Volatile organic compounds (VOC) for diagnosis in ex-preterm children
This project aims to investigate if there are difference in VOC profiles in exhaled breath condensate and urine in preterm infants with bronchopulmonary dysplasia when compared to preterm-born children without bronchopulmonary dysplasia and with term-born children.
Bronchopulmonary dysplasia (BPD) is a major respiratory consequence of premature birth (Walsh et al., 2006). In addition to respiratory symptoms, it can also associated with growth retardation, pulmonary arterial hypertension, neurodevelopmental delay, hearing defects, and retinopathy of prematurity (Allen et al., 2003).
Survivors of BPD have functional abnormalities persisting into adolescence and early adulthood, with concerns that BPD maybe a precursor of chronic obstructive pulmonary disease (COPD) (Baraldi and Filippone, 2007).
Although many studies report the long-term functional outcome of subjects with BPD, very little is known about the mechanisms as to why consequences of BPD persist in child/adulthood (Baraldi and Filippone, 2007).
Volatile organic compounds (VOC) are ubiquitous organic chemicals with high vapour pressure at room temperature. In the human body, VOCs are produced by various metabolic processes, thus forming a source of metabolic 'finger-printing'.
Exhaled adult human breath, for example, can contain several thousand VOCs which are considered to be markers of metabolic processes in the body (Miekisch et al., 2004). Breath VOCs are used for the early detection of cancer and related precision medicine (van der Schee et al., 2018, Buszewski et al., 2007).
More recently, this technology has been extended to test other human samples for detection of disease in preterm infants like feaces (Berkhout et al., 2018, de Meij et al., 2015) and tracheal aspirates (Rogosch et al., 2014). However, their utility in differentiating disease states or therapeutic response in ex-preterm children has not yet been tested.
Data collected from children recruited from the RHiNO (Respiratory Health Outcomes in Neonates) study. This study is investigating why prematurely born children develop lung disease and what might be the best treatment. The study provides an exciting opportunity to investigate VOCs in samples already collected. All samples have been collected after informed consent from the parents and children and has research ethical approval.
To investigate if there are difference in VOC profiles in exhaled breath condensate and urine in preterm infants with BPD when compared to preterm-born children without BPD and with term-born children.
Data collected from children recruited from the RHiNO (Respiratory Health Outcomes in Neonates) study. This study is investigating why prematurely born children develop lung disease and what might be the best treatment.
The study provides an exciting opportunity to investigate VOCs in samples already collected. All samples have been collected after informed consent from the parents and children and has research ethical approval.
School-aged children from three groups have been recruited:
- Group A: 50 children born preterm and developed BPD with respiratory sequelae
- Group B: 50 children born preterm but did not develop BPD and do not have respiratory complications
- Group C: 50 control children born at term.
- Identify the difference in the VOC profile between group A, B and C by:
- analysing EBC and urine samples by using the Cyranose C 320 system to detect VOC profiles.
- analysing data from C 320 using multivariate statistical analysis tools.
- building a score scatter plot according to the principal component analysis (PCA) model.
- Select putative biomarkers in the VOC profile variables by:
- building the so-called S-plot based on orthogonal projections to latent structures-discriminant analysis (OPLS-DA) models.
- using S-plot as a visualization tool to highlight in the VOC profile variables with a predominant role in the characterization of group A and B
- Identify the difference in the VOC profile between group B and C, and further selecting putative biomarkers.
- Repeat tasks 2 and 3.
The Urine and Exhaled Breath Condensate (EBC) collection (according to the American Thoracic Society/European Respiratory Society recommendations (Horvath et al., 2005) and urine samples are separately collected from people in group A, B and C.
Training and support
Appropriate training in the use of the equipment, the statistical and bioinformatical analysis and research logistics will be given. In addition we have an enthusiastic team of world class researchers focusing on lung disease of children (including many clinical trials) that will be able to help support the PhD.
You will be settling down in Cardiff, induction into the PhD programme and the University, reading about lung disease in prematurely born children, sourcing consumables and learning laboratory methods.
You will be optimising analysis methods, and learning statistical analysis (general and specific for this type of work.
You will be analysing samples in batches – controls first followed by cases. Analysis of data will continue concurrently, along with writing initial chapters of thesis (introduction, methods).
You will be ompleting final pieces of work resulting from trends seen in the results so far and the completion the thesis write-up.
Potential impact of the research
This research will quantify the role of VOCs in investigating lung disease that maybe consequent of development of BPD in infancy. Demonstrating the differences between those with and without lung disease will help identify targeted therapeutic options.
You should possess a minimum of an upper second class Honours degree, master's degree, or equivalent in a relevant subject.
Applicants whose first language is not English are expected to meet the minimum University requirements (e.g. 6.5 IELTS)
See details of accepted English Language qualifications for admissions.
How to apply
Consideration is automatic upon application for admission to the Doctor of Philosophy in Medicine with the following start dates:
In the research proposal section of your application, please specify the project title and supervisors of this project and copy the project description in the text box provided. In the funding section, please select ‘self-funded’