Osteoporosis treatment response assessed by micromechanical modeling of MRI data.

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DESCRIPTION (provided by applicant): Osteoporosis and low bone mass are currently estimated to be a major public health threat for almost 44 million U.S. women and men aged 50 and older. While the disorder affects both genders, approximately 80% of those having the condition are women. After menopause and the concomitant decline in estrogen levels, bone undergoes structural changes and reduction in tissue volume density resulting in reduced strength and increased fracture susceptibility. Even though effective treatment is available, the high cost and numerous side effects of antiresorptive and anabolic drugs have spurred the search for alternative therapies. Equally important, however, is that pharmacologic intervention is generally not indicated until subjects have progressed to a level of bone density commensurate with the diagnosis of osteoporosis. It has recently been shown that low-magnitude mechanical stimulation (LMMS) at frequencies of tens of Hertz is osteogenic, presumably via downregulation of the nuclear hormone receptor, PPAR, resulting in preferential differentiation of marrow stromal cells toward the osteoblastic instead of the adipocytic lineage. This competing renewal project seeks to further develop methods for image-based micro-finite-element modeling for quantifying various properties of skeletal mechanical competence, along with improved methods for high-resolution structural magnetic resonance (MR) imaging of cortical and trabecular bone, and measurement of bone marrow composition by MR spectroscopic imaging. The resulting protocol will be applied in a subsequent randomized, double-blinded, translational patient study to evaluate the hypothesis that early postmenopausal women, subjected to a daily 10-minute treatment of LMMS for one year, will show an improvement in trabecular and cortical stiffness and failure strength at the tibia, along with a reduction in vertebral marrow adiposity relative to their placebo-treated peers. The successful completion of the project will provide new insight into the potential for image-based computational biomechanics for monitoring prophylactic intervention.