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Imaging Epidemiology

The working group Imaging Epidemiology focuses on the combined – radiological and epidemiological – evaluation of population-based imaging data. Our aim is to exploit the potential of imaging data to characterize and identify novel risk factors for progression of subclinical disease states, with the goal to improve individualized diagnosis and prediction of diseases and overall health outcomes. Together with our AI Image Analysis Group at the University Freiburg (www.nora-imaging.de) and external cooperation partners, we further explore Radiomics and other novel image analysis methods to discover unknown information hidden in medical CT and MR data.

Studying subclinical disease states and disease progression is instrumental in advancing medical knowledge, improving patient outcomes, and promoting more effective and efficient healthcare practices. Subclinical disease states refer to the early stages of a disease that may not yet present noticeable symptoms. Understanding and monitoring these pre-pathological changes provides valuable insights into the mechanisms and progression of diseases, which might allow for identification of specific treatment targets and early intervention which often leads to better patient outcomes. We are particularly interested in modelling longitudinal trajectories of imaging data and track progression from subclinical to clinical presentations.

We mainly work with population-based cohorts, which investigate common chronic diseases, such as cardiovascular diseases, diabetes, lung diseases and cancer. Within these studies, imaging data provide a powerful tool to derive intricate insights into organ morphology and function, for example adipose tissue distribution, lung capacity, ectopic fat accumulation, skeletal muscle quality, or cardiac remodeling. Moreover, we analyze correlations and interactions of different organ systems using whole-body imaging. Monitoring subclinical disease states at the population-level contributes to a better understanding of the prevalence and distribution of diseases within populations. We are particularly interested in using imaging data to identify subphenotypes of prevalent diseases, and high-risk groups which could particularly benefit from tailored interventions.

By integrating imaging information with clinical risk factors from deeply phenotyped population-based studies (e.g. NAKO, UKBB, KORA, NSTL), we aim to use these rich data to get a better understanding of underlying disease processes and to assess the value of imaging data in predicting disease risk. As imaging data represent detailed information of organ structure and function and thus capture intermediate states along the pathway of health to disease, there is great potential for them to improve existing risk scores and prognostic models.

A main research focus of our working group is on cardiometabolic disease. Metabolic disorders such as diabetes, metabolic syndrome and steatotic liver disease, and cardiovascular diseases such as heart failure and myocardial infarction affect large numbers of the general population. From a public health perspective, they are a major cause of morbidity and mortality, and thus investigating potential disease pathways, risk factors and predictive markers is crucial to improve primordial and primary prevention.

Our projects in the cardiometabolic field include

  • Body composition: Assessment of body composition through the joint analysis of abdominal and ectopic adipose tissue, investigating patterns across the lifecourse, subphenotypes and their association with cardiometabolic disease and disease risk.
  • Muscle composition: Identification of determinants of muscle composition, fat infiltration, and muscle health, and development of diagnostic and prognostic models.
  • Liver health: Investigation of imaging-based measures of liver health, including morphology, fat content and iron content. Determination of distinct subgroups of liver steatosis and risk factors of progression of metabolic dysfunction-associated steatotic liver disease, in particular the role of hepatic iron.
  • Cardiac function: Assessment of patterns and predictors of left- and right ventricular function in different subgoups and association with different disease states.
  • Cardiac remodelling:  Evaluation of deviations from regular cardiac geometry, identification of subphenotypes based on patterns of imaging-derived morphology data, and assessment of their connection with heart failure and cardiovascular disease risk.

Our work is highly interdisciplinary, with collaborations with colleagues from medicine, statistics, computer science and many more.