Work package 5

Translating Genomic and Imaging Insights into Clinical Practice for Improved Bone Health

Integrated genomic profiling and Imaging clustering of patients (clinical setting): The objective of this work package is to translate genomic discoveries and imaging insights on skeletal fragility into clinical practice, with the aim of improving patient care. Genomic studies provide a wealth of information that enhances our understanding of the genetic basis of both monogenic and complex bone disorders. The insights gained from these studies have wide-ranging applications in clinical practice, including optimizing risk stratification, identifying novel risk factors and disease biomarkers, and improving drug discovery.

As a “proof of concept” approach, we will initially focus on risk stratification and the identification of biologically meaningful disease clusters among individuals with unresolved familial forms of osteoporosis (FAMOS Study). Additionally, we will apply our approach in different real-life clinical settings, starting with the Bone Expertise Centre of Erasmus MC, where genotyping will be conducted on osteoporosis patients signing informed consent during a one-year period. Furthermore, we will pursue proof of principle of the approach in monogenic conditions through collaboration, targeting patients with Paget’s disease, Craniosynostosis, and children suspected to have unresolved monogenic forms of bone fragility.

Erasmus University Medical Center Rotterdam – Erasmus MC, is a leading research center in Europe and worldwide. Our group within the Genetic Laboratory & Genomics Facility of the Department of Internal Medicine has a strong track record in genomic research and possesses all the necessary infrastructure and resources to conduct the proposed LEGENDARE program. This includes access to computing resources, genomic facilities, and epidemiological cohorts with existing approval from the Medical Ethics Review Committee (METC). Research involving patients will require independent METC approval.