are unclear. We aimed to characterise Alternaria sensitisation trajectories from childhood to adulthood; we compared skin prick testing (SPT), whole-extract specific IgE and rAlt a 1, and assessed their longitudinal association with asthma and rhinitis in the Isle of Wight birth cohort. METHODS:Participants were assessed at 4, 10, 18 and 26 years. Alternaria sensitisation was measured by SPT (all ages) and serum IgE to whole Alternaria extract and rAlt a 1 (10, 18, 26 years). "Any Alternaria" denoted positivity to any assay. Alternaria, asthma and rhinitis trajectories (10-18-26 years) were classified as Never, Any positive or Persistent. Associations used χ2 trend tests and Poisson regression with robust errors. RESULTS:Among 434 participants with complete Alternaria data, trajectories were Never 84.1%, Any 11.1% and Persistent 4.8%. Alternaria trajectories showed graded associations with asthma, and more strongly with rhinitis (trend p < 0.001 for both), with highest risks for persistent asthma and rhinitis. Across ages, rAlt a 1 discriminated asthma risk better than whole-extract in youth, whereas effects converged by 26 years. For rhinitis, effect sizes were similar across assays and ages. Assay agreement strengthened with age, becoming substantial by 18 years and near-perfect by 26 years. CONCLUSIONS:Alternaria sensitisation consolidates from late adolescence and, when persistent, tracks with persistent asthma and rhinitis. Longitudinal analysis shows rAlt a 1 discriminates asthma risk better than whole-extract in youth, while assays are largely interchangeable in adulthood.

Background: The relationship between neonatal DNA methylation (DNAm) and childhood atopy, particularly its temporal dynamics, remains inadequately characterized. Establishing this will provide insights into the epigenetic mechanisms underlying atopy development. Methods: Skin prick tests (SPT) for 11 common allergens were performed in the Isle of Wight third-generation birth cohort (IOWF2) at ages 1, 3, and 6 years. Atopy was defined as a positive response to one or more allergens on SPT. DNAm at birth at 294,265 CpGs from umbilical cords (n = 192) or Guthrie cards (n = 107) was screened (through R package ttscreening) for potential association with atopy. Pathway enrichment analysis of screened CpGs was performed using the missMethyl package in R. Associations between CpGs that passed screening and atopy status were assessed via logistic regressions with repeated measures, adjusting for age, sex, and birth weight. Age-specific associations were examined via DNAm × age interactions. CpGs showing age-specific association were further tested in the parental cohort (IOWBC (F1); n = 717). Multiple testing was controlled using FDR-adjusted p-values at 0.05. Results: In total, 601 CpGs passed screening. Pathway enrichment analysis identified enrichment of the cell activation pathway (GO:0001775; FDR-adjusted p-value = 0.017). DNAm at 502 CpGs in F2 showed age-specific associations with atopy. Among these, 102 CpGs showed consistent directions in F1, and 14 were statistically significant (p-value < 0.05). Except for cg01519508 (FOXF1), DNAm-atopy associations weakened over time at the remaining 13 CpGs. Conclusions: At certain CpGs, DNAm at birth is associated with childhood atopy in an age-dependent manner, and for CpGs showing association at an earlier age, such associations weaken at later ages.

Abstract Background Cluster modelling has demonstrated asthma heterogeneity across disease severities, but contemporary data integrating difficult-to-treat and mild asthma with systematic assessment of comorbidity-focused treatable traits remain limited. Objective To identify and characterise difficult-to-treat and mild asthma clusters in two UK cohorts: Wessex AsThma CoHort of Difficult Asthma (WATCH-DA) and a mild-asthma cohort from the Epigenetics of Severe Asthma study (EOSA-MA). Methods Separate K-means clustering was applied to WATCH-DA ( n = 498; 11 variables) and EOSA-MA ( n = 67; 12 variables). Post-hoc comparisons evaluated demographic, inflammatory, physiological, comorbidity and patient-reported outcome profiles. Results Six difficult-to-treat and two mild asthma clusters were identified respectively, all Type-2 (T2)-predominant. Difficult-to-treat asthma clusters differed by sex, age of asthma-onset, body mass index (BMI) and comorbidities. Two clinically controlled clusters, cluster-1 (Early-onset atopic controlled) and cluster-4 (Late-adult-onset non-atopic controlled), showed distinct comorbidity patterns despite lower overall morbidity. Three severe, exacerbation-prone, adult-onset, female predominant difficult-to-treat clusters (Adult-onset eosinophilic exacerbator [cluster-2], Young-adult-onset high-risk exacerbator [cluster-5], Adult-onset obese multimorbid symptomatic [cluster-6]) varied by blood eosinophil counts (BEC), spirometry, BMI, treatment needs, comorbidities, and quality of life. An Adolescent-onset obese atopic obstructive (cluster-3) showed fewer exacerbations but high BEC with worst spirometry and poor asthma control. In mild asthma, cluster-1 (Early-onset atopic mild) showed worse pathophysiological indices and asthma control than cluster-2 (Adolescent-onset mild) but similarly high comorbidity prevalence. Conclusion Characterisation of difficult-to-treat and mild asthma clusters reveals diverse associated clinical traits and outcomes across the asthma severity spectrum. Recognition of these clusters and their associated comorbidities should prompt early personalised asthma management to address both airway-centric and comorbid disease aspects.

Background Cluster modelling has demonstrated the heterogeneity of asthma but has previously focused mainly on severe disease with limited assessment of mild disease or treatable traits like comorbidities. Objective To identify and characterise difficult-to-treat and mild asthma clusters in two UK cohorts: Wessex AsThma CoHort of Difficult Asthma (WATCH-DA) and a mild-asthma cohort from the Epigenetics of Severe Asthma study (EOSA-MA). Methods Separate K-means clustering was applied to WATCH-DA (n = 498; 11 variables) and EOSA-MA (n = 67; 12 variables). Post-hoc comparisons evaluated demographic, inflammatory, physiological, comorbidity and patient-reported outcome profiles. Results Six difficult-to-treat and two mild asthma clusters were identified respectively, all Type-2 (T2)-predominant. Difficult-to-treat asthma clusters differed by sex, age of asthma-onset, body mass index (BMI) and comorbidities. Two clinically-controlled clusters, cluster-1 (early-onset–clinically-controlled–atopic disease) and cluster-4 (adult-onset–clinically-controlled–least-atopic disease), showed distinct comorbidity patterns despite lower overall morbidity. Three severe, exacerbation-prone, adult-onset, female predominant difficult-to-treat clusters (cluster-2, cluster-5, cluster-6) varied by blood eosinophil counts (BEC), spirometry, BMI, treatment needs, comorbidities, and quality of life. An adolescent-onset–obese–atopic–airflow-obstructive disease (cluster-3) showed fewer exacerbations but high BEC with worst spirometry and poor asthma control. In mild asthma, cluster-1 (early-onset-atopic-mild-asthma) showed worse pathophysiological indices and asthma control than cluster-2 (adolescent-onset-mild-asthma) but similarly high comorbidity prevalence. Conclusion Characterisation of difficult-to-treat and mild asthma clusters reveals diverse associated clinical traits and outcomes across the asthma severity spectrum. Recognition of these clusters and their associated comorbidities should prompt early personalised asthma management to address both airway-centric and comorbid disease aspects.

Summary Obesity in the first 18 years of life demonstrated sex‐specific associations with eczema. Exploratory analysis suggested that leptin is a potential mediator of the observed association.

BACKGROUND:Early pregnancy represents a critical developmental period and may influence later development of respiratory and allergic diseases in children. A growing body of evidence links prenatal exposures to atopic diseases, such as asthma, with sex-specific differences. METHODS:Children from the F2-generation (i.e., children born to male or female F1 participants of the original 1989 birth cohort) of the Isle of Wight Birth Cohort (IOWBC) (n = 617) were followed from pregnancy through 10 years. Prenatal exposures (12 weeks), including cooking heat source, dampness or fungi in the home, air pollution and vehicle traffic near the home, were determined via standardized questionnaires. The status of wheezing, asthma, allergic rhinitis, and eczema status were also determined through questionnaires. Generalized linear mixed models analyzed the association of these early in utero exposures with the risk of childhood atopic disease status and respiratory symptoms. RESULTS:Increased vehicle traffic near the home (10+ per hour) was associated with increased risk of wheezing during childhood (RR: 1.74; 95% CI: 1.02-2.99); particularly in girls. Any increased vehicle traffic was also associated with higher risk of repeated wheezing in girls compared to infrequent traffic. A significant additive interaction between outdoor air pollution and sex was observed for repeated wheezing (RERI = 0.46; 95% CI: 0.10-0.82), indicating that the combined effect of prenatal air pollution exposure and female sex exceeded the sum of their individual effects. However, no prenatal environmental factors were associated with increased risk of diagnosed asthma, allergic rhinitis, or eczema. CONCLUSIONS:Our findings suggest that prenatal exposures to vehicle traffic near home are associated with an increased risk of repeated childhood wheezing, and such associations are likely to be different between boys and girls. Early exposures to vehicle traffic and outdoor air pollution increase susceptibility to childhood wheeze without increasing the risk of atopic diseases.

Groundwater is a major source of drinking water in the United States (US). Groundwater chemistry can contribute to lead leaching from water supply pipes due to factors such as pH and mineral content that influence corrosion. Lead exposure disproportionately affects children from low-income neighborhoods. We evaluated the association of county-level groundwater chemicals with the percentage of children with blood lead levels >5 μg/dL (BLL5%) in 1,104 US counties served by public water utilities using groundwater. Out of the 4,844 BLL5% observations, 3,525 had values of "NA" for BLL5%. We used weighted least squares regression to evaluate the associations, adjusting for covariates such as county-level median household income, educational attainment, and poverty rates. Bayesian Kernel Machine Regression (BKMR) was used to assess the joint effects of all chemicals on BLL5%. Sensitivity analyses tested the robustness of our results by imputing missing BLL5% values. A one mg/L increase in arsenic, copper, dissolved oxygen, and selenium was associated with increases in BLL5% of 0.0512% (95% CI: 0.0002%, 0.1023%), 0.0358% (95% CI: 0.0208%, 0.0508%), 0.0956% (95% CI: 0.0225%, 0.1687%), and 0.3038% (95% CI: 0.1747%, 0.4420%), respectively. Alkalinity, pH, calcium, bicarbonate, and dissolved solids were not found to be statistically significant. BKMR identified calcium, lithium, and alkalinity (posterior inclusion probabilities = 1,000) as important, though with minimal effects. Sensitivity analyses showed variability in results depending on assumptions about missing data. Our findings highlight the importance of monitoring groundwater quality and implementing interventions to reduce childhood lead exposure risks in vulnerable populations, particularly minority, and low-income children.

ttScreening (TT) is an effective high-dimensional screening algorithm to identify important cytosine-phosphate-guanine dinucleotide (CpG) sites associated with DNA methylation. Via simulations, we aimed to examine the impact of influential outliers on TT’s performance. We simulated K = 2,000 and 10,000 CpG sites across n = 100 and 200 subjects, linearly associated with a continuous outcome, x_1 , and other latent variables with the error term following a normal or Cauchy distribution. Among the K CpGs, 10 were associated with x_1 (informative CpGs) while the remaining sites were not associated with x_1 (non-informative CpGs). We artificially created 1 to 5 influential points in one informative and one non-informative CpG site and compared TT’s accuracy to Bonferroni and false discovery rate (FDR)-based approaches. TT performed as well as or better than the FDR and Bonferroni-based approaches, across all degrees of influentiality. When focusing on non-informative CpG detection, regardless of sample size, all approaches had high accuracy (above 85

[This corrects the article DOI: 10.1016/j.waojou.2024.100976.].

DNA methylation (DNAm) is a developmentally dynamic epigenetic process; yet, most epigenome-wide association studies (EWAS) have examined DNAm at only one timepoint or without systematic comparisons between timepoints. Thus, it is unclear whether DNAm alterations during certain developmental periods are more informative than others for health outcomes, how persistent epigenetic signals are across time, and whether epigenetic timing effects differ by outcome. We applied longitudinal meta-regression models to published meta-analyses from the PACE consortium that examined DNAm at two timepoints—prospectively at birth and cross-sectionally in childhood—in relation to the same child outcome (ADHD symptoms, general psychopathology, sleep duration, BMI, asthma). These models allowed systematic comparisons of effect sizes and statistical significance between timepoints. Furthermore, we tested correlations between DNAm regression coefficients to assess the consistency of epigenetic signals across time and outcomes. Finally, we performed robustness checks, estimated between-study heterogeneity, and tested pathway enrichment. Our findings reveal three new insights: (i) across outcomes, DNAm effect sizes are consistently larger in childhood cross-sectional analyses compared to prospective analyses at birth; (ii) higher effect sizes do not necessarily translate into more significant findings, as associations also become noisier in childhood for most outcomes (showing larger standard errors in cross-sectional vs prospective analyses); and (iii) DNAm signals are highly time-specific, while also showing evidence of shared associations across health outcomes (ADHD symptoms, general psychopathology, and asthma). Notably, these observations could not be explained by sample size differences and only partly to differential study-heterogeneity. DNAm sites changing associations were enriched for neural pathways. Our results highlight developmentally-specific associations between DNAm and child health outcomes, when assessing DNAm at birth vs childhood. This implies that EWAS results from one timepoint are unlikely to generalize to another. Longitudinal studies with repeated epigenetic assessments are direly needed to shed light on the dynamic relationship between DNAm, development and health, as well as to enable the creation of more reliable and generalizable epigenetic biomarkers. More broadly, this study underscores the importance of considering the time-varying nature of DNAm in epigenetic research and supports the potential existence of epigenetic “timing effects” on child health.

BACKGROUND:The natural history of airway hyperresponsiveness (AHR) from childhood to adulthood and its association with asthma status are poorly understood. We aim to define the natural history of AHR in relation to asthma characteristics such as symptoms, atopy and lung function to improve our understanding of the changes in AHR with asthma pathophysiology during adolescence. METHODS:Methacholine bronchial challenge test (BCT) was undertaken in the Isle of Wight whole population birth cohort at 10 years (n = 783), 18 years (n = 585) and 26 years (n = 86). Data on wheeze, lung function, and atopy were collected at each time point. Definite AHR was defined as methacholine concentration provoking a 20% decrease in Forced Expiratory Volume in 1 s (PC20) at < 4 mg/mL. RESULTS:AHR prevalence was 21.6% (169/783) at 10 years and 5% (29/585) at 18 years of age (p < 0.01). In 406 participants, where methacholine BCT was performed at both 10 and 18 years, 80.9% of those with AHR at age 10 became negative at 18 years. At a population level, AHR trajectory was in the opposite direction to that of asthma (14.7% at age 10 to 17.6% at age 18; p = 0.004), atopy (26.9% at age 10 to 41.5% at age 18; p < 0.001) and airway obstruction (FEV1/FVC ratio of 0.88 at age 10 to 0.87 at age 18; p < 0.001). AHR prevalence remained stable between the ages of 18 and 26 years. CONCLUSION:The natural history of AHR is characterised by a marked decrease in prevalence during adolescence, in contrast to asthma and other asthma characteristics. Age should be considered when interpreting AHR as an asthma defining trait.

Biologic treatment options have expanded considerably for severe asthma, transforming patient management, but only ~70% of patients respond to the treatment following standard criteria. This preliminary study utilized data collected in the Wessex AsThma CoHort of difficult asthma (WATCH) study. DNAm was measured in n=15 severe asthma patients at baseline, i.e., before mepolizumab (MEPO), an interleukin-5 receptor antagonist, was administered and the response to MEPO was analyzed. We aimed to assess the potential of DNA methylation as a feasible biomarker to predict responses to MEPO among severe asthma patients. Our preliminary findings suggested that pre-biologic DNA methylation as a predictor of a patient's response status may outperform pre-biologic blood eosinophils cell counts.

OBJECTIVE:Substance use, violence, and HIV/AIDS (i.e. SAVA) are three adversities known to cluster and contribute to other poor health outcomes among marginalized communities due to structural factors including racism and poverty. Most research on SAVA has focused on negative outcomes (e.g. psychopathology) among those directly affected. To address important gaps in the literature, the current study explored how child gender moderates the associations between maternal SAVA severity and child individual, relational, community, and cultural resilience. METHOD:Participants included 263 children (Mage = 12.11, SD = 2.77; 59% girls; 82% Black) and their maternal caregivers. SAVA severity was examined as a continuous latent variable and resilience levels were calculated via confirmatory factor analysis based on manifest variables. RESULTS:After adjusting for covariates, linear regression analyses indicated that, among girls but not boys, lower maternal SAVA severity was associated with higher individual (β = -0.22, p = .04, d = 0.01) and community (β = -0.27, p = .02, d = 0.02) level resilience. Further, across all children, lower maternal SAVA severity was associated with higher cultural resilience (β = -0.24, p < .001, d = 0.04). The association between maternal SAVA and relational resilience was not statistically significant. CONCLUSIONS:Maternal SAVA impacts child resilience, but this effect is not uniform, as findings illustrated differential effects of SAVA by child gender and resilience level. This work emphasizes the critical need to assess and understand unique drivers of child resilience in order to intervene effectively on co-occurring adversities.

Kernel machine regression is a nonparametric regression method widely applied in biomedical and environmental health research. It employs a kernel function to measure the similarities between sample pairs, effectively identifying significant exposures and assessing their nonlinear impacts on outcomes. This article introduces an enhanced framework, the generalized Bayesian kernel machine regression. In comparison to traditional kernel machine regression, generalized Bayesian kernel machine regression provides substantial flexibility to accommodate a broader array of outcome variables, ranging from continuous to binary and count data. Simulations show generalized Bayesian kernel machine regression can successfully identify the nonlinear relationships between independent variables and outcomes of various types. In the real data analysis, we applied generalized Bayesian kernel machine regression to uncover cytosine phosphate guanine sites linked to health-related conditions such as asthma and smoking. The results identify crucial cytosine phosphate guanine sites and provide insights into their complex, nonlinear relationships with outcome variables.

Background:The increasing global prevalence of obesity and cardiovascular disease (CVD) represents a pressing public health challenge. Traditional obesity metrics, such as body mass index (BMI) and waist circumference (WC), have limitations in accurately predicting CVD risk. The weight-adjusted waist index (WWI), a novel metric combining WC and body weight, has been proposed as an alternative predictor of central obesity and its associated risks. This systematic review and meta-analysis aimed to evaluate the association between WWI and CVD. Methods:We conducted a systematic review of the literature in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the study was registered with PROSPERO (ID CRD42024629861), searching PubMed, Scopus, and Google Scholar for observational studies examining the relationship between WWI and CVD. Data extraction and quality assessment were performed independently by two reviewers. A random-effects meta-analysis and subgroup analyzes were conducted to pool effect sizes, expressed as adjusted odds ratios (aORs) or adjusted hazard ratios (aHRs), and heterogeneity was evaluated using I², T², and Q statistics. Results:Ten studies comprising 170,297 participants were included. The pooled analysis revealed a significant positive association between WWI and increased CVD risk, with a pooled OR of 1.33 (95% CI: 1.17-1.48, p < 0.01). Moderate heterogeneity was observed (I² = 38.0%). Subgroup analyses showed stronger associations in studies conducted in the United States (OR: 1.35; 95% CI: 1.24 - 1.47) compared to China (OR: 1.32; 95% CI: 1.17 - 1.48). No significant differences were found between cross-sectional (OR: 1.33) and cohort studies (OR: 1.37). Conclusions:This study suggests a potential association between WWI and CVD, supporting its utility as an alternative measure of central obesity compared to traditional metrics. Despite these findings, moderate heterogeneity warrants further investigation into population-specific factors and mechanisms underlying the relationship between WWI and CVD. Future research should validate these findings across diverse populations and explore the clinical applications of WWI in CVD prevention strategies. Systematic review registration:https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024629861.

AIM:We aim to assess association of DNA methylation (DNAm) at birth with total immunoglobulin E (IgE) trajectories from birth to late adolescence and whether such association is ethnicity-specific. METHODS:We examined the association of total IgE trajectories from birth to late adolescence with DNAm at birth in two independent birth cohorts, the Isle of wight birth cohort (IOWBC) in UK (n = 796; White) and the maternal and infant cohort study (MICS) in Taiwan (n = 60; Asian). Biological pathways and methylation quantitative trait loci (methQTL) for associated Cytosine-phosphate-Guanine sites were studied. RESULTS:Two total IgE trajectories, high vs. low, were inferred from each of the two cohorts. Associations of DNAm at 103 CpGs with IgE trajectories in IOWBC and at 476 CpGs in MICS were identified. Between the two cohorts, of the identified CpGs, one was in common, methQTL site cg16711274 (mapped to gene MINAR1), and 17 pathways were common with at least four linked to airway diseases. CONCLUSION:The findings suggest at-birth epigenetics may explain ethnicity differences in total IgE trajectories later in life.

We propose a Bayesian variable selection method in the framework of modal regression for heavy-tailed responses. An efficient expectation-maximization algorithm is employed to expedite parameter estimation. A test statistic is constructed to exploit the shape of the model error distribution to effectively separate informative covariates from unimportant ones. Through simulations, we demonstrate and evaluate the efficacy of the proposed method in identifying important covariates in the presence of non-Gaussian model errors. Finally, we apply the proposed method to analyze two datasets arising in genetic and epigenetic studies.

Early childhood wheezing is associated with asthma risk at later ages, emphasizing the need for understanding wheezing patterns and their implications for asthma development. Children in the F2-generation (n = 603) of the Isle of Wight Birth Cohort (IOWBC) were followed-up at 3, 6, 12, 24, 36, and 72 months. Prevalence of wheeze and wheeze type (general, infectious, and non-infectious) were recorded. Group-based trajectory models covering ages 3 to 36 months were used to identify early childhood wheezing trajectories for each type of wheeze. These trajectories were examined for their association with asthma status and lung function at 6 years and later. Distinct trajectories for general (“Persistent”, “Transient”, “Progressive”, and “Infrequent/Never”), infectious (“Persistent”, “Transient”, and “Infrequent/Never”), and non-infectious (“Progressive”, “Early Occurrence”, and “Infrequent/Never”) wheezing were identified. Compared to the “Infrequent/Never” trajectories, four trajectories were associated with an increased risk of asthma, namely “Progressive” non-infectious, “Early Occurrence” non-infectious, “Persistent” infectious, and “Persistent” general wheeze trajectories. The identification of wheeze trajectories across different etiologies as significant risk factors for asthma may aid in understanding the complex, multifactorial nature of asthma onset. The findings suggest that early identification of specific wheeze patterns, not just occurrence of wheezing, can inform clinical interventions and potentially mitigate the risk of developing asthma.