Personalized anti-fracture treatment

With increasing age of the population, industrialized countries across the globe are facing a substantial increase in osteoporotic fractures, which account for considerable disease burden and costs.

The aim of this five-year proposal is to improve personalized prevention, diagnosis, and treatment of osteoporosis and related fractures. Fracture risk is influenced by both bone strength and the risk of falls.

Bone strength is determined by trabecular and cortical bone density, bone size and bone quality, while the risk of falls depends on muscle mass, balance and coordination.

We propose that different underlying mechanisms exist for fractures at different bone sites.Signals associated with bone site-specific fracture risk will be identified using data from three omics platforms (genomics, extensive plasma proteomics, and metabolomics) available in unique biobanks. We will use these association signals in machine learning to develop markers for fractures.

Mendelian Randomisation will determine if the identified biomarkers are causal risk factors for fractures.

Promising fracture association signals will also be evaluated in translational studies as candidate bone site-specific fracture targets.

Proof of concept of our translational competence is our previous successful studies identifying novel fracture targets, starting from human genetic signals.Our vision is to develop fracture type-specific diagnostic tools and treatments facilitating a personalized anti-fracture treatment.