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Pathogenesis and Host Response

Susceptibility to Lower Respiratory Infections in Childhood is Associated with Perturbation of the Cytokine Response to Pathogenic Airway Bacteria

Vissing, Nadja Hawwa MD, PhD; Larsen, Jeppe Madura MSc, PhD; Rasmussen, Morten Arendt MSc, PhD; Chawes, Bo Lund Krogsgaard MD, PhD; Thysen, Anna Hammerich MSc, PhD; Bønnelykke, Klaus MD, PhD; Brix, Susanne MSc, PhD; Bisgaard, Hans MD, DMSci

Author Information
The Pediatric Infectious Disease Journal: May 2016 - Volume 35 - Issue 5 - p 561-566
doi: 10.1097/INF.0000000000001092


Lower respiratory infections (LRI) in childhood are common and account for considerable morbidity and health care utilization.1–3 The frequency of LRI varies significantly between otherwise healthy children, but extrinsic and intrinsic triggers of such variation are poorly understood. In the Copenhagen Prospective Studies on Asthma in Childhood2000 (COPSAC2000) cohort, we previously demonstrated that neonates colonized in the airways with Streptococcus pneumoniae (S. pneumoniae), Haemophilus influenzae (H. influenzae) or Moraxella catarrhalis (M. catarrhalis) have an increased risk of asthma,4 but also LRI in early childhood, independently of asthma.5 Furthermore, we demonstrated that children from the same cohort with asthma by school age exhibited an aberrant immune response to pathogenic airway bacteria in infancy.6 Therefore, we hypothesized that children developing LRI may also have an aberrant immune response to pathogenic bacteria in early life.

To investigate this hypothesis, we stimulated peripheral blood mononuclear cells (PBMCs) stored since age 6 months from the COPSAC2000 birth cohort with H. influenzae, M. catarrhalis and S. pneumonia. Release of key cytokine immune mediators involved in pathogen clearance (TNF-α, IFN-γ, IL-17, IL-5 and IL-13)7–12 and immune regulation (IL-10 and IL-2)13,14 were assessed and association with incidence of LRI was studied for the first 3 years of life.


Study Cohort

The COPSAC2000 is a longitudinal clinical birth cohort study of 411 children recruited in Denmark during 1998–2001 among pregnant mothers with a history of asthma.15 A key feature of the COPSAC2000 study is the intense clinical surveillance of the cohort by the COPSAC pediatricians including scheduled 6-monthly clinical investigations and clinic visits for any acute airway symptom supported by a day-to-day diary on the presence or absence of Troublesome Lung Symptoms (TROLS)16,17 filled from birth by the parents.

Data were collected according to Good Clinical Practice guidelines. The study was conducted in accordance with the Declaration of Helsinki and approved by the Copenhagen Ethics Committee (KF01-289/96) and the Danish Data Protection Agency (2008-41-1754). Written informed consent was obtained from both parents.

Lower Respiratory Infections

At each acute visit for respiratory symptoms, the children were examined, diagnosed and treated by the COPSAC pediatricians in accordance with predefined standard algorithms. LRI was defined as a diagnosis of clinical pneumonia or bronchiolitis. Pneumonia was defined by troublesome cough accompanied by tachypnea, fever and abnormal auscultation,18 whereas bronchiolitis was defined by symptoms of coryza progressing over a few days to cough, tachypnea, chest retractions, and auscultative widespread crepitation and/or rhonchi in a child below 2 years old.19 Diagnoses were based on clinical appearance independently of identified pathogen(s).

The research physicians thoroughly interviewed the parents at each 6-monthly scheduled visit about their child’s illnesses, use of medication and health care contacts during the previous 6 months. The respiratory diaries were reviewed by the doctor as a part of the interview.

If the child had been seen elsewhere suspected for respiratory illness, that is, at the general practitioner or hospital, details on clinical findings, diagnosis and treatment were obtained from the source. We previously validated these data on infectious episodes revealing that our records for respiratory illnesses are nearly complete.20

PBMC Immune Response to Bacterial Stimulations

Immune responses to pathogenic respiratory bacteria were studied as previously described.6 In brief, 5 × 105 PBMCs were stimulated in U-bottomed 96-well plates for 40 hours with UV-inactivated H. influenzae, M. catarrhalis or S. pneumoniae (50 µg/mL) in 200 µL/well completed RPMI 1640 medium [2 mM L-glutamine (Cambrex, East Rutherford, NJ); 0.1 M HEPES (Lonza); 100 U/mL penicillin/streptomycin (Lonza)] supplemented with 10% FBS. IFN-γ, TNF-α, IL-2, IL-5, IL-10, IL-13 and IL-17 were analyzed in supernatants using a custom multiplex assay from Meso Scale Discovery (MSD, Gaithersburg, MD).

Statistical Methods

Supernatant cytokine levels in response to H. influenzae, M. catarrhalis or S. pneumoniae were adjusted by subtracting baseline levels of unstimulated PBMCs for each donor. Measurements were subsequently square root transformed to improve normal distribution before further statistical analysis.

We previously learned that the cytokine levels show a high degree of interdependence.6 Based on these results, the a priori hypothesis was to look for a pattern rather than single cytokine associations.

The association between cytokine levels and incidence of LRI at age 0–3 years was analyzed by quasi Poisson regression. The associations were investigated in a hierarchy with 3 levels: (1) A multivariable model assessing the entire immune profile by combining the 7 cytokine responses across all 3 bacterial stimulations (1 test). To explore the results of the first analysis, we used (2) a trivariable model using each cytokine across all 3 bacterial stimulations (7 tests) and (3) a univariable model using each cytokine from each of the 3 bacterial stimulations (21 tests) estimating incidence risk ratios with 95% confidence intervals of LRI. The statistical models are described in details in the Supplemental Digital Content 1,

The presented P values are crude with a significance level of 0.05. Multiple testing is handled by using the Bonferroni method for the significance threshold at each hierarchy level.

To avoid bias from left censoring of the data due to low detection levels, an additional analysis was performed using cytokine data as a dichotomized variable based on whether the stimulation level was higher than the unstimulated level or not.

In addition, we performed 2 subgroup analyses: (1) An analysis excluding children who experienced LRI before sampling of PBMCs at age 6 months and (2) an analysis also excluding children who had asthma within the study period (0–3 years) to investigate potential confounding by underlying asthmatic disease.

To explore the findings from the multivariate model, we applied a principal component analysis (PCA), decomposing the complex dataset into fewer dimensions to capture the largest variability in the data. The PCA is described in details in the Supplemental Digital Content 1, All associations were adjusted for gender21–23 and season of birth.23–25 All statistical analysis are based on a linear quasipoison regression, while in the graphical presentation of the results, the children are partitioned into 3 groups based on the number of LRI. Statistical modeling was done in R version 2.15.1. and Matlab R2012b using in-house algorithms.



Full clinical follow-up till age 3 years was available in 334 (81%) of the 411 children in the COPSAC2000 cohort. PBMC collection and storage at age 6 months was successfully completed in 291 (87%) of these children. No differences between the study and dropout groups were observed (see baseline characteristics of the study groups in Table, Supplemental Digital Content 2,

Asthma was diagnosed in 45 (15%) children before age 3.

Incidence of LRI

Pneumonia was diagnosed on 301 occasions and bronchiolitis on 40 occasions, yielding a total of 341 episodes of LRI. A total of 162 children (56%) experienced at least 1 episode of LRI, and mean incidence was 1.2 episodes per child (median: 1, IQR: 0–2) throughout the 3 years. In the majority of cases (64%), the child was diagnosed by the COPSAC pediatricians at the research clinic during the infectious episode. Twenty-five (9%) children experienced LRI before sampling of PBMC at age 6 months.

PBMC Bacterial Immune Responses

The absolute cytokine levels measured in response to the bacterial challenges are displayed in Table 1. We did not detect any univariate association between incidence of LRI and the individual cytokine levels after the three different stimulations (Table 2).

Measured Cytokine Levels in Response to Pathogenic Airway Bacteria at Age 6 Months (n = 291)
Cytokine Levels and Incidence of LRI

The trivariable model is depicted in Figure 1 showing the level of each of the seven cytokines across the 3 bacterial stimulations stratified by incidence of LRI with corresponding P values. TNF-α was significantly associated with increased incidence of LRI in the trivariable model including TNF-α responses from all 3 bacterial stimulations, both in the overall analysis (P = 0.005, N = 291, Table 2), in the subgroup of children without LRI before PBMC sampling (P = 0.010, N = 267) and in children without asthma at any time-point from 0–3 years (P = 0.004, N = 229; Table, Supplemental Digital Content 3, IL-5 was also significantly associated with increased incidence of LRI in the trivariable model (P = 0.028, Table 2), but the association was neither significant after excluding children with LRI before the age of 6 months nor after excluding children diagnosed with asthma in the study period (Table, Supplemental Digital Content 3,

Mean cytokines levels across the 3 bacterial stimulations stratified by incidence of LRI (blank stimulation responses subtracted, error bars indicate standard deviation of the mean).HI, Haemophilus influenzae; MC, Moraxella catarrhalis; SP, Streptococcus pneumoniae.

The multivariable model encompassing the entire immune-profile pattern, as reflected by all 7 cytokines across all 3 bacterial stimulations, showed a highly significant association with the incidence of LRI (P value = 0.0062). The association remained significant when excluding children with LRI before sampling (P = 0.011) as well as when excluding children with concurrent asthmatic disease (P = 0.0098; Table, Supplemental Digital Content 3, The results from the trivariable model indicate that TNF-α and IL-5 are the key cytokines driving the association.

Dichotomized Data

Figure, Supplemental Digital Content 4,, depicts the percentage of stimulations were cytokines were detected. Table, Supplemental Digital Content 5,, displays the results of the uni-, tri- and multivariable analyses based on dichotomized cytokine data. The multivariable analysis based on dichotomized cytokine data also revealed a statistically significant pattern; the overall P value attenuated from P = 0.006 for the model on continuous data to P = 0.01 for the dichotomized data.

In the univariate and the trivariable analyses based on dichotomized data, TNF-α and IL-2 were significantly associated to incidence of LRI, but both cytokines were detectable in a high proportion of the stimulations, so dichotomization of these cytokines does not seem meaningful. As seen in Table 1 particularly IL-5, but also IL-13 and IL-17 were undetectable in a large proportion of stimulations. Limiting dichotomization to the cytokines with low detectability (IL-5, IL-13 and IL-17) and the rest of the cytokines as continuous variables also revealed a statistically significant pattern with P = 0.04.

PCA Analysis

The PCA analysis confirmed the significant association between the overall immune response and incidence of LRI, with an association built on a correlation structure between several cytokines and bacterial stimulations (see Supplemental Digital Content 6,, for details). The PCA analysis did not further elucidate the composition of the pattern describing susceptibility to LRI. In this model, TNF-α and IL-5 likewise carry important information, but they are not explaining the entire aberrant bacterial immune response displayed by children experiencing LRI.


Principal Findings

Children with increased incidence of LRI at age 0–3 years display an aberrant in vitro PBMC immune response to pathogenic airway bacteria in early life, which may contribute to the heterogeneous presentation of common lower airway infections in childhood.

Strengths and Limitations

The storage of PBMCs since infancy in a prospective cohort setting provided a unique material for assessing infant immune function preceding LRI enabling this first study of a possible link between functional immunity and increased susceptibility for LRI during early childhood.

We studied LRI based on a clinical diagnosis, regardless of pathogen or systemic inflammatory biomarkers, consistent with other studies on uncomplicated childhood LRI.2,3,26,27 The longitudinal single center study design with comprehensive prospective data collection, clinical evaluations and symptom assessment supported by daily respiratory diaries enhanced the validity of the clinical data. Even though some episodes of LRI were diagnosed outside COPSAC (36%), considerable efforts were made to collect all relevant information. The validity of this part of the data was confirmed in a previous validation study,20 finding less than 4% missed diagnoses in the record of the families’ general practitioner for LRI.

In the literature, incidence rates of LRI in young children vary greatly (13%–64%) depending on study design and case definitions,1,28–33 and evidence is hampered by the lack of a golden standard defining LRI.34 Our finding of 58% of children younger than 3 years having at least one incidence of LRI corresponds with other community-based studies utilizing similar case definitions.29,35

It is a limitation of the study that all children were born to asthmatic mothers since we previously demonstrated that children born of mothers with asthma, hay fever or eczema exhibit different airway immunity compared with children born of healthy mothers.36 Still, considering the high prevalence of asthma among adults of reproductive age in industrialized countries,37 our results are relevant to a substantial proportion of the population. In addition, the associations remained significant in the subgroup of children without concurrent asthma suggesting that the findings are not driven by the child’s propensity to develop asthma. This is further supported by the distinct immune profile describing increased risk of LRI which is different from our earlier described immune profile related to increased risk of asthma later in life.6

It is another limitation that the immune response was assessed in vitro, which may not adequately reflect the complex in vivo processes involving tissue-resident immune cells.

The choice of pathogenic stimulations can also be debated. We choose 3 distinct bacteria, but no viral stimulation. These 3 bacteria are the most common pathogens involved in bacterial airway infections in children38,39 and we have previously shown that children colonized in the airways as neonates with either of these pathogens have increased risk of LRI within the first 3 years of life.5

The PCA technique with an unsupervised approach with respect to the outcome is a strength of the paper and was chosen because it is specifically designed to identify patterns built on several sources of information, addressing the covariance between these sources. The dominating immunological trends are characterized in the first couple of components: PC1 describes the overall immune response for the individual children and PC2 describes the diverse response related to different stimulations. In the later components, less amplified patterns of variation in the data material are revealed, where we see the information that is associated to LRI burden. The PCA model is built independently of the outcome making it less sensitive to overfitting compared with supervised statistical methods handling multivariate data.


We demonstrated a specific functional immune fingerprint, identifying children at risk of LRI in early childhood. Thus, host immunity appears to play a role for individual susceptibility to LRI and exploring the functional immunology in early childhood may help understanding the population variability in LRI.

The pattern associated with increased disease incidence is described by a correlation structure across cytokines and the individual bacterial stimulations, where several cytokines carry relevant information. Our results suggest that children with increased incidence of LRI experience an aberrant immune response when compared with healthy children. It is a key finding in this study that the overall immune potential of the child does not reflect susceptibility to LRI; meaning that although some children generally respond with high levels whereas other children respond with low levels, these differences are not important in regards to susceptibility to LRI. This may be due to the redundancy of the immune system. The important differences arise when we look at differences within the stimulations for each child. It appears that children with a skewed response, meaning a high cytokine production in response to one bacteria, but a low production in response to another bacteria, are the ones more susceptible to LRI.

The PCA model indicates that TNF-α and IL-5 are important drivers of the significant pattern associated with increased susceptibility to LRI, and TNF-α and IL-5 are also significant in the trivariable model. However, even though TNF-α and IL-5 seem attributable for some part of the offset functional immunity against bacterial stimulations associated with an increased LRI propensity, the complexity of the response and the biological meaning at the individual mediator level remains to be fully elucidated. Thus, our main interpretation is that susceptibility to LRI is reflected in the functional immune response to common airway pathogens, but is not explained by single cytokine variations in response to single bacteria. Future functional studies should therefore explore broader patterns combining information from several different immune mediators instead of the traditional approach of studying unidirectional associations.

It is interesting that for some cytokines, for example, IL-5, very low levels predominate, but variations within these low levels still contribute importantly to the pattern. It could be questioned whether it is justifiable to use these data as continuous variables, and differences could be driven by chance findings caused by inadequacies in analysis techniques. However, sample storage and analyses were kept under very strict conditions, and the Meso Scale Discovery assay is a validated tool with a high accuracy and reproducibility, also for low concentrations, leaving us confident in the measurements. Furthermore, the analysis using cytokine data as a dichotomized variable did not alter the conclusions.

This is the first attempt to elucidate association between a functional cytokine-mediated immune response and susceptibility to LRI in early childhood. Other studies have looked at different aspects of immunologic host factors involved in susceptibility to LRI in early childhood. Particularly disorders of the humoral immune response has been linked to increased incidence of infections, including immunoglobulins,40,41 complement defects,42–44 mannose-binding lectin,45,46 defective antibody responses,47–49 etc. It appears that solitary immune defects have minor importance, and the strongest risk factor for recurrent infections is the coexistence of several partial immune defects,41,50 supporting that LRI susceptibility is a complex multidimensional trait as suggested by our findings.

Recurrent LRI might initiate a chronic inflammatory microenvironment in the host, and thus our findings could merely be a reflection of increased infectious load and not vice versa. However, we collected the immune cells in early infancy when only a minor proportion of children had suffered from LRI or other infections, and importantly, the associations were unchanged when we excluded children with LRI before PBMC sampling from the analyses.

The distinction between lower and upper airway infection in young children is quite arbitrary, and the tendency to present with LRI may merely be a phenotypic characteristic of children with vulnerable airways. It might be that some children have the capacity to confine their infection to the upper airways with limited symptoms, while others are more severely affected by similar infectious agents, reacting with lower airway symptoms due to impaired immunity. Improved understanding of these mechanisms would be of great importance and a potential target for clinical intervention.

Children on a trajectory to develop asthma may present with recurrent LRI.35 Thus, our findings could be due to a common pathogenesis to asthma and LRI or merely reflect a phenotypic characteristic of asthmatics. However, we previously investigated the association between functional immune response and asthma in the present cohort.6 This previous study showed that children developing asthma by school age exhibited an immune response characterized by asthma-associated type-2 responses, and thus an immune response different from the one associated to susceptibility to LRI. Furthermore, we performed a subgroup analysis excluding children with ongoing asthma to ascertain that the findings were not driven by confounding of underlying asthmatic disease. We saw that the results attenuated by this stratification, but remained significant. Importantly, the patterns describing increased susceptibility to LRI are not different in this subgroup.


Children at risk of LRI present a pertubed cytokine-based immune signature upon exposure to common airway pathogens in early life. This suggests that susceptibility to recurrent LRI in childhood is attributable to aberrant early life functional immune response of the host.


The authors thank the children and parents participating in the COPSAC2000 cohort, as well as the COPSAC study team. The authors thank Professor Karen Krogfelt, Statens Serum Institut, Copenhagen, Denmark for providing bacterial strains for these experiments and Lisbeth Buus Rosholm at DTU Systems Biology for technical assistance with cytokine analysis.


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lower respiratory infections; immune response; pneumonia; bronchiolitis; child

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