PREDICTIONS OF DISTAL RADIUS COMPRESSIVE STRENGTH BY MEASUREMENTS OF BONE MINERAL AND STIFFNESS ___________________________________________ A Thesis Presented to The College of Arts and Sciences, Ohio University ___________________________________________ In Partial Fulfillment of the Requirements for Graduation with Honors from the College of Arts and Sciences with the degree of Bachelor of Science in Biological Sciences ___________________________________________ by Maureen A Dean April 2016 2 Table of Contents Acknowledgements……………………………………….…….……......3 Abstract……………………………………………………….……….…4 1. Background…………………………………………………................5 1.1 Bone Health and Development…………………………….…….5 1.2 Osteoporosis Disease and Diagnosis……………………….…..8 1.3 Bone Stiffness and Strength………………………………….….12 1.4 Osteoporotic Fracture Sites………………………………….….18 1.5 Specific Aims……………………………………………………....21 1.6 Hypotheses…………………………………………………....……21 2. Methods………………………………………………………….…....22 2.1 Experimental Design……………………………………….……..22 2.2 Specimens…………………………………………………….…….25 2.3 Experimental Protocol……………………………………….…...30 2.4 Data Analysis……………………………………………….……..41 2.5 Statistical Analysis………………………………………….…….48 3. Results………………………………………………………….….….50 3.1 In Situ and Ex Situ Measurements………………………………50 3.2 Univariate Analysis…………………………………….…………51 3.3 Bivariate Analysis……………………………………….………...52 4. Discussion…………………………………………………….….….....61 4.1 Main Findings………………………………………….…………..61 4.2 Diagnostic Error Rate……………………………….……………61 4.3 Relation to Previous Research...…………………………………63 4.4 Strengths……………………………………………….…………...65 4.5 Weaknesses…………………………………………….…………..69 4.6 Future Research……………………………………….…………..72 References…………………………………………………..……….…...73 3 Acknowledgments This thesis was made possible by funding from the Student Enhancement Award, the Ohio Space Grant Consortium, and Dr. Anne Loucks of the Department of Biological Sciences. I am extremely thankful for those outside of Dr. Loucks’ Laboratory who aided me during this project. I want to personally thank Dr. Leatha Clark of the Ohio Musculoskeletal and Neurological Institute for being this project’s DXA technician and for teaching me how to analyze DXA images, as well as Dr. Betty Sindelar of the Ohio University Division of Physical Therapy, who allowed me to use her QMT system. A special thank you goes to my thesis advisor, Dr. Griffin, who guided me through the process of writing a senior thesis. Additionally, I am thankful to the human tissue banks, AdvancedMed and Science Care, for providing specimens for this project and to the donors themselves who gave their bodies to science. I would not have been able to complete this project without the extraordinary work accomplished by previous students in Dr. Loucks’ laboratory. I would especially like to acknowledge Emily Ellerbrock and Jennifer Neumeyer for their work on MRTA analysis, Tyler Beck for his analysis of ulna QMT data, and Gabrielle Hausfeld for laying the ground work on predictors of ulna bending strength and for making me laugh and enjoy my time in the laboratory. I want to express my gratitude for my family and cross country teammates who created an unbelievable support network for me during this project and inspired me to do better each day. Lastly, I would like to thank Dr. Loucks and Lyn Bowman who have encouraged, challenged, and guided me to accomplish more than I ever thought possible. I want to personally thank Dr. Loucks for accepting a previously inexperienced person such as myself into her laboratory and opening my mind to a different realm of science. I cannot express my gratitude enough to Lyn who has spent hours sitting patiently with me to teach me what I needed to know. This has been the most informative experience of my college career and I am fortunate to have been able to work under Dr. Loucks and Lyn for the past two years. 4 Abstract Osteoporosis is a systemic disease that is characterized by a decrease in bone strength leading to an increased risk of fracture. Currently osteoporosis is diagnosed by measuring bone mineral density (BMD) by dual-energy X-ray absorptiometry (DXA). Previous literature has shown that DXA measurements do not accurately predict which individuals will fracture, leaving physicians with a limited ability to target those who need preventative care. Ohio University is developing a new technology, mechanical response tissue analysis (MRTA), to measure the stiffness (EI) and estimate the strength of human ulna bones in vivo. EI is strongly associated with bone strength, but this technology’s diagnostic ability is limited unless it can predict the strengths of other long bones where osteoporotic fractures occur. Nineteen percent of osteoporotic fractures occur in the distal radius of the forearm, and those fractures are an indicator for future osteoporotic spine and hip fractures. This project compared the accuracies with which ultra-distal (UD) radius compressive strength was predicted by donor demographics, measurements of ulna bending and radius compressive stiffness (EI, EA), ulna bending strength, and DXA measurements from the UD and 1/3 regions of the radius. This study used 32 fresh frozen cadaveric arms from men and women ranging in age from 23-99 years and in BMI from 13-40 kg/m2. Ulna EI and bending strength, and UD radius EA and compressive strength were all measured using the gold standard method, Quasistatic Mechanical Testing (QMT). Unlike DXA and MRTA, QMT cannot be used in vivo. Simple linear regression analyses revealed that the most accurate predictor of UD compressive strength was UD radius EA (standard error of the estimate, SEE = 874 N), which cannot be measured in vivo. The accuracies of all other predictors were indistinguishable from one another. Confidence in these results is reduced, however, by certain outliers in the data, which inflated the SEE values of all predictors. 5 1. Background 1.1 Bone Health and Development Bone health will be increasingly important for Americans in the upcoming years as osteoporosis becomes a more prevalent disease. Osteoporosis is a systemic disease characterized by a decrease in bone strength causing an increased risk of fracture (25). In the United States alone, the World Health Organization estimates around ten million people are afflicted with this disease (8, 25). In the year 2005 two million osteoporotic fractures were recorded, but by the year 2025 the number of fractures has been predicted to rise by fifty percent. Additionally, this disease has a great economic impact on the United States. In 2005 the cost of care related to osteoporotic fractures was almost 17 billion dollars. That cost is predicted to rise over 25 billion dollars by 2025 (5). Bone tissue is composed of collagen, mineral and non-collagenous proteins. It functions by providing support, mobility, and protection for the body while acting as a storage site for essential minerals (7). Bone is comprised of two different types of tissue. Compact or cortical bone (Figure 1) has porosity (fluid-filled voids) in the range of 5-10%. It can be found within the shafts of long bones and as a shell or cortex around vertebrae and other spongy bones. This type of tissue makes up 80% of the mass of bones in young adults (14). Cortical bone contains Haversian canals with capillaries and nerves, Volkmann’s canals that connect Haversian canals with blood vessels and nerves, and resorption cavities or volumes created by osteoclast cells. 6 Figure 1. The dense microstructure of cortical bone (21). The second type of bone tissue, trabecular bone, otherwise known as cancellous or spongy bone (Figure 2), has porosity in the range of 75-95%. It can be found in flat bones, cuboidal bones, and the ends of long bones. Trabecular bone is comprised of an interconnecting network of thin plates or trabeculae that create a largely open microstructure within the bone. It comprises 80% of the bone surface area in young adults. As a largely open structure, it provides support without increasing the weight of the bone. Widening the surface area at the ends of long bones 7 reduces stress (force per unit area) on joints and concentrates that load onto the more compact and stronger cortical bone in the bone shaft (21, 29). Figure 2. Porous Trabecular bone (27). Bone turnover is a normal process that ensures the skeletal system adapts to changing conditions. Turnover occurs in two different ways: modeling and remodeling. Modeling refers to the growth of bone during childhood. As we achieve adulthood, the rate of modeling significantly decreases both radially and longitudinally (7). Remodeling is characterized by bone resorption and formation at the same site. It takes place in three stages 1) activation 2) resorption 3) formation (7, 21). The goals of remodeling include maintaining the balance of essential minerals in the blood, adapting bone to the mechanical environment, and repair of bone damage (6). Bone is removed and added through two types of cells: osteoclast cells act to resorb bone while osteoblast cells form new bone. The remodeling sequence functions through a basic multicellular unit (BMU) that is composed of approximately 10 osteoclasts and hundreds of osteoblasts. Activation begins when a chemical or mechanical signal causes osteoclasts to form and move to a spot on the bone. Over a course of three weeks, osteoclasts resorb bone tissue to create a groove on a bone surface or a tunnel 8 within cortical bone. Next, osteoblast cells begin to replace the tissue that was resorbed. This occurs over