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Dynamics of respiratory symptoms during infancy and associations with wheezing at school age

Usemann J^*, Xu B^*,Delgado-Eckert E, Korten I, Anagnostopoulou P, Gorlanova O, Kuehni C, Röösli M, Latzin P and Frey U on behalf of the BILD study group

* Equal contribution.

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Dynamics of respiratory symptoms during infancy and associations with wheezing at school age

Jakob Usemann^1,2*, Binbin Xu^1*, Edgar Delgado-Eckert¹, Insa Korten^1,2, Pinelopi Anagnostopoulou²,Olga Gorlanova¹, Claudia Kuehni³, Martin Röösli^4,5, Philipp Latzin^1,2 and Urs Frey¹ on behalf of the BILD⁶ study group

1 University Children`s Hospital Basel, UKBB, Basel, Switzerland

2 Pediatric Respiratory Medicine, Department of Pediatrics, Inselspital, Bern University Hospital, University of Bern, Switzerland

3 Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland

4 Swiss Tropical and Public Health Institute Basel, Basel, Switzerland

5 University of Basel, Basel, Switzerland

6 Basel Bern Infant Lung Development (BILD) cohort, current study group: Insa Korten, MD, Bern; Pinelopi Anagnostopoulou, MD, Bern; Urs Frey, MD, PhD, Basel; Olga Gornalova, MD, Basel; Philipp Latzin, MD, PhD, Bern; Elena Proietti, MD, PhD, Zurich; Anne Schmidt, MD, PhD, London; Jakob Usemann, MD, Basel.

* These authors contributed equally.

Corresponding author and reprint requests:

Urs Frey, University Children`s Hospital Basel, Basel, Switzerland Spitalstrasse 33, 4056 Basel, Switzerland

Phone: +41 – 61 – 7041900 Fax: +41 – 61 – 7041269

E-mail: [email protected]

Word Count: Abstract: 250/250, main body: 3564 Figures: 4, Tables 3

Key messages

 In this study, we developed a method to objectively characterize the dynamic symptom pattern of prospectively assessed respiratory symptoms during infancy.

 Utilizing this method, we identified a group of infants exposed to host factors and

environmental exposures, which were also at increased risk for wheezing and atopy at school age.

 This suggests that the assessment of dynamic symptom patterns may help to better characterize subjects susceptible for later disease.

Abstract

Background: Children with frequent respiratory symptoms in infancy have an increased risk for later asthma, but the association with the dynamic symptom pattern is unknown. We developed an observer-independent method to characterize the dynamics of symptoms and tested its association with respiratory morbidly at school age.

Methods: In this birth-cohort of healthy neonates, we prospectively assessed weekly respiratory symptoms during infancy, resulting in a time series of 52 symptom scores. For each infant, we calculated the probability of transition between two consecutive symptom scores. These transition probabilities were used to construct a Markov matrix, which characterized the dynamic symptom pattern quantitatively using a single entropy parameter. Based on this parameter we determined 4 phenotypes. Using logistic regression, we then determined the association with wheezing and atopy at school age.

Results: From 369 eligible neonates, 322 (87%) attended follow-up at 6 years and had complete data for >48 weeks of respiratory symptom scores during infancy (16864 observations).

Compared to the healthy reference phenotype, the high-risk phenotype, defined by the highest irregularity (and thus entropy parameter) of the Markov matrix, had an (adjusted odds ratio; 95%

CI) for wheezing of (OR 3.85; 1.11–13.43), and for atopy of (OR 3.45; 1.09–10.87) at school age. The high-risk phenotype was predominantly male (82%), and contained more infants exposed to maternal asthma (23%), and environmental tobacco smoke (41%).

Conclusion: Our study describes a novel method to characterize dynamics of respiratory symptoms at infancy, which may reflect susceptibility and recovery patterns of the airways.

Introduction

Wheezing disorders in early childhood have a high prevalence [1], with a major health issue [2], and methods to identify infants at risk for subsequent asthma are needed. Exposure to host factors (e.g. sex, maternal atopy), and environmental risk factors (e.g. childcare, siblings, environmental tobacco smoke (ETS) exposure, air pollution) influence the incidence and duration of respiratory symptoms during infancy [3, 4], and are associated with wheezing episodes during childhood [5]. In clinical practice, assessing risk factors, estimating the frequency of respiratory symptoms, and also the symptom pattern (e.g. episodic versus persistent symptoms) [6], may help to identify infants at risk for later asthma.

Especially the pattern of symptom deterioration and recovery (i.e. progression from a given symptoms state to another) may be informative, as it is determined by the dynamic symptom pattern that a subject undergoes during a given time window. While it is known that persistent wheeze in infants is more closely associated with later asthma and reduced lung function than episodic wheeze [6, 7], it is difficult to characterize the dynamic symptom pattern observer-independently, and to estimate its predictive value for persistence of respiratory symptoms during preschool age.

We hypothesize that the dynamic symptom pattern may not only be determined by exposure to infectious risk factors (siblings, childcare), but also by host factors and exposure to ETS or air pollution. The dynamic symptom pattern may thus contain information on susceptibility of the airways to infectious triggers. This hypothesis was supported by our previous study (Stern et al.

[8]), in which infants exposed to higher air pollution levels recovered more slowly from viral infections than those with lower exposure levels. Similarly, infants of allergic mothers [9], or those exposed to ETS [9], are more likely to suffer from persistent wheeze during childhood.

Previous studies used a Markov model the trajectory of asthma severity [10], and to model asthma control [11]. The dynamic pattern of subsequently assessed respiratory symptoms could be mathematically represented using this Markov model (also known as Markov matrix) [12].

Each row of this matrix encodes a conditional probability distribution, which can be measured using the Shannon entropy [13]. Consequently, by calculating entropy of the probability distributions encoded in the Markov matrix, we could objectively characterize the pattern of symptom deterioration and recovery.

The aim was to develop a method to characterize the dynamic pattern of weekly assessed respiratory symptom scores during infancy with a Markov matrix for each infant. First, we tested if we could identify specific dynamic phenotypes using these Markov matrices. Next, we tested whether these dynamic phenotypes predicted wheezing and atopy at school age. Lastly, we determined if environmental risk factors were more common in specific dynamic phenotypes, to explore if specific dynamic symptom patterns are influenced by host factors and environmental exposures.

Methods

Details are outlined in the appendix.

Study design

In the Basel-Bern infant lung development (BILD) birth-cohort study, we prospectively assessed weekly respiratory symptom scores (states 0 to 4) [14] during infancy, resulting in 52 consecutive observations. We used these symptom scores to develop a method which summarizes the dynamic symptom pattern with a Markov matrix for each infant. These Markov matrices were characterized using one single quantitative measure, namely entropy. First, we tested if we could identify specific dynamic phenotypes based on this entropy parameter. Next, we tested the association between dynamic phenotypes with wheezing and atopy at school age (primary outcomes). Allergic sensitization, upper respiratory tract infection (URTI), lung function and FeNO measurements at school age were secondary outcomes. Lastly, we compared the distribution of risk factors across dynamic phenotypes.

Study participants

This study comprised a group of unselected, healthy term born neonates recruited antenatally in two centers (Bern and Basel) in Switzerland [15]. From 1999 to 2015, 369 children from Bern were invited for a follow up at 6 years. The Ethics Committees of Bern and Basel, Switzerland approved the study. Written informed consent was obtained from parents before enrolment.

Exposures: respiratory symptoms during infancy

During the first year of life, research nurses called the parents weekly to assess the child’s health and respiratory symptoms using a standardized symptom score that groups symptoms into 4 levels according to severity [4, 14]. Weeks with respiratory symptoms were defined as the total number of weeks a child had any respiratory symptom, independent of type or severity; weeks with severe respiratory symptoms were defined as a symptom score of ≥3 (e.g repeated sleep disturbances during the night, or general practitioner consultation), as described previously [4, 14] (Table S1).

Markov matrix to assess dynamics of respiratory symptoms

We used a Markov model approach to examine trajectories between subsequently assessed symptom scores. We assessed transitions between different levels of the symptom scores: state zero: healthy, symptom score 0; states 1-4: symptomatic states, symptom scores 1-4. For each symptom state, we counted how often a transition to any other state, assessed in the subsequent week occurred. For each child, this count information can be displayed in a 5×5 matrix (vertical axis initial state, horizontal-axis target state). These counts are absolute frequencies, which were used to calculate relative frequencies for each transition (Figure 1 A-C). This matrix is called Markov matrix [12], which was then graphically represented using a three-dimensional landscape. Finally, the landscape’s irregularity was quantified using a single entropy value [16]

(Figure 2, supplementary methods).

Risk factors

At baseline, we used a standardized questionnaire to assess pre-and postnatal exposure to risk factors for respiratory symptoms during infancy [4] or asthma development (e.g. number of siblings, maternal asthma). Parental atopic disease was defined as self-reported, doctor-diagnosed asthma, hay fever, or eczema. Maternal education was categorized as low (3 years of secondary education) and high (≥4 years of secondary education). Duration of breastfeeding (exclusive or non-exclusive) was weekly assessed and binary coded (as <26 and ≥26 weeks). All risk factors were categorized as binary variables in order to compare under-or overrepresentation across phenotypes.

Outcomes: respiratory outcomes and atopic sensitization at school age

At 6 years, asthma and allergy were assessed by an adapted International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire [17]. We choose the following outcomes: “any wheezing between 1-6 years”; “current wheezing”, defined by wheezing over the past 12 months; upper respiratory tract infection (URTI), defined by ear infection, throat infection, or serious cold (defined by symptoms ≥2 weeks) over the past 12 months. Atopy was defined as

allergic rhinitis, allergic asthma, or atopic dermatitis. A skin-prick test (SPT) was determined positive if a wheal diameter of any of the seven tested aeroallergens was greater than positive control [15].

Spirometry was performed using the MasterLab setup (Jaeger, Wuerzburg, Germany) according to standard guidelines [18]. Forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), FEV₁/FVC ratio, and forced expiratory flow at 25-75% of FVC (FEF_25-75%) were expressed as z-scores [19]. We measured the fraction of exhaled nitric oxide (FeNO) using a commercially available analyzer (CLD 77 AM; Eco Medics AG, Duernten, Switzerland) according to current guidelines [20].

Statistical analysis

Entropy is a measure for disorder within a dynamic system, and higher entropy values correspond to more irregularity [13]. For each infant, we calculated entropy [21] of the Markov matrix, which provides a quantitative measure of the irregularity patterns of symptom deterioration and recovery. The frequency distribution of entropy visually appeared multimodal, with 4 modi (Figure 3A). Therefore, we defined 4 dynamic phenotypes based on the slope of the cumulative distribution function curve, and a slope of zero was used for cutoff. We defined 4 similarly sized reference phenotypes by the frequency distribution of total number of weeks with respiratory symptoms during infancy (Figure 3B).

Using logistic regression, we studied the association of the 4 dynamic phenotypes, the 4 reference phenotypes, and asthma risk factors, with the child’s outcomes. For “any wheezing”,

“current wheezing”, atopy and positive prick test of the child, analyses were adjusted for sex, maternal education and asthma, ETS exposure, childcare, and siblings. For FeNO, we additionally adjusted for asthma of the child and inhaled corticosteroid use. For lung function, we adjusted for maternal education, maternal asthma, ETS exposure, childcare, and siblings.

Chi² and Kruskal–Wallis tests were used to compare characteristics across phenotypes. A Bonferroni-corrected significance level was used to account for multiple pair-wise testing. We

used the weighted kappa-statistic [22] to compare agreement between dynamic and reference phenotypes.

For sensitivity analyses, we repeated the analysis in infants having ≥1 episode of symptom score

≥3, and within an additional, independent sample from our cohort of 242 infants. To explore that the entropy distribution was not an artifact of our analysis, we re-categorized the symptom states (0, 1, 2, (3+4)→3), we simulated data, and perturbed the existing data. Furthermore, we corrected our findings for potentially unobserved events.

Results

From 369 eligible subjects, 322 (87%) were studied (Figure S1), having >48 weeks of symptom series during infancy and complete data on risk factors and outcomes (Table 1). Demographic data and distribution of respiratory symptoms did not differ between infants followed-up and those lost to follow-up, but exposure to risk factors did (Table S2).

Distribution of respiratory symptoms

In infants followed-up, we had information for 16864 person–weeks. On average, the number of weeks with any respiratory symptom was (median; range) (4; 0-23 weeks). In contrast, severe symptoms were rare (0; range 0-6 weeks). Figure 1D shows the distribution of all symptom states.

Dynamics of respiratory symptoms assessed by the Markov matrix

Figure 2 shows the dynamic pattern of respiratory symptoms for 2 infants. Both had the same number of symptom weeks, but different patterns of the Markov matrix landscape. The shape of the matrix landscape, characterized by a single entropy parameter differed as well.

Outcomes at school age

From 322 children at follow-up, 105 (32.9%) had any wheezing, and 38 (11.7%) had current wheezing. There were 120 (37.5%) children with allergic diseases, and a subgroup of 270 completed a SPT, 37 (13.6%) of which were positive. Lung function was completed in 222 (68.7%) children, and 231 (71.7%) had FeNO measurements.

Associations of dynamic phenotypes, reference phenotypes and risk factors with outcomes at school age

We defined 4 dynamic phenotypes using the Markov matrix and 4 corresponding reference phenotypes by the distribution of respiratory symptom weeks (Figure 3). Compared to baseline phenotpye one, in the adjusted logistic regression model, male gender, maternal asthma, dynamic phenotype 4 and reference phenotypes 2 and 3 were associated with any wheezing during childhood (Table 2). Dynamic phenotype 4 and male gender were also associated with current