Variation in Cortical Osteocyte Lacunar Density and Distribution: Implications for Bone Quality Assessment

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Randee L. Hunter, B.S., M.A.

Graduate Program in Anthropology

The Ohio State University

2015

Dissertation Committee:

Clark Spencer Larsen, Advisor

Samuel D. Stout

Paul W. Sciulli

Amanda M. Agnew

Copyrighted by

Randee Linn Hunter

2015

Abstract

The purpose of this study is to investigate variation in cortical bone osteocyte cell populations using their lacunae as a proxy. The osteocytes and their lacunocanalicular network have been identified as the regulator of bone quality and function by exerting extensive influence over metabolic processes, mechanical adaptation, and mineral homeostasis. Recent research has shown that osteocyte malfunction and apoptosis leads to a decrease in bone quality and increase in bone fragility. However, these results are limited to mainly trabecular bone in clinical studies following biopsy or prosthetic replacement in osteoporotic patients and animal studies in which experimental data have been collected.

This study is the first to analyze cortical bone variation in osteocyte lacunar density from multiple skeletal sites to establish regional and systemic age and sex related trends. Bone samples were recovered from 30 modern cadaveric individuals (15 males and 15 females) ranging from 49 to 100 years old. Three anatomical sites were utilized for this study: the midshaft femur which is frequently used in anthropological studies of cross-section geometry, age estimation and behavioral interpretations as it is a major load bearing bone; the distal third of the diaphyseal radius as it represents a clinically relevant site for fractures associated with falls especially in older adults; and the midshaft of the

6th rib as this is frequently held as a systemic control. Thin ground histological (80 µm)

cross-sections were made and imaged under bright field microscopy. Bone size measurements (total subperiosteal area Tt.Ar; cortical area Ct.Ar) were collected using

ArcGIS for the larger femora and ImageJ for the radius and rib. ImageJ was then used to optimally manipulate the image in order to identify and count osteocytic lacunae

(Ot.Lc.N) automatically over the entirety of the cross-section. Intracortical porosity area

(Po.Ar) was collected using a semi-automatic requiring extensive manual verification and used to calculate bone area (B.Ar) for each section (Ct.Ar-Po.Ar). Each variable used for comparisons was normalized for size: osteocyte lacunar density (Ot.Lc.N/B.Ar and

Ot.Lc.N/Ct.Ar) and intracortical porosity (%Po.Ar). Independent samples t-tests indicated no significant sex differences between any variables in all elements. Pearson correlations demonstrated an overall decrease in osteocyte lacunar density

(Ot.Lc.N/B.Ar) and increase in intracortical porosity (%Po.Ar) with age for each element.

Intracortical porosity (%Po.Ar) was significantly negatively correlated with lacunar density (Ot.Lc.N/Ct.Ar) in each element. Lastly, a systemic trend in the decrease in osteocyte lacunar density (Ot.Lc.N/B.Ar) with age was identified between the femur and radius but no relationship between the rib and radius, or rib and femur. All correlations were explored for males and females separately as well as for the pooled sample and demonstrates varying strengths of relationships between sexes. These results indicate that although all elements are experiencing systemically influenced declines in osteocyte lacunar density, there appears to be a differential effect at each anatomical site. It is hypothesized here that the importance of the mechanical environment in osteocyte

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viability (and lacunar existence) may be the underlying cause of this differential intra- individual pattern.

This study provides a foundation upon which to build interpretations of osteocyte lacunar density values and distributions in past and present populations. The central role of the osteocyte makes it the “middle man” by which mechanical loading, systemic health, and environmental conditions directly affect change in the skeletal system. Thus, the osteocyte lacunocanalicular network should be utilized in bioarchaeological and clinical studies of bone quality assessment.

This work is dedicated to my grandmother Carolyn Ravens without whose support none of this was possible.

Acknowledgments

I would like to first thank my advisor, Dr. Clark Larsen, for taking a chance on someone with a unique background and facilitating my development as an anthropologist.

The support and guidance from the faculty and staff in the Department of Anthropology have been greatly appreciated. Thank you to Dr. Sam Stout for sharing his knowledge of skeletal biology and Dr. Paul Sciulli for his statistical prowess and unique perspective. I would especially like to thank Dr. Jim Gosman who encouraged me to look inside the

“black box” of bioarchaeological skeletal research from which this project stems.

I sincerely appreciate the support of my family: my Mom, Dad, Step-mom,

Grandma and Grandpa for always offering encouraging words and letting me know they are proud. This would not have been possible without The Forsters who have and continue to positively influence my life to no end, and to Kari, Clare, Jenny and Jenny who keep me sane and remind me to embrace life.

A special thanks to Dr. Tim Gocha and Victoria Dominguez who shared their extensive knowledge and training in histological techniques and software for analyses; but most importantly, who provided a friendly sounding board for concepts explored here.

Lastly, I am deeply grateful to Drs. Amanda Agnew and John Bolte for welcoming me into the IBRC which has challenged me to utilize unique perspectives in

my research and facilitated my growth as a researcher making this project possible.

Thank you to all the members of the IBRC for their help in obtaining samples and feedback throughout this process. A huge thank you to Mandy and John for their encouragement and support in fostering my developing career but especially for their friendship and guidance in life.

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Vita

2002...... Francis Howell High School, MO

2006...... B.S. Nuclear Medicine Technology, summa cum laude, Saint Louis University

2010...... M.A. Anthropology, with Distinction, Department of Anthropology, The Ohio State University

2009-2013 ...... Graduate Teaching Associate, Department of Anthropology, The Ohio State University

2011-present ...... Graduate Teaching Associate, Division of Anatomy, The Ohio State University

Fields of Study

Major Field: Anthropology

Area of Emphasis: Biological Anthropology

Minor: Anatomy

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Table of Contents

Abstract ...... ii Acknowledgments...... vi Vita ...... viii Fields of Study ...... viii Table of Contents ...... ix List of Tables ...... xiii List of Figures ...... xv

Chapter 1: Introduction ...... 1 1.1 Research Context...... 1 1.2 Hypotheses ...... 6 1.3 Significance ...... 10 1.4 Topics Addressed ...... 11

Chapter 2: Biological Foundation and Assessment of Cortical Bone Quality ...... 13 2.1 Introduction ...... 13 2.2 Cortical Bone Cells ...... 13 2.2.1 Osteoclasts ...... 14 2.2.2 Osteoblastic lineage ...... 17 2.3 Cortical Bone Metabolism ...... 23 2.3.1 Modeling ...... 23 2.3.2 Remodeling ...... 26 2.4 Assessment of Cortical Bone Quality ...... 31 ix

2.4.1 Definition of Bone Quality ...... 31 2.4.2 Modern Methods of Bone Quality Assessment ...... 36 2.5 Summary ...... 39

Chapter 3: Osteocytes ...... 40 3.1 Introduction ...... 40 3.2 Osteocyte Functions ...... 40 3.2.1 Regulation of the BMU ...... 40 3.2.2 Mechanosensation and Mechanotransduction ...... 48 3.2.3 Mineral Homeostasis ...... 52 3.3 Osteocyte Viability and Apoptosis ...... 53 3.3.1 Mechanical Influences and Microdamage ...... 54 3.3.2 Systemic Influences ...... 57 3.3.3 Micropetrosis ...... 60 3.4 Osteocyte Role in Bone Quality ...... 62 3.5 Summary ...... 66

Chapter 4: Materials and Methods ...... 67 4.1 Introduction ...... 67 4.2 Materials ...... 67 4.3 Methods ...... 70 4.3.1 Obtaining and Preparing Cadaveric Skeletal Samples ...... 70 4.3.2 Slide Preparation...... 71 4.3.3 Imaging ...... 74 4.3.4 Data Collection ...... 76 4.4 Data Analysis ...... 86

Chapter 5: Results ...... 87 5.1 Introduction ...... 87 x

5.2 Method Test...... 88 5.3 Age and Sex Correlations ...... 89 5.3.1 Sex Differences for variables normalized by size ...... 89 5.3.2 Osteocyte Lacunar Density (Ot.Lc.N/B.Ar) Intra-individual values ...... 93 5.3.3 Osteocyte Lacunar Density (Ot.Lc.N/B.Ar) Correlations with Age ...... 95 5.4 Intracortical Porosity and Osteocyte Lacunar Density Correlations per Element 101 5.5 Systemic Trends in Osteocyte Lacunar Density ...... 110 5.6 Differences between Osteocyte Lacunar Density calculations using “Ct.Ar” versus “B.Ar” ...... 120 5.7 Summary ...... 121

Chapter 6: Discussion ...... 123 6.1 Introduction ...... 123 6.2 Methodological Considerations...... 124 6.2.1 Effects of using Ot.Lc.N/Ct.Ar versus Ot.Lc.N/B.Ar ...... 124 6.3 Inter-individual Variation in Osteocyte Lacunar Density ...... 125 6.4 Intracortical Porosity and Osteocyte Lacunar Density ...... 129 6.4.1 Intracortical Porosity (%Po.Ar) and Age ...... 131 6.4.2 Osteocyte Lacunar Density Correlates with Intracortical Porosity per Element ...... 134 6.5 Systemic Trends ...... 139 6.6 Osteocyte Lacunocanalicular Network and Bone Quality ...... 144 6.6.1 Comparisons with Bone Mass Measurements ...... 144 6.6.2 Osteocyte Lacunar Density and Diseases ...... 146 6.6.3 Osteocyte Lacunar Density and Microdamage ...... 149 6.7 Osteocyte Lacunar Density in Bioarchaeology ...... 150 6.8 Hypotheses Revisited ...... 152 6.9 Limitations ...... 157

6.10 Future Work ...... 159

Chapter 7: Conclusions …………………………………………………………....…...162 References ...... 165 Appendix A: Raw Data Per PMHS Per Element ...... 180 Appendix B: Sex Specific Regression Trends ...... 188

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List of Tables

Table 4.1: PMHS demographics………………………..………………………………..69

Table 5.1: Method verification ……………...………………………………………….89

Table 5.2: Descriptive Statistics..………………………………………………………..90

Table 5.3: Male vs. Female t-test results....……………………………………………..91

Table 5.4: Pearson correlations variables with age………………………………………97

Table 5.5: Correlations between intracortical porosity and osteocyte lacunar density...107

Table 5.6: Systemic correlations for osteocyte lacunar density………………………...112

Table 5.7: Systemic correlations for intracortical porosity……………………………..116

Table A.1: Raw data for individual 6882 (49 yr old male)……………………………..181

Table A.2: Raw data for individual 6350 (59 yr old male)……………………………..181

Table A:3: Raw data for individual 6807 (66 yr old male)……………………………..181

Table A.4: Raw data for individual 6641 (69 yr old male)……………………………..181

Table A.5: Raw data for individual 6477 (71 yr old male)……………………………..182

Table A.6: Raw data for individual 6446 (77 yr old male)……………………………..182

Table A.7: Raw data for individual 6406 (79 yr old male)……………………………..182

Table A.8: Raw data for individual 6542 (79 yr old male)……………………………..182

Table A.9: Raw data for individual 6873 (80 yr old male)……………………………..182

Table A.10: Raw data for individual 6449 (83 yr old male)……………………………183 xiii

Table A.11: Raw data for individual 6450 (83 yr old male)……………………………183

Table A.12: Raw data for individual 6460 (86 yr old male)……………………………183

Table A.13: Raw data for individual 6889 (92 yr old male)……………………………183

Table A.14: Raw data for individual 6602 (94 yr old male)……………………………183

Table A.15: Raw data for individual 6527 (100 yr old male)…………………………..184

Table A.16: Raw data for individual 6448 (51 yr old female)………………………….184

Table A.17: Raw data for individual 6319 (63 yr old female)………………………….184

Table A.18: Raw data for individual 6453 (67 yr old female)………………………….184

Table A.19: Raw data for individual 6539 (68 yr old female)………………………….184

Table A.20: Raw data for individual 6633 (69 yr old female)………………………….185

Table A.21: Raw data for individual 6655 (69 yr old female)………………………….185

Table A.22: Raw data for individual 6817 (73 yr old female)………………………….185

Table A.23: Raw data for individual 6621 (78 yr old female)………………………….185

Table A.24: Raw data for individual 6353 (79 yr old female)……………………….…185

Table A.25: Raw data for individual 6531 (80 yr old female)………………………….186

Table A.26: Raw data for individual 6333 (81 yr old female)………………….………186

Table A.27: Raw data for individual 6610 (86 yr old female)………………….………186

Table A.28: Raw data for individual 6611 (86 yr old female)………………….………186

Table A.29: Raw data for individual 6501 (90 yr old female)………………….………186

Table A.30: Raw data for individual 6604 (98 yr old female)………………………….187

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List of Figures

Figure 2.1: Osteocyte lacunae viewed under bright field ...... 21

Figure 4.1: Distal radius cross-section VSI and 8-bit image ...... 80

Figure 4.2: ImageJ automated detection of Ot.Lc.N ...... 83

Figure 4.3: ImageJ semi-automated detection of Po.Ar ...... 85

Figure 5.1: Interval plot of Ot.Lc.N/B.Ar per element by sex ...... 93

Figure 5.2: Boxplot Ot.Lc.N/B.Ar demonstrating variation between elements ...... 95

Figure 5.3: Scatterplot Ot.Lc.N/B.Ar verus age in the femur ...... 98

Figure 5.4: Scatterplot Ot.Lc.N/B.Ar verus age in the radius...... 99

Figure 5.5: Scatterplot Ot.Lc.N/B.Ar verus age in the rib ...... 101

Figure 5.6: Scatterplot %Po.Ar verus age in the radius ...... 102

Figure 5.7: Scatterplot %Po.Ar verus age in the rib ...... 104

Figure 5.8: Scatterplot %Po.Ar verus age in the femur ...... 105

Figure 5.9: Scatterplot %Po.Ar verus Ot.Lc.N/Ct.Ar in the radius ...... 108

Figure 5.10: Scatterplot %Po.Ar verus Ot.Lc.N/Ct.Ar in the rib ...... 109

Figure 5.11: Scatterplot %Po.Ar verus Ot.Lc.N/Ct.Ar in the femur ...... 110

Figure 5.12: Scatterplot systemic Ot.Lc.N/B.Ar comparisons between elements ...... 114 xv

Figure 5.13: Scatterplot systemic %Po.Ar comparisons between elements ...... 118

Figure 6.1: Radius cross-sections comparing %Po.Ar 6882 vs 6527 ...... 132

Figure 6.2: Femur cross-sections comparing %Po.Ar and Ot.Lc.N/B.Ar (6882 vs 6527)

...... 137

Figure 6.3: Scatterplot bone mass (%Ct.Ar) versus Ot.Lc.N/B.Ar...... 145

Figure B.1: Scatterplot sex related trends in Ot.Lc.N/B.Ar versus age in the femur ..... 188

Figure B.2: Scatterplot sex related trends in %Po.Ar versus age in the femur ...... 189

Figure B.3: Scatterplot sex related trends in Ot.Lc.N/B.Ar versus age in the radius ..... 189

Figure B.4: Scatterplot sex related trends in %Po.Ar versus age in the radius ...... 190

Figure B.5: Scatterplot sex related trends in %Po.Ar versus age in the rib ...... 190

Figure B.6: Scatterplot sex related trends in %Po.Ar versus Ot.Lc.N/B.Ar in the femur

...... 191

Figure B.7: Scatterplot sex related trends in %Po.Ar versus Ot.Lc.N/B.Ar in the radius

...... 191

Figure B.8: Scatterplot sex related trends in %Po.Ar versus Ot.Lc.N/B.Ar in the rib ... 192

Figure B.9: Scatterplot sex related systemic trends in Ot.Lc.N/B.Ar between femur and radius ...... 192

Figure B.10: Scatterplot sex related systemic trends in %Po.Ar between femur and radius

...... 193

Figure B.11: Scatterplot sex related systemic trends in %Po.Ar between femur and rib

...... 193

xvi

Figure B.12: Scatterplot sex related systemic trends in %Po.Ar between radius and rib

...... 194

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Chapter 1: Introduction

1.1 Research Context

Bone, as a dynamic tissue influenced by a multitude of environmental factors over the course of an individual’s lifespan, has long been the investigative target of researchers seeking to understand its complex position in overall health. Of particular interest in reconstructing the biocultural histories of past populations is the concept of “skeletal health” as often osseous tissue is the last remaining biological material available.

Recently, the field has turned towards the more accessible concept of skeletal or bone

“quality” since “health” of the individual as a whole is an elusive category for which bioarchaeologists often lack the appropriate tools to assess. Less inaccessible is determining the health of the organ system with which we are left by investigating the indicators of systemic or environmental impacts on the quality and integrity of skeletal material. Clinically, assessments of bone quality are crucial for monitoring fracture risk and maintaining quality of life in an ever growing elderly population facing issues of age related bone loss. As technology has and continues to advance, skeletal biologists at the crossroads of both bioarchaeology and modern medicine have recognized the importance of incorporating measures of microarchitectural integrity into macroscopic assessments of bone quality. Bone strength, or the ability to resist fractures and successfully incur regular insults while maintaining proper functioning, is determined by its composition

and structure (Brandi 2009; Currey 2003; Seeman and Delmas 2006). Moving beyond solely considering measures of bone mass, research into the multi-faceted and hierarchical components of bone strength have increasingly included factors affecting the cellular machinery of bone. For both clinicians interested in the health of an individual and bioarchaeologists uncovering the evolutionary and biological histories of past populations, understanding variation at the cellular level of bone is crucial to illuminating the complex physiological interactions of systemic, mechanical and environmental factors as it relates to populational health.

At the center of recent inquiry, the osteocyte has been recognized as the most ubiquitous and uniquely positioned cell to maintain the macroscopic integrity of the entire skeletal system through maintenance of the microenvironment. Over the past two decades, the paradigm has shifted from osteocytes as a passive, buried “fossil” (Frost

1960c) to a multifunctional, active, long-lived cellular component of the skeletal system

(Bonewald 2007; Bonewald 2011). By regulating bone metabolism, the network of osteocytes has been touted as the “conductors” of the dynamic changes in bone extracellular matrix in response to microdamage, adaptation to mechanical loads, and mineral homeostasis demands (Schaffler et al. 2014; Seeman 2006). Additionally, the cellular population has filled a once elusive role in the ability of cortical and trabecular bone to sense and respond to its mechanical environment through the adaptive process of mechanotransduction (Han et al. 2004; Klein-Nulend et al. 2013; Nicolella et al. 2006).

Making up 90-95% of all osseous cells, osteocytes residing within their lacunae and extend their dendritic processes within tunnels of bone called canaliculi facilitating

communication with other bone cells and their precursor populations; this forms a complex lacunocanalicular network or functional syncytium. Viability or survival of the cells is dependent upon the microenvironmental physiological parameters for hormonal, molecular, nutritional and mechanical influences. With a surface area of more than 100 times that of trabecular bone and 400 times that of cortical bone, the osteocyte syncytium is both highly sensitive to changes within the system especially those produced by mechanical forces, and highly connected to facilitate maintenance of nearly all biological processes occurring in bone. Frost’s early work study osteocyte viability and fate within human bone (Frost 1960b; Frost 1960c). Martin (2000a), building upon the work of

Marotti and colleagues (Marotti 1996; Marotti et al. 1992a), placed osteocytes at the research forefront by suggesting an inhibitory role for the cell in regulating targeted bone remodeling. Seminal animal and molecular studies published since proposing an inhibitory role have adjusted Martin’s work (as will be discussed further in Chapter 3); however, the result of his work was to integrate the osteocyte syncytium into proposed theories of bone adaptation to its environment providing the key biological explanation to, for example, maintaining homeostatic strain as described in Frost’s Mechanostat theory (Hughes and Petit 2010). Maintaining physiological or optimal loading is essential to osteocyte viability such that disuse or overuse levels will result in osteocyte apoptosis. Thus, as the bones’ mechanosensing cell, osteocytes mediate the adaptive response to its loading environment by translating mechanical signals into chemical signals affecting bone metabolism, but are themselves dependent on mechanical load to properly function. The importance of the extensive lacunocanalicular network in

providing nutrients and communication between all types and stages of bone cells has proven to be imperative in carrying out this locally adaptive and dynamic task.

Equally important as the ability to sense and respond to changes in the mechanical environment, osteocytes function to maintain and protect the “integrity of the material and structural strength of bone” (Seeman and Delmas 2006:2253) through initiation of targeted remodeling. Osteocyte apoptosis (cellular death) can result from a multitude of endogenous and exogenous factors and can lead to bone fragility and increased fracture risk by an overall decrease in bone quality (Noble et al. 1997). A major factor in initiating osteocyte cell death is the disruption of interstitial fluid flow through the lacunocanalicular network providing nutrients and oxygen to the cell by accumulated linear microdamage (Bonewald 2007; Jilka et al. 2013; Vashishth et al. 2000; Verborgt et al. 2000). Initiation of the remodeling process is signaled through the remaining intact network by the apoptotic cell as well as its viable neighbors resulting in targeted removal and replacement of the damaged area (to be discussed in Chapter 3). Microfracture accumulation and repair is essential to preventing gross failures in bone, a recent and powerful argument against bone remodeling suppressants as a treatment option for osteoporosis. Replacing fatigued bone is yet another crucial role in osteocyte maintenance of bone quality. Yet, apoptosis is not only induced through microdamage but can occur due to systemic factors such as hypogonadism, reactive oxygen species accumulation, excess endogenous glucocorticoids, corticosteroid treatment, and mechanical disuse (discussed in Chapter 3). Due to their various susceptibilities resulting in apoptosis, osteocytes represent the link between the overall health of an

individual including environmental and behavioral factors and the subsequent manifestations on bone. In order to thoroughly understand the tissue level quality of the skeletal system and its ability to withstand and respond to stressors or physiological insults, skeletal biologists have focused on the mechanisms behind these factors influencing the osteocytic syncytium.

This study is designed to establish a baseline understanding of the variation in density and distribution of osteocytes in cortical bone both within and between individuals. Current research tends to compare patterns of osteocyte distribution, using osteocytic lacunae as a proxy, across bone types and locations despite research indicating the site specific nature of cell density and viability. Before these reported patterns can be interpreted, a systematic investigation into basic factors affecting variation in the osteocyte lacunar density, as a proxy for the cells that inhabit them, must be conducted.

Here, the author will investigate variation between skeletal sites as it relates to major factors that may affect osteocyte viability across a population including chronological age and sex. Relationships between lacunar density and the product of cellular apoptosis, intracortical porosity, are explored for each site. Lastly, the intra-individual systemic variation in lacunar density and porosity with age and between sexes is investigated.

Employing basic histological analyses of the midshaft rib, midshaft femur and distal radius from 30 cadaveric individuals, the specific aims of this study are as follows:

A) By quantifying osteocytic lacunae, provide a baseline assessment of inter- individual variation in osteocyte density with respect to age and sex in the femur, radius, and rib.

B) Analyze the relationships between age and sex related changes in osteocyte lacunar density between each element investigating systemic and regional patterns. In doing so, begin to contextualize the conflicting data on lacunar density available likely an artifact of comparing different skeletal sites and bone types (trabecular versus cortical) and diseased (osteoporotic) to healthy bone.

C) Quantitatively demonstrate the relationship between porosity and osteocytic lacunar density in cortical bone. Compare the use of including measures of porosity on the calculation of osteocytic lacunar density in complete histological cross sections of a skeletal element.

D) Provide a foundational understanding of the importance of quantifying microarchitectural elements, particularly osteocyte density and distribution, when assessing bone quality in any modern or bioarchaeological context.

1.2 Hypotheses

H10: In the femur, radius and rib, there is no evident age or sex related variation in osteocyte lacunar density (Ot.Lc.N/B.Ar) per element.

H1A: In the femur, radius and rib, there is an age related decline in osteocyte lacunar density (Ot.Lc.N/B.Ar) per element occurring in both males and females.

The viability of osteocytes and hence, healthy maintenance of the bone, relies on both systemic and mechanical factors. Systemic changes associated with increasing age, such as higher levels of reactive oxygen species, decreased sex hormones, reduction in mechanical loading have been shown to increase osteocyte apoptosis in cortical bone

(Almeida and O’Brien 2013; Jilka et al. 2013). As empty lacunae are either repaired or

hyperminearlized, an increase in osteocyte apoptosis results in a decrease in existing lacunae (Noble and Reeve 2000). It is generally accepted that osteocyte density decreases with age (Qiu et al. 2002b) due to influences that will be reviewed in chapter

3. Heretofore, conflicting data on the effects of sex on osteocyte density has also been reported. As estrogen loss has been studied as a major factor in higher levels of age associated bone loss in females than in males, decreases in osteocytic density may occur at disparate rates between the sexes. Emerton and colleagues (2010) found a two fold increase in osteocyte apoptosis in cortical bone of the midshaft femora in mice following ovariectomy. But in human bone, the picture is not as clear. Vashishth and colleagues

(2002b) found that the relationship between osteocyte density and aging is sexually dimorphic in vertebral trabecular bone. Conversely, this same group found no significant sex differences in lacunar density changes with age in the midshaft femur (Vashishth et al. 2000). Mullender and colleagues (2005) found higher osteocyte densities in healthy females than in healthy males and the relationship held in osteoporotic patients.

Although age related declines in the osteocyte population density have been demonstrated, the rate of loss between sexes is debated (Carter et al. 2013). As the sensitivity and susceptibility of osteocytes vary across bone types and anatomical locations, a baseline understanding of variation in age and sex related changes in density must be established upon which to make quality interpretations. This hypothesis investigates the effects of two major contributing factors-age and sex- to cortical osteocytic lacunar density at three very different skeletal sites: midshaft rib, midshaft femur and distal radius.

H20: Per element, there is no correlation between osteocyte lacunar density

(Ot.Lc.N/Ct.Ar) and intracortical porosity (%Po.Ar)

H2A: Per element, osteocyte lacunar density (Ot.Lc.N/Ct.Ar) is negatively correlated with intracortical porosity (%Po.Ar).

The number of osteocytes in any given skeletal location is determined by the end of growth and development. Newly embedded osteocytes only occur during occasional periosteal apposition but more commonly following remodeling events; thus, excluding the occasional later in life fracture repair or modeling event, osteocytes cannot be added to bone without initial resorption and formation of the basic multicellular unit (BMU). A direct relationship with increasing age and intracortical porosity is well established especially in females following the onset of estrogen loss. However, researchers have reported that the amount of bone loss attributable to hypogonadism is small (Almeida and

O’Brien 2013). As osteocyte apoptosis has been shown to precede and trigger resorption in a targeted fashion in both human and animal models (Aguirre et al. 2006; Emerton et al. 2010; Plotkin 2014) intracortical porosity created through remodeling activity is assumed to be correlated with osteocyte density. Porosity has been shown to correlate with lacunar density in the midshaft femur (Dong et al. 2014) but has yet to be directly investigated in the distal radius or the midshaft rib. As apoptosis increases with age due to changes in bone material properties, and systemic and mechanical influences, intracortical porosity increases with age as a result. The decrease in structural bone quality that accompanies an increase in cortical porosity is mediated through disruption of the osteocyte lacunocanalicular network. This study will investigate the relationship

between intracortical porosity and osteocyte lacunar density in each element to compare with previous work in the midshaft femur, and as the first to report this relationship in the distal radius and midshaft rib.

H30: Regional patterns of age and sex related changes in osteocyte lacunar density

(Ot.Lc.N/B.Ar) will correlate systemically among all three anatomical sites.

H3A: There exist regionally specific patterns of age and sex related changes in osteocyte lacunar density (Ot.Lc.N/B.Ar) which do not correlate systemically among all three anatomical sites.

Despite the prevailing acceptance that systemic factors associated with aging will encourage osteocyte apoptosis, due to the site specific mechanical influences on osteocyte viability and subsequent lacunar density, comparisons between studies originating from a variety of anatomical locations and osseous type require a level of caution. It is assumed the inverse relationship between age and osteocyte viability/lacunar density is ubiquitous; however, due to the various mechanical environments experienced by the midshaft rib, midshaft femur and distal radius, it can be expected that variation in osteocyte density decline exists between these regions. The only studies that have examined systemic patterns of osteocyte lacunar density variation have been completed in horses (Skedros et al. 2005) and osteoporotic sheep (Zarrinkalam et al. 2012) both reporting discrepancies in the amount of decline between anatomical locations in relation to mechanical environment and disease respectively. This study will be the first to report systemic correlations of osteocyte lacunar density declines in human cortical bone. Likely, the systemic factors affecting cellular viability will overcome the

anti-apoptotic influences of mechanical forces but potentially to a varying amount in each element. In this study, using samples from the same 30 individuals is a novel approach to exploring the systemic patterns of osteocyte lacunar density.

1.3 Significance

Although the goals for interpreting skeletal data vary between modern clinical investigations and bioarchaeological research, the same biological processes are necessary to understand the skeletal manifestations of health and environmental influences. In fact, each respective field has and should continue to contribute to the other. Using clinically established factors influencing bone mass and structure, provides a foundation for behavioral reconstructions of past populations. Bioarchaeologists have the unique ability and access to skeletal populations for avenues of study which are logistically impossible in a living population. In this case, the osteocyte has been identified ubiquitously in mammals, dinosaurs (Schweitzer et al. 2013), and

Australopithecus afarensis (“Lucy”) (Bromage et al. 2009) supporting its evolutionarily constrained existence. The osteocyte lacunocanalicular network is the mechanism by which endogenous and exogenous factors including health and lifestyle affect change within the skeletal system. Therefore, unraveling the complex and hierarchical effects on the cellular components of bone quality moves both fields toward more accurate interpretations of the skeletal system.

This study will provide basic knowledge of cortical bone osteocyte lacunar density as it changes with age and sex. Additionally, it will be the first to correlate intracortical porosity and lacunar density in human cortical bone at multiple skeletal sites,

and the first to investigate systemic or intra-individual variation. Understanding the systemic declines in osteocyte lacunar density is important to assess the appropriateness of comparing studies utilizing various anatomical sites as well as for skeletal remains by which there may only be a few elements with which to work. With 30 modern cadaveric individuals, this project will serve as a foundation on which future work can build in our understanding of how to apply osteocyte lacunar density to assessment of bone quality as affected by systemic and environmental influences in past and present human populations.

1.4 Topics Addressed

The context, hypotheses and significance of this study were briefly addressed in this chapter. The two chapters to follow expound upon the justification for this study and provide explanations of biological processes to situate osteocytes in a key role for bone quality determination. Chapter 2 can be divided into two major components: the biological foundation of bone quality and the operationalized definition and modern methods of assessment. This chapter will integrate the macroscopic and microscopic aspects of a functioning tissue as it relates to cortical bone. Chapter 3 will detail the lifespan of the cell in question from osteocytogenesis through to apoptosis. The importance of the osteocytic lacunocanalicular communicative network will be outlined including the molecular signaling that regulates the activity of fellow cells providing the crucial connection between osteocyte viability and the biological processes discussed in

Chapter 2. The mechanisms behind the major osteocyte functions of mechanosensation, mechanotransduction and microdamage repair are explained to position the cell as a

major component of bone quality. Lastly, Chapter 3 addresses the basis for inter- and intra-individual variation in osteocyte density. The factors affecting osteocyte viability and function which ultimately determine lacunar density, both mechanical and metabolic

(systemic) are outlined so as to establish the biological foundation for differences.

Chapter 4 describes the methodological approach for histological analysis and semi-automated quantification of osteocytic lacunar density. This chapter establishes the importance of a basic science approach to quantifying microarchitectural structures that have been studied using more complex technologies. Histological analyses were performed using bright field microscopy and quantification of osteocytic lacunar density and intracortical porosity completed using the free access program ImageJ ®; thus providing a more accessible tool for researchers investigating cortical osteocyte density.

Chapter 5 details the results and patterns found between density and chronological age, sex, and intracortical porosity. These results are discussed in Chapter 6 including possible physiological explanations of identified patterns in osteocyte lacunar density.

Finally, Chapter 6 ends with a discussion for the potential use of this variable in bioarchaeological investigations. Chapter 7 concludes this work.

Chapter 2: Biological Foundation and Assessment of Cortical Bone Quality

2.1 Introduction

This chapter will explore the biological foundation of bone quality including a brief description of the cellular components, their role in bone metabolism, and the basic processes of bone metabolism. Osteocytes will be discussed in further detail in Chapter 3 including their molecular influences on other bone cells and regulation of skeletal metabolism. In order to position quantitative measurements of osteocyte density into a collection of factors used to assess bone quality in human populations, the current mechanisms by which these assessments are carried out are briefly discussed. It is important to define “skeletal health” or as it is used in this study “bone quality,” and discuss how this concept is investigated due to the hierarchical nature of bone tissue. As this study concentrates on the response of cortical bone osteocytes to systemic and mechanical factors, this review will focus mainly on the bone envelopes containing cortical bone, their development and maintenance.

2.2 Cortical Bone Cells

Bone is a metabolically active connective tissue influenced not only by the mechanical loading it incurs but also by multiple systemic factors as it is but one component of a complex system that constitutes an organism. Capable of adapting to such influences, bone serves multiple functions including mineral homeostasis (as it

stores 99% of the body’s calcium), protection of vital organs and serves as the levers to produce movement (Brandi 2009). On a tissue level, the balance between the rigid mineral components preventing deformation and the flexible type I collagen components allowing for deformation of the bone extracellular matrix is crucial to maintain a properly functioning bone and resist gross failure. To accomplish and maintain the competency of the tissue, four cellular “actors” (osteoblasts, osteocytes, bone lining cells, and osteoclasts) on four skeletal surfaces (endosteal, intracortical or Haversian, periosteal and trabecular) carry out dynamic metabolic processes in response to mechanical and systemic factors (Gosman et al. 2011; Martin et al. 1998; Robling and Stout 2008) These surfaces or envelopes are unique in their roles in the mechanical response and health or quality of the bone (Burr and Akkus 2014) with variable sensitivities and metabolic rates.

By understanding the physiological functions and homeostatic parameters of the cellular machinery, variations then become informative for those investigating bone quality or making biocultural interpretations of the past.

2.2.1 Osteoclasts

Unlike any other cell in the body in its shape and digestive capabilities, the osteoclast is not only responsible for bone resorption but also interacts with the immune system and inflammatory responses (Cappariello et al. 2014; Charles and Aliprantis

2014). The large (~300 µm), multi-nucleated osteoclast is a part of the monocyte- macrophage family and derived from hematopoietic stem cells (HSC) supported by receptor activator of nuclear factor κB ligand (RANKL) and macrophage colony stimulating factor (M-CSF) (Bar-Shavit 2007; Ross 2013). Both proteins are produced

by mesenchymal stem cells (MSCs) and cells from the osteoblastic lineage and induce osteoclastogenesis (Boyle et al. 2003) encouraging the fusion of many mononuclear precursor HSCs to create a large multinucleated cell capable of migration across the bone surface (Bellido et al. 2014). RANKL activates its receptor on the polarized osteoclast which stimulates necessary structural changes required to initiate the resorption process including the formation of a sequestered microenvironment between the cell and the bone surface (Boyle et al. 2003; Ross 2013). The multinucleated cell is polarized with an apical membrane facing the bone surface and opposite to it, a basolateral membrane facing the vascular environment. At the apical membrane, the osteoclast forms a “sealing zone” with the surface of the bone precipitated by foot like structures referred to as

“podosomes;” these podosomes form a “belt” which provides a tight yet dynamic attachment between the cell and the surface of the bone (Cappariello et al. 2014; Charles and Aliprantis 2014). Irregularly shaped extensions of the osteoclast plasma membrane having fused with transport vesicles, form the ruffled border of the apical membrane

(Cappariello et al. 2014, Bellido et al. 2014). This combination of the podosome belt and the ruffled border effectively seal the resorption surface from the extracellular bone microenvironment (Cappariello et al. 2014). This “sealing zone” cordons off the location where bone resorption is to occur and contains the extent of the ruffled border which can be further divided into more specialized membrane “domains.” On the periphery near the sealing zone, the subdomains of the ruffled border known as the “fusion zone” release elements needed to dissolve the matrix within the intracellular compartment or

“Howship’s lacuna” (Cappariello et al. 2014). Meanwhile, the centrally located “uptake

zone” of the ruffled border provides a route for the degraded and released bone matrix products to be disposed of into the vascular environment abutting the basolateral membrane (Cappariello et al. 2014).

Multiple mechanisms are required for bone resorption to occur. The resorption cavity or Howship’s lacuna is acidified to a pH ~4.5 by the secretion of both protons (H+)

- - - and chloride ions (Cl ) supported by an anion exchanger (Cl /HCO3 ) located on the basolateral membrane (Bar-Shavit 2007, Cappariello et al. 2014, Bellido et al. 2014).

Acidifying the resorption lacuna by forming HCl- dissolves the inorganic hydroxyapatite portion of the bone matrix exposing the organic collagen portion. The secretion of proteolytic enzymes such as cathepsin-K along with matrix metalloproteinases (MMP) dissolves the exposed collagen and releases the products into the resorption lacuna

(Bellido et al. 2014). These degraded products are then internalized by the osteoclast and either digested by its lysosomes or released into the vascular environment via the functional secretory domain of the basolateral membrane through a transcytosis process

(Cappariello et al. 2014, Bellido et al. 2014). Alternatively, these can be directly released into the extracellular environment following the detachment of the sealing zone from the Howship’s lacuna.

Following completion of their resorption cavity or “cutting cone,” the fate of osteoclasts is programmed cell death or apoptosis (Bar-Shavit 2007). Though this process is not completely understood, apoptosis may occur under the influence or lack thereof of many factors. Detachment of the cell from the bone is not sufficient on its own to cause apoptosis (Bellido et al. 2014). Antiapoptotic cytokines such as M-CSF and

RANKL may promote osteoclast survival in addition to their osteoclastogenic role

(Bellido et al. 2014). Additionally, tumor necrosis factor alpha (TNF-α) and interleukin-

1 (IL-1) promote cell survival (Bellido et al. 2014). Nitric oxide (NO) promote detachment and apoptosis of the osteoclast and will be discussed in Chapter 3.

2.2.2 Osteoblastic lineage

Osteoblasts are derived from mesenchymal stem cells (MSC) and form bone through collagen matrix synthesis and secretion of calcium phosphate mineral (Gosman and Stout 2010). Anatomically, populations of MSCs are found in the deep layer

(cambium) of the periosteum or in the stromal tissue of the medullary cavity (Martin et al. 1998). These bone building cells constitute between 4-6% of total cells in the skeletal system and play an important role in achieving and maintaining healthy bone mass

(Capulli et al. 2014). Osteoblastogenesis, or the differentiation of osteoblasts from MSC requires the influence of multiple factors to drive the pluripotent cells towards the osteoblast lineage. These factors include bone morphogenetic proteins (BMPs) and the

Wnt signaling pathways which commit the MSC towards an osteogenic path under

RunX2 influence (Capulli et al. 2014, Bellido et al. 2014). The differentiation process is divided into mesenchymal progenitors, preosteoblasts, and mature osteoblasts although the classification of cells into discrete categories remains difficult (Long 2012).

Osteoprogenitor cells tend to express the gene RunX2, considered the “master gene” in osteoblast differentiation as RunX2 null mice are devoid of osteoblasts (Capulli et al.

2014). Mature osteoblasts are identified by the expression of type I collagen, alkaline phosphatase (ALP) and osteocalcin, the most abundant non-collagenous protein in bone

(Brennan-Speranza and Conigrave 2015; Long 2012). The entire osteoblastogenesis process from MSC to mature osteoblast requires approximately 2-3 days to complete

(Martin et al. 1998).

Once reaching mature status, the cuboidal shaped and polarized osteoblasts are aligned on the bone surface in a single row with cytoplasmic processes extending towards osteocytic processes within the bone matrix (Capulli et al. 2014). Bone matrix formation involves two steps: depositing the organic framework and subsequent mineralization

(Capulli et al. 2014). Deposition of the organic portion, called osteoid prior to mineralization, requires osteoblast secretion of mainly type I collagen, non-collagen proteins such as osteopontin and osteonectin, and proteoglycans (Capulli et al. 2014).

Osteocalcin expression is increased 200 fold at the bone surface serving to coordinate matrix mineralization (Brennan-Speranza and Conigrave 2015). The collagen component of bone is responsible for its characteristic flexibility and tensile strength (Martin et al.

1998). Mineralization of the collagen rich osteoid scaffolding begins with synthesis of hydroxyapatite crystals in the matrix vesicles derived from the cellular membrane of the osteoblast (Capulli et al. 2014, Bellido et al. 2014). These inorganic mineral crystals fill the spaces between the collagen fibrils during the process of mineralization and convey rigidity and compressive strength to the bone (Martin et al. 1998). Once bone formation is complete, mature osteoblasts face three potential fates: apoptosis, transition into bone lining cell, or transition into osteocytes. Approximately 60-80% of osteoblasts exit their cell cycle via apoptosis (Bellido et al. 2014).

Bone lining cells (BLC), once thought to be quiescent “retired” osteoblasts, are flattened cells that line the non-resorbing surface of bone (Bellido et al. 2014). However, as they appear to retain their receptors for parathyroid hormone, lining cells may regain

(Franz‐Odendaal et al. 2006) their ability to synthesize and secrete matrix (Allen and

Burr 2014; Bellido et al. 2014; Martin et al. 1998). As a component of the functional syncytium in communication with the dendritic processes of osteocytes via gap junctions

(see Chapter 3 for more details), BLCs may play a yet to be illuminated role in calcium and metabolite exchange between the bone and the extracellular environment (Bellido et al. 2014). Recently, a more well defined and active task for the BLCs has been investigated: the formation of a bone remodeling compartment (BRC). Prior to resorption, osteocytes may signal the cells covering the surface of the bone in need of replacement causing the BLCs to detach and form a canopy which houses the basic multicellular unit (BMU) and partitions it from the marrow environment (Bellido et al.

2014). The BRC maintains an environment within which progenitor cells are encouraged to differentiate to osteoblast or osteoclast lineages via molecular signals controlled by hormonal or mechanical stimuli (Bellido et al. 2014). No longer considered a stagnant cell, the formation of the BRC by bone lining cells is crucial to the process of bone remodeling (a process to be discussed in section 2.3.2).

Lastly, osteoblasts that do not undergo apoptosis nor become bone lining cells are fated to be embedded in the osteoid matrix becoming the most abundant and regularly distributed bone cell, the osteocyte. The proportion of osteoblasts that terminate as osteocytes varies across species (Franz-Odendaal et al. 2006) but has been estimated as

30% in mammalian bone (Parfitt 1990) and more recently between 5% and 20% in human cortical bone (Bellido et al. 2014). Osteocytes comprise 90-95% of all bone cells and have a lifespan that can reach decades (Bellido et al. 2014; Bonewald 2007;

Bonewald 2011) Frost (1960b) estimated the average half-life of human osteocytes to be

25 years; however, this likely varies based on anatomical location and skeletal metabolism. For this reason, these long lived cells correspond to the tissue age of bone rather than the chronological age of the individual (Manolagas and Parfitt 2010) and depending on anatomical site, bone type and turnover rate result in some populations surviving the entirety of an individual’s life (for example in ear ossicles that do not undergo remodeling) (Noble and Reeve 2000). Osteocytes exist in lacunae within the extracellular matrix of bone, specifically located between lamellae, and extend on average 50 cytoplasmic dendritic processes through tunnels called canaliculi (Martin et al. 1998, Bellido et al. 2014) (Figure 2.1). The dendritic processes form gap junctions with pre-existing neighboring osteocytes, the vascular supply, the extra-osseous space and the remaining bone lining cells on the quiescent surface. Via these gap junctions, proteins and biochemical molecules are exchanged rapidly and efficiently (Martin et al.

1998) promoting intercellular communication (Knothe Tate et al. 2004). The extensive osteocytic lacunocanalicular network forms a functional syncytium encompassing a proteoglycan extracellular fluid filled space (Knothe-Tate et al. 2004). The periosteocytic fluid flowing through the lacunocanalicular network serves to provide nutrients for the osteocytes themselves as well as facilitating the role of mechanosensation (see Chapter 3). Thus, the osteocytic syncytium is formed throughout

the bone and functions in both communication and transportation/exchange of metabolic materials as well as mechanosensation and transduction (the details of which will be further discussed in Chapter 3).

Osteocyte Lacunae

Figure 2.1: Osteocyte lacunae viewed under bright field microscopy

Though the factors regulating the selection of cells to become an osteocyte are still under investigation, the process of osteocytogenesis is becoming less obscure.

Initially, osteocytogenesis was argued to be a passive process by which some osteoblasts

“slowed” down enough that they were buried in the osteoid produced by its neighboring 21

cells (Bonewald 2011). However, there have been recent developments suggesting the process by which osteocytes are embedded within the matrix is anything but passive.

The transformation process from an immature osteoblast into a mature osteocyte has up to eight morphologically distinct phases though this is not agreed upon in the literature

(Franz‐Odendaal et al. 2006). However, in general during this transformation process, osteoblasts undergo a reduction in cellular organelles and size (Franz-Odendaal et al.

2006) while developing cytoplasmic dendritic processes. Franz-Odendaal and colleagues

(2006) suggest four possible ways in which osteoblasts become embedded based upon their polarity and the direction of osteoid deposition. The major active component of transformation from osteoblast into the postmitotic, mature, and embedded osteocyte involves the extension of dendritic processes towards the mineralization front, actively tunneling through matrix to maintain communication with cells on the bone surface

(Bonewald 2011). Furthermore, gene expression alterations driving differentiation into osteocytes support the active nature of osteocytogenesis. Initiation of the formation of dendritic processes is driven by the expression of both membrane associated proteins as well as MMP-14 (Bellido et al. 2014).

The explicit functions of mature osteocytes and the extensive lacunocanalicular network in the maintenance of the bony extracellular matrix will be discussed in detail in

Chapter 3. However, the foundation on which these functions can be successfully performed by the functional unit of the osteocytic network depends upon two major factors: the availability of osteoblasts to differentiate into osteocytes, and the viability of the osteocytes themselves which establishes a population density of cells within bone.

Lacunar density has been established as a common proxy for determining osteocyte density (Hernandez et al. 2004; Vashishth et al. 2002b) which is dependent on a balance between viability and apoptosis and the factors affecting each. These processes will be further elucidated in Chapter 3.

2.3 Cortical Bone Metabolism

The ability of bone to dynamically respond to its environment is precipitated by the metabolic processes carried out by cells discussed above. Two distinct processes function to develop and maintain the integrity of the bone: modeling and remodeling.

Though occurring under varying circumstances, these processes are the underlying foundation for osteocyte production and distribution and therefore must be defined.

2.3.1 Modeling

Modeling, as described by Frost (1985), functions to “sculpt” developing and intact organs, in this case, bone. In response to the mechanical loading environment under which a bone grows and is maintained, altering the shape and amount of compact cortical bone present is essential to function properly. Modeling occurs primarily during childhood development but can continue throughout life and generally results in a net increase in bone mass (Allen and Burr 2014). Unlike remodeling (to be discussed in the next section), modeling is carried out by osteoblasts and osteoclasts independently via processes termed “formation drift” and “resorption drift” respectively (Frost 1985, Allen and Burr 2014). Ultimately, by adding and removing bone at independent places in the periosteal or endosteal envelopes, bone shape and size are affected (Martin et al. 1998).

Modeling during growth ensures the achievement of the appropriate amount of bone mass

(Maggiano 2012) arranged in an adaptive or “customized” distribution (Martin et al.

1998) in relation to the perceived mechanical environment .

During development, growth and modeling drifts adjust bone architecture and mass by producing primary lamellar bone mainly in response to mechanical loads

(Robling and Stout 2008). Although considered to be independent, osteoclast resorption on the medullary cavity and osteoblast formation along the periosteal surface function together to alter bone size and shape (Maggiano 2012). The production of primary lamellar bone responds to three factors: the genetically programmed growth process, increasing body and muscle mass, and the mechanical loading environment (Frost 1985).

For growth purposes, in order to maintain shape during lengthening, resorption modeling and formation modeling act on the periosteal and endosteal surfaces respectively (Allen and Burr 2014). Although occurring on separate surfaces, this type of modeling at the metaphysis referred to as “metaphyseal cut back” is globally linked and dysregulation or dysfunction of either of these processes results in abnormal metaphyseal morphology

(Allen and Burr 2014). During ontogeny, the periosteal envelope is highly active especially in response to increasing body mass and accompanying mechanical demands

(Gosman and Stout 2010). Physiologically, modeling still occurs after skeletal maturity is attained but to a lesser extent (Seeman 2008).

Similar to ontogenetic changes, in order to maintain cortical thickness yet distribute bone mass appropriately in relation to its central axis, bone drift occurs by the processes of periosteal formation modeling (forming circumferential lamellae) and resorption modeling on the endosteal surface. For both radial growth and to attain an

adaptive geometry, drift includes modeling processes at both the periosteal and endosteal envelopes. Formation of primary lamellae on the lateral femoral periosteal surface and on the mirrored medial endosteal surface accompanied by lateral endosteal and medial periosteal resorption ultimately moves the bone through anatomical space (Maggiano

2012). The net ventral drift observed in ribs accommodates the expansion of the thorax during growth (Streeter and Stout 2003). As Maggiano (2012) purports, the modeling process occurs due to growth and development under the guise of genetic controls, but how the process occurs and to what geometric end is due to the mechanical environment.

In order to maintain mechanical integrity, bone has the ability to add, remove or replace matrix (Frost 1987). Frost (1982) suggests the minimum effective strain level as the threshold over which bone initiates an adaptive response to the in vivo tissue strain it is experiencing. Termed the “mechanostat” (Frost 1987; Frost 2003) a negative feedback system with optimal strain set-points (which vary between tissue type and anatomical location), explains the adaptive modeling response occurring both when and where the customary strain stimulus is exceeded. Hence, formation or resorption modeling function to return the in vivo tissue strain to the optimal strain levels by dynamically altering bone shape and distribution. This return to optimal strain is locally carried out by osteocyte alteration of its immediate surroundings which will be covered in Chapter 3.

Thus, drift may occur as a consequence of radial growth to maintain cortical thickness, or in response to mechanical loads to optimize the architecture of the bone as a whole.

Despite the underlying control, this phenomenon results in variances in tissue age as compared to the chronological age of the individual. Variations in the mean tissue age

have implications for the persistence of the long lived osteocyte deposited during initial primary lamellar formation (to be discussed in Chapter 3).

2.3.2 Remodeling

Unlike modeling, the process of remodeling involves the “tethered” or “coupled” actions of bone resorption and formation at same location. Remodeling can occur on all four bone surfaces (periosteal, endosteal, intracortical and trabecular) (Allen and Burr

2014; Stout and Crowder 2012). Simply, remodeling functions to remove older bone and replace it with new bone (Martin et al. 1998) through the coordinated actions of osteoclasts and osteoblasts respectively. In the intracortical envelope, the team of osteoclasts, osteoblasts and accompanying blood vessels is termed the “basic multicellular unit” or BMU. The result of the remodeling process is referred to as the

“basic structural unit” (BSU) or secondary osteon delineated by cement lines from the surrounding interstitial lamellae.

Remodeling can be considered either targeted or non-targeted. Targeted remodeling responds to a localized event such as microdamage induced osteocyte apoptosis, while non-targeted remodeling is stochastic or random (Allen and Burr 2014).

Initially, Parfitt (2002) and Burr (2002) suggested merely 30% of remodeling throughout the body could be attributed to targeted remodeling. However, with more recent understanding of osteocyte apoptosis and resulting control over osteoclast and osteoblast activity (to be discussed in Chapter 3), Allen and Burr (2014) posit the majority of remodeling should be attributed to locally initiated targeted events. Together, these two types of remodeling function to fulfill three major purposes:

1. Maintain systemic calcium homeostasis by exchanging calcium ions at the

resorption surface (Martin et al. 1998). (Non-targeted)

2. Allow for skeletal adaptation to the mechanical environment and localized

strains increasing “mechanical efficiency” (Martin et al. 1998: pg. 62)

(Targeted)

3. Provides a mechanism to repair fatigue damage or mechanically induced

microdamage to maintain the mechanical integrity of the bone preventing

accumulation and gross failure (Burr 2002; Parfitt 2002). (Targeted)

Maintaining healthy bone requires a systemic balance of both types of remodeling as well as a balance between osteoblastic and osteoclastic activity in order to meet the above needs.

Traditionally, remodeling was simplistically characterized by the activation, resorption, formation (ARF) sequence (Martin et al. 1998). However, the process has become more complex regardless of the initiating event or the function, the remodeling sequence is now classified by five distinct phases (Allen and Burr 2014; Henriksen et al.

2009) The first stage, activation, encompasses both the site localization on which the remodeling cycle will occur, and also the recruitment and differentiation of osteoclast precursors into mature osteoclasts (Allen and Burr 2014). HSC recruitment is initiated via biochemical signals released by the osteocyte and transported via the lacunocanalicular network to the surface where bone lining cells have detached to form the bone remodeling compartment (BRC) (Sims and Martin 2014). The Haversian vessel or marrow provide the osteoclastic precursors to undergo maturation under the influence

of RANKL and M-CSF (as discussed in 2.2.1) and initiate the second phase of the remodeling cycle: resorption. Intracortically, the mature osteoclasts “tunnel” through the bone creating a “cutting cone” which varies in depth. In the case of microdamage initiated osteocyte apoptosis, the cutting cone would function to remove the damaged area of bone. Generally, the diameter of the cutting cone within cortical bone reaches between 150 and 350 µm (van Oers et al. 2008) and moves at a speed of approximately

20 µm per day (Martin et al. 1998). To provide more osteoclast precursors in the event of osteoclast death, the release of vascular endothelial growth factor (VEGF) stimulates the growth of a capillary through the cutting cone. Although the cutting cone (and thus the resulting BSU or osteon) diameter is relatively consistent in size, the length of an osteon may vary greatly (Allen and Burr 2014).

Following resorption and osteoclast apoptosis, a brief reversal phase in which the remodeling process shifts from a catabolic to an anabolic metabolism. It is during this reversal phase, that complex and tightly regulated coupling factors play a vital role in the

BMU. The discovery of the EphrinB2 expression on osteoclasts and EphB4 expression on osteoblasts supports a bi-directional signaling between the cellular components of the

BMU (Ikeda and Takeshita 2014; Zhao et al. 2006). EphrinB2 supports osteoblastogenesis and leads to bone formation by binding to EphB4 while EphB4 inhibits osteoclastogenesis and decreases bone resorption by binding to EphrinB2

(Henricksen et al. 2009). At least four other osteoclast expressed factors have been identified in mouse models and may play an important role in coupling yet require further genetic studies (Ikeda and Takeshita 2014). Additionally, factors released into the BRC

from the bone matrix during resorption (transforming growth factor beta or TGF-β and

BMPs) positively influence osteogenic precursors to the osteoblast lineage (Allen and

Burr 2014). The osteocyte role in releasing or transporting coupling factors will be discussed in Chapter 3.

During the reversal phase prior to bone formation, the remaining collagen fragments must be cleaned from the resorption pit (Henricksen et al. 2009, Allen and

Burr 2014). A group of mononuclear cells know believed to be of the osteoblastic lineage and more specifically, specialized bone lining cells degrade the organic collagen matrix (Everts et al. 2002) Additionally, the BLCs smooth the scalloped resorption pit in preparation for their secretion of a thin, sulfur and proteoglycan rich reversal or cement line (Stout and Crowder 2012, Allen and Burr 2014). The reversal or cement line delineates the osteon from the surrounding interstitial lamellae and represents the size of the cutting cone. Controversy exists over the level of mineralization of the cement line that likely has some mechanical relevance in need of elucidation (Allen and Burr 2014).

The fourth phase of the remodeling cycle is bone formation. It is in this phase that osteocytogenesis occurs as mature osteoblasts secrete unmineralized organic osteoid to centripetally fill in the resorption pit (forming concentric lamellae) left by the catabolic activity of the osteoclasts. As bone formation proceeds, osteoblasts may reach one of their three afore mentioned fates including becoming embedded as an osteocyte. Due to the nature of osteoid deposition, the “oldest” osteocytes within a single BSU or osteon, are closest to the cement line. Following the completion of skeletal growth and development, bone formation during remodeling represents the sole source of new

osteocyte populations. The rate of osteoid refilling of the cutting cone decreases as progress towards the center leaving a vascular or Haversian canal in which a capillary persists to nourish the remaining cells (Martin et al. 1998). Mineralization of the new osteoid occurs initially rapidly incorporating calcium and phosphate ions into the matrix over the course of 2-3 weeks (Allen and Burr 2014). Secondary mineralization can take up to a year and consists of the last additions and maturation of hydroxyapatite crystals

(Allen and Burr 2014).

The fifth and final phase of the remodeling process is the termination phase.

During this time, newly formed osteocytes have maintained gap junction communication with the remaining osteoblasts. Osteocytes produce sclerostin which antagonizes the Wnt pathway preventing bone formation. Following termination of the remodeling cycle, the surface of the bone (within the Haversian canal) is lined with BLC and considered quiescent. During normal physiological conditions, the total remodeling cycle lasts approximates 4-6 months (Allen and Burr 2014). The formation portion of this time frame is about 4 to 5 times longer than the resorption portion which completes around 3 to 6 weeks. However, each of these phases may vary depending on anatomical location, bone tissue type, changes in magnitude of mechanical strain, or rate of osteoclast or osteoblastogenesis (Martin et al. 1998) so that although the process may be coupled it may not be balanced. Under physiological conditions, a healthy individual will experience a slightly negative bone balance at the BMU level due to the presence of the

Haversian canal (Allen and Burr 2014). That is, the amount of bone removed by the cutting cone is slightly larger than the amount of bone replaced during formation. The

rate of bone remodeling, often referred to as the activation rate or the initiation of the remodeling cycle, is positively associated with bone loss as more BMUs are formed, formation rate is slowed, and a negative balance still occurs at each BMU thus resulting in bone loss (Allen and Burr 2014). Commonly, age associated osteoporosis can be attributed to this process resulting in decreased bone mass and quality. The molecular explanation and other factors contributing to remodeling rates, coupling, balance and resulting age associated bone loss will be discussed in Chapter 3.

2.4 Assessment of Cortical Bone Quality

Clinical assessments of bone quality, especially in an aging population, are driven by fracture prevention, an outcome that can have serious consequences for the morbidity and mortality of an individual. Bioarchaeological analyses not only use bone quality as a similarly informative index but as a tool for biocultural interpretations of past populations. Due to the dynamic nature of osseous tissue, the resulting gross bone is a variable palimpsest of features affecting both its material composition and structural integrity. For this very reason, multi-scaled assessments must be employed to make any interpretations about the quality of such a complex tissue. This section will review the operationalized definition of bone quality as well as the macroscopic assessment tools in order to position osteocytic lacunar density within a useful framework.

2.4.1 Definition of Bone Quality

In order to fulfill tissue level functions, bone has unique yet dialectical features conveying properties of both rigidity and flexibility. Bone must function to preserve itself while ensuring self-repair and systemic mineral homeostasis mediated by the

remodeling process discussed above. Currey (2003) described bone as having a

“strength-safety factor” by which natural selection has promoted the attainment of enough strength to withstand muscle forces and load bearing without breaking under minor forces yet with the minimum amount of mass to do so successfully and healthily.

However, the consequence of impaired bone quality is fragility which ultimately increases fracture risk especially in aging adults. The composition of bone is adapted to maintain its structural strength (Burr 2011; Currey 2012; Seeman and Delmas 2006) and prevent gross failure. Strength is achieved by a combination of bone mass and bone quality and bone distribution (Burr 2011; Burr and Akkus 2014; Currey 2003, Ruff 2008,

Larsen 2015). Yet in defining the latter of these, it is evident these components are anything but mutually exclusive. Burr and Akkus (2014:22-23) in summarizing the generally accepted definition of bone quality include four primary parts which encompass both physiological and structural properties (the fifth listed here is included but they consider it independent of bone density):

1. The rate of bone turnover

2. Bone microarchitecture and geometry

3. Intrinsic material properties of the extracellular collagen-mineral matrix

4. Microdamage accumulation

5. Osteocyte density

Osteocyte density, the focus of this study, has a complex and direct relationship with most of these variables (to be discussed in Chapter 3). Skeletal metabolism or bone turnover is not only a source of new viable osteocytes and removes old or apoptotic

osteocytes, but is directly regulated by molecular signals from viable or apoptotic osteocytes via the lacunocanalicular network. Additionally, the accumulation of microdamage not only causes the apoptosis of osteocytes when disrupting lacunocanalicular communications (to be discussed in further detail in Chapter 3) and initiation of remodeling events to remove the damage, but is advantageous in directing the replacement of old bone tissue for preventative maintenance of future gross failure.

Young bone is less mineralized, has a longer fatigue life and is less likely to develop linear microfractures which could propagate to gross fracture if unrepaired; whereas, old bone is more mineralized, more brittle and therefore more likely to fracture (Burr 2003;

Seeman 2006). The density of apoptotic osteocytes has been significantly correlated with indices of remodeling (Hedgecock et al. 2007). Mice lacking osteocytes or with osteocyte dysfunction develop the bone fragility syndrome consistent with aging and osteoporosis (Chen et al. 2010). Thus, although one can simply designate five categories contributing to the quality of bone, they are not and should not be considered independent from each other. The complex relationship and factors affecting remodeling and microdamage repair mediated by osteocyte density in cortical bone will be the focus of

Chapter 3.

As previously discussed, the remodeling cycle is multifaceted and depends upon the proper functioning of the individual components of the BMU resulting in a delicate balance between bone resorption and formation. Slight variations that accompany changes in age, mechanical loading, or systemic hormonal factors can affect the physiological or homeostatic “stable state” of bone metabolism (Seeman and Delmas

2006). Age associated bone loss is propagated by any change that affects any stage of the remodeling cycle (Gosman and Stout 2010) and can be attributed to remodeling disorders

(Parfitt 2003). In both sexes following growth and development, the net BMU balance of bone production is negative (Seeman 2007). However, Seeman and Delmas (2006) argue that the rate of bone gain (during growth) or bone loss (following achievement of peak bone mass) due to a positive or negative BMU balance respectively is small. The compounding factor affecting overall bone gain or loss is driven by the remodeling rate.

Rapid remodeling is associated with decreased bone quality as it reduces material stiffness (replacing older more mineralized bone with younger less mineralized bone), creates centers of stress concentration in empty resorption pits due to a temporal lag following resorption prior to formation predisposing the site to microdamage, and alterations in the cross linking between collagen fibers (Seeman and Delmas 2006; Szulc and Seeman 2009). This increase in activation frequency can lead to decreased bone mass (Gosman and Stout 2010) and occurs following hypogonadism in females and later in life in both sexes (Szulc and Seeman 2009). As remodeling rate increases and bone loss becomes mainly endosteal and intracortical later in life, cortical porosity increases; thus contributing to bone fragility by affecting its ability to resist microfracture propagation and gross failure in the case of a traumatic fall (Seeman and Delmas 2006).

Contrary to the estrogen-centric paradigm that predominated the last few decades, researchers now recognize that factors leading to age related bone loss including hypogonadism are mediated through increased levels of oxidative stress and their effects

on the cellular machinery of bone (discussed in further detail in chapter 3) (Manolagas and Parfitt 2010).

Rapid remodeling rates produce microenvironmental conditions within the bone conducive to microdamage accumulation. Changes in material properties including the amount of mineralization and the collagen-mineral structure affect the mechanical integrity of the bone as well as the ability to resist microdamage accumulation (Burr and

Akkus 2014). Microdamage, both linear and diffuse, occurs during both physiological and pathological levels of loading. However, the amount of accumulated damage depends upon the balance between the amount produced and the amount repaired by normal remodeling processes (Seref-Ferlengez et al. 2015). Increased production or decreased ability to repair the damage in conjunction with increased bone loss further promotes bone fragility especially in postmenopausal females (Burr and Akkus 2014).

Microdamage accumulation with age in bone, particularly interstitial bone, is likely due to the concurrent decrease in osteocyte density (also mainly in interstitial bone) (Qiu et al. 2005); however, whether the decrease in osteocyte density or the increase in microdamage came first is unknown (Manolagas and Parfitt 2010) but could represent a positive and cyclic feedback mechanism for which decreasing bone quality could be the result. The role of osteocytes in repair of microdamage will be discussed in further detail in Chapter 3.

Traditionally, fracture risk and decreased bone strength have been attributed to decrease in bone mass. However, a new paradigm in assessing the multivariate elements of skeletal strength and integrity (quality) has recognized that mass is not the only

determinant of risk for fracture. In fact, increasing age has been shown to increase fracture risk independently from measured bone mass or mineral density (Reid 2013).

The importance of environmental factors to support the genetic maximum potential of bone mass in an individual has emphasized the importance of diet and activity during ontogeny for both trabecular and cortical bone (Gosman et al. 2011). Functional adaptation of the size, shape and quality of bone in response to adolescent mechanical loading provides the adult skeleton with the basis upon which age related loss will occur.

Thus, although this study focuses on the age and sex related changes occurring later in life, the importance of “early gain” to prevent “later loss” and ultimately stave off increased fragility and fracture risk occurs during ontogeny (Garn 1970).

2.4.2 Modern Methods of Bone Quality Assessment

Assessing the integrity or quality of bone in clinical populations seeks to prevent fractures especially in the elderly population; however, the underlying causative agent to fragility is the bone’s ability to function properly and regulate itself with respect to the demands placed upon it. A frequently employed non-invasive tool is the dual energy x- ray absorptiometry or DXA. Established in the 1980s, this technology has become the most widely used measurement of bone mineral density (BMD) especially in the postero- anterior spine and femoral neck sites for which extensive population data have been collected (Chun 2011). These sites represent two of the most frequent osteoporotic fractures; however, most fractures occurring after age 65 are non-vertebral and cortical in nature accompanying the mainly cortical (not trabecular) bone loss at appendicular sites

(Zebaze et al. 2010). If only considering prevalence of vertebral fractures before and

after administration of anti-resorptive pharmaceutical therapy, changes in BMD cannot explain about 70-96% of variation in vertebral fracture reduction (Seeman 2007). The remaining variation is due to microstructural features which are inaccessible to the two dimensional DXA (Burghardt et al. 2011). Additionally, discordant BMD results (as compared using a “Z” score compared to an age matched population) between elements attributed to soft tissue attenuation severely limits assessments and hinders diagnostic accuracy (Blake et al. 2012). DXA limitations stem from its inability to assess microarchitecture or microstructural properties of bone and should only be used as a

“quick and dirty” assessment of bone quality (Blake et al. 2012).

To address this issue, quantitative computed tomography (QCT) has been used to assess both BMD as well as microstructural properties of bone (Glüer 2014). However, current practices utilizing QCT of the spine and femoral neck, and peripheral QCT

(pQCT) of the distal radius and distal tibia in situ are still lacking in their ability to assess fracture risk reduction following treatments. The resolution cannot detect changes in intracortical porosity, differentiate between endosteal and periosteal regions to assess apposition, or incorporate material properties of the bone (Glüer 2014). High-resolution pQCT (HR-pQCT) have been able to alleviate some of these issues but are restricted when doing in situ scans. The resolution of this technology ex vivo is ~1µm but in vivo scanning is ~130µm (Burghardt et al. 2011). Despite the difference in resolution between ex vivo and in vivo, HR-pQCT at the distal radius has successfully measured cortical thickness and intracortical porosity in males and females of all ages (Allen and Krohn

2014). Emerging from high resolution CT scans is the ability to perform finite element

analysis (FEA) on bone allowing for assessment of mechanical properties and bone strength (Allen and Krohn 2014; Glüer 2014). Computer simulations are run in which each “element” or node is assigned parameters for material properties, and a “load” is applied to the bone as a whole or to specifically cortical or trabecular portions so that relative contributions to overall strength can be assessed. Limitations to FEA stem from the initial assigned material properties which may not represent the true stiffness of the bone as amounts of hypo or hypermineralized bone can significantly affect these (Allen and Krohn 2014). Lastly, nano-QCT at submicron resolution has been successfully performed on mice including visualization of the osteocyte lacunocanalicular network

(Schneider et al. 2007; Voide et al. 2009). This technology has yet to include human studies due to limitations in machine size and ionizing radiation dosages.

As technology continues to advance to produce higher resolution at lower dosages of ionizing radiation, it will eventually be possible to assess the microstructure of cortical bone in vivo including the extent and health of the osteocyte lacunocanalicular network.

Although ionizing dosage levels are not of concern for bioarchaeologists, the ability to scan an intact bone and reach submicron resolution level would eliminate the need for destructive histological techniques. Physiologically, osteocyte density plays a crucial role (discussed in Chapter 3) in mitigating changes in bone quality the effects of which can be assessed at a tissue level by technologies discussed above. However, a true picture of the health of a bone has to include the network of cells that function to maintain it important for clinical and biocultural determinations of bone quality.

Establishing a foundation for systemic variation in the osteocyte lacunocanalicular

network prior to the ability to quantify this in living individuals will facilitate informed interpretations of bone quality.

2.5 Summary

This chapter outlined the biological underpinnings of cortical bone quality. The cellular machinery and processes by which cortical bone is maintained were introduced.

A working definition of bone quality and modern technological assessment tools were also discussed. Advances in imaging technologies allowing submicron resolution will allow for non-invasive and non-destructive access to the underlying regulators of bone tissue for clinicians and bioarchaeologists. Chapter 3 will delve into the physiological mechanisms by which osteocytes affect the cells and processes addressed in this chapter.

Chapter 3: Osteocytes

3.1 Introduction

Osteocytes have been recognized as the primary cells responsible for the maintenance of extracellular bony matrix as well as the mediator for bone’s ability to adapt to its mechanical environment. They are ideally situated to integrate not only the mechanical stimuli but also metabolic (hormonal) stimuli (Bellido et al. 2013) affecting their local microenvironment which in turn governs the bone as a whole. This chapter will situate the functions of the most numerous of the bone cells and their osteocyte lacunocanalicular syncytium within the gross quality of the tissue. First, the major roles osteocytes play in the mechanosensation, regulation of osteoblast and osteoclast differentiation and activity, and mineral homeostasis will be discussed. Next, the factors affecting the viability and therefore, the density of cells and lacunae will be addressed.

Lastly, this information will be used to position osteocytes as the major players in the cellular machinery to affect parameters of bone quality.

3.2 Osteocyte Functions

3.2.1 Regulation of the BMU

The functional adaptation of a tissue like bone which is exposed to a highly dynamic mechanical environments is essential for its survival (Knothe Tate et al. 2004).

The health and quality of the bone depends upon the ability for mechanosensation and

transduction to influence the remodeling process which provides the basis for adaptation.

By influencing the BMU using molecular signals produced during mechanotransduction, osteocytes have the ability to influence both osteoblastic and osteoclastic differentiation and activity (Klein-Nulend et al. 2003). In fact, much like other systems in the body, the regulatory osteocytes direct their “effector” cells (Schaffler et al. 2014) in response to

“input” perceived and relayed by the functional syncytium. The osteocyte is now viewed as the major regulator and initiator of the remodeling process (Bonewald 2007). Many of the mechanisms by which this occurs have been discovered, however, there remains ambiguity in portions of the osteocytic medicated regulation and coupling of the BMU.

Bone Formation: Martin (2000a) and Marotti (1996) first posited the inhibitory role of the osteocyte network on osteoblast activity. However, other researchers have found that bone formation rate is directly linked to lacunar density and higher density could be the mechanism by which woven bone forms faster than lamellar bone (Britz et al. 2012; Hernandez et al. 2004). Contradictory studies on osteocyte control of bone formation have created some confusion surrounding the process (Komori 2013). In mouse models, diphtheria toxin induced osteocyte death resulted in severely reduced bone formation (Komori 2013); however, others have found an inverse relationship between osteocyte density and bone formation (Qiu et al. 2002b). These results have led some to conclude that osteocytes suppress bone formation during physiological conditions (Komori 2013). Unfortunately, this is an oversimplification of the process. In fact, osteocyte signaling affects bone formation via osteoblast differentiation and activity in two distinctive yet opposite ways depending on the mechanical environment. First, in

response to physiological strain levels, osteocytes synthesize and secrete anabolic paracrine molecules to promote osteoblast differentiation and function (Schaffler et al.

2014). These signaling molecules include prostaglandins (especially PGE2) and nitric oxide (NO). The downstream effects of PGE2 result in the activation of the cAMP/PKA pathway to support the canonical Wnt/β-catenin promotion of osteoblast differentiation

(Bonucci 2009; Schaffler et al. 2014). NO has dose dependent results on bone formation.

Low concentrations of NO released under physiological mechanical conditions acts to promote osteoblastogenesis and survival, while conversely, high levels secreted by osteocytes promote osteoblast apoptosis (Schaffler et al. 2014). Lastly, osteocyte expressed insulin-like growth factor (IGF-1) has anabolic effects on bone by mediating the loading induced activation of Wnt/β-catenin pathway (Sheng et al. 2014).

Upregulation of osteocyte expression of IGF-I following mechanical stimuli acts as an autocrine signal for the same cells to increase expression of Wnt10b while suppressing

SOST expression (to be discussed briefly); together, the IGF-I, PGE2 and Wnt10b activate the canonical Wnt pathway in osteoblast precursors to increase bone formation

(Sheng et al. 2014). To support this effect, Reijnders and colleagues (2007) observed increased endosteal bone formation in rat tibia following 4-point bending which resulted in increased osteocyte expression of IGF-I.

In opposition to the osteogenic signaling just discussed and in support of Marin and Marotti’s original “inhibitory” theory, osteocytes have the ability to inhibit bone formation by antagonizing the Wnt/β-catenin pathway. Osteocytes are the major source of sclerostin (a product of the SOST gene) which downregulates osteoblastogenesis by

binding to the lipoprotein receptor related proteins (LRP4 and LRP5) associated with the

Wnt pathway (Schaffler et al. 2014, Bellido 2014). Targeted deletion of SOST in mice results in high bone mass and greater bone strength (Bellido 2014). In addition to sclerostin, osteocytes also produce dickkopf-1 (DKK-1) which performs a similar though mutually exclusive antagonistic role as sclerostin (Schaffler et al. 2014, Bellido 2014).

The secretion of the molecules is mediated by mechanical strain and in term is responsible for the mechanism behind bone functional adaptation (Bellido 2014). Both sclerostin and DKK-1 expression is decreased in response to physiological loading and high strain levels effectively allowing the local Wnt pathway to be uninhibited resulting in an anabolic response (Bellido 2014). During rat ulnar loading, SOST transcription and resulting sclerostin levels were differentially diminished in portions of the cortex experiencing higher strains (Robling et al. 2008). Conversely, in disuse or severe unloading, sclerostin expression is increased and maintains an inhibitory effect on bone formation. Importantly, sclerostin has a negative effect on bone formation without a compensatory effect on resorption which lends itself to being a therapeutic target in the treatment of age related bone loss.

Van Oers and colleagues (2011) used computer modeling to argue for the necessary cooperation of the above “stimulatory” and “inhibitory” action of osteocytes on bone formation. Through varying biochemical pathways, these two scenarios are not mutually exclusive although the “inhibitory” effect may be more severe (Schaffler et al

2014). The importance of both effects on the bone formation portion of the remodeling process and subsequent mechanical adaptation will be discussed shortly.

Bone resorption: Viable osteocytes control bone resorption via the production of

RANKL, a cytokine crucial for osteoclast differentiation and survival, and its antagonist osteoprotegerin (OPG). All members of the osteoblast cell lineage have the capability to produce RANKL in different levels and at different states of maturity (Sims and Martin

2014). The relative contribution of each cell type has yet to be discovered; however, the production of RANKL by viable osteocytes has been established in mice studies. The direct production of RANKL by osteocytes at higher levels than stromal cell precursors, is supported by the osteopetrotic phenotype found in mice with osteocyte RANKL deletion (Nakashima et al. 2011; Xiong et al. 2011). However, the mechanism by which soluble or membrane bound RANKL reaches osteoclast progenitors is unclear. It appears to require direct cell to cell contract between dendritic processes and hematopoietic stem cells to induce osteoclast differentiation (Bellido 2014; Sims and Vrahnas 2014). Under the bone remodeling compartment, following initiation of a remodeling event, this communication is more likely; however, communication by which RANKL produced by embedded osteocytes reaches distant locations (such as the metaphyseal periosteum) during modeling is unknown (Sims and Vrahnas 2014). Basal levels of systemic parathyroid hormone maintain bone resorption by promoting osteocyte derived RANKL secretion (O'Brien et al. 2013; O'Brien et al. 2008). Conversely, estrogen functions to suppress bone resorption by downregulating RANKL production in osteocytes (O'Brien et al. 2013). Meanwhile, the secretion of osteoprotegerin (OPG), the antagonist to

RANKL, also functions to control bone resorption in an inhibitory manner. OPG, a decoy receptor for RANK on osteoclast precursors, downregulates osteoclast

differentiation. The localized OPG/RANKL balance is an important regulator of bone turnover (O'Brien et al. 2013) and alters in response to mechanical loading and microdamage. Diptheria toxin deletion of osteocytes results in high levels of resorption either due to the inhibitory effects of OPG on osteoclastogenesis or due to osteocytic apoptosis (Sims and Martin 2014) though likely a combination of both.

A second mechanism by which osteocytes affect remodeling is through their death. Osteocytic apoptosis not only promotes osteoclastogenesis but precedes it

(Seeman 2006). Apoptosis can be induced due to a variety of mechanical and metabolic factors (to be discussed in section 3.3) such as fatigue induced microdamage, increasing age, hypogonadism, oxidative stress, pathological metabolic bone disease, and hypoxia.

The death of osteocytes not only signals the bone lining cells to initiate the formation of the BRC, but acts as a “beacon” or localization mechanism for the subsequent remodeling event to occur (Bellido 2014). The relationship between osteocyte apoptosis and mechanical usage follows a “U” shaped curve in which both unloading and excessive loading result in cellular death (Aguirre et al. 2006; Jilka et al. 2013). Aguirre and colleagues (2006) demonstrated the importance of maintaining physiological mechanical strain to prevent apoptosis in mouse models of immobilization; osteocyte apoptosis occurred within 3 days and was followed by increased resorption, intracortical porosity and cortical thinning. Verbort and colleagues (2000) have reported osteocyte apoptosis as soon as 1 day following fatigue induced loading. From another perspective, Noble and colleagues (1997) have reported apoptotic osteocytes in regions of bone experiencing high rates of turnover.

Regardless of the mechanism of death, it is clear that biochemical signaling initiates and directs a remodeling event to the affected area to replace apoptotic cells and the extracellular matrix surrounding it. This “targeting” of the remodeling event makes osteocytes “martyrs” for the integrity of the bone, sacrificing themselves to maintain the matrix (Seeman 2006). The plethora of studies demonstrating an increase in resorption following the loss of osteocytes led many to propose an “inhibitory” theory on osteocyte control of osteoclasts (loss of osteocytes results in loss of the inhibitory signal to BLCs and resulting initiation of a remodeling event) (Martin 2000a). However, no such BLC repression molecule has been isolated. Rather osteocyte apoptosis is creating a permissive environment in which resorption can occur, and evidence suggests that proresorptive signals are released by the surviving neighbor osteocytes during death

(Schaffler et al. 2014). Although the exact molecules used by the dying osteocytes to signal to their neighboring cells is unknown, the increased production of RANKL directs the BMU formation. The dying cell may release “apoptotic debris” that plays a yet to be discovered role in communication to non-apoptotic cells. More importantly, the immediately adjacent surviving osteocytes upregulate RANKL and downregulate OPG within a few hundred micrometers of the damage (Kennedy et al. 2012) which is effectively far enough to reach the population of hematopoietic stems cells in either the endosteal or periosteal bone surface or in the vascular supply (Schaffler et al. 2014).

Upregulation of RANKL surrounding the apoptotic osteocyte results in osteoclast recruitment to the site and the progression of a remodeling event. There is evidence that osteoclasts resorb towards the dead osteocytes at the tip of the cutting cone attracted

towards the apoptotic debris much as macrophages are attracted to the same when endothelial cells die (Klein-Nulend et al. 2003). Meanwhile, non-apoptotic osteocytes in the area upregulate anti-resorption signals effectively constraining the cutting cone to the damaged area. Factors stimulating osteocyte apoptosis will be discussed in section 3.3.

Osteocyte control of the differentiation of mesenchymal and hematopoietic stem cells and the location of the initiation of the BMU provides a unified explanation of the mechanism behind local mechanical adaptation of bone. Locally, osteocyte biochemical control over the direction and size of the cutting zone represents adaptation at each remodeling event leaving the resulting basic structural units (BSUs or secondary osteons) aligned in the primary direction of mechanical strain (a direct result of the mechanotransducing ability of the osteocytic lacunocanalicular network). Osteoclasts find their way through the pre-existing bone matrix by NO signaling (Burger et al. 2003).

Differential canalicular flow patterns around the cutting cone and reversal zone during loading result in directing existing resorption and subsequent recruitment of osteoclasts.

Low flow near the cutting cone reduces NO production and encourages apoptosis in nearby osteocytes; this apoptosis attracts more osteoclasts and leads to further excavation of bone in the direction of loading (Burger et al. 2003; Klein-Nulend et al. 2003). Near the reversal zone, enhanced canalicular flow results in osteocytes increasing production of NO and encouraging the detachment of osteoclasts. Osteon infilling is controlled by osteocyte density and differential expression of sclerostin. Densities of lacunae are twice as high near the cement line of Haversian system than near the central canal from

90,000/mm3 to 40,000/mm3 (Hannah et al. 2010). As bone is deposited quickly near the

reversal line and rate declines as matrix moves closer to the central canal, osteocyte incorporation is lower (Qiu et al. 2003a). The inhibitory signal, sclerostin, is secreted differentially by newly embedded osteocytes to ensure proper infilling and adequate central canal size. Although the mechanism was yet to be identified (sclerostin), this supports Martin’s inhibitory theory for osteocyte-derived inhibition of bone formation

(Martin 2000a; Martin 2000b).

3.2.2 Mechanosensation and Mechanotransduction

Osteocytes are uniquely positioned both in their ubiquity but also in their physical location within the matrix to be the long sought mechanosensors of bone (Bonewald

2007; Bonewald 2011). As Seeman (2006:1446) accurately describes: “no part of the bone is more than a few microns from an osteocyte.” The extensive nature of the entire osteocytic lacunocanalicular network lends itself to detection and communication about the mechanical environment of the bone. As discussed above in section 3.2.1, the ability of osteocytes to affect change through biochemical signaling molecules is not only dependent but often in response to mechanical strain levels experienced in the functional syncytium. Two key features of osteocytes as mechanosensors are the ability to detect mechanical stimuli and send appropriate signals to effector cells (discussed above) (Han et al. 2004; You et al. 2008). This process of converting mechanical stimuli to chemical signals is termed “mechanotransduction” and is unique in bone to the osteocyte.

Over the past twenty years, the role of osteocytic lacunocanalicular network in adaptation of bone mass and architecture has been slowly illuminated (Klein-Nulend et al. 2013). Osteocytes, due to their unique multi dendritic structure located within the

mineralized matrix of lacunae and canaliculi are sensitive to fluid shear stresses resulting from tissue deformation (Klein-Nulend et al. 2013; Klein-Nulend et al. 1995; You et al.

2000). It is unlikely that osteocytes are responding directly to the same magnitude of strain experienced by the bone as a whole (Schaffler et al. 2014). Measured perilacunar in vivo peak strain was demonstrated to be an order of magnitude higher than gross macroscopic strain spurring questions surrounding the amount of microdeformation in canaliculi to displace such high stress levels (Nicolella et al. 2006). It is highly unlikely that healthy osteocytes are experiencing such high magnitude strains within the matrix, suggesting an alternative to mechanical stimuli with the lacunocanalicular network.

However, other groups have predicted the in vivo strain experienced by the osteocytic cell body to be less than 0.2% that of the gross applied strain arguing flaws in in vitro measured strain, and the magnitude would be too small to stimulate mechanotransduction

(Wang et al. 2008). Alternatively, physiological mechanical loading causes interstitial fluid flow throughout the network producing shear stress on the cell membrane and dendritic processes. Shear stress is defined as the direct mechanical effect of fluid flowing over the surface of the cell (Klein-Nulend et al. 1995). Weinbaum and colleagues (1994) were among the first to propose the fluid shear stress model and demonstrated that osteocytic dendritic processes experienced the same order of magnitude in fluid shear stress as endothelial cells on the surface. Subsequently, this mechanism has been experimentally tested as the major mechanical stimuli acting on osteocytes (Schaffler et al. 2014).

Based upon both in vivo and in vitro studies of osteocytes, the portion of the cell more sensitive to mechanical strain or responsible for mechanosensation is still debated

(Klein-Nulend et al. 2013). The current paradigm suggests that the dendritic processes within their canaliculi are the major mechanosensors of bone. However, the osteocyte cell body in vivo may experience matrix deformation in the presence of a loading event with a large enough magnitude and could be directly involved in mechanosensation via these matrix strains on their cytoskeleton (Klein-Nulend et al. 2013). A crucial anatomical feature to sensing changes in fluid flow strain is the tethering of the cell processes to the matrix walls of the canaliculi via focal adhesions as well as β3 integrin proteins regularly spaced throughout the process (Schaffler et al. 2014). Additionally, the dendritic membrane contains the transmembrane protein CD44 directly connecting the peridendritic environment to the internal cytoskeleton of the process (Schaffler et al.

2014). Due to the tightly cross linked actin proteins comprising their cytoskeleton, dendritic processes are much more rigid than the osteocyte cell body which predicts increased mechanosensitivity in response to fluid flow stress. This tethering produces fluid drag and increases the amount of strain experienced by the processes; a phenomenon that can ameliorate the argument that in vivo tissue strains are not large enough to initiate a mechanical response. To orient the processes within Weinbaum and colleagues (1994) original strain amplification theory, Wang and colleagues (2008) tested whether the integrin adhesions would focally amplify strains enough to excite the osteocyte. Their results support the strain amplification at attachment sites two orders of magnitude larger than whole tissue strain (Wang et al. 2008). Strains this large stimulate

the opening of mechanosensitive stretch-activated ion channels which allow for the influx of Ca2+ into the cell resulting in the excitation of the osteocyte (Wang et al. 2008; You et al. 2000).

Mechanosensitivity of the osteocytic functional syncytium mediates biomechanical adaptation via the regulation of effector cells (as discussed in 3.2.1). The transduction of the magnitude of strain into biochemical signals results in downstream effects on bone remodeling. The relative expression of antagonistic molecules discussed above (such as OPG/RANKL), sclerostin versus NO and PGE2 is controlled by not only the size but type of mechanical stimuli. Different strains can produce different signaling pathways (Bonucci 2009). Mullender and colleagues (2004) demonstrated that NO and

PGE2 secretion increased after pulsatile fluid flow whereas only NO production was increases following cyclic strain. Robling and colleagues (2008) found that sclerostin production decreased in a strain-dose dependent manner along the diaphysis of mice ulnae which corresponded with a strain-dose dependent increase in bone formation

(Robling et al. 2008). IGF-I, NO and PGE2 are early responders following mechanical loading and upregulate the Wnt pathway. During physiological levels of loading, the osteoclastogenic ability of osteocytes is hindered by the decreased production of RANKL and increased production of OPG (You et al. 2008). Signaling molecules produced by osteocytes during the mechanotransduction process and transportation through the lacunocanalicular network to progenitor cells allows for regulation of local microarchitectural adaptations in bone.

3.2.3 Mineral Homeostasis

Embedded osteocytes play an important role in the maintaining both local and systemic mineral homeostasis via control of their microenvironment as well as through the hormonal production of factors affecting calcium and phosphate levels. The surface area of the entire lacunocanalicular network is over 400 times greater than Haversian and

Volkmann’s canal systems in cortical bone and over 100 times greater than trabecular bone area promoting the rapid exchange of minerals (Marotti et al. 1992b; Mullender et al. 1996). Thus, some of the same features (particularly the extensiveness and communication abilities of the network) that lend the system to biomechanical bone adaptation also allow for it to function in mineral homeostasis.

As previously discussed, osteocytic control over osteoclastogenesis is highly mechanically regulated. However, response to systemic factors such as parathyroid hormone (PTH) can produce the same effect to stimulate osteoclast activity and direct the liberation of calcium stores. However, Parfitt (2003) argues that calcium homeostasis in response to the PTH mediated “set-point” is unlikely to be maintained solely by osteoclastic activity due to temporal length and the evidence that anti-resorptive drugs do not affect long term calcium levels. Although to what extent is yet to be discovered, an osteocyte mechanism driven by PTH may be the alternative. Osteocytes retain a minimal ability to degrade organic portions of bone (cathepsin K and matrix metalloproteinases) as well as synthesize an appreciable amount of type 1 collagen (Schaffler et al. 2014).

The process of perilacunar resorption and modification of its own microevironment is termed “osteocytic osteolysis” and was introduced decades ago (Belanger et al. 1967).

This localized exchange of liberated minerals between the bone mineral and tissue fluid is much more rapid than osteoclast resorption (Schaffler et al. 2014). Tazawa and colleagues (2004) found enlarged osteocytic lacunae in rats given daily injections of

PTH. And in circumstances of severe calcium demand such as lactation, perilacunar remodeling has been demonstrated (Qing et al. 2012). However, the same results were produced in ovariectomized rats suggesting osteocytic osteolysis can occur in stress states without severe calcium demand (Schaffler et al. 20141).

Global mineral homeostasis is also affected by osteocyte function. The production of fibroblast growth factor 23 (FGF23) acts on distal convoluted tubules of the kidneys to suppress reabsorption of phosphate (Schaffler et al. 2014). In the case of

X-linked hypophosphatemic rickets (HYP), the undermineralized osteoid and reduced renal tubular phosphate reabsorption may be attributed to dysfunction in the osteocyte specific phosphate-regulating endopeptidase homolog, X-linked (PHEX). PHEX deficiencies likely contribute to elevated serum FGF23 which would result in a hypophosphatemic skeletal phenotype but is not yet fully understood.

3.3 Osteocyte Viability and Apoptosis

In order for the osteocytic lacunocanalicular network to perform the above listed functions, the long living cells must remain viable. Osteocyte apoptosis itself has been characterized as a form of tissue damage on its own prior to the resulting effects mediated by mechanisms discussed in section 3.2 (Seeman 2006). Manolagas and colleagues

(2010) estimate that 2.5% of osteocytes die each year. But in fact, the life span of an osteocyte has the potential to correspond to the chronological age of the organism as

evidenced by the cells within the ear ossicles. As these bones do not remodel, the present osteocytes at time of death were established during the development of the bones (Bloch et al. 2012). However, in all other bony tissue that experience turnover, the age of the cell corresponds to the localized tissue age (Qiu et al. 2005). The existence and state the lacunae, the focus of this study, is dependent on the viability of the cell contained within it (Knothe Tate et al. 2004). Many factors both mechanical and metabolic affect the viability of the osteocyte and are discussed here.

3.3.1 Mechanical Influences and Microdamage

Mechanical loading promotes both viability as well as apoptosis in osteocytes in a dose-dependent manner. There exists a range of physiological customary strain levels that promote osteocyte viability whereas, perceived strains outside of the range promote apoptosis (Hughes and Petit 2010). Osteocytes are sensitive to hypoxia and require the fluid flow induced by mechanical loading to deliver oxygen and metabolites in addition to removing waste crucial for cellular survival. Dodd and colleagues (1999) were the first to identify osteocyte hypoxia and death in response to unloading in bird ulnae.

Mechanical stimuli serve to protect osteocytes from other proapoptotic influences such as glucocorticoids by activating the Wnt signaling pathway (Bonewald and Johnson 2008).

As previously discussed, the relationship between viability and mechanical loading follows a “U” shape curve in that disuse causes high levels of apoptosis as does excessive pathological loading. Apoptosis in these cases is caused by different mechanically mediated mechanisms. Death caused by unloading and disuse likely results from the severe decrease in interstitial fluid flow and delivery of nutrients to cells; whereby, death

caused by excessive loading results from accumulated microdamage disrupting the osteocytic lacunocanalicular network.

Frost (1960a) recognized that linear microcracks disrupting the canalicular network were likely the stimulus by which bone recognizes damage and mounts a repair response. Linear microdamage induces apoptosis by the loss of canalicular integrity, detachment of osteocytes from the extracellular matrix, rupture of dendritic processes, and lack of oxygen and nutrients to cells (Jilka et al. 2013). It appears that the linear form of microdamage that occurs most often in older interstitial bone is the culprit for induction of osteocyte apoptosis rather than diffuse damage (Burr 2014). Whereas, diffuse damage which occurs prior to linear microdamage but at the sublamellar scale does not induce osteocyte apoptosis and is repaired via a separate process from remodeling (Seref‐Ferlengez et al. 2014). Fatigue induced microdamage initiated osteoclastogenesis results from apoptosis of osteocytes in the immediate vicinity of microcracks. As previously and briefly touched upon, the neighboring osteocytes increase expression of anti-apoptotic protein Bcl-2 resulting in localization of the damaged tissue, apoptotic cells and necessary remodeling event. The death of osteocytes is spatially and temporally associated with microdamage and results in targeted intracortical remodeling (Cardoso et al. 2009; Verborgt et al. 2000) as a preventative measure for gross failure. Yet microdamage is not solely a product of excessive loading, instead accumulation occurs as a product of normal repetitive mechanical loading

(O'brien et al. 2005). Accumulation of damage results in reduction of strength, stiffness and energy to fracture of the bone (Burr 2003; Burr 2011). Paradoxically, the

physiological role of microdamage reduces fracture risk by dispersing energy that could have caused gross fracture (Agnew and Bolte IV 2012), and by stimulating apoptotic osteocyte signaled removal and repair. Thus, Seeman (2006) has characterized osteocytes as “martyrs for bone strength” in that they “sacrifice” themselves in order for old, damaged bone to be replaced. The balance between damage and repair ensures the integrity and function of the bone and is argued to be an evolutionarily advantageous mechanism (Burr 2011).

The spatial relationship between osteocyte apoptosis, density, and linear microcracks supports a causality for which the direction is unknown (Qiu et al. 2005).

Osteocyte apoptosis increases with age and either initiates a remodeling event or the lacunae fill in with hypermineralized matrix (see section 3.3.3). If microdamage occurs first causing apoptosis, then the observed reduced osteocyte lacunar density surrounding linear microcracks may be a result of failure or delayed initiation of a BMU (Busse et al.

2010; Qiu et al. 2005). Conversely, if osteocyte death occurs first and results in hypermineralization and increased brittleness, then this region of bone would be more susceptible to microdamage (and a reduced number of osteocyte lacunae would subsequently be observed). Likely, there is a positive feedback loop occurring that combines these two scenarios especially with increasing chronological age and associated senescence of osseous tissue leading to the accumulation of more microdamage, deficiencies in osteocyte cell populations and an overall resulting increase in bone fragility.

3.3.2 Systemic Influences

In conjunction with the mechanical influences, multiple systemic factors associated with aging also influence the life span of the osteocyte though not in a mutually exclusive fashion. It is rather more appropriate to examine this in light of

“senescence” rather than chronological age as this represents a more biologically informative state (Crews and Ice 2012); however, the data required to quantify allostatic load are not available in this sample. Thus, chronological age is used to approximate the senescent changes within the human body. Advancing age often leads to decreased physical activity and accumulation of microdamage in hypermineralized bone tissue both leading to osteocyte apoptosis. However, there are other systemic and hormonal changes that occur to which osteocytes are sensitive.

Parathyroid hormone (PTH) has opposite effects on the bone depending on dosage and temporal administration. Chronic excess of the hormone, as occurs in secondary hyperparathyroidism which is common with increasing age, results in the upregulation of RANKL secretion by osteocytes and resultant increase in osteoclastogenesis (Bellido et al. 2013). Thus, remodeling rate is increased and can induce bone loss. Conversely, intermittent injections of PTH have an anabolic effect on bone metabolism by increasing osteoblast activity and bone formation. This is also likely mediated through the PTH-receptor on osteocytes whereby Sost (the gene that controls sclerostin production) mRNA was markedly decreased so that sclerostin secretion was low (Bellido et al. 2013). This downregulation of Sost/sclerostin results in upregulation of the Wnt pathway and subsequent bone formation (O'Brien et al. 2008). The anabolic

effects of PTH on periosteal bone formation result from this same process in osteocyte signaling (Bellido et al. 2013).

With advancing age, loss of sex steroids (estrogen and androgen) has a deleterious effect on osteocyte viability (Bellido 2014). Hypogonadism that results with age, more drastically in females but later in life in both sexes, results in the increased apoptosis of osteocytes. Estrogen exerts an anti-apoptotic effect on the cells of the MSC lineage via the activation of survival kinases (ERKs and P13-K) (Bellido and Hill Gallant 2014).

Bone loss associated with estrogen withdraw in females is mediated by the increased resorption initiated by osteocyte death (increased osteoclastogenesis); a mechanism for the increased bone turnover rate associated with age (Khosla et al. 2012). Tomkinson and colleagues (1997) demonstrated a significant reduction in viable osteocytes in premenopausal female cancellous iliac bone biopsies after 6 months of treatment for endometriosis using hormones to induce a hypoestrogenic state. In mouse cortical bone of the femoral diaphysis following ovariectomy, osteocyte apoptosis increased 2-fold particularly in the posterior cortex and preceded, but was accompanied by, increased endocortical resorption in areas adjacent to apoptotic cells (Emerton et al. 2010).

Conversely, treatment with estrogen replacement in vivo or estradiol in vitro resulted in prevention of osteocyte apoptosis due to estrogen mediated NO cascade which degrades the pro-apoptotic protein Bcl-2 in osteocytes (Khosla et al. 2012).

Though the effects of estrogen or androgen removal on increased remodeling are strong, there has been a call to shift the paradigm of age related bone loss and osteoporosis away from sex steroid deficiency and towards oxidative stress (Manolagas

and Parfitt 2010). The long lived osteocytes are susceptible to a decrease in cellular function and other age related changes including metabolic dysfunction (Almeida and

O’Brien 2013). Although little quantitative work has been done on the molecular damage of osteocytes as a result of age, multiple studies have demonstrated a decrease in viability and density with age (Busse et al. 2010; Manolagas and Parfitt 2010; Qiu et al. 2002b).

Almeida and O’Brien (2013) argue that hypogonadism and hyperglucocorticoid effects on bone remodeling are mediated by oxidative stress (which should be considered the culprit for cellular death and dysfunction). Free radicals or reactive oxygen species

(ROS) create oxidative stress in tissues throughout the body in various disease states

(cardiovascular, diabetes, cancer); yet, they are a product of normal cellular metabolism.

In bone, ROS inhibits the Wnt/β-catenin pathway adversely affecting bone formation and osteocyte viability (Almeida and O’Brien 2013). In mice following gonadectomy, administration of an antioxidant returned bone mass to previous levels. Similarly, in mice with global deletion of an antioxidant gene who experienced low bone mass which worsened with age, antioxidant administration normalized these effects (Almeida and

O’Brien 2013). Increase levels of endogenous glucocorticoids that occur with increasing age also induces osteocyte apoptosis and prolongs osteoclast lifespan likely potentiated through oxidative stress (Almeida 2010). Glucocorticoids are known to induce osteoblast death (Almeida 2010) creating a downstream effect of decreasing osteocyte populations.

Any factor that affects the osteoblastic lineage by reducing numbers decreases the density of osteocytes within the matrix (Bellido 2014). The reduction in osteocyte density with age is due to an increase in apoptosis relating to microdamage accumulation, increased

osteoblast apoptosis, decreased physical activity, or increased oxidative stress. However, these factors are not mutually exclusive and likely combine to affect the population of cells. The ramifications of the death or survival of the “conductor of the bone orchestra” manifest in bone quality. Increased rates of apoptosis with age result in increased intracortical porosity, deficient mechanical properties of the tissue, altered geometry and poor quality (Plotkin 2014).

3.3.3 Micropetrosis

As summarized by Vashishth and colleagues (2002a): osteocytic lacunae are the most prevalent measure of the osteocyte population within bone. The above sections have discussed the paramount role of the osteocytic lacunocanalicular network in regulating the processes to assess and maintain the quality of bone extracellular matrix including the resulting signaling processes following osteocyte apoptosis. Yet there remains another phenomenon surrounding age related changes in this cell referred to as

“micropetrosis” (Frost 1960c). Frost coined the term to represent the hypermineralization of lacunae and canaliculi following osteocyte apoptosis found primarily in extra-

Haversian bone and characteristic of aging. Additionally, Frost (1960b) found an increasing prevalence of empty lacunae with age varying from 1% at birth to 40% by 70 years old in osteons and up to 75% vacancy in interstitial bone. Schaffler et al. (2014) found 30% of lacunae in the rib were empty in elderly individuals. Conversely,

Mullender and colleagues (1996) reported no significant correlation between the percentage of empty lacunae and age between a control group and an osteoporotic group.

Thus it remains unclear as to the mechanisms or biological explanation as to why some

osteocyte apoptosis stimulates osteoclast resorption while at other times it results in empty lacunae that will become hypermineralized (Atkins and Findlay 2012).

Normally, the pericellular space consists of a thin layer of hypomineralized bone separating the cell from the lacunar walls and allowing for the fluid flow and strain amplification discussed earlier (Busse et al. 2010; Nicolella et al. 2008). Healthy osteocytes maintain an inhibitory regulation on the mineralization of their perilacunar space and it is possible that upon their death, these controls are released and they no longer maintain their unique microenvironment (Atkins and Findlay 2012; Nicolella et al.

2008). Increased mineralization of lacunae and canaliculi increases the brittleness of bone and makes it more susceptible to microdamage (Frost 1960c). To compound the increased susceptibility to fracture, age related increase in mineralization (in general) plus hypermineralized lacunae results in a severe decrease in strain signal to the mechanosensing cells under physiological loading conditions potentially contributing to development of osteoporosis and poor bone quality on top of the already occurring age related influences (Nicolella et al. 2008). Busse and colleagues (2010) found a decrease in lacunar density and increase in hypermineralized lacunae with age differentially in the periosteal and endocortical cortex of the femur. They suggest that the slight periosteal apposition that occurs with age results in fewer old and hypermineralized lacunae in the periosteal cortex; whereas, high levels were found in older interstitial bone. The delay in recognition or failure to repair microdamage following osteocyte apoptosis (due to any of the factors discussed above associated with aging) may be the explanation to micropetrosis; aging cells with delayed signaling to initiate remodeling may leave enough

time for the lacunar environment to become mineralized further impairing the syncytium response to mechanical stimuli and microdamage. This phenomenon contributes an additional factor to the aging and bone fragility biological connection (Busse et al. 2010).

3.4 Osteocyte Role in Bone Quality

There is a reciprocal relationship between the structure of the bone and the load which it can withstand (Seeman 2006), and as the mechanosensory portion of the organ, the osteocyte functional syncytium is pivotal in orchestrating this relationship and affecting bone mass and bone quality (Klein-Nulend et al. 2013). Mullender and Huiskes

(1995) posited that osteocytes may fulfill Roux’s hypothesis that adaptation in bone is regulated by cells under the influence of local stress. The functions of the osteocyte syncytium, especially their position in mechanosensation and therefore biomechanical adaptation to maintain integrity of the bone as a whole, have lead authors to position the cell into long held theories of mechanobiology and bone functional adaptation (Chen et al. 2010; Hughes and Petit 2010). The mechanostat has been argued by Frost (1987) to be the mechanism by which natural selection achieved Currey’s “strength-safety” factor in bone. It is the strain levels experienced by osteocytes that set the threshold values for the mechanostat (Hughes and Petit 2010). The mechanically stimulated production of

NO and PGE2 immediately following loading are continued for hours after the event is alleviated thus acting as a potential “memory” or “set point” for the bone (Knothe Tate et al. 2004). These minimum effective strain (MES) levels then partition mechanically mediated bone metabolism into “disuse,” “physiological,” and “overuse” ranges thereby affecting modeling and remodeling processes. More recently, Skerry (2006) revised the

MES into the customary strain stimulus (CSS) which takes into account not only the magnitude of strain, but also the frequency, amount of rest between loading, and distribution of loading across the bone. The response to CSS and subsequent bony adaptation is mediated through osteocyte viability or apoptosis such that in disuse apoptosis due to a lack of nutrients to the cell and overuse apoptosis caused by microdamage disruption both function to increase remodeling rates (Hughes and Petit

2010) and have tangible effects on bone mass and quality. During physiological conditions, osteocytes stimulate osteoclastogenesis (RANKL) and inhibit osteoblast differentiation (sclerostin); however, during physical activity (yet still within the physiological range of CSS), these effects are reversed (Komori 2014). Thus, osteocyte role in bone quality is mitigated via a mechanically regulated balance of antagonistic

(RANKL/OPG), inhibitory (sclerostin), and stimulatory (Wnt) molecular secretion.

Following microdamage induction, the ratio of RANKL/OPG increases significantly from the neighboring healthy cells at the behest of the dying osteocyte promoting and directing initiation of remodeling (Burr 2014). Ma and colleagues (2008) found that osteocyte density significantly correlated with measures of the mechanical integrity and quality of the bone. Density was positively correlated with maximum loading values and bone mineral content while negatively correlated with microcrack length in vertebral bodies of rats (Ma et al. 2008). In rib sections from postmenopausal females, Qiu and colleagues (2005) found that the length and number of microcracks was

5 times higher in interstitial bone where lacunar density was 17% lower than osteonal bone. In this sample, the calculated risk for microdamage was 3.8 times higher in bone

with less than 728 lacunae per mm2 (Qiu et al. 2005). However as noted above, osteocyte apoptosis is crucial for maintenance of bone quality and repair of damage through initiation of a BMU (Plotkin 2014). In fact, pharmacological prevention of osteocyte apoptosis under fatigue loading prevents the activation of intracortical remodeling

(Herman et al. 2010). The successful maintenance of the bone necessitates enough viable osteocytes to sense and repair damage which in turn requires their death. As osteocytes die and signal resorption, they are replaced by a new and full complement of young and viable cells (Qiu et al. 2002b). As age increases, osteocytes stochastically and importantly die at a rate of 2.5% per year (Manolagas and Parfitt 2010). Stochastic death can only result when a cell has outlived its lifespan which likely occurs in cortical areas of bone where turnover is low. Thus, in “deep” bone (~45 µm from the surface) osteocyte density declines with age approaching an asymptote at age 75 of approximately

40% of density at peak bone mass (Qiu et al. 2002a). The lack of autophagy or protective death of osteocytes leads to low turnover and low bone mass in experimentally treated mice which mimicked the wild type aged mouse population (Manolagas and Parfitt

2013). Therefore, a healthy functioning network or syncytium maintains a balance between viability and repair to the extent that Manolagas and Parfitt (2010) argue that the most important aspect to bone quality is maintaining osteocyte viability with age.

Importantly, age related changes in the microstructure and composition of bone can explain the disparity between increasing fracture risk and decreasing BMD (Manolagas and Parfitt 2013). Bone matrix volume has been correlated to osteocyte lacunar number

(Vashishth et al. 2002b). A causal relationship has been established between cortical

porosity and osteocyte apoptosis (Jilka et al. 2013) and age associated decrease in bone strength due to porosity has been reported as 76%. The dysregulation of remodeling accompanying osteoporosis and increasing porosity can be linked to osteocyte apoptosis due to aging factors mediated through oxidative stress. As the disease state continues, the connectivity of the syncytium is disrupted leading to further apoptosis, increased resorption, decreased infilling and the cycle continues (Knothe Tate et al. 2004). The role of osteocytes in other conditions has been explored as well. In multiple myeloma, osteocytes lose control of sclerostin secretion. An increased proportion of hypermineralized lacunae was found in osteoarthritic bone (Carpentier et al. 2012). In cases of hyperparathyroidism, osteoclastic activity is increased, yet with intermittent administration of PTH, sclerostin levels are decreased and bone mineral density improves

(Compton and Lee 2014).

We now understand that the cell upon which PTH, glucocorticoids and estrogen work to affect change in the remodeling process is the osteocyte. Osteocyte apoptosis as the underlying cause of targeted bone remodeling now illuminates the exact mechanism upon which estrogen and bisphosphonates achieve their antiremodeling effects (Bellido

2014). Anti-RANKL antibodies are being used to target osteocyte production to reduce excessive resorption. Emerging as an important target in osteoporosis treatments, likely due to the adverse effects of long term usage of anti-resorptive drugs, is the osteocyte derived sclerostin. Using a sclerostin antibody, serum levels are lowered and osteogenic response is increased via the promotion of osteoblast differentiation and survival

(Compton and Lee 2014). It is likely that more osteocyte specific genes will be

discovered and targeted in the treatment of metabolic bone diseases as it lies at the center of determining bone quality.

3.5 Summary

The position of osteocytes within bone allows for successful monitoring and maintenance of quality (Watanabe and Ikeda 2010). Decreased osteocyte viability directly affects the density of the osteocytic network, and as discussed in this chapter, negatively affects its functions and the strength and quality of the bone. Increased osteocyte apoptosis accompanies bone fragility that characterizes conditions such as hypogonadism, hyperparathyroidism, endogenous glucocorticoid excess, mechanical disuse and aging (Bellido 2014). The strong case for considering osteocyte density as an additional indicator of bone quality has been made and supported repeatedly (Ma et al.

2008). Using the well established method of assessing lacunar density as a proxy for cell density, this study will add to the growing body of evidence that osteocytes should not be overlooked when discussing bone health, fragility and fracture risk.

Chapter 4: Materials and Methods

4.1 Introduction

This chapter will outline the materials and methods used to complete this project.

The processes by which samples were obtained, histological slides created and images taken will be described. All measured and calculated variables obtained will be defined as well as the methods employed to collect these data. Lastly, a brief description of the statistical analyses performed will be introduced (and further elucidated in Chapter 5).

4.2 Materials

For this project, skeletal samples were obtained from modern cadaveric individuals received through The Ohio State University’s Whole Body Donor program.

Basic demographics and cause of death for the post-mortem human subjects (PMHS) are known. A total of 30 individuals (15 males and 15 females) with a comparable age range were included in this study (Mann-Whitney test p=0.618). Of these individuals, 29 were embalmed and used for dissection based anatomy courses; one individual was a fresh cadaver used for PMHS shoulder injury testing in the Injury Biomechanics Research

Laboratory. All skeletal samples are curated in the Skeletal Biology Research Laboratory

(SBRL) under Dr. Amanda Agnew. Individuals for this study were chosen to be representative of a modern population for which bone quality assessments are routinely

performed. Cause of death was not a determining factor as these individuals were experiencing a myriad of conditions from which different populations may suffer which would contribute to osteocyte viability or apoptosis (as discussed in Chapter 3). As this study seeks to establish and understand the variation in osteocyte lacunar density, chronic and/or acute issues leading to death did not exclude individuals from this sample resulting in a random and representative subset of the population. Thus, the only exclusion criteria were macroscopic changes to the skeletal sites of interest which could have included healed infections, periosteal reactions, prosthetics, or gross evidence of bony metastases.

Table 4.1 summarizes the demographics and causes of death for the 30 PMHS included in this study. Ages for the entire sample range from 49 to 100 years old with a mean of 76.83 years. The 15 male subjects range in age from 49 to 100 years old with an average age of 77.8 years. The 15 female subjects range from 51 to 98 years old with an average age of 75.87 years. In an attempt to move beyond the constraints of using chronological age categories as an informative factor in bone quality, age is treated as a continuum with no attempt to arbitrarily divide the sample into subgroups. This approach is designed to give a more biologically informative assessment of the effects of age and sex on osteocyte lacunar density. The ancestry of all individuals was classified as

“white” by the donor antemortem or the donor’s family through their Whole Body

Donation Program designation paperwork.

Subject Sex Age (years) COD/Medical information ID 6882 Male 49 Metastatic cancer of unknown primary 6448 Female 51 Breast cancer 6350 Male 59 Lung cancer 6319 Female 63 Lung cancer 6807 Male 66 Septic shock/Acute myeloid Leukemia 6453 Female 67 Septic shock 6539 Female 68 Coronary artery disease 6641 Male 69 Chronic obstructive pulmonary disorder 6655 Female 69 Alzheimer’s disease 6633 Female 69 Pneumonia/Lung cancer 6477 Male 71 Lung cancer 6817 Female 73 Breast cancer 6446 Male 77 Chronic obstructive pulmonary disorder 6621 Female 78 Hemorrhagic brain stroke 6406 Male 79 Respiratory failure/Lung cancer 6542 Male 79 Failure to thrive/Liver cancer 6353 Female 79 Acute cerebrovascular accident 6873 Male 80 Congestive heart failure 6531 Female 80 Gastrointestional bleeding/scleroderma 6333 Female 81 Respiratory failure/Emphysema 6449 Male 83 Probable cardiac dysrhythmia 6450 Male 83 Chronic obstructive pulmonary disorder 6460 Male 86 Acute myocardial infarction 6610 Female 86 Chronic obstructive pulmonary disorder 6611 Female 86 Cerebrovascular accident 6501 Female 90 Failure to thrive/Acute myocardial infarction 6889 Male 92 Pneumonia/Alzheimer’s disease 6602 Male 94 Congestive heart failure 6604 Female 98 Dementia 6527 Male 100 Abdominal cancer Table 4.1 PMHS demographics for this sample including age, sex, and cause of death. All individuals were self-identified ancestry as “white.”

Each PMHS is represented by three anatomical locations (with the exception of

6807 whose rib was reserved for another project in the SBRL) (n=89 elements): midshaft femur, distal one third of the diaphyseal radius, and midshaft of the 6th rib. These sites were chosen not only due to their variation in loading environments, but also their

ubiquitous use in both anthropological and clinical contexts. The midshaft femur and midshaft rib are utilized for various histological analyses including age at death estimations, cross-sectional geometry analyses of mechanical adaptation, and age related bone loss or intracortical porosity. The cortical bone in the distal one third of the diaphysis of the radius is becoming an important clinical indicator of bone fragility and fracture risk as three dimensional advanced imaging technologies are becoming more prevalent. High resolution micro-computed tomography scans on the distal radius of clinical patients are replacing the antiquated Dual energy X-ray absorptiometry (DEXA) or bone densitometry to assess bone strength as a predictor of bone strength (Dalzell et al.

2009; Fonseca et al. 2013; MacNeil and Boyd 2008). Therefore, these three sites were chosen due to their relevance in both fields allowing for a seamless addition of osteocytic lacunar density analyses to already established research practices and sampling protocols.

4.3 Methods

4.3.1 Obtaining and Preparing Cadaveric Skeletal Samples

Skeletal elements were extracted by the author from 30 modern cadaveric subjects following partial or complete dissection courses. Musculature was dissected away from the entire femur, radius and sixth rib to determine the appropriate location for sampling.

Each anatomical location was inspected for any gross macroscopic pathological conditions both prior to and immediately following sectioning. When possible all elements were taken from the left side; however, due to variances in dissections and the condition of each individual cadaver, if the left side element was not available, the right side element was obtained. Using a Stryker oscillating autopsy saw to section in the

transverse plane, 2-3 inch blocks were obtained from the midshaft femur. Similarly, a 1-

2 inch block was removed from the midshaft of the sixth rib. Radius samples (1-2 inch blocks) were obtained from the distal one third of the diaphysis not including the epiphysis; this portion of the radius corresponds to the area of the diaphysis beneath the pronator quadratus muscle in humans (White et al. 2012). As per protocol in the Skeletal

Biology Research Laboratory (SBRL), the distal aspect of each element was notched using the Stryker saw to maintain orientation. Excess musculature was carefully dissected from the blocks prior to further processing.

Maceration of sections was performed using a mixture of warm water and Biz laundry detergent for 4 to 8 hours to free any remaining muscle attachments as well as the marrow from in the medullary cavity. After removal from the solution, each element was rinsed and carefully cleaned before degreasing. The sections were submerged in

Richard-Allan ScientificTM Clear-RiteTM liquid overnight initially, evaluated the following morning and allowed to remain submerged as need be. Once the degreasing process was complete, all bones were set out to dry prior to sectioning.

4.3.2 Slide Preparation

All stages of slide preparation were performed using equipment in the SBRL. The femora and radii elements did not require embedding prior to sectioning as their cortex remained intact during trial slide preparation. Femoral blocks were positioned into a large chuck perpendicularly so as to avoid oblique sectioning. A Buehler Isomet 1000

Precision metallurgical saw with a 15HC diamond-edged blade was used to cut 2 mm thick transverse sections from the center of each block. Each transverse section was then

ground using Buehler Apex Diamond Grinding Discs on a Buehler Ecomet 250 variable- speed grinding unit. While manually securing the section beneath a sand paper encased glass slide, a 35 μm grit disc was used initially for a variable amount of time at a speed of

120 rpm. Periodically, the thickness of each section was measured using a Marathon

Electronic Digital Micrometer so as to obtain a uniform thickness of 80 μm (±10%). This thickness was chosen after a pilot study during which serial sections of 10 radii from the sample were each sectioned and mounted at three arbitrarily chosen varying thicknesses:

80 μm, 100 μm and 120 μm. The same region of interest was chosen from each thickness for each subject to determine the most appropriate procedures for automated detection in

ImageJ (to be discussed in section 4.3.4) It was determined that the 80 μm (±10%) sections were most suited to both even grinding with little to no fracturing of the sample itself, and for automated data collection. Femora and radii were measured throughout the grinding process across multiple anatomical sites (anterior, posterior, medial, and lateral, as well as the offsets of these) from the cortex and encompassing all regions (periosteal, intracortical and endosteal). An average of these measurements was calculated so that despite small variations in sample thickness across the cortex, the overall sample was within the designated 80 μm (±10%) range. Once sections were near the 80 μm thickness, the grinding disc was switched to a 15 μm grit to finish and polish. Each sample was measured to check the final thickness, gently rinsed using distilled water, folded into a

Kimwipe and clamped between two slides overnight to dry.

Due to the thin nature of the cortex and high percentage of trabecular bone in the rib cross section, all 29 rib sections were embedded prior to sectioning. If needed, each

initial rib sample was trimmed using a Dremmel to fit into the rectangular plastic canister in which a 1 cm base of embedding material had previously hardened. A mixture of

Buehler EpoThinTM Expoxy Resin and Buehler EpoThinTM Hardener was prepared by weight using a 100:39 ratio respectively. The resin and hardener were combined per manufacturer instruction stirring until the mixture achieved a clear consistency. Each rib in its plastic container was then covered using the liquid mixture of EpoThinTM. Ribs were repositioned parallel to the edges of what will become the hardened plastic block so that sectioning can be performed transversely through the rib. Using a Fisher Scientific

Isotemp® Model 281A vacuum oven and a Fisher Scientific Maxima® C-plus Model

M4c vacuum pump, excess air was removed from the EpoThinTM mixture during three rounds of vacuum evacuation to 25 in. Hg pressure. This process also encourages the liquid EpoThinTM to permeate the rib sample to ensure uniform embedding. After vacuuming, the containers are removed and placed in atmospheric pressure fume hood to set overnight resulting in hardened resin blocks able to be secured in the large bone chuck.

Rib sections were cut and ground with the same equipment and procedures as listed above for the femora and radii. The integrity of the rib sections were maintained within the EpoThinTM while grinding only to a measured thickness of 120 μm (±10%).

Upon visual inspection under bright field microscopy, this thickness was determined to both retain the complete cortex without fracturing out of the embedding material as well as remaining thin enough for proper analyses. Thus, all rib sections were ground and polished to a uniform thickness of 120 μm (±10%). After polishing with the 15 μm grit

grinding disc, the sections were given a final measurement, gently rinsed with distilled water and clamped between two slides within a Kimwipe overnight allowing for drying prior to mounting.

All dry thin-sections were mounted to glass slides using Fischer Chemical

PermountTM Mounting Medium and covered with a glass cover slip. Excess embedding material was trimmed from the rib sections to assure proper coverage by the cover slip.

Femoral and radii cross sections were mounted without any further modifications and orientation was maintained for future analyses. Mounted slides were allowed to dry for at least 2 days prior to imaging; however, as PermountTM requires between 2 to 3 weeks in order to dry completely, some slides remained in the drying trays for a longer amount of time. There were no visual differences in histological structures or coloring of the bone sections between drying times as PermountTM mounting medium is non-yellowing.

4.3.3 Imaging

All 89 sections were imaged under bright field microscopy for visualization of osteocytic lacunae. An Olympus VS120® -S5 virtual slide scanner system with a manually loaded motorized stage coupled with an Allied Vision Pike F-505C five megapixel camera was used to obtain digital images of the complete cross-sections. The

VS-ASW is a cellSens® digital imaging software provided with the VS120®-S5 scanner which produces virtual slide image (VSI) file type of the complete cross-section. This image acquisition software allows for designation of specific regions of interest (ROI) to be scanned resulting in reduced data file sizes. Once loaded onto the automated stage, an overview is performed using automatic specimen recognition under 20x magnification

(10x binoculars and 2x objective) with VS-ASW autoexposure and autofocusing. The

VS120®-S5 has a virtual Z-scanning function allowing for the system to focus through the depth of the region of interest as denoted by the user. Following the overview acquisition, the Virtual Slide Acquisition Wizard program allows for selection of magnification and Extended Focal Imaging (EFI). All complete cross section VSI were acquired at 40x magnification (10x binoculars and 4x objective). The selection of EFI Z- mode enables acquisition of images with potentially unlimited depths of focus by creating a composite image from multiple variably focused images as the stage moves through a range of Z-positions. Thus, the resulting composite image of the entire section displays all levels of the sample within focus which is why uniform thickness of samples within elements is important. By choosing the “Automatic Z spacing and Z range,” each sample is optimally imaged bringing all levels into focus in the resulting composite VSI. The

“Z-range” is the distance between top and bottom focus layers throughout the sample;

“Z-spacing” is defined as the distance between individual focus layers. The “Default” setting for the EFI Z-mode acquisition software produces the optimal Z-spacing between two focal planes which is determined by the depth of focus calculated from the objective’s numerical aperture and is consistent across all sections. Next, by editing the scan area, the user can determine the ROI which in all cases for this study included the entire cross-section. Using a live feed from the accompanying BX61 microscope, the optimal exposure time was selected for each image. Lastly, a high density focus map was created by user denotation of focus areas across the entire ROI whereby the VS120® automatically focuses at each designated area. Thus, the optimal Z-position is

determined for each focus area across the entire sample and a profile for focus Z-planes is created and saved prior to image acquisition upon which the individual EFI images are built. The final composite image is extracted from the saved initial overview imaged producing a VSI file type which can only be opened within the cellSens® Dimension software. The scale bar and orientation labels were “burned” into the image so that it remains visible at all times, and the image is saved as a tagged image file format (TIFF) which can be opened in any program used for data collection (4.3.4). The use of EFI Z- mode imaging creates a sharply focused image displaying histological components throughout the entire depth of field; this however, reinforces the necessity for uniformity across samples as discussed above so as to eliminate differences in osteocytic lacunar counts due to thicker sections with more focal planes in focus. See Figure 4.1 for an example of a seamlessly stitched VSI image.

4.3.4 Data Collection

4.3.4.1 Cross-sectional Area Measurements

In order to normalize counts of osteocytic lacunae for size, two dimensional density values were calculated for analysis. Therefore, measures of bone area for each sample were obtained either using ArcGIS version 10.1 (ESRI© 2012) for the larger femoral sections or ImageJ for the smaller radii and rib sections. Measured variables of bone size include:

A. Total Subperiosteal Area (Tt.Ar): total cross-sectional area expressed in mm2

B. Endosteal Area (Es.Ar): area within the endosteal border expressed in mm2 (in

ArcGIS. In ImageJ, it is possible to create a “donut” polygon after measuring

Tt.Ar to achieve a cortical area measurement)

C. Cortical Area (Ct.Ar): amount of cortical bone within a cross-section

expressed in mm2 (calculated using Tt.Ar-Es.Ar; or measured directly)

D. Percent Cortical Area (%Ct.Ar): size normalized value calculated by

(Ct.Ar/Tt.Ar)*100 and has no units.

Femoral Tt.Ar and Es.Ar were measured using ArcGIS version 10.1 (ESRI©

2012) which has been introduced as a valuable analytical tool for histomorphometry

(Rose et al. 2012). Cross sectional TIFF images with a visible scale bar were imported into the program in a new map document for each sample. The program builds pyramids so as to manage file size and resolution providing a seamless user interface especially when moving between viewing the entire cross section to a smaller region of interest.

Scaling of images was completed as described by (Rose 2011) and verified using a measurement tool in ArcGIS to measure the known length of the burned in 2mm scale bar. Polygon features were created to manually trace the periosteal and endosteal borders resulting in Tt.Ar and Es.Ar measurements. These were then used to calculate the cortical area of each femoral cross-section. Trabecular bone was excluded as this study is investigating solely cortical bone.

Radii and rib Ct.Ar were obtained using the free Java-based National Institutes of

Health program ImageJ. Images are opened using the program and the scale was set using the burned in scale bar. Images from the VS120® are universally set at a resolution

of 580 pixels/mm. The “polygon selections” tool is used to trace the periosteal border.

This polygon is added to the ROI Manager and measured to produce Tt.Ar. By holding the “Alt” key, the endosteal border of the periosteal polygon can be cut out producing a donut shaped polygon encompassing the cortical area of the cross section. By adding this polygon to the ROI manager and measuring, a value for Ct.Ar is obtained. After outlining the cortex, the background of the image is extracted by using the EditClear

Outside function. This removes any aspect of the slide that may be errantly included in osteocytic lacunar counts due to their size or circularity.

4.3.4.2 Image Manipulation in ImageJ

In order to maximize the ability of ImageJ to automatically detect osteocytic lacunae and measure intracortical porosity, the variation in bone color within and between samples is addressed by image manipulation within ImageJ. The following procedures were performed for all 89 images to optimize the image for data collection.

1. To reduce the color of the extracellular matrix, the brightness of the image is

increased (ImageAdjustBrightness/contrast). This step is dependent on the

color variation in the bone sample; however, an adjustment of the Brightness

scale from 255 to between 235 and 245 is sufficient to retain the “dark” osteocytic

lacunae while lightening the bone color.

2. Each image is converted to a 16-bit type (ImageType16-bit).

3. The image is then thresholded and converted to a binary image which produces a

white background with black osteocytic lacunae. This image manipulation utilizes

the “Threshold” tool under “ImageAnalyze.” By choosing the “over/under”

option, the maximum slider was moved to 255 (absolute white) while the

minimum slider was adjusted from 0 (absolute black) until the dark spaces

(lacunae and Haversian canals) were filled with blue. The ideal threshold value

for all samples was uniformly set at 180 so as to reduce error between sections.

By selecting “Apply,” the conversion to an 8-bit binary image with white bone

and black objects is complete. If the resulting image is a black background with

white lacunae, “Invert” the image under the “Edit” function.

4. To avoid errant counts within Haversian canals, resorption spaces, or Volkmann’s

canals as well as preparing for semi-automated measurements of intracortical

porosity, the “Fill Holes” function will fill in complete openings with black fill.

Using further manipulation to ensure the closure of these canals or spaces can be

done through the following: ProcessBinaryClose. ProcessBinaryDilate.

ProcessBinaryFill Holes. If the “dilate” option was utilized to “fill holes,” it

is important to undo this function by selecting the “Erode” option under

ProcessBinary (especially prior to area measurements for intracortical

porosity).

5. As often there remain Haversian or Volkmann’s canals and resorption spaces that

do not fully fill in, the paintbrush tool is used to draw in the remaining border

prior to a second round of “Fill Holes.” This is most effectively performed on the

binary image with the original RGB image “Stacked” beneath it for easy reference

and movement between images to assure accurate filling in of spaces. Once, the

entire cross-section has been manually checked, perform the “Fill Holes” function

again.

Save the resulting binary image as a separate TIFF file from the original image. This image is now prepared for both osteocytic lacunar and intracortical porosity measurements and will be used for the automated collection portion of these variables

(Figure 4.1).

Figure 4.1: VSI original RGB image of distal radius cross-section (individual 6477) on the left. 8-bit binary image resulting from image manipulation with pores and lacunae filled in “black.”

4.3.4.3 Automated Procedures in ImageJ

ImageJ is used to semi-automatically count osteocyte lacunae. The following variables are measured and calculated:

E. Osteocytic lacunar number (Ot.Lc.N): raw counts of lacunae in cortical bone.

F. Osteocytic lacunar density (Ot.Lc.N./B.Ar): normalized for size of the bone;

gives number of lacunae per mm2

A pilot study was performed to assess the capacity of ImageJ to automatically detect and count osteocytic lacunae accurately. Using a subsample of 10 radii, the previously described image manipulation and subsequent automated procedures were performed for each entire cross-section. Following the completely automated process, the same image was manually counted using a point count method. There were no significant differences in osteocyte lacunar number (Ot.Lc.N) between automated and manual counts of each section (reported in section 5.2). Therefore, to reduce any error and standardize the procedure, the “Analyze Particles” function in ImageJ was used to automatically count Ot.Lc.N. Under the “Analyze” menu, the “Analyze Particles” function counts and measures objects in a binary image. The command searches the image for the edges of an object within the specified parameters, outlines the encountered object, measures it, adds it to the ROI manager then proceeds to scan the rest of the image repeating the process. The following parameters have been optimized (during many trial and error attempts) for detection of osteocytic lacunae:

1. Size (mm2): only particles within the size range will be added to the ROI manager

to be counted and measured. For all samples, the size range was set to 0.00001 to

0.0002 mm2. The upper limit of this range was determined to exclude the smallest

Haversian canals.

2. Circularity: the range of circularity values established by 4 ×(Area/Perimeter2)

encompass shapes from an elongated polygon or line (value of 0) to a perfect

circle (value of 1.00). Due to the variation in appearances of lacunae, circularity

values from 0.04 to 1.00 were chosen so as to prevent the inclusion of cement

lines yet capture any elongated lacunae.

3. Show: a dropdown box offers options for displaying results. To expedite manual

verification, the option “Overlay Outlines” is chosen. This provides an outline of

each object identified to fall within the parameters as an overlay. The overlay

including all the counted outlines can be layered back onto the original image

during visual inspection and manual adjustment.

ImageJ produces a count and area measurement of all objects which fall within the above specified parameters as an overlay (Figure 4.2). The entire overlay (all identified and outlined objects) is saved from the ROI manager as a compressed Zip file that can be opened in conjunction with any image without altering said image (ROI

ManagerMoreSave). The binary image is no longer needed and should be closed while the original RGB image is re-opened. Additionally, open the saved ROI zip file for the sample in question; this should open and populate the ROI manager. In order to view the overlay outlines on the original RGB image, select the box marked “Show All” in the

ROI manager.

Figure 4.2: ImageJ automated identification of osteocyte lacunae in magenta overlay. Overlay opened on original RGB image.

To assess the effects of age and sex on the viability of osteocytes via the maintenance and survival of their lacunae, two dimensional density values must be calculated to normalize the data. The Ot.Lc.N values produced by the semi-automated

ImageJ procedure with manual verification are used to calculate densities for each individual section for all 30 individuals in the sample.

G. Osteocyte Lacunar Density (Ot.Lc.N/Ct.Ar): reported in #/mm2: normalized

for the amount of bone available within a cross-section.

Porosity data were collected following the semi-automated ImageJ procedures

(detailed in Cole 2014). The pre-prepared binary images for each section are again opened in ImageJ. The “Colony Blob Count Tool” is used to identify and measure pores in the bone excluding osteocytic lacunae. This includes Haversian canals, Volkmann’s

canals and large resorption spaces (which should all be filled in black by this point). The semi-automatic procedure tested on rib, tibiae and femoral cross-sections was reported to produce statistically insignificant results from manual calculation of total porosity area

(p=0.0532) (Cole 2014). Following the image manipulation described in 4.3.4.2, the

“Colony Blob Count Tool” plugin is assigned measurement values of 0.0002 mm2

(minimum Haversian canal size) to “Infinity” (maximum set by program). The plugin then analyzes the image in the same way described above for “Analyze Particles” and adds black spaces that meet the size requirements to the ROI manager. After the entire image is complete, the ROI overlay is saved and reopened on the original RGB image.

The manual portion of the method is employed here. The entire cross-section is checked for accuracy of identification prior to measurement counts. As the “Fill holes” function may result in darker bone areas being filled in and errantly included in the colony blob count, using the original RGB image allows for misidentified pores to be manually deleted. Additionally, any pores that were not filled in accurately can be deleted and manually redrawn then added to the ROI manager (Figure 4.3). Once the process is complete, use the “Measure” command in the ROI manager to obtain a summed measurement of intracortical porosity (Total Pore Area).

H. Total Pore Area (Po.Ar): reported in mm2

I. Percent Porosity (%Po.Ar): calculated by (Ct.Ar/Po.Ar)*100

Total Pore Area (Po.Ar) is then used to calculate Bone Area (B.Ar) to determine the actual amount of bone present.

J. Bone Area (B.Ar): reported in mm2. Calculated by Ct.Ar-Po.Ar.

As bone area (B.Ar) is a better representation of the amount of bone available in which there could exist an osteocyte, Ot.Lc.N was normalized using this value.

K. Osteocyte Lacunar Density (Ot.Lc.N/B.Ar): reported in #/ mm2: normalized

by the absolute amount of bone present within the cross-section

This value is used in all comparisons with age and sex and between elements for systemic comparisons. As it accounts for intracortical porosity, Ot.Lc.N/B.Ar cannot be used in correlations between lacunar density and porosity for which Ot.Lc.N/Ct.Ar is the more appropriate variable. However, as reported in section 5.6, using Ot.Lc.N/Ct.Ar instead of

Ot.Lc.N/B.Ar can affect results and interpretations due to the confounding element of porosity.

Figure 4.3: ImageJ semi-automated identification of intracortical porosity (numbers represent count and not area in this case). Size parameters prevent the plugin from detecting the smaller lacunae in porosity measures.

4.4 Data Analysis

After completing data collection (all raw data reported in Appendix A for each individual), analysis was performed using IBM SPSS statistical software. All variables were tested for normality using Shapiro-Wilk test which is more appropriate for small sample sizes. Normalized values (%Ct.Ar, %Po.Ar, Ot.Lc.N/Ct.Ar and Ot.Lc.N/B.Ar) were tested for differences between males and females using independent samples t-tests for each of the three elements. Normality was then tested for these variables (using

Shapiro-Wilk) for males and females separately as well as pooled together. Age and sex related trends for osteocyte lacunar density (Ot.Lc.N/B.Ar) were investigated using

Pearson correlations accompanied by scatterplots to assess visual trends. These same tests were run on the Ot.Lc.N/Ct.Ar and %Po.Ar variables for additional interpretations.

All data were assessed at a significance level of 0.05. Correlations were run for males only, females only and the pooled sample for each of the three elements. Intracortical porosity (%Po.Ar) and lacunar density (Ot.Lc.N/Ct.Ar) relationship within each element was investigated using the same methods (Pearson correlations and scatterplots).

Systemic trends were similarly assessed comparing Ot.Lc.N/B.Ar in the femur versus rib, rib versus radius, and radius versus femur. Correlation coefficients (r) were used to assess the strength and direction of the relationships tested here. Meanwhile, regression lines were added to each scatterplot that demonstrated a trend.

Chapter 5: Results

5.1 Introduction

This chapter will begin by presenting the results of a pilot methodological investigation for this project. Descriptive statistics are included for the pooled sample as well as for males and females separately for each of the measured and calculated variables. All variables are reported for each element: femur, radius, and rib. Osteocyte lacunar density is always normalized by bone area (Ot.Lc.N/B.Ar) unless otherwise indicated (as in comparisons with intracortical porosity). First, sex differences are investigated for variables including cortical area, osteocyte lacunar density, and porosity for each element to determine if pooling the sample (n=30) is appropriate. Subsequently, osteocyte lacunar density values are reported for each element and their relationship with age. To investigate the patterns of lacunar density and intracortical porosity within this sample, relative porosity area is also investigated with respect to age. After establishing the inter-individual variation and trends within this sample, the intra-individual relationships in lacunar density and intracortical porosity are compared between anatomical skeletal sites. Importantly, the amount of porosity within each element is then compared to the lacunar density of the same element to determine the interrelatedness of these variables expected from the physiological knowledge presented in chapters 2 and 3.

The systemic intra-individual variation in both lacunar density and intracortical porosity

is then investigated and reported. Lastly, the impacts of using Ot.Lc.N/Ct.Ar versus

Ot.Lc.N/B.Ar is reported to understand how interpretations may be confounded by calculating density without accurately considering the amount of bone present. Appendix

A contains primary data for all measured and calculated variables for each individual in this sample.

5.2 Method Test

A subsample of radii from ten individuals was chosen to establish any significant differences between automated identification of lacunae in ImageJ compared to manual identification. Original images were manipulated for the automated process described in section 4.3.4.2. Osteocyte lacunar number (Ot.Lc.N) was recorded. For comparison, lacunae were point counted across the entire radii cortex on the original unmanipulated image using ImageJ and lacunar number (Ot.Lc.N) recorded. Table 5.1 displays the raw counts, raw difference and amount of error. There were no significant differences between the automated and manual counts for this subsample (p=0.725). %Error between the actual (Manual) and experimental Ot.Lc.N ranges between 0.124% and

3.457%. The low amount of error combined with the non-significant differences between automated method and manual (actual) counting demonstrates the accuracy of the automated method and supports its use for all samples.

Manual PMHS Auto Ot.Lc.N Difference %Error Ot.Lc.N 6319 66182 68175 1993 2.9234 6333 61961 60241 -1720 2.8552 6350 127297 12745 158 0.1240 6406 88984 91187 2203 2.4159 6446 90295 92447 2152 2.3278 6448 80955 79982 -973 1.2165 6450 100913 101343 430 0.4243 6453 53625 51833 -1792 3.4573 6477 97834 97458 -376 0.3858 6633 48252 47917 -335 0.6991 Table 5.1: Method verification demonstrating no significant differences between automated and manual Ot.Lc.N counts (p=0.725). %Error calculated using (Manual Ot.Lc.N- Auto Ot.Lc.N/Manual Ot.Lc.N)*100.

5.3 Age and Sex Correlations

5.3.1 Sex Differences for variables normalized by size

Descriptive statistics for each element are reported in Table 5.2. For each element

(femur, radius, rib) males and females are reported separately for relative bone size

(%Ct.Ar), relative intracortical porosity (%Po.Ar), osteocyte lacunar density using cortical area for normalization (Ot.Lc.N/Ct.Ar), and osteocyte lacunar density using bone area for normalization (Ot.Lc.N/B.Ar). Independent sample t-tests were performed to investigate any sex differences between these four variables in each element. Table 5.3 reports the results of these t-tests demonstrating no significant differences between males and females. Thus, correlations for age and intra-individual comparisons are made using the entire group (n=30 for radius and femur; n=29 for ribs).

Ot.Lc.N/Ct.Ar Radius %Ct.Ar %Po.Ar Ot.Lc.N/B.Ar (#/mm2) (#/mm2) Males (n=15) Mean 62.544 11.800 958.643 1084.684 Std. Deviation 10.634 5.491 132.753 118.069 Normality p- 0.323 0.353 0.310 0.766 value Females (n=15) Mean 61.791 12.258 939.1886 1064.3054 Std. Deviation 9.1504 6.228 185.4283 157.3706 Normality p- 0.936 0.135 0.856 0.882 value All (n=30) Mean 62.168 12.029 948.916 1074.495 Std. Deviation 9.755 5.773 158.759 137.088 Normality p- 0.471 0.200 0.853 0.682 value

Ot.Lc.N/Ct.Ar Rib %Ct.Ar %Po.Ar Ot.Lc.N/B.Ar (#/mm2) (#/mm2) Males (n=14) Mean 31.3267 13.6447 794.5021 919.0929

Std. Deviation 8.2240 5.2425 126.8595 124.3732 Normality p- 0.758 0.091 0.302 0.835 value Females (n=15) Mean 36.0187 13.53 829.0925 958.0328

Std.Deviation 12.4806 5.68 138.4467 140.9798 Normality p- 0.188 0.120 0.887 0.332 value All (n=29) Mean 33.7536 13.58401 812.3611 939.2342

Std. Deviation 10.7228 5.373 131.7723 132.3317

Normality p- 0.038* 0.344 0.959 0.597 value Table 5.2: For each element, all variable descriptive statistics reported for males and females separately as well as for the entire sample. All variables are normally distributed with no necessary transformation. Continued

Table 5.2: Continued

Ot.Lc.N/Ct.Ar Femur %Ct.Ar %Po.Ar Ot.Lc.N/B.Ar (#/mm2) (#/mm2) Males (n=15) Mean 68.6879 12.82 853.7195 973.9493

Std. Deviation 7.8140 5.35 187.9308 183.1850 Normality p- 0.557 0.755 0.312 0.181 value Females (n=15) Mean 65.4837 13.44 786.6524 905.6427

Std.Deviation 8.8179 5.03 166.233 172.4534 Normality p- 0.554 0.279 0.767 0.821 value All (n=30) Mean 67.0858 13.13 820.186 939.7960

Std. Deviation 8.3468 5.12 177.6333 178.2240 Normality p- 0.370 0.327 0.494 0.364 value

Radius df t p-value %Ct.Ar 28 0.208 0.837 %Po.Ar 28 -0.213 0.833 Ot.Lc.N/Ct.Ar 28 0.330 0.744 Ot.Lc.N/B.Ar 28 0.401 0.691 Rib df t p-value %Ct.Ar 27 -1.186 0.246 %Po.Ar 27 0.058 0.954 Ot.Lc.N/Ct.Ar 27 -0.699 0.491 Ot.Lc.N/B.Ar 27 -0.786 0.438 Femur df t p-value %Ct.Ar 28 1.053 0.301 %Po.Ar 28 -0.329 0.745 Ot.Lc.N/Ct.Ar 28 1.035 0.309 Ot.Lc.N/B.Ar 28 1.052 0.302 Table 5.3: There are no significant differences between males and females for any variable or in any element supporting the decision to pool the entire sample (n=30) for these investigations.

Figure 5.1 shows the mean and 95% confidence intervals for osteocyte lacunar density (Ot.Lc.N/B.Ar) for males and females in the femur, radius and rib. Considerable overlap of confidence intervals and no significant p-values reported in Table 5.3 indicate similar processes occurring in males and females that will be further explored with respect to age and intra-individual variation in the following sections. Due to the size standardization, sexual dimorphic influences on area or density values are removed and all variables are normally distributed for each sex individually as well as the pooled sample (Table 5.2) with the exception of %Ct.Ar for the sample. However, normality plots suggests this variable is approaching normality and as it is not directly related to testable hypotheses here, the data were not transformed.

Interval Plot Osteocyte Lacunar Density 95% CI for the Mean

1200 )

r 1100

c L

. 1000

(

s n

e 900 D

800 Sex Males Females Males Females Males Females Rib Radius Femur

Figure 5.1: Interval plot for osteocyte lacunar density for males and females for each element indicating no significant differences between sexes.

5.3.2 Osteocyte Lacunar Density (Ot.Lc.N/B.Ar) Intra-individual values

As evidenced in Figure 5.1, the radius demonstrates the highest osteocyte lacunar density values relative to size for both males and females. The mean of the entire sample

(n=30) Ot.Lc.N/B.Ar per elements were compared using a one-way ANOVA with

Bonferroni posthoc tests. Significant differences in intra-individual osteocyte lacunar density were found (F-statistic=7.968; p=0.001). The radius had a significantly higher density than the rib or femur (p=0.003 for both post hoc comparisons). The rib and femur were not significantly different from each other (p=1.00) as evidenced in their mean

values listed in Table 5.2 (939.2342 mm-2 and 939.7960 mm-2 respectively). The large amount of variation or spread in the femur for males, females and as a pooled sample is indicative of the amount of expected variation in mechanical changes experienced by an elderly population in load bearing bone (Figure 5.2). Figure 5.2 shows the intermediate amount of variation in osteocyte lacunar density for males and females for the radius compared to the femur and rib. The rib, as expected due to the constant mechanical strain accompanying ventilation, demonstrates the least amount of variation in this sample.

Figure 5.2 Boxplot showing variation between elements

5.3.3 Osteocyte Lacunar Density (Ot.Lc.N/B.Ar) Correlations with Age

Osteocyte cell population and therefore, lacunar density is expected to decrease with age due to the myriad of factors discussed in Chapter 3. Although there are no significant differences between sexes in Ot.Lc.N/B.Ar, to investigate any variation in

trends between males and females, correlations with age are considered for each subsample as well as the total sample for each anatomical site (element). For the femur, in males, density values range from 598.263 mm-2 to 1222.948 mm-2 (individuals 6873,

80 year old and 6477, 71 year old respectively); in females, density values range from

515.166 mm-2 to 1169.116 mm-2 (individuals 6611, 86 year old and 6448, 51 year old respectively). For the radius, in males density values range from 871.246 mm-2 and

1245.406 mm-2 (individuals 6873, 80 year old and 6641, 69 year old respectively); in females, ranges are from 738.262 mm-2 to 1301.556 mm-2 (individuals 6501, 90 year old and 6448, 51 year old). Lastly, for the rib in males, ranges are from 722.893 mm-2 to

1113.885 mm-2 (individuals 6542, 79 year old and 6882, 49 year old respectively); in females, ranges are from 620.913 mm-2 to 1128.808 mm-2 (individuals 6333, 81 year old and 6610, 86 year old respectively). Table 5.4 shows the Pearson correlation coefficients

(or strength of the relationship; Pearson’s r) and two tailed significance for each variable and chronological age.

Femur Radius Rib r p R2 r p R2 r p R2 Males Ot.Lc.N/B.Ar -0.604 0.009 0.365 -0.290 0.147 0.084 -0.519 0.029 0.269 Ot.Lc.N/Ct.Ar -0.656 0.004 0.430 -0.586 0.011 0.343 -0.700 0.003 0.491 %Po.Ar 0.540 0.038 0.292 0.831 0.000 0.691 0.628 0.016 0.394 %Ct.Ar -0.376 0.168 0.141 -0.223 0.424 0.050 -0.147 0.617 0.022 Females Ot.Lc.N/B.Ar -0.335 0.112 0.112 -0.405 0.067 0.164 0.039 0.446 0.002 Ot.Lc.N/Ct.Ar -0.459 0.042 0.211 -0.537 0.020 0.288 0.016 0.477 0.0003 %Po.Ar 0.569 0.027 0.324 0.669 0.006 0.447 0.062 0.827 0.004 %Ct.Ar -0.660 0.007 0.435 -0.121 0.666 0.015 0.207 0.458 0.043 All Ot.Lc.N/B.Ar -0.456 0.006 0.208 -0.337 0.034 0.114 -0.241 0.104 0.058 Ot.Lc.N/Ct.Ar -0.542 0.001 0.294 -0.537 0.001 0.288 -0.344 0.033 0.119 %Po.Ar 0.545 0.002 0.297 0.738 0.000 0.545 0.339 0.072 0.115 %Ct.Ar -0.487 0.006 0.237 -0.175 0.354 0.031 0.033 0.865 0.001 Table 5.4: Results of correlations performed between each variable and chronological age. Pearson correlation coefficients (and their direction), significance values and R2 values reported for males and females separately as well as pooled. Significant values are bolded at α = 0.05 level. P-values approaching significance are italicized.

Figures 5.3, 5.4, and 5.5 represent the inter-individual correlations between osteocyte lacunar density (Ot.Lc.N/B.Ar) and age for each element. For the femur, males alone show the strongest significant negative correlation with age (correlation coefficient:

-0.604); female osteocyte lacunar density is not correlated with age in the femur although

Figure 5.3 indicates a decreasing trend for this group (gray). In the femur, for the entire sample (n=30) there is a significant negative correlation (correlation coefficient: -0.456) between lacunar density and increasing age though not as strongly correlated as when considering males alone. The trend line in Figure 5.3 for the sample (n=30) has an R2 value of 0.208 indicating age explains only 20.8% of variation in osteocyte lacunar density; whereas, when only considering males, age can account for more variation in

lacunar density (0.365). However, most telling for all density correlations with age is the decreasing trend which corresponds to the expected increased osteocyte apoptosis

(decreased viability) that accompanies aging or more specifically senescing. Additional plots with separate sex specific regression lines can be found in Appendix B.

Figure 5.3: Scatterplot of Ot.Lc.N/B.Ar versus Age in femoral midshaft for males and females (red and gray respectively). Regression line added for the pooled sample as there are no statistically significant differences between males and females.

In the radius, the highest relative osteocyte lacunar density element, the pooled sample shows a significant negative correlation between osteocyte lacunar density and increasing age. Figure 5.4 demonstrates that as age increases, osteocyte lacunar density

displays a decreasing trend. The one tailed significance for the entire sample (n=30) is significant at the α=0.05 level (p=0.034). However, regression fit line indicates only

11.4% of variation in lacunar density is explained by increasing age for the entire sample; males are slightly lower at 8.4% while females are slightly higher at 16.4%. Females display a moderately negative though insignificant correlation with age (-0.405); correlation coefficient for Ot.Lc.N/B.Ar and age in males is weak (-0.290). The decreasing changes in density as an individual ages in the radius is likely due to continued though slightly suppressed mechanical usage during such actions as pronation

(to be discussed in Chapter 6).

Figure 5.4: Scatterplot of Ot.Lc.N/B.Ar versus Age in the cortex of the distal third of the radius for males and females (red and gray respectively). Regression line added for the pooled sample as there are no statistically significant differences between males and females.

Lastly, for the rib, there are also no significant correlations between density and increasing age in females or the pooled sample but a significant relationship in males

(Figure 5.5). As is the case in the radius, both males and females demonstrate a decreasing trend. When considering only males, density is moderately negatively correlated with age with a correlation coefficient of -0.519 and significant (p=0.029).

Likely the weak relationship between lacunar density in females and age can be attributed to difficulty in defining the endosteal border of a rib cross section attributed to trabecularization of the cortex to be discussed later. As density values are standardized using bone area, artifacts may be introduced in females undergoing a large amount of post-menopausal endosteal bone loss.

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Figure 5.5: Scatterplot of Ot.Lc.N/B.Ar versus Age in the midshaft 6th rib for males and females (red and gray respectively). Regression line added for the pooled sample as there are no statistically significant differences between males and females.

5.4 Intracortical Porosity and Osteocyte Lacunar Density Correlations per Element

As expected, cortical bone loss occurs with age. In this sample, intracortical porosity is positively correlated with age in the femur, radius, and rib (Table 5.4).

Pearson correlation coefficients (r) were also used to establish the strength of the relationship between intracortical porosity and chronological age prior to comparing porosity and lacunar density to each other. The radius demonstrates the strongest relationship with age such that it is best described as quadratic (Figure 5.6) both when considering sexes independently, as well as for the total sample. A strong and significant

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positive correlation for the entire sample (p-value=0.000) reveals that the radius experiences a severe increase in intracortical porosity as individuals age. This bone loss increases more drastically in the later decades of life (80 to 100 years old).

Figure 5.6: Scatterplot of Intracortical Porosity (%Po.Ar) versus Age in the radius. Regression line applied to pooled sample (n=30)

More difficult to quantify due to severe trabecularization of the cortex, the rib displays a more complicated and sex specific relationship (Figure 5.7). Males demonstrate a moderate significant positive correlation with age (correlation=0.628, p-

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value=0.016) whereas, females do not (correlation=0.062; p-value=0.827). Considering the entire sample, intracortical porosity does increase with age though it displays a weak and non-significant relationship (correlation=0.339; p-value=0.072). Unlike the radius, where age explains 56% of the variance in intracortical porosity, age only explains 11.5% of variation in cortical bone loss in the rib. Again, these data are unlikely to be refuting the biological and physiological effects of increased remodeling rate that has been reported in the rib in the past but more likely to be due to the well recognized (Zebaze and Seeman 2015) difficulties in identifying the endocortical border essential for accurate quantification of porosity area relative to amount of bone present in the cortex.

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Figure 5.7: Scatterplot of Intracortical Porosity (%Po.Ar) versus Age in the rib. Regression line applied to pooled sample (n=29)

Though it is intermediate to the radius and rib, the femur experiences significant bone loss with age (Figure 5.8). When considering the entire sample, a moderate and significant positive correlation (correlation=0.545, p-value= 0.002) exists between femoral intracortical porosity and increasing age. Age explains 29.7% of variation in porosity in the femur; again an intermediate amount between the radius (56%) and the rib

(11.5%) suggesting the presence of other factors influencing bone loss.

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Figure 5.8: Scatterplot of Intracortical Porosity (%Po.Ar) versus Age in the femur. Regression line applied to pooled sample (n=30)

In order to investigate the relationship between intracortical porosity and osteocyte lacunar density, Pearson correlation coefficients were employed as well as plotting porosity (%Po.Ar) against density (Ot.Lc.N/Ct.Ar) to determine directional trends. Age is intrinsically controlled for in the paired data sets for these individuals. It is critical to note that previous reports of osteocyte cell population as measured using lacunar density have been reported and analyzed after normalization using bone area

(B.Ar) which provides a more accurate representation of density per unit area of bone.

As discussed in Chapter 4, bone area (B.Ar) takes into account the porosity area

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measured in the cortex of each element. To avoid conflation, comparison between lacunar density and porosity necessitates the use of cortical area (Ct.Ar) which does not include porosity area (Po.Ar) in its quantification of the amount of bone present to normalize both variables. Thus, for the comparisons within this section, osteocyte lacunar density is calculated using Ot.Lc.N/Ct.Ar and expressed in #/mm2. Table 5.5 displays the results of correlations between intracortical porosity and osteocyte lacunar density. These are expected to be negatively correlated so that as porosity area is high, density of osteocytes is low. As with porosity and age, the radius represents the strongest relationship, the femur is intermediate, and the rib displays the weakest.

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Femur Radius Rib %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/Ct.Ar Males Correlation coefficient -0.638 -0.685 -0.506 p-value 0.003 0.003 0.033 R2 0.407 0.469 0.256 Females Correlation coefficient -0.560 -0.790 -0.476 p-value 0.015 0.000 0.037 R2 0.314 0.624 .227 All Correlation coefficient -0.626 -0.746 -0.486 p-value 0.000 0.000 0.004 R2 0.392 0.556 0.236 Table 5.5: Pearson correlation of Intracortical Porosity (%Po.Ar) and Osteocyte Lacunar Density (Ot.Lc.N/Ct.Ar) for each individual and each skeletal site. Significant values are bolded (α=0.05).

Figure 5.9 shows the decreasing trend in intracortical porosity as osteocyte lacunar density increases in the radius. In males and females, the correlation is significant, though stronger in females (Table 5.5). All sampling groups display a negative correlation between these variables. The strong relationship in the radius suggests that although there may be some metabolic influences on intracortical porosity, osteocyte lacunar density can explain 55.6% of the variation in intracortical porosity for the entire sample. As age explains a nearly identical percentage of variation in porosity, this suggests that age related changes in lacunar density may be the mediating factor in the relationship between intracortical porosity and age at this anatomical site.

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Figure 5.9: Scatterplot of Intracortical Porosity (%Po.Ar) against Osteocyte Lacunar Density (Ot.Lc.N/Ct.Ar) in the radius. Regression line applied to total sample (n=30)

In the rib, males, females and the pooled sample displayed moderate and significant correlations. Figure 5.10 demonstrates the negative relationship between osteocyte lacunar density and intracortical porosity in the rib. Lacunar density explains a larger percentage of variation in porosity in the rib than age does (23.6% compared to the

11.5% demonstrated in Figure 5.7). Although the relationship is only moderate within the rib, I believe that future work to more accurately define the endocortical border, remove this error from measurements of the amount of bone present, will lead to a stronger relationship between intracortical porosity and lacunar density.

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Figure 5.10: Scatterplot of Intracortical Porosity (%Po.Ar) against Osteocyte Lacunar Density (Ot.Lc.N/Ct.Ar) in the rib. Regression line applied to total sample (n=29)

The femur is again positioned between the radius and rib in strength of the relationship between intracortical porosity and lacunar density (Figure 5.12). Males demonstrate a slightly stronger negative correlation than females (Table 5.5). As with the radius and rib, there are no significant differences between males and females and very slight variation in the strength of the relationship between porosity and density in any element. The same is the case for the femur: for the entire sample (n=30), intracortical porosity is significantly and negatively correlated with osteocyte lacunar density

(correlation= -0.626; p-value=0.000). As is the case in the rib, lacunar density explains

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more variation in intracortical porosity across the whole group than age alone supporting the importance of osteocytes in age related changes of porosity.

Figure 5.11: Scatterplot of Intracortical Porosity (%Po.Ar) against Osteocyte Lacunar Density (Ot.Lc.N/Ct.Ar) in the femur. Regression line applied to total sample (n=30)

5.5 Systemic Trends in Osteocyte Lacunar Density

To test the hypothesis that there exist intra-individual systemic trends in osteocyte cell density reduction with age and sex, lacunar density was compared between anatomical sites for each individual (femur vs. radius, femur vs. rib, radius vs. rib).

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Again, as these are intra-individual comparisons, it was not necessary to extrinsically control for age. Table 5.6 demonstrates both the correlation coefficients and the two tailed significance of the Pearson correlation tests. If the decreasing lacunar density found in each of the elements were to display intra-individual systemic variation, significant correlations would be expected; Table 5.6 and Figure 5.12 indicate this is not the case. These data do indicate a moderate and significant positive correlation between the femur and radius lacunar density in males only. However, for the pooled sample, R2 value is low suggesting a large percentage of variation is explained by other variables.

Figure 5.12b and 5.12c show the lack of relationship between the lacunar density of the radius and rib and femur and rib respectively.

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Femur Radius Rib Males Ot.Lc.N/B.Ar Ot.Lc.N/B.Ar Ot.Lc.N/B.Ar

Femur Ot.Lc.N/B.Ar (r) 1 0.561 0.106 p-value - 0.029 0.718

Radius Ot.Lc.N/B.Ar (r) 0.561 1 -0.197 p-value 0.029 - 0.499

Rib Ot.Lc.N/B.Ar (r) 0.106 -0.197 1 p-value 0.718 0.499 - Femur Radius Rib Females Ot.Lc.N/B.Ar Ot.Lc.N/B.Ar Ot.Lc.N/B.Ar

Femur Ot.Lc.N/B.Ar (r) 1 0.108 -0.287 p-value - 0.072 0.299

Radius Ot.Lc.N/B.Ar (r) 0.108 1 0.055 p-value 0.072 - 0.845

Rib Ot.Lc.N/B.Ar (r) -0.287 0.055 1 p-value 0.299 0.845 - Femur Radius Rib All Ot.Lc.N/B.Ar Ot.Lc.N/B.Ar Ot.Lc.N/B.Ar

Femur Ot.Lc.N/B.Ar (r) 1 0.312 -0.125 p-value - 0.093 0.519

Radius Ot.Lc.N/B.Ar (r) 0.312 1 -0.050 p-value 0.093 - 0.798

Rib Ot.Lc.N/B.Ar (r) -0.125 -0.050 1 p-value 0.519 0.798 - Table 5.6: Correlations for systemic intra-individual variation in osteocyte lacunar density (Ot.Lc.N/B.Ar). Significant values are in bold.

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The lack of strong intra-individual trends in osteocyte lacunar density suggests that although there may be systemic factors affecting osteocyte cell viability/apoptosis, there appear to be site specific micro-environmental changes occurring to affect each of these cell populations differentially. The changes in osteocyte cell density mitigated through their apoptosis facilitates increased remodeling rate that corresponds with aging supported by the strong relationships between lacunar density and porosity reported in section 5.4.

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)) Figure 5.12: Scatterplots of Intra-individual Osteocyte Lacunar Density for Systemic Comparisons. A) Femur to Radius: the only significant correlation for lacunar density systemically. Regression line applied to the pooled sample. B) Radius to Rib indicating no relationship between lacunar densities. C) Femur to Rib also indicating no obvious relationship between lacunar densities Continued

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Figure 5.12 continued:

In order to further elucidate this relationship, intracortical porosity was also compared on a systemic level using the same methods as systemic comparisons of lacunar density. Table 5.7 lists the strength of the relationship (Pearson correlation coefficient) and two tailed significance.

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Males Femur %Po.Ar Radius %Po.Ar Rib %Po.Ar

Femur %Po.Ar (r) 1 0.711 0.699 p-value - 0.003 0.005 Radius %Po.Ar (r) 0.711 1 0.794 p-value 0.003 - 0.001 Rib %Po.Ar (r) 0.699 0.794 1 p-value 0.005 0.001 - Females Femur %Po.Ar Radius %Po.Ar Rib %Po.Ar Femur %Po.Ar (r) 1 0.568 0.477 p-value - 0.027 0.095 Radius %Po.Ar (r) 0.568 1 0.142 p-value 0.027 - 0.614 Rib %Po.Ar (r) 0.477 0.142 1 p-value 0.095 0.614 - All Femur %Po.Ar Radius %Po.Ar Rib %Po.Ar Femur %Po.Ar (r) 1 0.643 0.566 p-value - 0.000 0.001 Radius %Po.Ar (r) 0.643 1 0.425 p-value 0.000 - 0.022 Rib %Po.Ar (r) 0.566 0.425 1 p-value 0.001 0.022 - Table 5.7: Pearson correlations for systemic intra-individual variation in intracortical porosity. Significant values are bolded.

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As opposed to the lack of strong trends found in lacunar density within the individuals of this group, intracortical porosity displays an obvious systemic trend between these three anatomical sites. For males, females, as well as the entire pooled sample, the femur and radius were found to be positively and significantly correlated

(Table 5.7 and Figure 5.13a). Figure 5.13b demonstrates the positive and significant correlation of intracortical porosity between the radius and the rib. This relationship is strong in males, non-significant in females, and moderate and significant in the pooled group (correlation= 0.425; p-value= 0.022). Lastly, Figure 5.13c demonstrates the intra- individual relationship between the femur and the rib (significant in males, and the pooled sample though not in females alone). Again, likely the endocortical trabecularization of the rib especially in females lends to artifacts in comparisons between the rib and the radius or femur. R2 values are highest between the femur and radius, followed by the femur and rib, and lastly the radius and rib (Figure 5.13).

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Figure 5.13: Scatterplots of Intra-individual Intracortical Porosity (%Po.Ar) for Systemic Comparisons. Regression lines applied to the pooled sample. A) Femur to Radius. B) Radius to Rib. C) Femur to Rib. Continued

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Figure 5.13: Continued

To sum, the strongest and only significant systemic relationships in osteocyte lacunar density (Ot.Lc.N/B.Ar) are in males between the femur and radius (correlation coefficient = 0.561; p-value= 0.029). No other trends were identifiable systemically in lacunar density. However, intracortical porosity was systemically related in the individuals of this sample. Again, males show the strongest relationships between the femur and radius and the radius and rib (0.711 and 0.794 respectively). Yet, the pooled sample comparisons of intracortical porosity between all anatomical sites are significantly correlated.

In the previous sections, these data have demonstrated a significant negative correlation for osteocyte lacunar density with age; a positive correlation and trend for 119

intracortical porosity and age for each element; significant negative correlations between intracortical porosity and osteocyte lacunar density for each element. This section investigated the systemic trends for both osteocyte lacunar density and intracortical porosity. These data and relationships suggest site specific rates or trends in decreasing osteocyte cell population density, yet systemically related consequences of these decreases depicted in the correlated intra-individual porosity. The potential biological explanation behind these relationships will be discussed in Chapter 6.

5.6 Differences between Osteocyte Lacunar Density calculations using “Ct.Ar” versus “B.Ar”

The effects of including porosity area in the calculation of osteocyte lacunar density results in differences of significance when investigating age and sex related patterns. Although using “bone area (B.Ar)” as the denominator in determining osteocyte lacunar density is a more representative depiction of the actual number of lacunae per mm2, using cortical area in its place results in smaller density calculations per element

(see Appendix A). “Cortical area (Ct.Ar)” as the size normalization for lacunar density results in stronger correlation coefficients for each group and for each element (see Table

5.4). However, the trends remain the same between Ot.Lc.N/Ct.Ar and Ot.Lc.N/B.Ar although differences in significance which may alter interpretations are found in some elements and groups. For the femur, using “Ct.Ar” to calculate lacunar density results in correlations with age which are stronger (as represented by the correlation coefficients) in all samples compared to using “B.Ar” for standardization (and achieved significance in females changing the p-value from 0.223 using Ot.Lc.N/B.Ar to p=0.043 using

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Ot.Lc.N/Ct.Ar). One tail significance is achieved in males and the pooled sample but not in females for the rib when correlating Ot.Lc.N/Ct.Ar with age instead of Ot.Lc.N/B.Ar.

For the radius in males and females, non-significant decreases in lacunar density

(Ot.Lc.N/B.Ar) became significant when using cortical area (Ot.Lc.N/Ct.Ar) (Table 5.4); unsurprising that the greatest effects of including porosity areas on lacunar density measurements would occur in this element as the increase in intracortical porosity is more severe with age thus creating larger differences in density between individuals of each group.

5.7 Summary

In this sample, there were no significant differences in any variable between males and females. However, as demonstrated, there were slight differences between the sexes and thus, all correlations and relationships were tested for all groups (males, females and pooled) and across all anatomical sites (femur, radius, rib). Osteocyte lacunar density showed a negative correlation with age as expected though not significant for all groups or elements. Intracortical porosity and lacunar density were correlated in each element supporting the closely tethered physiological system between these variables. Lastly, there was little evidence for strong systemic trend in osteocyte lacunar density changes with age suggesting, in conjunction with the element specific data, that these changes are occurring differentially between anatomical sites. How this relates to the physiological control over intracortical porosity which is systemically correlated between the femur, radius and rib will be discussed in Chapter 6. Lastly, this chapter presented the differences in interpretations that could occur when using cortical area as

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opposed to the more accurate representation of bone area in the calculating density. This should be a caution for comparisons of values across studies in terms of osteocyte lacunar density.

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Chapter 6: Discussion

6.1 Introduction

The purpose of this research was to establish a systemic and standardized foundation for understanding the variation in cortical bone osteocyte lacunar density in relation to major factors of clinical and bioarchaeological relevance (age and sex). In order to assess bone quality or population health in past and present populations, we must not ignore the most ubiquitous bone cell provided its regulatory function and its susceptibility to mechanical and metabolic factors highly influenced by lifestyle and behavior. Thus, an additional goal of this work is to illuminate the utility of osteocyte lacunar density as an indicator of bone quality for bioarchaeologists assessing the skeletal record barring preservation issues and regulations on destructive methods. Here, chapters

2 and 3 have set the stage for physiological processes that link influences at the organism and tissue levels to the cellular level after which osteocytes respond appropriately to affect change that maintains or alters the bone as a whole (tissue level). Understanding patterns of inter-individual variation in lacunar density with age and between sexes allows for interpretations of data falling outside of these trends and investigation into the cause and resulting consequences both clinically and archaeologically. As noted in chapter 1, our understanding of osteocyte density alterations demonstrate conflicting

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results due to the comparisons across skeletal sites. However, the anatomically site specific osteocyte sensitivity and function cannot be ignored (Skedros et al. 2005).

Therefore, the lack of systemic patterning of osteocyte lacunar density found here is crucial to ameliorate the disparities currently reported in the literature. This study will add to the pool of work being done to position the osteocyte into a dominant role in maintaining bone quality and function as the first to examine multiple elements from within individuals.

6.2 Methodological Considerations

6.2.1 Effects of using Ot.Lc.N/Ct.Ar versus Ot.Lc.N/B.Ar

Although trends were unchanged, interpretations of the strength and significance of relationships between lacunar density and age are affected by the choice to utilize cortical area (Ot.Lc.N/Ct.Ar) as opposed to bone area (Ot.Lc.N/B.Ar) to account for size.

As displayed in Table 5.4, nearly all values of Ot.Lc.N/Ct.Ar are significantly inversely correlated with age with the exception of the rib in females only. In all cases, the strength of correlations with age values (r) are stronger using Ot.Lc.N/Ct.Ar over the corresponding Ot.Lc.N/B.Ar (except female only ribs). However, as established in this sample, intracortical porosity (which does not include lacunar area measurements; only

Haversian canals, Volkmann’s canals and resorption spaces) increases with age in all elements; thus, the significance and strength of Ot.Lc.N/Ct.Ar which does not account for the amount of porosity (spaces in which there cannot exist lacunae) relationship with age is more representative of porosity than of actual changes in osteocyte lacunar density. In this sample, these differences encourage the standardization of density calculation

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procedures when comparing variables across anatomical sites and individuals. The use of bone area (Ot.Lc.N/B.Ar) allows for a more accurate representation of the amount of bone available for osteocytes to inhabit and maintain. Therefore, change in lacunar density between elements or individuals is not due to the confounding amount of bone loss or remodeling but truly the amount of osteocytic lacunae in the bone available.

Thus, differences of porosity between individuals are controlled and not affecting comparisons of lacunar density supporting the use of “bone area” in density calculations.

The only appropriate use for Ot.Lc.N/Ct.Ar here was for comparisons between lacunar density and intracortical porosity within each element. As these variables are matched between samples, there was no need to control for porosity using Ot.Lc.N/B.Ar (which would create redundancy as porosity is accounted for in the %Po.Ar variable for comparison). For histomorphometric comparisons, it is important to control for intracortical porosity in normalizing osteocyte lacunar density.

6.3 Inter-individual Variation in Osteocyte Lacunar Density

The estimated variation in osteocyte lacunar density is not consistently reported in either osteoporotic or healthy human bone (Carpentier et al. 2012). Additional issues in establishing normal variation of osteocyte lacunar density in a population includes the incongruity of sampling sites and interpretations made across anatomical sites, bone type, and small sample sections (Carter et al. 2013). Osteocyte cell populations are variable based on a number of factors that vary across the skeletal system including mechanical loading environment which affects the mechanosensitivity and customary strain stimulus between elements and likely even within an element experiencing complex loading.

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Thus, this study first attempts to establish a standardized comparison using three anatomical sites: the midshaft femur (a frequently used area in anthropological investigations of cross sectional geometry, aging, etc.), the distal cortex of the radius (a clinically relevant site for increased bone fragility resulting in susceptibility to traumatic fracture) and the midshaft rib (utilized in both anthropological and clinically relevant studies as a systemic control as it is generally unaffected by changes in physical activity).

Comparisons to patterns found in other studies and explanations of trends are presented here.

Osteocyte density (as represented by lacunar density) has been shown to decrease with age in males and females in both trabecular and cortical bone (Mullender et al. 2005;

Mullender et al. 1996; Qiu et al. 2002b; Vashishth et al. 2000) and without significant differences between sexes in the femur (Vashishth et al. 2000). This study also found an inverse relationship between age and osteocyte lacunar density in the three anatomical sites studied. Similar to other research groups (Busse et al. 2010), there were no significant sex differences found in this sample (although plots with sex specific trends are included in Appendix B for reference). However, these results are not directly comparable to any of the previous studies listed here or in chapter 3 as sampling sites and regions of interest vary greatly. Qiu and colleagues (2005) demonstrated in cortical bone of the rib, interstitial (older) bone has 17% fewer osteocytic lacunae than osteonal bone.

Power and colleagues (2002) found that osteocyte viability was similar in osteonal bone in the cortex of the intracapsular femoral neck between healthy and osteoporotic individuals. Lastly, Busse and colleagues (2010) demonstrated no significant differences

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between sexes but a significantly more severe decline in osteocyte lacunae in regions of interest in the endosteal compared to the periosteal envelope of the proximal femoral cortex. Thus, these site-specific effects on osteocyte viability and lacunar density illuminate the need for comparable datasets before truly assessing variation. This study has established a baseline onto which future work and comparisons can be made, increasing the accuracy of interpretations of the physiological processes and environmental factors affecting these in past and present human populations.

All elements, despite the sampling groups (males, females, pooled), demonstrated a decreasing trend in lacunar density with age. The radius was significance (p=0.034) in the pooled sample as well as the males for the rib (p=0.029). The femur demonstrated significant correlations in the males only and pooled sample groups (p= 0.009 and 0.005 respectively). There seem to be sex related differences within this sample when correlating age despite the lack of significant differences in lacunar density between the sub-groups (see Appendix B).

Interestingly and perhaps in support of the paradigm shift away from estrogen- centric effects on bone mass and quality, females do not show the strength of relationship with age that males do at these three sites. Estrogen has anti-apoptotic effects on osteocytes (Khosla et al. 2012; Sharma et al. 2012; Tomkinson et al. 1997) which would result in the maintenance of higher densities of occupied lacunae and decreased initiation of remodeling. Yet in human iliac cancellous bone (metabolically more sensitive to estrogen withdraw due to larger surface area and high rate of turnover), osteocyte density was found to decrease beginning near or shortly after skeletal maturity with no significant

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acceleration post-menopause (Qiu et al. 2002b). In the present sample, although menopausal status is unknown, the age range spans nearly 50 years of later life, and it is expected that a majority of these females were menopausal or post-menopausal.

Hormone replacement or anti-resorptive treatments may be confounding the age related decline especially in relation to the males of the group. Interestingly though, if this was the case for some or all of these females, despite the effects of these treatments, osteocyte lacunar density is still inversely related to age in the femur and radius to varying degrees.

Conversely, in the metabolically sensitive rib where the thin cortex and large medullary cavity would place most of the osteocytic lacunae within range of osteoclast progenitors

(HSC), males show a stronger correlation than females (r=-0.519 and 0.039 respectively).

In fact, the rib demonstrates an unexpected positive relationship with age in females

(Figure 5.5) but this may be due to one female with a very low Ot.Lc.N/B.Ar value. It is possible that hormone replacement therapy or pharmacological treatment to prevent bone loss could create artifacts for females in this sample; however, intracortical porosity

(%Po.Ar) is still increasing with age more strongly in females than males at the femoral midshaft but not in the radius or rib. Likely, as has been discussed briefly throughout chapter 5, issues with identifying the endosteal boundary in females may be accentuated due to hypogonadism driven endocortical bone loss and trabecularization (Zebaze and

Seeman 2015). This creates issues with defining the endosteal border and subsequent porosity area used to normalize lacunar density counts.

Factors accompanying senescence such as increased oxidative stress, hypogonadism, increase in endogenous glucocorticoids, increased mineralization of bone

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and resulting microdamage accumulation may not be best represented by chronological age. Reduced osteocyte density can result from an increase rate of osteoblast apoptosis

(fewer cells to differentiate into mature osteocytes) that accompanies sex steroid withdraw (Bellido et al. 2014). Although these factors all influence osteocyte viability

(as discussed in section 3.3), their relationship with chronological age is tenuous and variable due to lifestyle factors. Chronological age does not always correspond to or capture the amount of allostatic or stress load (Crews 2003; Crews and Ice 2012). Thus, the weak explanatory power of chronological age on variation in osteocyte lacunar density in each element is not surprising. The presence of an inverse relationship suggests that some of the senescent changes occurring in this sample are captured by chronological age yet future work is needed to tease out these individual components.

To sum, osteocyte lacunar density was inversely related to age in the femur, radius and rib to varying degrees. There were no significant differences in mean values of lacunar density (Ot.Lc.N/B.Ar) or intracortical porosity (%Po.Ar) between sexes despite the effects of menopause induced endocortical bone loss in females or the continuing periosteal apposition seen in males suggesting perhaps an “early gain” protective mechanism in which a baseline level of osteocytes are attained to attenuate the

“later loss” in life.

6.4 Intracortical Porosity and Osteocyte Lacunar Density

As Allen and Burr (2014) have revised their ideas on the amount of remodeling that is locally targeted and as many have demonstrated the osteocytic control over initiation of a BMU, intracortical porosity was used in this study as a link between

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osteocyte lacunar density and the effects of reduction in density with age. Localized initiation and orchestration of a remodeling event is choreographed by the apoptosis of one or multiple osteocytes; a causal relationship supported by the ubiquitous apoptosis preceding osteoclastogenesis in remodeling events (O'Brien et al. 2013; Seeman 2006).

Apoptosis can be induced by a multitude of factors addressed in section 3.3 including oxidative stress, induced by glucocorticoids and estrogen or androgen loss, promotion of osteocyte death which links systemic hormonal changes to increased intracortical porosity. Yet a major player in osteocyte apoptosis and disruption of the lacunocanalicular network is microdamage especially in this sample with an age range from 49 to 100 years old. Although the cause and effect relationship between osteocyte density and microdamage is unknown (Qiu et al. 2005) yet likely builds upon one another, both microdamage and intracortical porosity increase with age and osteocytes are the causal link.

Kennedy and colleagues (2012) demonstrated osteocyte apoptosis induced by fatigue loading mice ulna resulting in increased RANKL production by healthy osteocyte neighbors in the region and initiation of intracortical remodeling. Their study was among the first to demonstrate that the initiation of osteoclastogenesis stems from neighboring cells and not the apoptotic cell itself; they also demonstrated that RANKL expression in adult cortical bone osteocytes is rather low and must be upregulated as a result of damage. Similar to the results of this study, Vashishth et al. (2000) found no significant differences between sexes in lacunar density or porosity in the posterior cortex of the femur. They argue that the similarities in exponential probability between lacunar

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density, microdamage and porosity suggest regulation by a similar biological phenomenon. As discussed in chapter 3, the physiological mechanism governing the relationship between these variables is the functioning of the osteocytic lacunocanalicular network.

6.4.1 Intracortical Porosity (%Po.Ar) and Age

Increasing intracortical porosity with age has been demonstrated in various anatomical sites (Cooper et al. 2007; Feik et al. 1997; Nishiyama et al. 2010; Thomas et al. 2005) and has important consequences for fragility and biomechanical integrity (Bell et al. 2000; Kazakia et al. 2011) negatively affecting Young’s modulus (Zioupos 2001) and toughness (Yeni et al. 1997). This study found a positive correlation with intracortical porosity (%Po.Ar) in all elements and groups (Table 5.4) except for female ribs. The strongest exponential relationship was identified in the distal radius (r=0.738 for the pooled sample). For qualitative comparison, see Figure 6.1. These radii are representative of the youngest (49 year old) and oldest (100 year old) males in this sample and represent the lowest and highest relative porosities (respectively) in the distal radius.

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Figure 6.1: A) The distal radius cross-section original VSI and ImageJ manipulation for identification of intracortical porosity for individual 6882 (49 year old male) whose %Po.Ar value is 4.417%. B) The distal radius cross-section original VSI and ImageJ manipulation for identification of intracortical porosity for individual 6527 (100 year old male) whose %Po.Ar value is 25.409%.

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Though not continually load-bearing, the distal cortex of the radius transmits forces placed on the palmar surface of the hand (compressive) as well as muscle forces sustained during the act of pronation (pronator quadratus muscle attachments). The complexity of this loading environment likely does not vary largely with increasing age and decreasing load-bearing physical activity as our upper limbs can remain active even when unable to ambulate. This scenario offers a potential environmental explanation for the exponential relationship of intracortical porosity and age at this site. As age related changes occur in the distal radius to alter the material properties of the bone including increased mineralization and brittleness (Allen and Burr 2014), microdamage accumulates, osteocyte apoptosis increases and initiation of remodeling events to repair the damage result in increased intracortical porosity. As the use of forearm and mechanical environment of the radius likely does not change in later decades of life, this effect may be compounded resulting in an exponential relationship. However, this is not the case in the rib which would also maintain a consistent mechanical environment due to ventilation through life and undergo the same systemic age related changes in material properties. The linear increase in porosity seen in males and the pooled sample with age is likely confounded by the difficulties in identifying the endocortical border. Lastly, in this sample, intracortical porosity in the femur was significantly positively and linearly correlated with age in all groupings. The intermediate position of the femur in strength of relationship between lacunar density and age (situated between the radius and the rib) indicates that changes in lifestyle and load bearing activities in this older sample ranging

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into elderly years may have a larger impact on intracortical porosity than age alone can explain.

An additional explanation for the patterns seen here in the radius and femur involves the remodeling rate. Qualitatively, the distal radius retains a significant amount of primary unremodeled interstitial bone in which osteocyte age is older than remodeled osteonal (secondary) bone. Whereas, the femur, under load bearing mechanical forces contains a relatively larger amount of remodeled bone (save for the endosteal lamellar pocket identified by Maggiano (2011) and periosteal apposition compensating for endosteal bone loss). Qiu and colleagues (2005) found 80% of microcracks located in older interstitial bone as compared to 3% in osteonal bone. O’Brien and colleagues

(2003) demonstrated that fatigue induced microdamage initiated in interstitial bone with a lower osteocyte density; Vashishth (2000) demonstrated that this correlates with older age. Additionally, the prevalence of empty lacunae and micropetrosis is higher in interstitial bone than in secondary osteons or Haversian systems (Frost 1960b; Frost

1960c). Thus, one can posit that the higher amounts of unremodeled interstitial bone surviving to later decades in life of the distal radius may promote microdamage accumulation and increasing remodeling rates. As this is a cross-sectional study and does not include quantification of microdamage, this cannot be directly tested here.

6.4.2 Osteocyte Lacunar Density Correlates with Intracortical Porosity per Element

This study did not address within element distribution of osteocyte lacunae but rather considers the variation for the entire cross-section as more representative of the tissue level function while recognizing these thin sections represent only the specific

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anatomical location from which they came. As described in Chapter 3, osteocyte derived molecular signaling resulting from apoptosis initiates remodeling events. Thus, it is expected that lacunar density used as a proxy for the viability of the osteocyte population would negatively correlate with the amount of intracortical porosity. Previous work at different anatomical sites (including trabecular bone) found a negative relationship in their small sampling sections but did not investigate the entire cross-section of their sampling site. In 0.71mm2 sections of the superior, inferior, anterior and posterior cortical bone of the femoral neck, Power and colleagues (2001) found that for every 1% increase in porosity, lacunar density decreased by 2.35/mm2. Using synchrotron radiation

CT, Dong and colleagues (2014) found that for every 1% increase in porosity, lacunar density decreases by 104.61/mm3 in multiple small sections taken from the midshaft femur of two elderly females. Similarly, Vashishth and colleagues (2002b) found in the cortical bone of the midshaft femur and the trabecular bone of the vertebral body a highly correlated nonlinear relationship between the extracellular matrix volume and osteocyte lacunar density. They argued, which has subsequently been physiologically proven through the identification of RANKL, OPG and sclerostin secretion by osteocytes, that the existence of osteocytes rather than the amount of work performed by osteoblasts and osteoclasts are important in bone turnover and determining age related changes in bone mass. These studies purport that osteocyte density as measured by their lacunae determines bone volume fraction or bone mass. This is mediated by the osteocytic control over bone turnover discussed in Chapter 3. However, Colopy and colleagues

(2004) found that osteocyte density (though significantly related to porosity) only

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explained 49% of the variation in bone volume fraction in the fatigue loaded rat ulna and suggest other factors are responsible for regulating loss of extracellular matrix during targeted remodeling.

This study found similar relationships between porosity and lacunar density as discussed above in each element (though complicated in the rib). Table 5.5 and Figures

5.9 and 5.11 show significant negative linear correlations between intracortical porosity

(%Po.Ar) and osteocyte lacunar density (Ot.Lc.N/Ct.Ar). Figure 5.10 displays the weak and not statistically significant negative relationship between these variables in the rib.

Again, the rib is likely confounded with issues of trabecularization (Zebaze and Seeman

2015). The linear nature of these relationships might be due to the age range (49 to 100 years old). Vashishth and colleagues (2002) found a non-linear relationship in the cortex of the midshaft femur but their age range spanned from 16 years old to 73 years old. I expect that including younger individuals in this sample would have produced not only stronger relationships between lacunar density and age but also with intracortical porosity. For qualitative representation, the femora from the same individuals represented in Figure 6.1 are presented in Figure 6.2 with their associated intracortical porosity and lacunar density values.

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Figure 6.2: A) Femoral cross-section original VSI and ImageJ manipulation for identification of intracortical porosity from 6882 (49 year old male) with %Po.Ar and Ot.Lc.N/Ct.Ar values of 7.710% and 976.523/mm2 respectively. B) Femoral cross- section original VSI and ImageJ manipulation for identification of intracortical porosity from 6527 (100 year old male) with %Po.Ar and Ot.Lc.N/Ct.Ar values of 24.265% and 526.157/mm2 respectively.

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The spatial and temporal relationship between microdamage, osteocyte apoptosis and intracortical remodeling has been established in both mouse and human cortical bone and discussed in various sections here. Osteoclastogenesis induced by the production of

RANKL by the healthy neighboring osteocytes who were signaled (the nature of which is yet to be determined) by their apoptotic neighbors directly links the process of microdamage induced initiation of repair. Although changes in material properties of older bone would lend themselves to accumulate microscopic damage, the osteocyte lacunocanalicular network functions to repair such damage in a protective mechanism against gross failure (by replacing old bone with new less mineralized bone with a longer fatigue life). The increased initiation of remodeling (or increased bone turnover) accompanying increasing age is likely due not only to accumulating microdamage in brittle bone but systemic causes of osteocyte death (discussed in section 3.3).

Intracortical porosity in the midshaft femur has been studied (without lacunar density data) and indicates that 81% of the variance in porosity is due to a coalescence of pores and only 12-18% due to increase in pore density (Thomas et al. 2006); results echoed by

Cooper and colleagues (2007) also in the midshaft femur. Coalescence is mediated by the combined effects of increased spatial and temporal initiation of remodeling events as well as the decreased amount of bone formation in composite (coalescing BMU) osteons leading to overall fragility especially in regions of high mechanical strain (Bell et al.

2000).

Mechanical loading both provides an anti-apoptotic effect for osteocyte viability but can induce microdamage dependent on the material properties of bone. Disuse and

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weightlessness have been shown to increase osteocyte apoptosis (due to lack of interstitial fluid movement) which is followed by osteoclastogenesis, bone resorption, increased cortical porosity, decreased cortical thickness and decreased bone strength in mice (Aguirre et al. 2006). Fatigue induced microdamage follows the same pattern although osteocyte apoptosis is mediated by disruption of lacunocanalicular networks and nutrient/oxygen access for the cells rather than a lack of fluid flow. Despite the environmental or behavioral cause, the process remains the same with a resulting increase in intracortical porosity with decreasing osteocyte viability (measured here as lacunar density). In this sample, despite the variance in loading environments between anatomical sites, bone loss and osteocyte lacunar density show a statistically significant negative relationship.

6.5 Systemic Trends

To the author’s knowledge, only two studies of osteocyte lacunar density have investigated and reported systemic or intra-organism variation, neither of which utilized human bone (Skedros et al. 2005; Zarrinkalam et al. 2012). This study seeks to be the first to examine intra-individual osteocyte lacunar density to establish if the site specific trends display a systemic relationship. As each element shows a decreasing trend with age, and correlations were run for matched data points for the femur to rib, rib to radius and radius to femur, if a systemic pattern in lacunar density decrease with age is present, then a significant correlation (or noticeable trend) is expected. Table 5.6 and Figure 5.12 report the results of this investigation. The only apparent systemic trend indicated in this sample is between the midshaft femur and the distal radius in males (p=0.029), but not in

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females or the pooled sample (p= 0.072 and 0.093 respectively). Comparisons between the radius and rib and femur and rib do not indicate a systemic trend in lacunar density decrease with age. Although, the issue with endosteal border identification in female ribs was presented, systemic correlations were also non-significant between the femur and rib and radius and rib for males alone. Unlike the identifiable trends when plotting rib

Ot.Lc.N/B.Ar against age, this does not appear to be the case for systemic Ot.Lc.N/B.Ar between these elements (Figure 5.12 B and C). Although the decline in lacunar density with age has been demonstrated (including in this study), the rate of decline is unknown

(Carter et al. 2013). I suggest there is a mechanically mediated component interfering with organism wide decreases in osteocyte viability so that age related changes

(hypogonadism, excess glucocorticoids, increased oxidative stress) affect apoptosis differentially at these anatomical sites.

The function of the osteocyte lacunocanalicular network as the major mechanosensory and transducer of mechanical energy into biochemical signals has been established (see Section 3.2.2). This ability allows for bone to adapt to its mechanical environment and the role these cells play is expounded in section 3.4. The complexity of the hierarchical nature of bone construction affects how forces applied to the organ level are transmitted and distributed to the cellular level and is unclear (Nicolella et al. 2008).

The typically less mineralized perilacunar region surrounding the cell is approximately 2 to 8 µm and acts to amplify the macroscopic strain applied to the bone (Nicolella et al.

2008; Nicolella et al. 2006). The lacunar density variation reported across anatomical location and bone type may be a factor of the type and magnitude of strain reaching the

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osteocyte population in those regions (as well as their established customary strain levels which are beyond the scope of this study but likely genetically based and mechanically adapted). Skedros and colleagues (2005) reported contradictory results between the horse calcanei and third metacarpals: in the calcanei, lacunar densities were significantly increased in cortices undergoing mainly compressive forces (compared to tension cortices); whereas, in the metacarpals, areas of high compression have lower lacunar densities compared to tension. Carter and colleagues (2013) found decreased lacunar density around the neutral bending axis (sagittal plane from anterior to posterior) of the proximal femoral diaphysis whereas the highest were found in the medial and lateral quadrants which undergo compression and tension respectively during load bearing activities (Young-Hoo et al. 2001).

Mechanical microenvironment variation between the consistently loaded rib and the complex loading environments of the radius and femur may explain the lack of systemic trends in osteocyte decrease with age. The midshaft femur experiences bending forces (compression and tension) from loading that varies depending on the gait phase in addition to muscle forces associated with movement of the lower limb (Goldman et al.

2005). However, recently Skedros and colleagues (2015) identified and reviewed the issues with focusing on compression and tension strain types arguing that shear strain has a profound effect on bone matrix adaptation. The distal radius can experience a compressive loading environment as the forces incurred at the hand are transmitted through the scaphoid and lunate through the trabecular and cortical bone of the radius.

However, like the midshaft of the femur, the loading environment is complex and must

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include tensile muscle forces experienced during pronation and movements of the forearm. These complex loading environments for the femur and radius (combined compression and tension, or compression and torsion) result in shear strains on cortical bone which can be the most deleterious (Skedros et al. 2015). Meanwhile the simplified loading environment for the rib is thought to be cyclically loaded in bending: the pleural cortex in compression and the cutaneous cortex in tension. However, this does not take into account the forces placed on the rib from intercostals muscles of inspiration but rather only the action of the expanding thoracic volume. The difficulty in identifying how a bone (or in this case a localized portion of the bone) is loaded in vivo limits these comparisons and none of the current available options (force plates, strain gauges or finite element models) are ideal (Currey 2012).

The location of samples was not chosen to represent or isolate a specific loading environment or type of strain. Nor were any experimental data collected to correlate strain types with osteocyte lacunar distribution in this study. As these three locations experience complex strains, and there has been little (and contradictory) experimental data on the osteocyte lacunar density found in cortical bone undergoing specific types of strain, interpretations will be constrained to potential behavioral/lifestyle changes that may affect the systemic patterns seen here. The weak trends found in the rib both with age and intracortical porosity, and now the lack of systemic trends found between the rib and other elements may stem from the consistent loading environment as ventilation continues until death. Intracortical porosity was found to increase with age indirectly suggesting that osteocyte apoptosis was also increasing. However, barring any trauma

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that would significantly alter the loading environment during breathing, the osteocyte cell populations within the ribs are adapted to a constant, cyclic and consistent magnitude of strain. Thus, although evidence here supports some level of decrease in osteocyte viability as evidenced by decreased lacunar density and increased porosity, these effects are likely systemic and are producing the weakest relationships. Alternatively, the stronger relationships with age, intracortical porosity and systemic trends between osteocyte lacunar density in the femur and radius could have interrelated explanations.

As age increases, generally lifestyle changes ensue and mechanical loading decreases.

Cellular senescence due to systemic factors encourages delayed responses in remodeling which allows for micropetrosis and hypermineralization of bone (especially in older interstitial lamellar bone) (Busse et al. 2010). Thus, either the reduction or variation in mechanical loads promote osteocyte apoptosis through lack of interstitial fluid flow; or the continuation of load bearing activities in both the radius and femur in conjunction with increasing micropetrotic interstitial bone encourages linear microdamage propagation and resulting apoptosis. Likely, these two scenarios are not mutually exclusive as the hypermineralization of the perilacunar matrix results in increased stiffness and decreased osteocyte sensation of normal magnitude loads (Nicolella et al.

2006). I posit that this combination of changes in material properties of the bone and the variation in loading environments experienced in the appendicular skeleton could result in a systemic trend between the femur and radius but not with the rib (nor can we assume with other elements until this is tested). Why this relationship is strongest and significant

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only in males (although present in females but not statistically significant) is unknown at this stage but could potentially be resolved with a larger sample size in the future.

Lastly, the systemic relationships in intracortical porosity reported in Table 5.7 and Figure 5.13 can lend support to the mechanically mediated intra-individual variances in osteocyte lacunar density. For the pooled sample, all elements show a positive correlation for intracortical porosity (%Po.Ar). It can be argued that this supports the systemic influences on osteocyte apoptosis and the need to explore mechanical explanations for the discrepancies in lacunar density trends (discussed above).

6.6 Osteocyte Lacunocanalicular Network and Bone Quality

6.6.1 Comparisons with Bone Mass Measurements

Assessing bone quality in past or present populations ultimately boils down to the ability to function without failure. Clinically, much research in older populations has been directed at osteoporotic fractures occurring in the trabecular bone of the vertebrae or femoral neck; however, 80% of fractures associated with morbidity and mortality with increasing age are non-vertebral and concern the appendicular skeleton (Zebaze et al.

2010). Three-dimensional imaging techniques (discussed in Chapter 2) are often employed to assess cortical thickness and cortical volume particularly pre- and post- treatment with pharmacological anti-resorptives (bisphosphonates) or anabolic agents

(PTH) in osteoporotic patients (Zebaze and Seeman 2015). QCT technology allows for the combination of multiple predictors of fracture risk including BMD and structural properties such as cross-sectional area and percent cortical volume which when combined can define biomechanical strength indices of the imaged bone (Glüer 2014). As this

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study was two dimensional, I do not have access to cortical bone volume; however, as a proxy percent cortical area (%Ct.Ar) was calculated for each section. Using %Ct.Ar mimics a bone mass variable obtained from modern imaging methods of assessing fracture risk. Yet, bone strength declines disproportionately to declines in measures of bone mass (Manolagas and Parfitt 2010). It is within this discrepancy that the role of microstructural properties including osteocyte cell population is situated.

To contribute to this argument that bone mass is not the only predictor of bone strength and resistance to fracture, %Ct.Ar was plotted against osteocyte lacunar density

(Ot.Lc.N/B.Ar) for this sample (Figure 6.3).

Figure 6.3: %Ct.Ar compared to Ot.Lc.N/B.Ar for all 30 individuals. No significant trends between these variables were found.

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The lack of trends between these two variables indicates that %Ct.Ar does not offer any information on the “health” of the osteocyte lacunocanalicular network supporting it. Figure 6.3 also supports the physiological processes by which osteocyte lacunar density affects whole bone tissue quality via its regulation of remodeling and subsequent porosity (as %Ct.Ar does not include intracortical porosity). Thus, in lower resolution imaging technologies where only the periosteal and endosteal borders are possible to identify, %Ct.Ar or percent cortical volume may be a weak indicator of actual bone strength.

6.6.2 Osteocyte Lacunar Density and Diseases

Malfunction of the osteocyte lacunocanalicular network results in bone fragility associated with aging and osteoporosis (Chen et al. 2010). As the cell has the ability to modify its environment via osteocytic osteolysis, changes in locally sensed strain due to globally applied loads may allow for the cell to modify its environment to maintain physiological or homeostatic ranges by altering the mineralization of the perilacunar matrix. The increase in mineralization with age (Akkus et al. 2004) would lead to stiff perilacunar material, a decrease in local strain applied to the osteocyte resulting in apoptosis, initiation of remodeling and eventually osteoporosis (Nicolella et al. 2008).

Significant differences in older groups between periosteal and endocortical lacunar densities and hypermineralized lacunae (endocortical envelope displays fewer lacunae as well) suggests impaired bone remodeling and may mediate the age associated trabecularization of endocortical bone. Fewer hypermineralized lacunae were found near the periosteal than the endocortical surface resulting from the slight periosteal apposition

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that continues with age (Seeman 2008) resulting in younger bone and younger osteocytes near the periosteum (Busse et al. 2010). Decreased lacunar density and resulting decrease interstitial fluid flow compounded by reduction in activity and mechanical forces, add to the impaired or delayed bone repair. Systemic effects of estrogen/androgen loss, hyperparathyroidism, and oxidative stress on osteocyte apoptosis combined with an impaired cellular response results in increased bone loss pathognomonic of fragility conditions such as osteopenia and osteoporosis.

Osteocyte regulation of osteoblast and osteoclast activity (section 3.2.1) suggests that loss of osteocytic control would manifest in an imbalance between bone formation and resorption seen in metabolic bone disease (Watanabe and Ikeda 2010). When this balance is negative, the resulting porosity, bone loss and cortical thinning characterize osteoporosis. Osteoporotic bone has been found to contain fewer osteocytic lacunae than their controls (Mullender et al. 2005; Qiu et al. 2003b). However, other studies have reported increased lacunar density in patients with femoral neck fracture compared to postmortem samples (Jordan et al. 2003). Carpentier et al. (2012) found that hypermineralized lacunae were a normal aspect of bone physiology and were present in osteoporotic, osteoarthritic as well as healthy bone. This suggests that hypermineralization alone is not the underlying cause of osteoporotic bone loss. In one study, it did not appear that the structural decay associated with osteoporosis is the effect of changing geometry in osteocytic lacunae but rather a change in material properties of the surrounding tissue (McCreadie et al. 2004). Zarrinkalam and colleagues (2012) found a differential decrease in osteocyte density between the lumbar spine and iliac crest in

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osteoporotic sheep. This differential reduction in trabecular bone of each region may represent a similar systemic relationship discussed here.

The downfall to interpreting lacunar density data from these studies lies in the lack of understanding not of the biology, but of the variation between skeletal sites.

Nearly all of these studies were performed in trabecular human or animal bone.

However, thanks to increasing amounts of intracortical porosity, cortical bone surface area increases substantially over trabecular surface area leading to more area in which remodeling events can occur; thus most bone loss later in life is cortical in nature

(Seeman 2008). Therefore, variation in cortical bone osteocyte function with age is a necessity. It is unknown, though likely that some of the individuals in this sample would have been diagnosed as osteoporotic. An arbitrary and non-accurate classification (for argument’s sake only) could be made based on males and females in this group as the age range spans well beyond menopause. Interestingly, if we consider males a “control” group and females as the potential osteoporotic group, there were no significant differences in osteocyte lacunar density (Ot.Lc.N/B.Ar) for any element. This differs from the studies using mainly trabecular bone listed above but not unexpectedly. First, this is a crude approximation of the individuals who can be classified as osteoporotic in this sample. Secondly, and perhaps more importantly, it has already been established that anatomical location and particularly bone type affects lacunar density (although the mechanisms behind determination of lacunar density are still unknown). Thus, future work in cortical osteocyte lacunar density should be done in conjunction with

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osteoporosis diagnoses using advanced high resolution imaging before a clear picture of their pivotal role in the disease process can be established.

6.6.3 Osteocyte Lacunar Density and Microdamage

Burr (2011) has described the microdamage trade-off that exists in bone which requires toughness, stiffness, and flexibility all within one biological material. The accumulation of microdamage diminishes bone’s strength, stiffness and energy to gross fracture; paradoxically, the initiation and propagation of microdamage functions to dissipate energy that may have caused the bone to fail (Burr 2003; Burr 2011; Burr 2014;

Kennedy et al. 2012). Additionally, linear microdamage induced osteocyte apoptosis and subsequent remodeling replaces older more mineralized bone with younger less mineralized bone with a new complement of osteocytes (Herman et al. 2010). Ma and colleagues (2008) argue there must be enough osteocytes to properly function in maintenance of the extracellular matrix by repairing microdamage and that osteocyte density negatively correlates with the crack length of linear microdamage. Although lacunae may act as stress concentrators to initiate microcracks, they also act to reduce the propagation of these by dissipating energy which would have otherwise increased crack length (Nicolella et al. 2006).

Although the directionality of cause and effect is still unknown between microcracks and osteocyte apoptosis, these variable are highly correlated (Qiu et al.

2005) and subsequent effects on bone quality are profound. Intracortical porosity is likely responsible for substantial amount of bone loss and resultant structural deterioration after age 65 (Nicks et al. 2012; Zebaze et al. 2010). Delayed and impaired

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response to mechanical loading (mechanosensation) or apoptosis affects the bone’s ability to adapt or repair itself (Busse et al. 2010). If signaling is impaired or delayed as cell function decreases with senescence especially in older osteocytes inhabiting interstitial bone, then the empty lacunae becomes hypermineralized instead of replaced with new bone. Micropetrosis increases with age (Frost 1960c) and affects the tissue properties and propagation of linear microdamage in interstitial bone.

6.7 Osteocyte Lacunar Density in Bioarchaeology

The only investigation into osteocyte lacunar density in an anthropological context thus far was published by Bromage and colleagues (2009) using confocal scanning optical microscopy of “Lucy’s” femur. They were able to identify 63 lacunae within a specified volume and extrapolated a density of 23,333/mm3. In comparing this value to human (Goldman et al. 1999) and non-human primate (McFarlin et al. 2008) densities, Bromage concluded that for body size, primates incorporate fewer osteoblasts into osteocytes than other mammals (Bromage et al. 2009). However, this has yet to be investigated further. The encouraging results of this study are two-fold: the evolutionary importance of the osteocyte in bone function and the ability to detect lacunae in a 3 million year old fossil. Although diagenesis will likely confound investigations of osteocyte lacunar density, the quest is not impossible.

As the knowledge of osteocyte biology, genetic expression and molecular signaling under various environmental and systemic factors continues to increase, bioarchaeologists gain a new and highly sensitive tool for investigating bone quality and health in the skeletal populations. The osteocyte has been situated as the major

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orchestrator of all other bone cells (Bellido 2014; Schaffler et al. 2014) which ultimately produce the macroscopic organ we study. The sensitivity of the cell to systemic and mechanical factors allows for interpretations of biocultural processes affecting the population including behavioral changes, physical activity, pathologies, nutrition; the effect of which are all mediated through the osteocyte lacunocanalicular system.

Vashishth and colleagues (2002b) argue that the importance and effects of mechanical loading on determining bone mass and architecture is only in so far as it affects the viability and function of osteocytes. Robling and colleagues (2008) found that sclerostin secretion (the antagonist of osteoblasts and bone formation) was proportional to the mechanical load applied; during increased loading in mice ulnae, sclerostin production was decreased resulting in a release on the inhibitory effects of local osteogenesis. The central role of the osteocyte lacunocanalicular network in mechanical adaptation (both in sensation, transduction and maintenance of threshold strain levels) should be used to inform cross sectional geometry and robusticity interpretations. The environmental plasticity of the cortex of load bearing bones has been used to interpret bone strength and create behavioral reconstructions of past populations (Ruff 2008). As the mechanosensors of bone, the addition of osteocyte lacunar density and distribution strengthens these interpretations by providing another level data in the hierarchy of bone tissue and a biological basis for cross-sectional changes. Patterns in skeletal health indicators of many past populations have been investigated in relation to biocultural adaptations, cultural status discrepancies and differential access to resources, mobility, subsistence transitions, warfare and conquest, and other factors. (Katzenberg and

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Saunders 2008; Larsen 1995; Larsen 1997; Larsen 2002; Larsen 2015; Ruff et al. 1993;

Steckel et al. 2002). Though the role of the osteocyte in conditions such as periostitis, cribra orbitalia, or porotic hyperostosis has not been directly investigated, their contribution to bone functional adaptation has (Chen et al. 2010; Hughes and Petit 2010) and can be used to both validate and enhance biocultural interpretations about the past.

Osteocytes as the cellular “middleman” by which systemic health, cultural behaviors and environmental conditions affect bone quality should be the new and powerful tool of bioarchaeological investigations.

6.8 Hypotheses Revisited

The following section revisits hypotheses laid out in Chapter 1 and summarizes results discussed above.

H10: In the femur, radius and rib, there is no evident age or sex related variation in osteocyte lacunar density (Ot.Lc.N/B.Ar) per element.

H1A: In the femur, radius and rib, there is an age related decline in osteocyte lacunar density (Ot.Lc.N/B.Ar) per element occurring in both males and females.

Table 5.2 displays the mean osteocyte lacunar densities for each anatomical site.

Means are reported for males, females and the pooled sample. Table 5.3 shows the results of independent samples t-tests for lacunar density and indicate no significant sex differences for any variable utilized in analyses. Finally, Table 5.4 gives the results of

Pearson correlation tests between osteocyte lacunar density and age. The null hypothesis is refuted for males and the pooled sample in the femur (p= 0.09 and 0.006 respectively) indicating a statistically significant decline in femoral osteocyte lacunar density with

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increasing age. Although there are no statistical differences between males and females in lacunar density of the femur, when assessed independently for correlations with age, females do not show a significant decrease with age; however, Figure 5.3 demonstrates a decreasing trend for females which supports the alternative hypothesis.

For the radius, the pooled sample refutes the null hypothesis (p=0.034) and in the rib, only males show significant correlations with age (p=0.029). However, as Figure 5.4 and 5.5 demonstrate, there is a decreasing trend in osteocyte lacunar density with increasing age at these locations. As this sample only consists of 30 individuals ranging from 49 to 100 years old, likely a larger sample size encompassing younger ages would result in a significant correlation based on the trend seen with these data. The rib is likely confounded by issues with cortex determination in females as discussed above. Thus, the midshaft femur is the only anatomical site displaying significant decreases in osteocyte lacunar density with age, all elements support the alternative hypothesis by displaying a decreasing trend that would likely be amplified with the inclusion of younger individuals.

These data suggest the inter-individual differences between osteocyte lacunar density at each site correlates with age; however, despite the decreasing trend, there may be other mechanically mediated factors confounding the relationship. The stronger relationships shown in the femur with age in this sample and larger variation demonstrated in the boxplot Figure 5.2, is consistent with a decline in physical activity

(and load bearing mechanical forces in the femur) in an older population that was likely highly variable in this sample (i.e. some individuals may have been wheel chair bound or unable to be as mobile as perhaps the younger age decades of this group).

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Additional support for the alternative hypothesis is displayed in Table 5.4 and Figures

5.6, 5.7 and 5.8. As expected, intracortical porosity increased with age significantly at each skeletal site (the relationship between lacunar density and porosity will be discussed in H3). This line of evidence, as a result of increasing osteocyte apoptosis, lends support to decreasing osteocyte cell population (and therefore, lacunar density) with increasing age.

H20: Per element, there is no correlation between osteocyte lacunar density

(Ot.Lc.N/Ct.Ar) and intracortical porosity (%Po.Ar)

H2A: Per element, osteocyte lacunar density (Ot.Lc.N/Ct.Ar) is negatively correlated with intracortical porosity (%Po.Ar)

As discussed above in H1, osteocyte lacunar density and intracortical porosity both correlate with age (negative and positive respectively) demonstrating inter- individual variation at each anatomical site. To investigate the relationship between these two variables within each element, osteocyte lacunar density (Ot.Lc.N/Ct.Ar) and intracortical porosity (%Po.Ar) were correlated. The use of “cortical area (Ct.Ar)” to calculate density here is important so as to not confound the effects of porosity area on lacunar density while trying to compare lacunar density to porosity area. The variation in interpretations between Ot.Lc.N/B.Ar (a more representative measure of lacunar density) and Ot.Lc.N/Ct.Ar discussed in section 5.6 demonstrate the effects of intracortical porosity on quantifying lacunar density. However, in this comparison between

Ot.Lc.N/Ct.Ar and %Po.Ar, we are comparing their intra-individual and intra-site relationship expecting that as lacunar density decreased, intracortical porosity will

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increase with the advent of osteocyte apoptosis driven initiation of remodeling. The physiological relationship between these two variables is expected to underlie a strong correlation between them. Table 5.5 and Figures 5.9, 5.10, and 5.11 display the negative correlations for the radius, rib and femur (respectively). All groups (males, females and pooled sample) in each element refute the null hypothesis at an alpha level of 0.01 (with the exceptions of the rib in females which is significant at an alpha of 0.05). Figure 5.10 demonstrates the same negative trend expected between lacunar density and intracortical porosity and thus is in support of the alternative hypothesis (though without significance).

H30: Regional patterns of age and sex related changes in osteocyte lacunar density

(Ot.Lc.N/B.Ar) will correlate systemically among all three anatomical sites.

H3A: There exist regionally specific patterns of age and sex related changes in osteocyte lacunar density (Ot.Lc.N/B.Ar) which do not correlate systemically among all three anatomical sites.

Correlations between the osteocyte lacunar density of each element were run to determine if the decreasing changes with age demonstrated a systemic pattern (in which case significant relationships between each element were expected). Table 5.6 and

Figure 5.12 demonstrate the lack of significant correlation between the radius and rib, and the rib and femur. There is however, a moderate (r=0.561) and significant (p=0.029) positive relationship between lacunar densities in the femur and radius in males only. In females, and in the pooled sample, the femur and radius approach significance (p=0.072 for females and p=0.093 for pooled sample). These two elements are likely correlated because of the relative changes in loading with age that the rib does not undergo as its

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mechanical environment stays relatively consistent (ventilation occurs until death) despite age. Thus, the mechanically mediated support of osteocyte viability or promotion of apoptosis varies from site to site. Although lacunar density shows a decreasing trend at each site, the lack of strong correlations across the femur, radius and rib indicate that the systemic factors in osteocyte viability do not outweigh the mechanically mediated effects on the cellular population. Therefore, we can reject the null hypothesis for the radius to rib comparison, and the femur to rib comparison in males, females and the pooled sample. Although males show a significant relationship between the femur and radius, this relationship is not significant in females or in the pooled sample also refuting the null hypothesis. In order to fully investigate this further and clarify the relationship, other elements must be included.

As in the comparisons between individual elements, intracortical porosity was investigated systemically as indirect evidence of a systemic trend in lacunar density decrease with age as these two variables are a part of the same physiological process.

Unlike the seemingly lack of correlation between trends in lacunar density, intracortical porosity demonstrates significant relationships as shown in Table 5.7 and Figure5.13. All comparisons (femur to radius, radius to rib and femur to rib) demonstrate a positive and significant %Po.Ar relationship for males and the pooled sample. For females, as in the above tests for systemic trends in lacunar density, comparisons to the rib are the only non-significant relationships (again likely due to the endocortical issue previously discussed).

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Systemic correlations of intracortical porosity in conjunction with data reported for H1 and H2 (decrease in lacunar density with age at each element and correlations within elements between lacunar density and intracortical porosity), indicates there is indeed a systemic trend occurring within this sample. However, by broadly refuting the null hypothesis here, and the lack of clear relationships between all elements, it can be argued that variation in the mechanical environment experienced in each element is driving the variation in lacunar density changes.

6.9 Limitations

This study has several limitations, the first of which is sample size. Although the goal of this work was to establish a working baseline understanding of changes in cortical osteocyte lacunar density, only 30 individuals were included. These were sex matched

(15 males and 15 females) and age distributions were comparable. All PMHS were

Caucasian and representative of an older population. Exclusion criteria were limited to gross pathologies present on sections of interest. These circumstances were purposefully chosen to mimic a clinical population as well as representative of an archaeological population of unknown biocultural history. Results from this study should be cautiously applied to other populations or to other skeletal sites. In as much as general bone biology and physiological effects of age and sex produce similar trends in the skeletal system can these trends be applied to other studies. However, this study was designed to be a foundation on which future work can build.

Another limitation is the use of lacunae as a proxy for osteocytes themselves.

Although, as mentioned, lacunar density is frequently used to quantify osteocyte cell

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density (Hernandez et al. 2004; Qiu et al. 2002a; Vashishth et al. 2000) there is a temporal limitation inherent to this assumption. Empty lacunae following osteocyte apoptosis are expected to initiate a BMU and subsequently be removed (Noble and Reeve

2000) or fill in with mineral as in Frost’s described micropetrosis; however, with increasing age in humans, an increasing prevalence of empty lacunae have been found.

In the cortical rib, 95% of lacunae are filled during adolescence which reduces to about

70% at peak bone mass and does not substantially decrease thereafter (Frost 1963).

Lumbar spine lacunar vacancy did not increase with age in a human sample but did change by 30% in iliac cancellous bone from subjects aged 10-29 years to those 70-89 years old (Dunstan et al. 1990). Regardless of the conflicting evidence concerning empty lacunae which is likely site-specific, vacant lacunae should either be resorbed (evidence suggests within 10-14 days (Kennedy et al. 2012) or hypermineralize (Frost 1960c) (the timeline for this process is unknown). In fact, Qiu and colleagues (2002a) found a very strong positive correlation between lacunar density and occupied lacunae in human trabecular bone (r2=0.972) Thus, the static histomorphometry performed on these samples gives a snapshot of the lacunae present at time of death with likely a portion of them containing apoptotic osteocytes either in the process of initiating a remodeling event or hypermineralizing. Hypermineralization is a general feature of bone biology as age increases and cannot be avoided (Busse et al. 2010) nor detected in the bright field microscopy methods used here. However, although static histomorphometry without cellular staining cannot control for empty but present lacunae, hypermineralized lacunae would not be visible using this method. Additionally, if the temporal relationship of a

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vacated lacunae and initiation of BMU in healthy, younger individuals is within 2 weeks

(as mentioned above), then current evaluations of lacunar density are representative of the individual’s lifespan with some error in the last two weeks prior to death.

6.10 Future Work

This study sampled the entire cross section of each of the 89 elements included.

An advantage to this sampling strategy is that it encompasses the variation in osteocyte lacunar density for the whole cross-section. Bones function as a whole, therefore, capturing the “big picture” variation in this indicator of bone quality allows for further inquiry into spatial variation within the element. Several researchers have investigated the osteocyte density effects on osteon wall thickness (Metz et al. 2003; Power et al.

2012; Skedros et al. 2011) and presence and severity of microdamage (Colopy et al.

2004; Herman et al. 2010; Qiu et al. 2005; Reilly 2000; Verborgt et al. 2000). The data presented here should be compared to other histomorphometric parameters to add to the current body of knowledge especially in light of the novel systemic data presented between these three anatomical sites.

To fully assess the effect of osteocyte lacunar density on bone strength, a dynamic or quasi-static loading environment producing data on structural properties of the bone is necessary. This can be accomplished by applying loads to a whole bone or “coupon” section of a bone and measuring the required energy for fracture and then performing histological analysis of osteocyte lacunar density. The peak strain values within and between individuals for multiple elements provides a tangible measurement of bone strength in relation to osteocyte lacunar density that can be used to create an index of

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bone quality applicable to fracture risk. This represents one of many avenues that determining patterns of lacunar density and their direct effects on bone quality in a cadaveric population can be utilized as a tool for bioarchaeologists to assess the skeletal integrity in past populations. Thus, in addition to dynamically testing cadaveric bone and investigating lacunar densities, future work includes comparisons to archaeological populations (with decent preservation and permission for destructive analyses).

This particular method limits its usefulness for many bioarchaeological investigations as it is destructive to the bone as whole. However, using the remaining bone from this sample, I would like to scan regions of interest using synchrotron radiation computed tomography. By doing so on the same sample and testing comparable hypotheses, the results are two-fold: directly connecting the basic histomorphometric method that can sample the entire cortex to advanced imaging technology for comparable results, and investigating the potential segmenting techniques which would produce the most representative density value for the entire cortex (if possible) to reduce the amount of destruction necessary. Both of these products would be relevant to the clinical and bioarchaeological realm when advanced, non-destructive technologies are available (in the not so distant future).

Lastly, having argued that the lack of a strong systemic trend in osteocyte lacunar density indicates a site-specific mechanically mediated influence on osteocyte viability

(and density), using cross-sectional geometry, these relationships can be directly investigated. Carter and colleagues (2013) did so in the proximal femoral cortex of a 20 year old male (using synchrotron radiation CT) and found mechanically relevant areas of

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higher lacunar density around bending axes than neutral axes. This needs to be elucidated in a larger sample size and between different anatomical skeletal elements within individuals. Including experimental testing of cultured living osteocyte populations will directly link the gross manifestations in bone mass and distribution to the cellular activity in response to mechanical or systemic factors.

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Chapter 7: Conclusions

The purpose of this study was to quantify the variation in cortical bone osteocyte lacunar density. Though animal studies have illuminated the biology and centralized role of the cell in regulation and maintenance of bone, there is a lack of understanding of the basic variation throughout skeletal sites obfuscating the appropriateness of comparisons between current human studies. A baseline foundation of the variation in lacunar density, used as a proxy for the cell populations which inhabit them, as it relates to age, sex, and resulting intracortical porosity was reported here. There were no significant differences between males and females in lacunar density (Ot.Lc.N/B.Ar) or intracortical porosity

(%Po.Ar). Ot.Lc.N/B.Ar was found to decrease with age in males and females though with varying strengths of this relationship. %Po.Ar increased with age again with varying strengths. In other studies of limited scope, intracortical porosity, the product of increasing remodeling events and decreasing BMU balance, have correlated negatively with osteocyte lacunar density. Incorporating three skeletal sites that experience varying mechanical environments but that are under systemic influences, intracortical porosity was found to be negatively correlated with osteocyte lacunar density at each site. Lastly, this study is the first to investigate intra-individual variation in decreasing cell populations with age. The data presented here do not indicate a strong global rate of decrease in osteocytes with age in either sex despite the decreasing trends found when 162

considering elements individually. Intracortical porosity does show a systemic trend, indicating that other factors are mediating the rate of decline in Ot.Lc.N/B.Ar between these three anatomical sites. It is suggested here that the mechanical environment in conjunction with age related changes in the material properties of the bone may fulfill this role.

Osteocytes are the mechanism by which bone is able to adapt and respond to its environment on multiple levels. Localized control over remodeling functions to maintain the mechanical integrity of the bone as a whole. While the entire osteocyte syncytium in each bone is functioning to regulate its microenvironment, these effects combine to affect change at the tissue level. These are facilitated through influences on the viability or death of the cell itself. As has been discussed here through extensive literature review, the osteocyte can be touted as the “middle man” between environmental, behavioral and systemic factors and the bone. As clinicians are concerned with bone quality and preventing fracture risk, so too are bioarchaeologists investigating the health and quality of life in past populations. The ability of the osteocyte lacunocanalicular network to affect change in the cellular machinery of bone provides the explanatory link between mechanical and metabolic manifestations in bone. Thus, adding osteocyte lacunar density to the index used to reconstruct biocultural pathways can enhance interpretations and shine a light into the biological “black box.” As technology continues to advance, the resolution needed to non-invasively and non-destructively quantify lacunar density will be available for clinical and skeletal populations. Therefore, establishing variation across cortical sites provides a foundation on to which future research can build

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employing the major “orchestrator” of bone quality to inform assessments in past and present populations.

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Appendix A: Raw Data Per PMHS Per Element

180

Males:

6882 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 525736 687.451 538.375 78.315 38.600 499.776 7.170 976.523 1051.944 Radius 107517 156.021 110.187 70.623 4.866 105.321 4.417 975.768 1020.855 Rib 32160 82.342 29.759 36.141 1.396 28.363 4.692 1080.681 1133.885 Table A.1: 49 yr old male

6350 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 567091 676.067 514.347 76.079 43.426 470.921 8.443 1102.545 1204.216 Radius 127297 188.722 120.335 63.763 5.93608 114.399 4.933 1057.855 1112.757 Rib 28109 78.729 30.783 39.100 4.204 26.579 13.655 913.135 1057.546 181 Table A.2: 59 yr old male

6807 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 398153 544.032 431.430 79.302 45.986 385.443 10.659 922.869 1032.974 Radius 108416 148.150 101.190 68.302 7.818 93.372 7.726 1071.410 1161.122 Rib n/a n/a n/a n/a n/a n/a n/a n/a n/a Table A.3: 66 yr old male

6641 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 298540 580.178 368.502 63.515 54.753 313.749 14.858 810.145 951.526 Radius 97129 149.735 84.561 51.459 6.571 77.989 7.771 1148.626 1245.406 Rib 17081 60.225 20.634 34.262 1.991 18.643 9.648 827.808 916.208 Table A.4: 69 yr old male

181

6477 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 460167 696.639 416.384 59.770 40.107 376.277 9.632 1105.151 1222.948 Radius 97834 154.737 91.507 59.137 5.873 85.634 6.418 1069.142 1142.460 Rib 8283 44.462 8.692 19.549 1.254 7.438 14.424 952.945 1113.567 Table A.5: 71 yr old male

6446 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 445683 675.706 441.890 65.397 46.673 395.217 10.562 1008.583 1127.691 Radius 90295 124.074 91.093 73.418 8.902 82.191 9.772 991.240 1098.599 Rib 21003 68.256 26.750 39.191 3.496 23.254 13.070 785.159 903.208 Table A.6: 77 yr old male

6406 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 403282 682.308 459.438 67.336 70.525 388.913 15.350 877.773 1036.946

182 Radius 88984 125.247 85.929 68.608 14.398 71.531 16.756 1035.553 1243.997

Rib 19467 104.617 29.248 27.957 4.048 25.200 13.839 665.584 772.490 Table A.7: 79 yr old male

6542 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 370199 592.007 399.411 67.467 41.634 357.777 10.424 926.863 1034.721 Radius 89733 145.395 88.636 60.962 8.799 79.837 9.927 1012.376 1123.946 Rib 10283 82.069 15.755 19.197 1.530 14.225 9.713 652.682 722.893 Table A.8: 79 yr old male

6873 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 214414 618.051 428.568 69.342 70.174 358.394 16.374 500.304 598.263 Radius 50048 156.021 110.187 49.059 10.888 57.444 15.934 732.424 871.246 Rib 15150 72.459 18.865 26.035 3.042 15.823 16.123 803.074 957.446 Table A.9: 80 yr old male

182

6449 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 403482 488.190 404.2522 82.806 16.195 388.057 4.006 998.095 1039.749 Radius 67453 115.262 75.189 65.233 9.563 65.626 12.718 897.113 1027.832 Rib 18789 58.367 27.458 47.044 3.838 23.620 13.977 684.281 795.464 Table A.10: 83 yr old male

6450 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 340056 680.815 385.030 56.554 72.237 312.799 18.760 883.192 1087.140 Radius 100913 167.984 117.547 69.975 17.382 100.165 14.787 858.491 1007.465 Rib 17725 81.125 25.163 31.018 3.317 21.846 13.182 704.407 811.363 Table A.11: 83 yr old male

6460 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar

183 Femur 379850 741.205 467.607 63.087 72.533 395.074 15.511 812.328 961.466 Radius 109030 168.142 98.605 58.644 13.431 85.174 13.601 1105.725 1280.078 Rib 20562 97.460 28.067 28.798 4.274 23.793 15.229 732.604 864.220 Table A.12: 86 yr old male

6889 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 269582 647.770 398.979 61.592 72.328 326.650 18.128 675.680 825.292 Radius 8472 192.535 68.469 35.562 4.866 105.321 15.120 975.768 1020.855 Rib 14343 84.714 17.54 20.705 3.092 14.448 17.629 817.731 992.735 Table A.13: 92 yr old male

6602 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 296962 586.478 436.976 74.508 35.473 401.503 8.118 679.585 739.626 Radius 79427 118.304 93.729 79.227 10.971 82.758 11.705 847.411 959.753 Rib 16429 58.911 19.196 32.585 1.603 17.593 8.348 855.855 933.811 Table A.14: 94 yr old male

183

6527 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 254022 739.954 482.788 65.246 117.150 365.638 24.265 526.157 694.737 Radius 58912 137.792 81.537 59.174 20.718 60.819 25.409 722.519 968.637 Rib 16938 70.761 26.176 36.992 7.197 18.979 27.495 647.081 892.463 Table A.15: 100 yr old male

Females:

6448 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 434641 582.489 414.971 71.241 43.202 371.769 10.411 1047.400 1169.116 Radius 80955 122.435 64.119 52.370 1.920 62.199 2.995 1262.574 1301.556 Rib 10896 42.003 12.915 30.748 1.082 11.833 8.380 843.670 920.839 Table A.16: 51 yr old female

6319 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar

184 Femur 343819 453.932 343.617 75.698 25.214 318.403 7.338 1000.587 1079.823

Radius 66182 78.427 60.739 77.446 5.786 54.953 9.526 1089.613 1204.340 Rib 10310 39.157 13.639 34.832 3.004 10.635 22.027 755.921 969.467 Table A.17: 63 yr old female

6453 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 279193 494.731 294.476 59.522 41.566 252.910 14.115 948.102 1103.923 Radius 53625 91.255 51.041 55.932 5.295 45.746 10.374 1050.626 1172.239 Rib 13709 61.02 14.595 23.918 1.057 13.538 7.241 939.294 1012.614 Table A.18: 67 yr old female

6539 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 212276 454.211 310.334 68.324 27.695 282.639 8.924 684.025 751.050 Radius 52737 99.421 67.042 67.432 8.337 58.705 12.435 786.627 898.338 Rib 16182 43.485 17.984 41.357 2.404 15.580 13.369 899.800 1038.658 Table A.19: 68 yr old female

184

6633 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 308450 505.355 385.730 76.329 47.136 338.594 12.220 799.652 910.972 Radius 48252 100.628 51.782 51.459 6.693 45.089 12.926 931.830 1070.152 Rib 12159 43.942 16.517 37.588 3.163 13.354 19.149 736.151 910.512 Table A.20: 69 yr old female

6655 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 222665 463.314 301.560 65.088 31.019 270.541 10.286 738.377 823.037 Radius 53786 103.085 67.050 65.043 5.857 61.194 8.733 802.177 878.937 Rib 18460 49.196 24.244 49.280 2.375 21.869 9.795 761.426 844.106 Table A.21: 69 yr old female

6817 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar

185 Femur 312847 485.869 394.337 81.161 25.763 368.574 6.533 793.349 848.803

Radius 61566 90.351 68.869 76.224 6.019 62.850 8.740 893.958 979.576 Rib 15703 41.772 16.399 39.258 1.067 15.332 6.505 957.558 1024.182 Table A.22: 73 yr old female

6621 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 246855 403.426 304.425 75.460 32.477 271.948 10.668 810.890 907.7291 Radius 69203 102.631 69.595 67.811 4.992 64.603 7.173 994.367 1071.209 Rib 18910 40.297 18.889 46.874 2.030 16.859 10.747 1001.112 1121.661 Table A.23: 78 yr old female

6353 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 195708 483.833 309.326 63.932 70.696 238.631 22.855 632.691 820.129 Radius 61147 114.547 76.086 66.423 17.179 58.907 17.179 803.656 1038.032 Rib 11953 51.686 18.833 36.437 3.957 14.876 21.013 634.684 803.530 Table A.24: 79 yr old female

185

6531 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 169423 493.284 297.847 60.380 56.745 241.102 19.052 568.825 702.703 Radius 71273 100.406 60.383 60.139 3.717 56.666 6.156 1180.349 1257.781 Rib 11428 39.301 13.015 33.116 3.004 10.011 23.079 878.064 1141.509 Table A.25: 80 yr old female

6333 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 273812 479.284 299.636 52.517 30.764 268.872 10.267 913.817 1018.374 Radius 61961 87.909 54.330 57.657 3.645 50.685 6.708 1140.456 1222.465 Rib 10965 93.529 19.888 21.264 2.229 17.659 11.205 551.337 620.913 Table A.26: 81 yr old female

6610 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 216229 492.057 274.702 55.827 35.572 239.130 12.949 787.141 904.233

186 Radius 53967 107.379 67.965 63.295 13.924 54.042 13.924 794.041 998.621 Rib 10081 46.430 9.725 20.946 0.794 8.930 8.168 1036.607 1128.808

Table A.27: 86 yr old female

6611 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 149723 645.184 363.582 56.353 72.952 290.630 20.065 411.780 515.166 Radius 52715 123.350 52.513 42.572 6.937 45.576 13.209 1003.847 1156.636 Rib 9026 52.917 10.781 20.373 1.695 9.086 15.723 837.214 993.412 Table A.28: 86 yr old female

6501 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 248348 495.595 289.104 58.335 56.707 232.397 19.615 859.027 1068.638 Radius 37507 109.158 63.429 58.108 12.625 50.804 19.903 591.323 738.262 Rib 9965 38.849 14.420 37.118 2.408 12.012 16.699 691.045 829.583 Table A.29: 90 yr old female

186

6604 Ot.Lc.N Tt.Ar Ct.Ar %Ct.Ar Po.Ar B.Ar %Po.Ar Ot.Lc.N/Ct.Ar Ot.Lc.N/B.Ar Femur 266133 635.396 330.969 52.088 54.019 276.950 16.321 804.103 960.942 Radius 58574 126.340 76.830 60.812 16.843 59.987 21.922 762.384 976.437 Rib 24910 40.683 27.327 67.171 2.681 24.646 9.809 911.553 1010.697 Table A.30: 98 yr old female

187

Appendix B: Sex Specific Regression Trends

Only variables that demonstrate a visible trend are included in this appendix.

Figure B.1: Sex related trends in femoral osteocyte lacunar density versus age

188

Figure B.2: Sex related trends in femoral intracortical porosity versus age

Figure B.3: Sex related trends in radius osteocyte lacunar density versus age

189

Figure B.4: Sex related trends in radius intracortical porosity versus age

Figure B.5: Sex related trends in rib intracortical porosity versus age

190

Figure B.6: Sex related trends in femoral intracortical porosity versus osteocyte lacunar density

Figure B.7: Sex related trends in radius intracortical porosity versus osteocyte lacunar density

191

Figure: B.8: Sex related trends in rib intracortical porosity and osteocyte lacunar density

Figure B.9: Sex related systemic trends in osteocyte lacunar density between femur and radius

192

Figure B.10: Sex related systemic trends in intracortical porosity between femur and radius

Figure B.11: Sex related systemic trends in intracortical porosity between femur and rib

193

Figure B.12: Sex related systemic trends in intracortical porosity between radius and rib

194