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

Health effects associated with exposure to secondhand smoke: a Burden of Proof study

Despite a gradual decline in smoking rates over time, exposure to secondhand smoke (SHS) continues to cause harm to nonsmokers, who are disproportionately children and women living in low- and middle-income countries. We comprehensively reviewed the literature published by July 2022 concerning the adverse impacts of SHS exposure on nine health outcomes. Following, we quantified each exposure-response association accounting for various sources of uncertainty and evaluated the strength of the evidence supporting our analyses using the Burden of Proof Risk Function methodology. We found all nine health outcomes to be associated with SHS exposure. We conservatively estimated that SHS increases the risk of ischemic heart disease, stroke, type 2 diabetes and lung cancer by at least around 8%, 5%, 1% and 1%, respectively, with the evidence supporting these harmful associations rated as weak (two stars). The evidence supporting the harmful associations between SHS and otitis media, asthma, lower respiratory infections, breast cancer and chronic obstructive pulmonary disease was weaker (one star). Despite the weak underlying evidence for these associations, our results reinforce the harmful effects of SHS on health and the need to prioritize advancing efforts to reduce active and passive smoking through a combination of public health policies and education initiatives.

Antibiotics in pregnancy influence nasal microbiome and respiratory morbidity in infancy

Background

The effects of prenatal antibiotic exposure on respiratory morbidity in infancy and the involved mechanisms are still poorly understood. We aimed to examine whether prenatal antibiotic exposure in the third trimester is associated with nasal microbiome and respiratory morbidity in infancy and at school age, and whether this association with respiratory morbidity is mediated by the nasal microbiome.

Methods

We performed 16S ribosomal RNA gene sequencing (regions V3-V4) on nasal swabs obtained from 296 healthy term infants from the prospective Basel-Bern birth cohort (BILD) at age 4-6 weeks. Information about antibiotic exposure was derived from birth records and standardised interviews. Respiratory symptoms were assessed by weekly telephone interviews in the first year of life and a clinical visit at age 6 years. Structural equation modelling was used to test direct and indirect associations accounting for known risk factors.

Results

α-Diversity indices were lower in infants with antibiotic exposure compared to nonexposed infants (e.g. Shannon index p-value 0.006). Prenatal antibiotic exposure was also associated with a higher risk of any, as well as severe, respiratory symptoms in the first year of life (risk ratio 1.38, 95% CI 1.03-1.84; adjusted p-value (padj)=0.032 and risk ratio 1.75, 95% CI 1.02-2.97; padj=0.041, respectively), but not with wheeze or atopy in childhood. However, we found no indirect mediating effect of nasal microbiome explaining these clinical symptoms.

Conclusion

Prenatal antibiotic exposure was associated with lower diversity of nasal microbiome in infancy and, independently of microbiome, with respiratory morbidity in infancy, but not with symptoms later in life.

Pollen exposure is associated with risk of respiratory symptoms during the first year of life

BACKGROUND

Pollen exposure is associated with respiratory symptoms in children and adults. However, the association of pollen exposure with respiratory symptoms during infancy, a particularly vulnerable period, remains unclear. We examined whether pollen exposure is associated with respiratory symptoms in infants and whether maternal atopy, infant's sex or air pollution modifies this association.

METHODS

We investigated 14,874 observations from 401 healthy infants of a prospective birth cohort. The association between pollen exposure and respiratory symptoms, assessed in weekly telephone interviews, was evaluated using generalized additive mixed models (GAMMs). Effect modification by maternal atopy, infant's sex, and air pollution (NO₂ , PM_2.5 ) was assessed with interaction terms.

RESULTS

Per infant, 37 ± 2 (mean ± SD) respiratory symptom scores were assessed during the analysis period (January through September). Pollen exposure was associated with increased respiratory symptoms during the daytime (RR [95% CI] per 10% pollen/m³ : combined 1.006 [1.002, 1.009]; tree 1.005 [1.002, 1.008]; grass 1.009 [1.000, 1.23]) and nighttime (combined 1.003 [0.999, 1.007]; tree 1.003 [0.999, 1.007]; grass 1.014 [1.004, 1.024]). While there was no effect modification by maternal atopy and infant's sex, a complex crossover interaction between combined pollen and PM_2.5 was found (p-value 0.003).

CONCLUSION

Even as early as during the first year of life, pollen exposure was associated with an increased risk of respiratory symptoms, independent of maternal atopy and infant's sex. Because infancy is a particularly vulnerable period for lung development, the identified adverse effect of pollen exposure may be relevant for the evolvement of chronic childhood asthma.

Individual illness dynamics: An analysis of children with sepsis admitted to the pediatric intensive care unit

Illness dynamics and patterns of recovery may be essential features in understanding the critical illness course. We propose a method to characterize individual illness dynamics in patients who experienced sepsis in the pediatric intensive care unit. We defined illness states based on illness severity scores generated from a multi-variable prediction model. For each patient, we calculated transition probabilities to characterize movement among illness states. We calculated the Shannon entropy of the transition probabilities. Using the entropy parameter, we determined phenotypes of illness dynamics based on hierarchical clustering. We also examined the association between individual entropy scores and a composite variable of negative outcomes. Entropy-based clustering identified four illness dynamic phenotypes in a cohort of 164 intensive care unit admissions where at least one sepsis event occurred. Compared to the low-risk phenotype, the high-risk phenotype was defined by the highest entropy values and had the most ill patients as defined by a composite variable of negative outcomes. Entropy was significantly associated with the negative outcome composite variable in a regression analysis. Information-theoretical approaches to characterize illness trajectories offer a novel way of assessing the complexity of a course of illness. Characterizing illness dynamics with entropy offers additional information in conjunction with static assessments of illness severity. Additional attention is needed to test and incorporate novel measures representing the dynamics of illness.

Novel Methods of Measuring Adherence Patterns Reveal Adherence Phenotypes with Distinct Asthma Outcomes

RATIONALE

Poor adherence with asthma controller medication contributes to worse symptom control and increased exacerbation risk. Adherence is often expressed as the mean proportion of prescribed doses taken over a period of 6-12 months. New metrics may capture individual day-to-day variability patterns linked with distinct clinical outcomes.

OBJECTIVES

To test the hypotheses that novel time- and dose-based adherence variability metrics offer independent value to mean adherence in identifying distinct adherence patterns, that are associated with symptom control (Asthma Control Test [ACT] score) and exacerbation risk, using electronically-recorded medication data from a 6-month cluster randomized trial examining the effect of inhaler reminders on adherence.

METHODS

Adherence metrics were calculated from the first two months (months 0-2) of the study period. In addition to mean adherence (%prescribed puffs/day taken, PTmean), we examined novel metrics including: time adherence area-under-curve (T-AUC), reflecting cumulative gaps in adherence over time, entropy (H), reflecting disorder in the ways in which a patient changed their medication dose adherence from day to day, and standard deviation of the %prescribed puffs/day taken (PTSD). Dominant metrics identified from factor analysis were included in hierarchical clustering analysis. We compared the resultant clusters in terms of outcomes over months 2-6, and exacerbation risk over the entire study period.

RESULTS

Two factors explained >65% of the total variance in adherence, primarily driven by T-AUC and H. Two main patient clusters based on their adherence metrics were identified: Cluster 1 ("high time adherence", n=75) had better T-AUC, i.e. fewer gaps between medication-taking days, than Cluster 2 ("low time adherence", n=23). Though both clusters had similar symptom control at 2 months, Cluster 1 showed less subsequent decline in ACT over months 2-6 (median(IQR) change in ACT score: 1(-1, 4) vs -2(-3.75,0.75); p=0.012), and had better symptom control at 6 months (ACT score: 20(17, 23) vs 17 (15, 20); p=0.034). There were no significant differences between the clusters in terms of proportion of exacerbators or time to exacerbation.

CONCLUSIONS

Novel metrics showed that low time adherence was associated with greater risk of decline in asthma symptom control. Adherence patterns may exhibit 'memory' relevant to future clinical status, warranting validation in a larger dataset.

Childhood asthma- pathogenesis and phenotypes

In the pathogenesis of asthma in children there is a pivotal role for a type 2 inflammatory response to early life exposures or events. Interactions between infections, atopy, genetic susceptibility, and environmental exposures (such as farmyard environment, air pollution, tobacco smoke exposure) influence the development of wheezing illness and the risk for progression to asthma. The immune system, lung function and the microbiome in gut and airways develop in parallel and dysbiosis of the microbiome may be a critical factor in asthma development. Increased infant weight gain and preterm birth are other risk factors for development of asthma and reduced lung function. The complex interplay between these factors explains the heterogeneity of asthma in children. Subgroups of patients can be identified as phenotypes based on clinical parameters, or endotypes, based on a specific pathophysiological mechanism. Paediatric asthma phenotypes and endotypes may ultimately help to improve diagnosis of asthma, prediction of asthma development and treatment of individual children, based on clinical, temporal, developmental or inflammatory characteristics. Unbiased, data-driven clustering, using a multidimensional or systems biology approach may be needed to better define phenotypes. The present knowledge on inflammatory phenotypes of childhood asthma has now been successfully applied in the treatment with biologicals of children with severe therapy resistant asthma, and it is to be expected that more personalized treatment options may become available.

Dynamics of inhaled corticosteroid use are associated with asthma attacks

Inhaled corticosteroids (ICS) suppress eosinophilic airway inflammation in asthma, but patients may not adhere to prescribed use. Mean adherence-averaging total doses taken over prescribed-fails to capture many aspects of adherence. Patients with difficult-to-treat asthma underwent electronic monitoring of ICS, with data collected over 50 days. These were used to calculate entropy (H) a measure of irregular inhaler use over this period, defined in terms of transitional probabilities between different levels of adherence, further partitioned into increasing (H_inc) or decreasing (H_dec) adherence. Mean adherence, time between actuations (Gap_max), and cumulative time- and dose-based variability (area-under-the-curve) were measured. Associations between adherence metrics and 6-month asthma status and attacks were assessed. Only H and H_dec were associated with poor baseline status and 6-month outcomes: H and H_dec correlated negatively with baseline quality of life (H:Spearman r_S = - 0·330, p = 0·019, H_dec:r_S = - 0·385, p = 0·006) and symptom control (H:r_S = - 0·288, p = 0·041, H_dec: r_S = - 0·351, p = 0·012). H was associated with subsequent asthma attacks requiring hospitalisation (Wilcoxon Z-statistic = - 2.34, p = 0·019), and H_dec with subsequent asthma attacks of other severities. Significant associations were maintained in multivariable analyses, except when adjusted for blood eosinophils. Entropy analysis may provide insight into adherence behavior, and guide assessment and improvement of adherence in uncontrolled asthma.

Dynamic Transitions of Pediatric Sepsis: A Markov Chain Analysis

Pediatric sepsis is a heterogeneous disease with varying physiological dynamics associated with recovery, disability, and mortality. Using risk scores generated from a sepsis prediction model to define illness states, we used Markov chain modeling to describe disease dynamics over time by describing how children transition among illness states. We analyzed 18,666 illness state transitions over 157 pediatric intensive care unit admissions in the 3 days following blood cultures for suspected sepsis. We used Shannon entropy to quantify the differences in transition matrices stratified by clinical characteristics. The population-based transition matrix based on the sepsis illness severity scores in the days following a sepsis diagnosis can describe a sepsis illness trajectory. Using the entropy based on Markov chain transition matrices, we found a different structure of dynamic transitions based on ventilator use but not age group. Stochastic modeling of transitions in sepsis illness severity scores can be useful in describing the variation in transitions made by patient and clinical characteristics.

Respiratory Symptoms in Post-infancy Children. A Dutch Pediatric Cohort Study

Aim: To study the pattern of respiratory symptoms in children in the general population. Method: We followed a cohort of children for up to 2 years through parents completing weekly online questionnaires in the Child-Is-Ill study ("Kind-en-Ziekmeting" in Dutch); the study was running 2012-2015. Inclusion criteria were "an ordinary child" (according to the parents) and <18 years old at inclusion. We especially encouraged participation of post-infancy children. Age at inclusion, sex, smoking exposure, allergy in the family, and frequent infections in the family were noted. Pearson's correlation, principal component analysis, latent class analysis, latent profile analysis, linear regression, and linear mixed effects regression were used in the statistical analyses. Results: Data were collected on 55,524 childweeks in 755 children (50% girls; median age, 7 years; interquartile range, 4-11 years, 97% ≥2 years at inclusion), with reported symptom(s) in 8,425 childweeks (15%), leading to school absenteeism in 25%, doctor's visits in 12%, and parental sick leave in 8%; symptoms lasting ≥3 weeks were rare (2% of episodes). Linear mixed effects regression showed significant, but only limited, effects of season on the proportion of "symptom(s) reported" per individual child. Only runny nose showed a significant, but very small, age effect. However, the variability between the children was considerable. There were no obvious subgroups of children with specific symptom combinations. Conclusion: In any randomly chosen week, the vast majority of children (85%) in our-mainly-post-infancy cohort derived from the general population did not have any symptom, even in the younger age group, even in winter. The children showed considerable variability; no clear subgroups of symptom patterns could be identified, underlining the difficult position of healthcare providers. These results support our opinion that post-infancy children in the general population should not be evaluated as if they are infants when they have recurrent respiratory symptoms. If they clearly deviate from the above-described most common pattern, it is wise to keep an eye on potential, maybe even rare, serious underlying causes.

Exercise Science and Child Health: A Tale of Many Journeys

Children are the most naturally physically active human beings; reduced physical activity is a cardinal sign of childhood disease, and exercise testing provides mechanistic insights into health and disease that are often hidden when the child is at rest. The physical inactivity epidemic is leading to increased disease risk in children and, eventually, in adults in unprecedented ways. Cardiopulmonary exercise testing (CPET) biomarkers are used to assess disease severity, progress, and response to therapy across an expanding range of childhood diseases and conditions. There is mounting data that fitness in children tracks across the life span and may prove to be an early, modifiable indicator of cardiovascular disease risk later in life. Despite these factors, CPET has failed to fulfill its promise in child health research and clinical practice. A major barrier to more accurate and effective clinical use of CPET in children is that data analytics and testing protocols have failed to keep pace with enabling technologies and computing capacity. As a consequence, biomarkers of fitness and physical activity have yet to be widely incorporated into translational research and clinical practice in child health. In this review, the author re-examines some of the long-held assumptions that mold CPET in children. In particular, the author suggests that current testing strategies that rely predominantly on maximal exercise may, inadvertently, obfuscate novel and clinically useful insights that can be gleaned from more comprehensive data analytics. New pathways to discovery may emanate from the simple recognition that the physiological journey that human beings undertake in response to the challenge of exercise may be far more important than the elusive destination of maximal or peak effort.