Endotypes of childhood wheezing after severe RSV lower respiratory tract illness in infancy in socially vulnerable Argentinian children

Lung diseases are a major cause of ill health and premature death globally, and particularly in low- and middle-income countries. Lower respiratory tract infection caused by respiratory syncytial virus (RSV) is the main cause of hospital admissions in infants worldwide. Every year, millions of infants who are infected with RSV are hospitalised, many progress to experience long-term respiratory illnesses such as asthma, and >100,000 die; 99% of these deaths occur in developing countries. Many children with early-life RSV illness progress to develop recurrent wheezing and asthma, although others do not. In fact, it has been proposed that RSV infection in susceptible children may cause asthma. Recurrent wheezing and asthma reduce quality of life, create significant health care costs, and may affect respiratory health and lung function beyond childhood. Low lung function in young adulthood increases the likelihood of early death from all causes and is an important risk factor for development of chronic obstructive pulmonary disease, which is responsible for 5% of all deaths worldwide. About 90% of those deaths occur in low or middle-income regions. Early identification of infants at high-risk for chronic respiratory symptoms and low lung function in later life may allow us to develop new interventions to avert persistent illness and the serious consequences from loss of lung function. While this is an urgent need worldwide, it is particularly pressing in low-income populations, where infants experience more severe RSV infections and long-term lung illness of greater severity.

Our program aims to tackle this lifelong problem in the early months of life. We propose that severe RSV infection causes specific subtype(s) of asthma (but not others), and that different wheeze trajectories and asthma subtypes are linked with different types of immune responses to viruses which can be measured in respiratory secretions, thereby allowing early recognition of children at risk. Our overarching goal is to identify different patterns (or subtypes) of wheezing illness through childhood among children who experienced a severe RSV infection using novel mathematical modelling, and to discover early-life risk factors and molecules which predict these different subtypes of wheezing. We will leverage a unique study of 1,153 children in a low-income region of Argentina. Of these, 419 had severe RSV infection in infancy, 344 a severe non-RSV infection, and 390 are healthy controls. Extensive clinical data has been collected through the initial hospital admission, and biological samples were obtained for future analyses. Participants attended several follow-ups to age 3 years, with excellent retention (91%). We will extend follow up to the age of 6 years. We will derive subtypes of wheeze using sophisticated machine learning techniques, and conduct detailed assessments of lung function at ages 4-6 years. In parallel, we will conduct a series of studies in saved biological samples from the airways to identify types of antiviral immune responses during severe infection in infancy and their relationship with clinical outcomes. Concentrations of multiple molecules which are secreted by certain cells of the immune system and have an effect on other cells will be assessed in respiratory samples. We will identify patterns of immune responses and compare clinical outcomes between different patterns.

This study represents a unique opportunity to identify RSV-specific subtype(s) of chronic wheezing and asthma, and define subtype-specific indicators of progression to long-term respiratory illness. Importantly, this information comes from a population highly susceptible to respiratory illness: a group of infants living in extreme poverty. Early identification of infants at risk for progression to long-term wheezing illness may allow interventions to avert persistent disease and loss of lung function in the future.

Respiratory syncytial virus (RSV) lower respiratory tract illness (LRTI) is the main cause of hospitalisation in infants worldwide. Severe RSV LRTI may contribute to asthma development, but it identifying infants who will progress to chronic symptoms is not possible to date. We hypothesise that RSV LRTI causally contributes to one or more specific asthma endotypes, We further postulate that different wheezing trajectories from infancy to school age are associated with different patterns of cytokine & gene expression profiles at initial LRTI in infancy, allowing early recognition of infants at risk. We will test our hypotheses in a unique prospective study of 1,153 children in low-income region of Argentina; 419 with severe RSV LRTI in infancy, 344 with severe non-RSV LRTI, and 390 healthy controls. Extensive data has been collected through the initial hospitalisation, and biological samples from the initial episode obtained for future analyses. Participants were followed to age 3 years (retention 91%). We propose to extend follow up to age 6 years and derive subtypes of wheeze using machine learning applied to longitudinal data. In parallel, we will carry out a series of mechanistic studies in biobanked respiratory secretions to identify immunophenotypes of antiviral responses and their relationship with outcomes. Concentrations of 39 cytokines will be assessed, and machine learning used to cluster children based on their cytokine levels. We will also investigate the transcriptional response to infection in the airway and perform unbiased analyses for differentially expressed genes and pathways in severe RSV infection and infection caused by and other viruses. We will investigate developmental profiles of wheezing through childhood across clusters using longitudinal regression models. This study represents a unique opportunity to identify RSV-specific chronic wheezing endotypes and define endotype-specific indicators of progression to long-term respiratory illness.

Who might benefit from this research?

The proposed project will multiply the effects of previous investments, thereby having an overall scientific impact much greater than its level of requested funding. We will generate insights into intersections between early childhood RSV lower respiratory tract illness (LRTI), trajectories of wheezing, and subsequent lung function development, thereby identifying pathways that may provide information for targeted interventions to reduce the impact of severe RSV and non-RSV LRTI in South American population living in poverty. As the origins of adult chronic respiratory disease arise from childhood exposures, better understanding of the causes of respiratory disease in early childhood are key to designing new preventive interventions against long term morbidity.

We will build an infrastructure for large scale interdisciplinary collaborations to conduct cutting edge science, using existing and newly collected data resources, to produce health benefit for the Argentinian population, and broader. Respiratory diseases pose a particularly large burden in South America, where health-care policy makers need to take notice of the growing epidemic of COPD and start taking measures to both prevent and treat COPD effectively. The results of the proposed project may lead to the development of methods for prevention of chronic respiratory diseases, early mortality and premature death, and may reveal life-style choices to be made to prevent long-term adverse health outcomes. This will be of potential great value to patients, society, health-care professionals and industry.

How might they benefit from this research?

An important component of this programme, which is crucial to sustainability is the proposed investment in capacity development through training of young researchers. We will build on a strong history of collaborative training between the study investigators. The ability to access shared analysis resources in South America and the UK will be of great value for training and development of researchers, and the ability to access example analyses and expert advice will reduce their learning curve. Enabling the networking of datasets, expertise and methods for data preparation and analysis can help drive greater value from existing investments. Building South American capacity in statistical methodologies applied to longitudinal measures, and in novel analytical methods including latent class analysis and machine learning approaches, will ensure skills transfer to clinicians and researchers in South America.

Our findings may represent potentially valuable intellectual property, which we will seek to commercialise in collaboration with companies invested in diagnostics and/or therapeutics. Participating institutions have mechanisms and structures in place for exploring industrial applications. Partnerships such as the one described in this application help to make the UK an attractive location to retain research activities, and help expose academics to the process of translating science into products.