Data-Driven Approaches to Explore COPD Endotypes

PhD defence by Line Egerod Lund

Assessment Committee

Associate professor Ole Hartvig Mortensen, Department of Biomedical Sciences, University of Copenhagen (Chairperson)
Professor Ole Lund, Technical University of Denmark
Associate professor Helena Backman, Umeå University

Supervisors

Professor Nils Billestrup
Jannie Marie Bülow Sand
Cecilie Liv Bager
Ramneek Gupta

Department

Department of Biomedical Sciences

Graduate Programme

Biostatistics and Bioinformatics

Place

Building: Building 13, Room: Holst Auditoriet, Blegdamsvej 3B, DK-2200 København N

Email address to gain access to the thesis: line.egerod@gmail.com.
You will either receive a copy of the thesis or be informed where you can read a physical copy.
Recipients of copies of the thesis are not allowed to share or distribute it due to copyright compliance.

Short description of the thesis

Chronic obstructive pulmonary disease (COPD) is a major global health challenge and one of the leading causes of death worldwide. Despite sharing a common diagnosis, patients with COPD can differ in the biological mechanisms driving their disease. This thesis applies advanced statistical and machine learning approaches to data from the ECLIPSE cohort, a large international study of COPD, to identify and characterize biological subtypes, known as endotypes, using blood-based biomarkers of tissue injury and repair. The findings provide evidence that endotypes exist within COPD and are associated with different disease trajectories and clinical outcomes. By improving our understanding of COPD heterogeneity, this work supports the development of more precise approaches to diagnosis, prognosis, and treatment.