PURPOSE:Urinary proteomics may help us improve our understanding and classification of asthma. We sought to identify clusters of children based on urinary protein profiles and explore associations between these clusters, specific proteins, and asthma. METHODS:We analyzed urine samples from 146 children (Kindergarten-Grade 8) using tandem mass-spectrometry. The presence of 4,080 urinary proteins was assessed. Unsupervised k-means clustering (KMN) identified clusters. Ten notable proteins per cluster were identified. We then tested three asthma prediction approaches: (1) all proteins only, (2) all proteins and non-protein features (e.g., socio-demographics, clinical), and (3) clusters and non-protein features. Each approach was run using four supervised machine learning (ML) algorithms. Traditional binary logistic regression (traditional analysis) was also conducted. RESULTS:We identified two clusters from the KMN. Among the notable proteins, only collagen alpha-1 chain was statistically different between those with and without asthma (p = 0.02). Approach 3 (cluster and non-protein features) using random forest methods had the best predictive performance. While cluster was listed as an important variable within Approach 3, it was not associated with asthma based on traditional analysis. Wheeze and mother's history of asthma were consistently associated with asthma. Unique predictors for the ML included %predicted FEF25-75%, %predicted FEV1/FVC, child's age, and mother's history of allergy; whereas, urban residence was unique to traditional analysis. CONCLUSION:We identified collagen alpha-1 chain as a potential biomarker for asthma. External validation in independent cohorts and stratification by clinical phenotypes are needed to confirm its diagnostic utility and clarify its role in asthma pathophysiology.

RATIONALE:Access to polysomnography, the recommended standard for the diagnosis of OSA in children, is limited in many jurisdictions. Many children undergo treatment for OSA without confirmatory testing, are denied treatment in the absence of testing, or have a delay in treatment of other sleep disorders until OSA can be ruled out. METHODS:An expert panel conducted a systematic review and meta-analysis examining alternative testing to polysomnography. Recommendations formulation followed a process of proposal, discussion, revisions and voting, considering the evidence, panel members' judgment, as well as patient and family preferences. RESULTS:A total of 250 articles across 5 types of clinical assessment were included. Most articles excluded children with comorbidities or did not report exclusions (62% and 26%, respectively). Only 13% of articles included children under 2-years of age. Limited data were appropriate for meta-analysis. CONCLUSIONS:The panel upholds polysomnography as the recommended standard for the diagnosis of OSA in children given the absence of an alternative test that could be considered a replacement. Level 3/Home Sleep Apnea Testing is recommended as an alternative option to diagnose OSA in otherwise healthy children over 5-years of age for whom access to polysomnography is effectively absent. While the Pediatric Sleep Questionnaire and overnight oximetry add information to the clinical assessment, we suggest that these should not be used to diagnose OSA in children. Given patient and family preferences for testing at home, further investment is needed in developing accurate and accessible home-based testing options to diagnose OSA in children.

Background:Understanding the factors leading to the development of allergic disease is a critical area of research. We studied the development of allergic disease in identical and fraternal twins to identify potential differences in environment versus genetic factors. Methods:Twins aged up to 4 years were selected for inclusion in this long-term follow-up study. Regular questionnaire results, allergen levels, and other indicators were examined. Results:A total of 80 twins were included in this study. Over time, the incidence of atopic dermatitis (AD) decreased, and the incidence of rhinitis and wheezing increased. The incidence of AD, rhinitis, and food allergy was significantly higher in identical twins than fraternal twins. The consistency of positive inhaled allergens and positive food allergens was significantly higher in the identical twins than fraternal twins. The factors influencing allergic diseases were analyzed. In the identical twins, AD was more frequent in males, those with a birth weight <2,500 g, and having siblings; rhinitis was more frequent in those living in a bungalow style home, having pets, and carpeting; and wheezing was more frequent in males, having a birth weight <2,500 g, and having siblings. In the fraternal twins, AD was more frequent in those born <37 weeks gestation, and having flowers and plants in the house; rhinitis was more frequent in those born <37 weeks gestation, those with a history of neonatal asphyxia, and having a household smoking; and wheezing was more frequent in those born <37 weeks gestation, those with a history of neonatal asphyxia, with central heating, and household smoking. No factors were found to affect the occurrence of food allergy. Conclusions:Allergic diseases in children have a strong genetic predisposition, but are also influenced by environmental factors. The environmental factors affecting the occurrence of allergic diseases in identical and fraternal twins differ.

BackgroundThe coexistence of childhood asthma and mental health (MH) conditions can impact management and health outcomes but we need to better understand the etiology of multimorbidity. We investigated the association between childhood asthma and MH conditions as well as the determinants of their coexistence.MethodsWe used data from the Canadian Health Survey of Children and Youth 2019 (3-17 years; n = 47,871), a cross-sectional, nationally representative Statistics Canada dataset. Our primary outcome was condition status (no asthma or MH condition; asthma only; MH condition only; both asthma, and a MH condition (AMHM)). Predictors of condition status were assessed using multiple multinomial logistic regression. Sensitivity analyses considered individual MH conditions.ResultsMH condition prevalence was almost two-fold higher among those with asthma than those without asthma (21.1% vs. 11.6%, respectively). There were increased risks of each condition category associated with having allergies, other chronic conditions, and family members smoking in the home while there were protective associations with each condition status category for being female and born outside of Canada. Four additional variables were associated with AMHM and MH condition presence with one additional variable associated with both AMHM and asthma. In sensitivity analyses, the associations tended to be similar for most characteristics, although there was some variability.ConclusionThere are common risk factors of asthma and MH conditions along with their multimorbidity with a tendency for MH risk factors to be associated with multimorbidity. MH condition presence is common and important to assess among children with asthma.

Congenital heart disease (CHD) is one of today’s leading birth anomalies. Children with CHD are at risk for adaptive functioning challenges. Sleep difficulties are also common in children with CHD. Indeed, sleep-disordered breathing, a common type of sleep dysfunction, is associated with increased mortality for infants with CHD. The present study examined the associations between adaptive functioning and sleep quality (i.e., duration and disruptions) in children with CHD (n = 23) compared to healthy children (n = 38). Results demonstrated associations between mean hours slept and overall adaptive functioning in the CHD group r(21) = .57, p = .005 but not in the healthy group. The CHD group demonstrated lower levels of adaptive functioning in the Conceptual, t(59) = 2.12, p = .039, Cohen's d = 0.53 and Practical, t(59) = 2.22, p = .030, Cohen's d = 0.55 domains, and overall adaptive functioning (i.e., General Adaptive Composite) nearing statistical significance in comparison to the healthy group, t(59) = 2.00, p = .051, Cohen's d = 0.51. The CHD group also demonstrated greater time awake at night, t(56) = 2.19, p = .033, Cohen's d = 0.58 and a greater instance of parent-caregiver reported snoring, χ2 (1, N = 60) = 5.25, p = .022, V = .296 than the healthy group. Further exploration of the association between adaptive functioning and sleep quality in those with CHD is required to inform clinical practice guidelines.

We report 4 cases of primary ciliary dyskinesia in unrelated indigenous North American children caused by identical, homozygous, likely pathogenic deletions in the DNAL1 gene. These shared DNAL1 deletions among dispersed indigenous populations suggest that primary ciliary dyskinesia accounts for more lung disease with bronchiectasis than previously recognized in indigenous North Americans. (J Pediatr 2023;261:113362).

Objective The objective of this study was to determine the extent of machine learning (ML) application in asthma research and to identify research gaps while mapping the existing literature.Data Sources We conducted a scoping review. PubMed, ProQuest, and Embase Scopus databases were searched with an end date of September 18, 2020.Study Selection DistillerSR was used for data management. Inclusion criteria were an asthma focus, human participants, ML techniques, and written in English. Exclusion criteria were abstract only, simulation-based, not human based, or were reviews or commentaries. Descriptive statistics were presented.Results A total of 6,317 potential articles were found. After removing duplicates, and reviewing the titles and abstracts, 102 articles were included for the full text analysis. Asthma episode prediction (24.5%), asthma phenotype classification (16.7%), and genetic profiling of asthma (12.7%) were the top three study topics. Cohort (52.9%), cross-sectional (20.6%), and case-control studies (11.8%) were the study designs most frequently used. Regarding the ML techniques, 34.3% of the studies used more than one technique. Neural networks, clustering, and random forests were the most common ML techniques used where they were used in 20.6%, 18.6%, and 17.6% of studies, respectively. Very few studies considered location of residence (i.e. urban or rural status).Conclusions The use of ML in asthma studies has been increasing with most of this focused on the three major topics (>50%). Future research using ML could focus on gaps such as a broader range of study topics and focus on its use in additional populations (e.g. location of residence).Supplemental data for this article is available online at http://dx.doi.org/ .

STUDY OBJECTIVES:Children with late-onset (2-5 years) or persistent (3 months-5 years) sleep-related breathing disorder (SRBD) have an increased risk of behavior problems compared to children with no or early-onset SRBD. We sought to determine whether a combination of urine metabolites and sleep questionnaires could identify children at risk for SRBD-associated behavior problems. METHODS:Urine and data were analyzed from the Edmonton site of the CHILD birth cohort study. We measured urine metabolites (random, mid-stream) at age three-years among a sub-cohort of participants (n = 165). Random Forest with a Boruta wrapper was used to identify important metabolites (creatinine-corrected, z-scores) for late/persistent SRBD versus no/early SRBD (reference). An algorithm was subsequently generated to predict late/persistent SRBD in children with a history of snoring using a metabolite composite score (z-scores < or ≥ 0) plus the SDBeasy score defined as [age (yrs.) of most recent positive SRBD]2 - [age (yrs.) first reported ever snoring]2. RESULTS:Of the 165 children with SRBD data, 40 participants had late/persistent SRBD. Seven urinary metabolites in addition to the SDBeasy score were confirmed as important for late/persistent SRBD (AUC = 0.87). Among children with an ever-snoring history and a metabolite composite score ≥0, those with SDBeasy score ≥3 were over 13-fold more likely to have late/persistent SRBD (OR 13.7; 95%CI: 3.0, 62.1; p = 0.001). This algorithm has a Sensitivity of 69.6%, Specificity of 85.7% and a positive likelihood ratio (+LR) of 4.9. CONCLUSIONS:We developed a predictive algorithm using a combination of questionnaires and urine metabolites at age three-years to identify children with late/persistent SRBD by five-years of age.

Background:Previous studies showed bacterial lysates were effective for pediatric asthma. However, evidence of polyvalent bacterial lysate Qipian is lacking. Methods:In this real-world retrospective cohort study, data of children with asthma, aged six months to 14 years old, attending to Jiangxi Provincial Children's Hospital from January 2021 to April 2022, prescribed routine treatment for asthma plus Qipian (Qipian group) or not (control group) were extracted. To minimize the impact of confounders on the outcomes, baseline characteristics were utilized to perform propensity score matching through a multivariable logistic regression model. After matching, asthma control, exacerbation, etc. were compared. Results:Totally, 795 patients were included (337 in the Qipian group and 458 in the control group), with 278 pairs (556 patients) matched. Most baseline characteristics were well-balanced. The proportion of males were 68.3% and 70.1% in the two groups. The Qipian group favored better asthma control, with more "controlled" [3-month: 257 (92.4%) vs. 240 (86.3%); 6-month: 246 (88.5%) vs. 235 (84.5%)], and fewer "poorly/very poorly controlled" patients, compared with the control group (P=0.004 and 0.025, respectively). Patients in the Qipian group had lower risks of exacerbation. Incidence rate ratios (IRR) for any exacerbation were 0.56 [95% confidence interval (CI): 0.33 to 0.93] in the 3-month period and 0.83 (95% CI: 0.55 to 1.26) in the 6-month period. IRR for severe exacerbations were 0.09 (95% CI: 0.01 to 0.71) in the 3-month period and 0.20 (95% CI: 0.06 to 0.70) in the 6-month period (compared to the control group). Qipian significantly reduced the cumulative dose of short-acting beta-agonist (3-month: 3.22±10.37 vs. 8.08±16.71 mg; P<0.001; 6-month: 6.56±16.23 vs. 11.81±24.41 mg; P=0.002). There was no difference in incidences of respiratory tract infection or fever due to respiratory tract infection between the two groups. Numbers of antibacterial agent prescription were fewer in the Qipian group compared to the control group (3-month: 0.67±1.16 vs. 1.04±1.45; P=0.001; 6-month: 1.14±1.69 vs. 1.51±2.12; P=0.023). Conclusions:According to this retrospective study, Qipian may be effective for improved pediatric asthma control. Safety profile and mechanisms of action of Qipian need further investigation. Further randomized controlled trials are warranted to confirm our results.

Respiratory syncytial virus (RSV) is associated with bronchiolitis in infancy and the later development of asthma. Research on RSV in vitro requires preparation of a purified RSV stock. The objective for this work was to develop best methods for RSV purification, while monitoring the samples for potential contaminating proinflammatory mediators. Using polyethylene glycol concentration, and sucrose-gradient ultracentrifugation, we collected samples at each step of purification and measured the values of RSV titer, total protein (µg/mL), and proinflammatory cytokines (ELISA). We analyzed the efficacy of each step in the purification procedure. In so doing, we also determined that despite optimal purification methods, a well-known chemokine in the field of allergic disease, CCL5 (RANTES), persisted within the virus preparations, whereas other cytokines did not. We suggest that researchers should be aware that CCL5 appears to co-purify with RSV. Despite reasonable purification methods, a significant level of CCL5 (RANTES) persists in the virus preparation. This is relevant to the study of RSV-induced allergic disease.

Metabolomics is a dynamically evolving field, with a major application in identifying biomarkers for drug development and personalized medicine. Numerous metabolomic studies have identified endogenous metabolites that, in principle, are eligible for translation to clinical practice. However, few metabolomic‐derived biomarker candidates have been qualified by regulatory bodies for clinical applications. Such interruption in the biomarker qualification process can be largely attributed to various reasons including inappropriate study design and inadequate data to support the clinical utility of the biomarkers. In addition, the lack of robust assays for the routine quantification of candidate biomarkers has been suggested as a potential bottleneck in the biomarker qualification process. In fact, the nature of the endogenous metabolites precludes the application of the current validation guidelines for bioanalytical methods. As a result, there have been individual efforts in modifying existing guidelines and/or developing alternative approaches to facilitate method validation. In this review, three main challenges for method development and validation for endogenous metabolites are discussed, namely matrix effects evaluation, alternative analyte‐free matrices, and the choice of internal standards (ISs). Some studies have modified the equations described by the European Medicines Agency for the evaluation of matrix effects. However, alternative strategies were also described; for instance, calibration curves can be generated in solvents and in biological samples and the slopes can be compared through ratios, relative standard deviation, or a modified Stufour suggested approaches while quantifying mainly endogenous metabolitesdent t‐ test. ISs, on the contrary, are diverse; in which seven different possible types, used in metabolomics‐based studies, were identified in the literature. Each type has its advantages and limitations; however, isotope‐labeled ISs and ISs created through isotope derivatization show superior performance. Finally, alternative matrices have been described and tested during method development and validation for the quantification of endogenous entities. These alternatives are discussed in detail, highlighting their advantages and shortcomings. The goal of this review is to compare, apprise, and debate current knowledge and practices in order to aid researchers and clinical scientists in developing robust assays needed during the qualification process of candidate metabolite biomarkers. © 2019 John Wiley & Sons Ltd. Mass Spec Rev

Asthma and chronic obstructive pulmonary disease (COPD) are common respiratory disorders that have similar clinical presentation and misdiagnosis may lead to improper treatment. There is a need for a better, non-invasive test for the differentiation of asthma and COPD. In this study, we developed a new validated LC-MS/MS method for 17 urinary organic acids that could serve as potential biomarkers. Human urine samples were collected from adults with asthma or COPD. LC-MS/MS was performed using the differential isotope labeling approach. 4-(Dimethylamino) phenacyl bromide (DmPA) was used for derivatization using two different carbon isotopes, allowing for the formation of internal standard for each metabolite. Gradient elution was employed on a C18 column while the LC-MS/MS operated in the multiple reaction monitoring mode (MRM). Regulatory guidelines were used for method validation. Partial Least Squares Discriminative Analysis (PLS-DA) was applied to the log-transformed values of metabolites in each group of asthma and COPD subjects. Full validation in targeted metabolomics is scarce with usually limited number of metabolites, unlike fit-for-purpose approach. Due to the endogenous nature of the metabolites, numerous challenges were encountered during method development and validation, such as the lactic acid interference from the surrounding environment. The required specificity, accuracy and precision was successfully achieved. The method was fully validated, ensuring robustness and reproducibility when analyzing patient samples. The method was applied to analyze human urine samples and PLS-DA analysis showed differentiation of asthma and COPD subjects (R2 0.89, Q2 0.68). As targeted metabolomics is expanding to the clinical sphere, more validated methods/strategies are needed. Our work will expand the current knowledge-base regarding targeted metabolomics.

Objective: Studies have reported lower asthma prevalence in rural compared to urban areas. While environmental factors have mostly been implicated for these differences, the lower asthma prevalence could also be linked to asthma under-diagnosis in rural children. We investigate if rural children experience under-diagnosis of asthma more compared to urban children. Methods: In 2013, we conducted a cross-sectional survey of schoolchildren across an urban-rural gradient in Saskatchewan, Canada. The participants formed sampling frame for future studies. In 2015, we approached those who gave consent in 2013 for further testing, repeated the survey, and conducted clinical testing. Based on survey responses, children were classified into "no asthma," "at-risk-for-asthma," and "diagnosed asthma." We then classified asthma status as either "no asthma" or "probable asthma" based on a validated asthma algorithm. Results: The study population of 335 schoolchildren (aged 7-17 years) comprised of 73.4% from large urban, 13.7% from small urban, and 12.8% from rural areas. Proportion with report of physician-diagnosed asthma was 28.5% (Large urban), 34.8% (Small urban), and 20.9% (Rural). Mean percent predicted FEV1 and FEF25%-75% were lower in rural compared to small urban and large urban children (p < 0.05). Among those not classified as with "diagnosed asthma" by the survey, the algorithm further identified presence of asthma in 5.5% large urban, 8.1% small urban, and 18.8% rural children (p = 0.03). Conclusion: The study revealed evidence of asthma underdiagnosis in rural areas and further supports the use of objective measures in addition to symptoms history when investigating asthma across urban-rural gradients.

The ability to monitor the severity of respiratory diseases is difficult in the typical clinical setting, particularly for diseases of the upper airway, such as allergic rhinitis (AR). Patients often do not recognize how severe their symptoms are because of progressive “tolerizing” to symptoms. Chronic nasal congestion becomes the new normal. Clinical trials of AR therapies are associated with high placebo response rates to the degree that the US Food and Drug Administration has indicated AR as one of the few conditions in which including a placebo arm is not only ethically justified but also mandatory.

Urine is an ideal matrix for metabolomics investigation due to its non-invasive nature of collection and its rich metabolite content. Despite the advancements in mass spectrometry and 1H-NMR platforms in urine metabolomics, the statistical analysis of the generated data is challenged with the need to adjust for the hydration status of the person. Normalization to creatinine or osmolality values are the most adopted strategies, however, each technique has its challenges that can hinder its wider application. We have been developing targeted urine metabolomic methods to differentiate two important respiratory diseases, namely asthma and chronic obstructive pulmonary disease (COPD).

Targeted metabolomics requires accurate and precise quantification of candidate biomarkers, often through tandem mass spectrometric (MS/MS) analysis. Differential isotope labeling (DIL) improves mass spectrometric (MS) analysis in metabolomics by derivatizing metabolites with two isotopic forms of the same reagent. Despite its advantages, DIL-liquid chromatographic (LC)-MS/MS can result in substantial increase in workload when fully validated quantitative methods are required. To decrease the workload, we hypothesized that single point calibration or relative quantification could be used as alternative methods. Either approach will result in significant saving in resources and time. To test our hypothesis, six urinary metabolites were selected as model compounds. Urine samples were analyzed using a fully validated multipoint dansyl chloride-DIL-LC-MS/MS method. Samples were reprocessed using single point calibration and relative quantification modes. Our results demonstrated that the performance of single point calibration or relative quantification was inferior, for some metabolites, to multipoint calibration. The lower limit of quantification failed in the quantification of ethanolamine in most of participant samples using single point calibration. In addition, its precision was not acceptable in one participant during serine and ethanolamine quantification. On the other hand, relative quantification resulted in the least accurate data. In fact, none of the data generated from relative quantification for serine was comparable to that obtained from multipoint calibration. Finally, while single point calibration showed an overall acceptable performance for the majority of the model compounds, we cannot extrapolate the findings to other metabolites within the same analytical run. Analysts are advised to assess accuracy and precision for each metabolite in which single point calibration is the intended quantification mean.

The diagnosis of asthma and chronic obstructive pulmonary disease (COPD) can be challenging due to the overlap in their clinical presentations in some patients. There is a need for a more objective clinical test that can be routinely used in primary care settings. Through an untargeted H-1 NMR urine metabolomic approach, we identified a set of endogenous metabolites as potential biomarkers for the differentiation of asthma and COPD. A subset of these potential biomarkers contains 7 highly polar metabolites of diverse physicochemical properties. To the best of our knowledge, there is no liquid chromatography-tandem mass spectrometry (LC-MS/MS) method that evaluated more than two of the target metabolites in a single analytical run. The target metabolites belong to the families of monosaccharides, organic acids, amino acids, quaternary ammonium compounds and nucleic acids, rendering hydrophilic interaction liquid chromatography (HILIC) an ideal technology for their quantification. Since a clinical decision is to be made from patients data, a fully validated analytical method is required for biomarker validation. Method validation for endogenous metabolites is a daunting task since current guidelines were designed for exogenous compounds. As such, innovative approaches were adopted to meet the validation requirements. Herein, we describe a sensitive HILIC-MS/MS method for the quantification of the 7 endogenous urinary metabolites. Detection was achieved in the multiple reaction monitoring (MRM) mode with polarity switching, using quadrupole-linear ion trap instrument (QTRAP 6500) as well as single ion monitoring in the negative-ion mode. The method was fully validated according to the regulatory guidelines. Linearity was established between 6 and 21000 ng/mL and quality control samples demonstrated acceptable intra-and inter-day accuracy (85.7%-112%), intra-and inter-day precision (CV% <11.5%) as well as stability under various storage and sample processing conditions. To illustrate the method's applicability, the validated method was applied to the analysis of a small set of urine samples collected from asthma and COPD patients. Preliminary modelling of separation was generated using partial least square discriminant analysis (R-2 0.752 and Q(2) 0.57). The adequate separation between patient samples confirms the diagnostic potential of these target metabolites as a proof-of-concept for the differentiation between asthma and COPD. However, more patient urine samples are needed in order to increase the statistical power of the analytical model. (c) 2018 Elsevier B.V. All rights reserved.