JURY REPORT SWIERINGA AWARD
General
All three submissions were of very high quality, both in terms of content (with many new insights highly relevant to health), methodology (state-of-the-art technology), and structure and presentation. In the end, the jury selected the thesis of Mahmoud Abdel-Aziz Ibrahim, which effectively places the described research in its context, with a clear vision for its application in patient care and diagnostics, as well as urgent unresolved research questions.
Mahmoud Abdel-Aziz Ibrahim
Omics-guided precision medicine: microbiomics and breathomics in asthma phenotyping
January 19, 2022
New Insights into Respiratory Health and Disease
In adult patients with asthma, patient clusters or phenotypes could be distinguished based on the analysis of sputum microbiome, transcriptome, proteome, and eicosanoids. The findings demonstrated that a multi-omics approach:
Can help unravel underlying biological processes.
Can contribute as a biomarker to characterize different asthma phenotypes.
Can contribute to determining the risk of SARS-CoV-2 infection and/or morbidity.
Characterization of exhaled breath profiles using eNose technology in asthma patients allowed for a reliable distinction between:
Atopic and non-atopic patients.
Asthma patients and healthy controls, independently of a rhinovirus challenge in a longitudinal study.
Therefore, eNose analysis is a promising non-invasive method for asthma phenotyping and patient monitoring.
The Societal Relevance of the Research Question
The societal relevance of the research is significant. Asthma is a common respiratory condition with a highly heterogeneous clinical presentation. Many of the underlying biological mechanisms are still unknown, making the classification of asthma, a prerequisite for the development of precision medicine, very challenging. This thesis provides many starting points for valuable asthma phenotyping.
Originality of the Research Method
The technology used is state-of-the-art, utilizing multi-omics data and eNose profiles, processed through machine learning, to distinguish various patient groups, such as atopic and non-atopic.
In summary, both the introduction, experimental chapters, and the general discussion of the thesis testify to the exceptional scientific quality, dedication, and independence of Dr. Andel-Aziz.
Anneke ten Brinke,
Reinoud Gosens,
Rudi Hendriks
November 30, 2022