This NSF Faculty Early Career Development (CAREER) Program grant will develop a research and educational program that is expected to identify and predict how the changes in bone tissue composition that occur in osteoporosis will affect bone fracture behavior. The research program is expected to clarify the underlying causes of osteoporotic fractures and help to identify diagnostic indicators for them. This has important potential benefits for society and the US economy because hip fractures are an important cause of disability, and prevention of fragility fractures represents an opportunity to reduce a large and growing segment of healthcare expenditures in the US. The educational program is expected to benefit society by 1) enhancing infrastructure for education by developing curricular materials for composite materials and biomaterials, and making them available in a national database of teaching materials and 2) broadening participation of underrepresented groups and advancing discovery while promoting teaching, training, and learning by engaging underrepresented minority undergraduates in long-term research positions
Although osteoporosis is known to cause pathologic changes in bone tissue composition that are statistically associated with fragility fracture, the mechanisms by which pathologic changes in bone tissue composition affect fracture risk are unknown. The objective of this project is to mechanistically link the effects of pathologic changes in microscale composition to fracture behavior at the millimeter length scale in bone. In particular, we address microscale variation in material properties, i.e., heterogeneity, which is emerging as an important contributor to bone toughness. The research approach is to establish composition-nanomechanical property relationships by mapping properties in cortical tissue from samples that span a wide spectrum of macroscopic hip fracture morphologies. Then, the research team will create finite element models of the specimens, incorporating specimen-specific distributions of material properties to predict fracture behavior at larger length scales. This work is transformative because it integrates experimental orthopedic research with computational fracture mechanics by combining patient data from the former discipline with the rigorous modeling of the latter discipline.
