Variation in Osteon Circularity and Its Impact on Estimating Age at Death

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Arts in the Graduate School of The Ohio State University

By

Jesse Roberto Goliath, B.A.

Graduate Program in Anthropology

The Ohio State University

2010

Thesis Committee:

Dr. Sam D. Stout, Advisor

Dr. Douglas E. Crews

Dr. Clark Spencer Larsen

Copyright by

Jesse Roberto Goliath

2010

Abstract

Researchers have implemented many histomorphometric techniques to estimate age at death for human skeletal remains. While previous studies have reported relations between osteon size and age, few studies have focused on the shape of secondary osteons.

Osteon circularity (On.Cr) is a factor potentially affecting histological estimations.

Additionally, with age the numbers of observable osteons and osteon fragments increase and an asymptotic value for osteon population density (OPD) is eventually achieved. The cortex of reaches asymptote by 60 years of age, but it can occur as early as age 50.

Once asymptote is reached, histological methods can no longer produce reliable age at

death estimations. The purpose of this study is to establish if circularity differs between young and old age groups, and whether observed On.Cr is due to the effects of increasing

OPD per unit area and osteon size (area; On.Ar) on the shape of osteons. To determine circularity, osteons were measured from thin (~100µm) cross-sections of femora and ribs of 29 individuals under and over the age of 50 from a modern cadaver sample of known age at death. The observed results support the observations of Currey (1964) and Britz et al. (2009) that osteon cross-sectional shape becomes more circular with age. With the increase in the number of osteons and their fragments per unit area (OPD) with age, the probability of eccentric and larger osteons surviving to be measured decreased considerably. This finding may be useful to help identify if a has reached its OPD asymptote, or even help refine our ability to estimate age for older individuals.

ii

Dedication

I dedicate this thesis to my family. Without their patience, understanding, support and most of all love, the completion of this work would have not been possible. I also want to

dedicate this to my Notre Dame family for encouraging me and keeping me grounded.

iii Acknowledgements

I would like to express my thanks to Dr. Sam Stout, my advisor, for all his direction and

guidance in this project. I acknowledge Dr. Douglas E. Crews and The Ohio State

Statistical Consulting Service of The Ohio State University for their assistance in the

statistical analysis of the research data. Additionally, special thanks go to Dr. Clark

Spencer Larsen for his suggestions and advice on earlier drafts of this thesis.

iv Vita

May 2003…………………………………...Charleston Catholic High School

2006…………………………………………Induction into the Lambda Alpha Honor Society

May 2007…………………………………...B.A., Anthropology Honors, University of Notre Dame

2008…………………...... The Ohio State University Graduate Enrichment Fellow

2009 to present……………………………..Graduate Teaching Associate, Department of Anthropology, The Ohio State University

Fields of Study

Major Field: Anthropology

v Table of Contents

Abstract…………………………………………………………………..…………..…..ii

Dedication………………………………………………………………………….….…iii

Acknowledgments………………………………………………..………………..…….iv

Vita………………………………………………………………………………………..v

List of Tables……………………………………………………………………………vii

List of Figures……………………………………………………………………….....viii

Chapter 1: Introduction…………………………………………………………………1

Chapter 2: Methodology…………………………………………………………………6

Chapter 3: Results………………………………………………………………………14

Chapter 4: Discussion…………………………………………………………………..21

Chapter 5: Conclusion………………………………………………………………….26

References……………………………………………………………………………….27

vi List of Tables

Table 1. Summary of sample data...... 7

Table 2. Descriptive Statistics for the sample data...... 12

Table 3. One-sample Kolmogorov-Smirnov Test for all histomorphometric parameters...... 13

Table 4. Summary of statistics from univariate analysis in relation to age...... 15

Table 5. Summary of statistics from univariate analysis in relation to sex…………19

Table 6. Summary of statistics from paired T-test and correlation...... 20

vii

List of Figures

Figure 1. Example of the point at which OPD asymptote is reached……………..…..4

Figure 2. Examples of intact and fragmentary osteons………………………………..9

Figure 3. Images of osteons using programs Spot Basic 3.5.9.1 and ImageJ……….11

Figure 4. Osteon Circularity (On.Cr) for the sample data…………………………..16

Figure 5. Osteon Population Density (OPD) for the sample data……….…………...17

Figure 6. Mean Osteonal Area (On.Ar) for the sample data………………………...18

viii

Chapter 1: Introduction

Several histological methods have been developed for age estimation of archaeological and forensic skeletal remains (Kerley, 1965; Kerley and Ubelaker, 1978;

Ahlqvist and Damsten, 1969; Thompson, 1980; Stout and Paine, 1992; Cho et al., 2002).

Because these methods rely upon well-established increases in the number of intact and fragmentary osteons within defined fields or per unit area with age, osteon size (On.Ar) and circularity (On.Cr) potentially affect histological age estimates. The dimensions of osteons have been reported to vary with age, notably an age-dependent decrease in osteon size in humans (Jowsey, 1968; Singh and Gunberg, 1970; Ortner, 1975; Evans, 1976;

Stout and Simmons, 1979; Martin et al., 1980; Thompson, 1980; Thompson and Galvin,

1983; Pfeiffer, 1998; Watanabe et al., 1998; Streeter and Stout, 2003). This decrease has been found in macaques as well (Burr, 1992; Havill, 2004). However, few studies have focused on the relationship between age and shape of osteons. Moreover, when aspects of

On.Cr are examined the results are frequently limited to qualitative associations in regards to strain mode (Skedros et al., 1994) and location with the cortex (Pfeiffer, 1998).

Some notable exceptions include Currey (1964) and Britz et al. (2009). Currey (1964) reported that the osteons of older individuals are nearly circular whereas younger individuals have more irregularly shaped osteons. Britz and colleagues (2009) found that circularity increased with age in the femur. Because osteons are histomorphological

1 products of bone remodeling, a brief review of the remodeling process will be given

before the present study is discussed.

Bone Remodeling

During skeletal development, the processes of growth, modeling (shape change)

and remodeling (turnover) work together to adapt bone for its typical peak biomechanical

demands. After the completion of longitudinal and radial growth, a bone’s potential for

significant modeling activity is greatly reduced. Bone remodeling, however, is

continuous throughout the life of the individual. Remodeling is considered to exist in two

basic forms. Systemic remodeling is stochastic and most likely serves a metabolic

function, e.g., mineral homeostasis. Many authors have suggested a second form of bone

remodeling that is primarily biomechanical in function and targeted to repair microdamage in bone (Parfitt 1983; Burr, 1993; Bentolila et al., 1998). The breakdown

and renewal of bone that occurs during remodeling aids in skeletal maintenance, and

helps alleviate mechanical stresses such as weight, posture, and physical activity (Wolff,

1892; Woo et al., 1981; Kumar et al., 2005). Bone is remodeled via a complex multicellular unit that tunnels longitudinally through cortical bone. This basic multicellular unit (BMU) consists of and . Osteoclasts resorb existing bone, leaving a tunnel-like cutting cone or resorptive bay behind them. At the edges of the resorptive bay, mononuclear cells lining the resorptive bay deposit a special

2 thin layer of matrix called a reversal (cement) line, which separates an osteon from the surrounding cortex. Osteoblasts then move in and begin to lay down new matrix in concentric lamellae, starting from the edges of the resorptive bay and moving to the inside of the tunnel, where a central canal is left (Frost, 1969; Parfitt, 1990; 1994). These canals are known as Haversian canals, and they house blood vessels. The entire structural unit of reversal line, lamellae, and formed by this process is known as an osteon or Haversian system (Cooper et al., 1966; Frost, 1969; Widmaier et al., 2001).

Based on its morphology, the osteon can be regarded as an independent bone unit because it contains its own cellular and blood supply systems.

Because bone remodeling begins at or before birth and continues until death, the number of secondary osteons increases per unit area with age (Stout and Gehlert, 1979).

However, as the numbers of observable osteons and osteon fragments increase with age, an asymptotic value for osteon population density (OPD) is eventually achieved (Fig. 1).

OPD is the sum of the observed intact and fragmentary osteons per unit area for a bone sample (Wu et al., 1970; Stout and Teitelbaum, 1976). The OPD asymptote is the number of osteons/mm2 at which new osteons begin to remove the evidence of preexisting ones

(Frost, 1987). The cortex of bones usually reaches asymptote by 60 years of age, but it can occur as early as age 50 (Wu et al., 1970; Frost, 1987). Robling and Stout (2000) point out that once asymptote is reached, histological methods can no longer produce accurate age estimations.

3

Figure 1. Image showing the point at which OPD asymptote is reached (Robling and Stout, 2000)

There are many factors affecting bone-remodeling rates. It is well established that bone-remodeling rate increases in both males and females in their seventh decade, and then declines during the next two decades (Martin et al., 2004). Heavy mechanical loading, for example can accelerate remodeling rates in certain bones and thus potentially yield estimates above actual age (Wolff, 1892; Woo et al., 1981, Kumar et al., 2005).

Additionally, remodeling rates can be altered through decreased levels of physical activity or decreased responsiveness to loading (Kohrt, 2001; Pearson and Lieberman,

2004). Diet may also be a factor affecting remodeling rates. Cao and colleagues (2010) suggest that obesity induced by a high fat diet increases that may dampen any positive effects of increased body weight on bone. Numerous pathological conditions can also affect remodeling rates and, in turn, age estimations. Diabetes tends to slow remodeling, and hyperparathyroidism tends to accelerate it (Robling and Stout, 2000).

These factors have received considerable attention in the literature, and a number of

4 researchers have attempted to account for pathological conditions in age estimations

(Ericksen, 1991; Robling and Stout, 2000; Paine and Brenton, 2006).

Hypothesis

Osteon circularity is a variable potentially affecting age at death estimation that is

still not well understood. This is one of the first studies quantitatively examining osteon

shape using a circularity index in both femoral and rib bone. The purpose of this study is to evaluate the impact osteon shape (circularity) has on age at death estimations. More specifically, I hypothesize that osteon circularity will increase with age in both femur and rib cross-sections, and that because OPD increases and osteon area decreases, smaller more circular osteons are more prevalent as age increases and OPD asymptote is reached.

This is due to the greater likelihood of smaller more circular osteons surviving intact for measurement.

5 Chapter 2: Methodology

Sample Material

The study sample includes 12 males and 17 females, ages 17-82 years (with a mean age of 60.4 years). The 29 individuals are a subset of a dissecting room cadaver collection obtained from Washington University, St. Louis, Missouri. The slides of bone cross-sections were previously prepared for another study comparing cortical bone remodeling rates among three archaeological populations and a modern autopsy sample

(Stout and Lueck, 1995). Undecalcified sections were dehydrated and embedded in methylmethacrylate. Transverse sections, approximately 200µm in thickness, were cut with a high-speed rotary saw, ground manually to a thickness of approximately

100 µm, and mounted on glass slides (Frost, 1958). Age at death, sex, and cause of death are known for the individuals (Table 1). The subset contained only individuals of

European ancestry. Each individual was represented by femur and rib sections. All rib samples were taken from the middle-third of the rib and all femur samples were taken from the mid-shaft of the femur. These two sampling sites were chosen because they represent bones of different size and biomechanical loading histories (Robling and Stout,

2000), both factors that affect bone remodeling rate, and therefore age at which OPD asymptote is reached.

6 Table 1. Summary of sample data Individual Bones Examined Age Sex Cause of Death 1 Femur, Rib 17 M Aspiration of gastric contents Seizure disorder 2 Femur, Rib 35 F Diabetes mellitus (Liver failure) 3 Femur, Rib 39 F Hemorrhage Metastatic Breast Cancer 4 Femur, Rib 47 F Suicide (Drug overdose) 5 Femur, Rib 52 M Carcinomatosis 6 Femur, Rib 53 M Pending 7 Femur, Rib 53 M Carcinomatosis 8 Femur, Rib 54 F Intracerebral hemorrhage 9 Femur, Rib 55 F Metastatic Colon Cancer 10 Femur, Rib 57 F Liver Failure 11 Femur, Rib 59 F Ovarian Cancer 12 Femur, Rib 59 F Breast Cancer 13 Femur, Rib 60 M Cardiac Arrest 14 Femur, Rib 60 F Bladder Cancer 15 Femur, Rib 61 F Cerebral Hemorrhage 16 Femur, Rib 62 F Cardiac Arrest 17 Femur, Rib 65 F Metastatic Breast Cancer 18 Femur, Rib 66 M Lung Cancer 19 Femur, Rib 66 F Carcinomatosis 20 Femur, Rib 67 M Heart Attack 21 Femur, Rib 68 M Pneumonia 22 Femur, Rib 68 M Chronic Congestive Heart Failure 23 Femur, Rib 70 M Lung Cancer 24 Femur, Rib 72 F Pending 25 Femur, Rib 72 F Metastic Breast Cancer 26 Femur, Rib 75 M Gastric Carcinoma 27 Femur, Rib 77 F Cerebrovascular Accident 28 Femur, Rib 81 F Cerebral Hemorrhage 29 Femur, Rib 82 M Ventricular Fibulation 7 Histomorphometric Variables

Intact and fragmentary osteons (Fig. 2) were defined and counted following a

point-count grid method in order to determine osteon population density (OPD,

osteons/mm2). Half or more of an osteon’s area had to fall within the counting field

(square grid) of the eyepiece reticule to be counted (Pirok et al., 1966; Wu et al., 1970;

Stout and Teitelbaum, 1976). Osteon population density is defined as the number of

intact and fragmentary osteons per unit area (Wu et al., 1970; Stout and Teitelbaum,

1976). An intact osteon is an osteon in which at least 90% of the Haversian canal

exhibits no evidence of remodeling by subsequent osteon generations. Fragmentary

osteons are those in which the evidence of formation or resorption is present in more than

10% of its Haversian canal (Cho et al., 2002). For determining osteon circularity (On.Cr,

unitless), only structurally complete intact osteons with round haversian canals were

measured. On.Cr was measured using circularity index (4*pi*area/square root of

perimeter) the shape factor that indicates to what extent a measured object is similar to a

true circle. One represents a true circle and values approaching zero represent

increasingly elongated shapes (Russ, 1990). Osteon area represented the total area

contained within the cement lines of structurally complete osteons for each specimen. For determining mean osteonal area (On.Ar, mm2) the average area of 30-35 osteons per

individual/per bone (femur/rib) was calculated. Histomorphometric analyses were

8 performed according to standard criteria (Parfitt et al., 1987) and all abbreviations are

according to the standard nomenclature described by Parfitt et al. (1987).

Figure 2. Intact and fragmentary osteons. Photomicrograph of an unstained nondecalcified transverse section of a tibia. Thin arrows point to the cement line of an intact osteon (i) that has partially eliminated an earlier formed osteon to create an osteon fragment (f). Open arrow indicates a Haversian canal. Heavy arrows point to the cement line of the osteon fragment. At a 10x magnification (Stout, 1982).

9 Image Analysis

Thirty to thirty-five osteons were measured for each of the femur and rib thin

sections. As osteon size tends to vary in different areas of the bone cortex, perhaps

relating to regional differences in strain levels (van Oers et al., 2008a, b), osteons were sampled from throughout the entire thin section to obtain representation from all regions.

The prepared slides of bone thin (~100µm) sections were all examined under transmitted light with an Olympus BX51 research microscope with integrated eyepiece grid to allow microscopic field delineation and perform area measurements (Kimmel and Jee, 1983). A camera mounted on the microscope captured polarized and semi-polarized images of the

slides and transmitted them to a computer. Using SPOT Basic 3.5.9.1 software

(Diagnostic Instrument Inc.) and ImageJ software platform (v 1.42; National Institutes of

Health), an outline of each osteon was manually drawn, from which the computer

calculated osteon circularity and area (Fig. 3). A drawing pen tablet (Intuos3, Wacom Co.

Ltd., Japan) was employed for manual outlining of osteon boundaries. Each individual

osteon was outlined separately and served the basis for the calculation of osteon

geometric properties. All osteons which had well defined boundaries were outlined. This

included osteons with ‘classic’ circular shapes and more ‘irregular’ elongated shapes.

Where the majority of an osteon outline (≥ 75%) was visible and the remainder could be

inferred, it was also included. This inclusive approach was taken because it avoided

10 subjective judgment regarding circularity. Calibration at 10x was established with a stage

micrometer. All histomorphometric variables are measured using a 10x magnification.

Figure 3. Spot Basic images from the cortex of the rib of a 17 yr old male at a magnification of 10x. The second image shows two examples of manually drawn outlines used to calculate area and circularity.

Statistics

Statistical analyses were performed using SPSS 17.0 (SPSS Inc., Chicago, IL,

USA). Mean values for On.Cr, On.Ar, and OPD were calculated for each individual and these were employed in subsequent analyses. Summary descriptive statistics for the

sample are provided in Table 2. Komolgorov-Smirnov 1-sample tests confirmed that the

11 collection of mean values was distributed normally for all histomorphometric variables

(Table 3). Univariate analysis of variance was conducted to evaluate the relationship

between osteon geometry and age (fixed factor). The same analysis was done using sex

as a factor. Additionally, a paired T-test and paired correlations were utilized to evaluate

relationships between femur osteon geometry and rib osteon geometry. For all analyses,

significance level was set at p < 0.05 and log-transformed age was used to improve the

linearity of the relationship with the histomorphometric variables.

Table 2. Descriptive Statistics for the sample data Descriptive Statistics

Std. N Minimum Maximum Mean Deviation Variance

Statistic Statistic Statistic Statistic Std. Error Statistic Statistic

Age (yrs) 29 17 82 60.41 2.583 13.912 193.537

Femur On.Cr 29 .8327 .9272 .902286 .0048094 .0258996 .001 (unitless)

Femur On.Ar 29 .0138 .0653 .034074 .0022828 .0122934 .000 (mm2)

Femur OPD 29 9.718 41.100 23.43614 1.173137 6.317534 39.911 (osteons/mm2)

Rib On.Cr 29 .857 .924 .90268 .003111 .016753 .000 (unitless)

Rib On.Ar (mm2) 29 .01230 .04255 .0248192 .00178163 .00959435 .000

Rib OPD 29 12.59 42.33 22.9106 1.16426 6.26971 39.309 (osteons/mm2)

Valid N 29 12

Table 3. One-sample Kolmogorov-Smirnov Test for all histomorphometric parameters One-Sample Kolmogorov-Smirnov Test

Femur Femur Femur On.Cr On.Ar OPD Rib On.Cr Rib On.Ar Rib OPD

N 29 29 29 29 29 29

Normal Parametersa Mean .902286 .034074 23.43614 .90268 .0248192 22.9106

Std. .0258996 .0122934 6.317534 .016753 .00959435 6.26971 Deviation

Most Extreme Absolute .223 .158 .091 .172 .155 .131

Differences Positive .168 .158 .091 .101 .155 .131

Negative -.223 -.063 -.067 -.172 -.096 -.074

Kolmogorov-Smirnov Z 1.201 .851 .492 .924 .837 .708

Asymp. Sig. (2-tailed) .112 .464 .969 .360 .486 .699

a. Test distribution is Normal.

13 Chapter 3: Results

As predicted, there is an age related increase in circularity in both femoral and rib

bones. In addition, osteon circularity was significantly related to age (p=0.000), as were

OPD (p=0.002) and On.Ar (p=0.056) (Table 4). This relation was positive for On.Cr and

OPD and negative for On.Ar. That is circularity and density increased with age while osteonal area decreased with age.

Scatter plots of the relationships among all variables with age (untransformed) are provided in Figures 4-6. As reported by Britz et al. (2009), this study found no significant

relationship between sex and circularity (p=.735). Moreover, there was no statistically

significant relationship between sex and On.Ar (p=0.142) nor between sex and OPD

(p=0.841) (Table 5).

A comparison between femur and rib osteon geometry found all three histomorphometric variables significantly correlated (p= < 0.05), and a paired T-test showed no significant difference in means for On.Cr (p=0.845) or OPD (p=0.631).

However, there was a significant difference for On.Ar (p=0.000), with the femur having larger osteons. Results of the paired T-test and correlations are provided in Table 6.

14 Table 4. Summary of statistics from univariate analysis in relation to age Tests of Between-Subjects Effects

Dependent Type III Sum of Source Variable Squares df Mean Square F Sig.

Corrected Model On.Cr .024a 22 .001 13.853 .000

On.Ar .004b 22 .000 1.813 .056

OPD 1433.672c 22 65.167 2.893 .002

Intercept On.Cr 42.935 1 42.935 547528.801 .000

On.Ar .047 1 .047 438.325 .000

OPD 26757.808 1 26757.808 1187.716 .000

LogAge On.Cr .024 22 .001 13.853 .000

On.Ar .004 22 .000 1.813 .056

OPD 1433.672 22 65.167 2.893 .002

Error On.Cr .003 35 7.842E-5

On.Ar .004 35 .000

OPD 788.508 35 22.529

Total On.Cr 47.266 58

On.Ar .058 58

OPD 33368.407 58

Corrected Total On.Cr .027 57

On.Ar .008 57

OPD 2222.180 57

a. R Squared = .897 (Adjusted R Squared = .832)

b. R Squared = .533 (Adjusted R Squared = .239)

c. R Squared = .645 (Adjusted R Squared = .422)

15

Figure 4. Osteon Circularity Index (unitless). As age increased, there was an increase in circularity index for both rib and femoral bones.

16

Figure 5. Osteon Population Density (osteons/mm2). As age increased, there was an increase in the osteon population density for both rib and femoral bones.

17

Figure 6. Osteonal Area (mm2). As age increased, there was a decrease in mean osteonal area for both rib and femoral bones.

18

Table 5. Summary of statistics from univariate analysis in relation to sex Tests of Between-Subjects Effects

Dependent Type III Sum of Source Variable Squares df Mean Square F Sig.

Corrected Model On.Cr 5.502E-5a 1 5.502E-5 .116 .735

On.Ar .000b 1 .000 2.220 .142

OPD 1.603c 1 1.603 .040 .841

Intercept On.Cr 45.818 1 45.818 96504.877 .000

On.Ar .050 1 .050 362.587 .000

OPD 30144.521 1 30144.521 760.205 .000

Sex On.Cr 5.502E-5 1 5.502E-5 .116 .735

On.Ar .000 1 .000 2.220 .142

OPD 1.603 1 1.603 .040 .841

Error On.Cr .027 56 .000

On.Ar .008 56 .000

OPD 2220.577 56 39.653

Total On.Cr 47.266 58

On.Ar .058 58

OPD 33368.407 58

Corrected Total On.Cr .027 57

On.Ar .008 57

OPD 2222.180 57 a. R Squared = .002 (Adjusted R Squared = -.016) b. R Squared = .038 (Adjusted R Squared = .021) c. R Squared = .001 (Adjusted R Squared = -.017)

19 Table 6. Summary of statistics from paired T-test and paired correlation Paired Samples Correlations

N Correlation Sig.

Pair 1 Femur On.Cr & Rib On.Cr 29 .961 .000

Pair 2 Femur On.Ar & Rib On.Ar 29 .557 .002

Pair 3 Femur OPD & Rib OPD 29 .572 .001

Paired Samples T-Test

Paired Differences Sig. (2-

Mean Std. Deviation Std. Error Mean t df tailed)

Pair 1 Femur On.Cr - Rib On.Cr -.0003969 .0108261 .0020104 -.197 28 .845

Pair 2 Femur On.Ar - Rib On.Ar .00925528 .01056866 .00196255 4.716 28 .000

Pair 3 Femur OPD - Rib OPD .525586 5.821087 1.080949 .486 28 .631

20 Chapter 4: Discussion

These results support the hypothesis that age is significantly associated with osteon shape in both rib and femoral bone. Observed results support the observations of

Currey (1964) and Britz et al. (2009) that osteon cross-sectional shape becomes more circular with age. Additionally, increase is continuous in this sample and continues beyond the point at which cortical bone reaches OPD asymptote. Further, the observed increase in circularity with age is consistent with a suggestion by Takahashi and colleagues (1965) that the decreasing osteon area with age is because larger osteons are more likely to be overlapped by subsequent remodeling events. Moreover, an increase in osteon population density in both femur and rib bone cross-sections with age is observed.

An increase in OPD per unit area with age, decreases the probability that eccentric and larger osteons survive to be measured. More secondary osteons appear with age, creating more and more osteon fragments. The largest osteons are therefore the most vulnerable to having part of their area removed by new osteons and are the least likely to survive intact as the individual ages (Robling and Stout, 2000).

Conversely, the osteonal area decreased with age in both rib and femoral bones sampled. As previously stated, a decrease in osteon size with age is well documented in the literature. In this study, there was a significant difference in rib and femoral osteonal area. Ribs have a relatively thin cortex, are less affected by physical activity than are load-bearing long bones, like the femur (Raab et al., 1991; Tomerrup et al., 1993). As

21 reported in this study, Pfeiffer (1998) found that the mean osteonal area of ribs was significantly smaller than the mean osteonal area of femora. Pfeiffer argues that osteon size is correlated with bone size, for example, smaller bones have smaller osteons.

The increase in OPD and a decrease in On.Ar with age appears to result in symmetrical, more circular shaped osteons. Additionally, it is important to consider the effect of mechanical loading, especially when dealing with a load-bearing bone, such as the femur. Compression or tensile loading increases cross-sectional circularity while torsion resistance forms osteon shapes that are less resistant to bending (e.g. ellipse)

(Alexander, 1968; Rothschild and Panza, 2007). Also, van Oers and colleagues (2008a,

2008b) found that smaller osteons were located in areas of higher strains. Furthermore, the presence of reversal lines permits them to act as 'crack stoppers'. Smaller, more circular osteons may improve the fatigue life of cortical bone as it relates to microdamage. As age increases, the density of microcrack damage increases in both males and females (Schaffler et al., 1995). Microcrack propagation will tend to follow cement lines and lamellar boundaries along the tensile side of a strained element (Martin and Burr, 1982). It has been suggested that the concentric organization of Haversian lamellae also acts to limit the progression of a microcrack (Currey, 1962; Saha and

Hayes, 1977; Martin and Burr, 1982; O’Brien et al., 2005; Gibson et al., 2006).

Extending fatigue life through Haversian remodeling is considerably more efficient metabolically than the alternative of dramatically increasing cross-sectional area (i.e.,

22 bone thickness) (Lipson and Katz 1984). In biomechanics, circularity implies optimum

resistance to "all strain-inducing modes" (Lovejoy et al., 1976: 505).

This and previous studies show that osteons in cortical bone vary in size, even

between two adjacent osteons (Landeros and Frost, 1964; Martin and Burr, 1989; Qiu et

al., 2003). The mechanism(s) for the formation of different sized osteons remains

unclear. It has been suggested that osteon size is determined by the quantity of bone removed by osteoclasts in one resportive tunnel (Landeros and Frost, 1964; Takahashi

and Frost, 1965). However, a change in osteon size may be considered a desirable adaptive response. For example, while Moyle and colleagues (1978) found no significant relation between osteon diameter and toughness in canine femoral bone, specimens that failed testing had significantly larger osteons. Additionally, a decrease in osteoclastic activity may contribute to this trend (van Oers et al., 2008a). Van Oers et al. (2008a,

2008b) propose that strain-induced signals inhibit activity and therefore affect osteon size. If there is a decreased responsiveness to loading (Kohrt,

2001; Pearson and Lieberman, 2004) with age, one might expect an increase in osteon size. However, Martin and colleagues (1980) have linked a decrease in osteon size to a decrease in osteoclastic activity, and Tappen (1977) observed that resorption events do

not always occupy all the space (e.g. highly mineralized older bone) available to them.

Finally, another potential benefit of decreased osteon size may be that, as bone density

23 increases, the introduction of smaller spaces reduces the size of temporary defects, which

might contribute to bone failure.

To eliminate the potential effects of ancestry on the results, I restricted the

examination to only individuals of European ancestry. There is evidence that ancestral

differences exist for bone mass and structure (Ericksen, 1979; Pollitzer and Anderson,

1989; Parfitt, 1997; Cho et al., 2002). In looking at the bone samples themselves there were some assumptions made. Since autopsies had been performed, it is assumed that the listed cause of death correctly reflects the state of health for each individual. It is also assumed that the accident victims represent typical bone turnover rates for their cohort.

Chronically ill individuals probably do not represent typical bone turnover rates, but they were retained in this sample to insure the broadest data set. This seemed appropriate as these findings are meant to be applied to unidentified modern skeletal remains or to archaeological skeletal specimens, which are not necessarily healthy populations. This data set is particularly well suited to forensic anthropology, since forensic cases would be from a population similar in age and health distribution to the sample individuals.

In examining the data, there were also some contributing factors that may have affected the results of this study. Osteon accumulation is limited by the fact that osteons soon begin to overlap one another as more and more are added (Martin et al., 2004). As osteons overlap each other it is more difficult to accurately measure circularity. An earlier generation of osteon may appear to be a fragment but may in fact be a circular intact

24 osteon that has been partially covered. The impact of menopause is another confounding factor. Older females in the sample were post-menopause. Bone loss associated with an imbalance in remodeling and cortical thinning (which increases with menopause) is accelerated in females (Kaptoge et al., 2003; Russo et al., 2006). With an increase in remodeling, younger menopausal females may have a higher circularity index at an earlier age. Finally, it is unclear whether the BMUs themselves actually form smaller osteons as age increases or whether the observed smaller osteons are merely a factor of osteon crowding with age. Further research should continue to address the study of osteon shape and its potential applications using circularity index as an additional variable to examine with current aging methods. For example, in this study, a circularity index of 0.89 and greater was a clear indication mark for identifying an individual over the age of 60.

25 Chapter 5: Conclusion

In summary, it was found that with age there is a decrease in osteon size and an increase in circularity and osteon density. Additional research is needed to better clarify the underlying cause of the link between age and osteon shape as well as changing osteon size with age. The present study suggests that the observed increase in osteon circularity with age can be explained by the effects of increasing osteon population density and/or decreased osteon size on the shape of osteons. With the increase in osteon population density (OPD) per unit area with age, the probability of eccentric and larger osteons surviving to be measured decreases. Those left to measure are smaller and more circular in shape. It is proposed that including On.Cr, along with other variables, including osteonal area and OPD, with other known aging methods may enhance our ability to estimate age at death for older individuals, especially after OPD asymptote is reached. A better understanding of how osteon shape and size changes with aging would be helpful in refining forensic and bioarchaeological techniques in which size and shape are a factor.

26 References

Ahlqvist J, Damsten O. 1969. A modification of Kerley’s method for the microscopic determination of age in human bone. J Forensic Sci 14: 205-212.

Alexander RM. 1968. Animal Mechanics. London: Sidgwick and London.

Bentolila V, Boyce TM, Fyhrie DP, Drumb R, Skerry TM, Schaffler MB. 1998. Intracortical remodeling in adult rat long bones after fatigue loading. Bone 23: 275-281.

Britz HM, Thomas CD, Clement JG, Cooper DM. 2009. The relation of femoral osteon geometry to age, sex, height, and weight. Bone 45: 77-83.

Burr DB. 1992. Estimated intracortical bone turnover in femur of growing macaques: implications for their use as models in skeletal pathology. Anat Rec 232: 180-189.

Burr DB. 1993. Remodeling and the repair of fatigue damage. Calcified Tissue Int 53: S75-S81.

Cao JJ, Sun L, Gao H. 2010. Diet-induced obesity alters bone remodeling leading to decreased femoral trabecular bone mass in mice. Ann N Y Acad Sci 1192: 292- 297.

Cho H, Stout SD, Madsen RW, Streeter MA. 2002. Population-specific histological age- estimating method: a model for known African-American and European- American skeletal remains. J Forensic Sci 47: 12-18.

Cooper RR, Milgram JW, Robinson RA. 1966. Morphology of the osteon. An electron microscope study. J Bone Joint Surg Am 48: 1239-1271.

Currey JD. 1962. Stress Concentrations in Bone. Quart J Microscopial Sci 103: 111- 133.

Currey JD. 1964. Some effects of ageing in human Haversian systems. J Anat 98: 69-75.

Ericksen MF. 1979. Aging changes in the of the proximal femur in American blacks and whites. Am J Phys Anthropol 51: 563-570.

27 Ericksen MF. 1991. Histologic Estimation of Age at Death Using the Anterior Cortex of the Femur. Am J Phys Anthropol 84: 171-179.

Evans FG. 1976. Age changes in the mechanical properties and histology of human compact bone. Yearb Phys Anthropol 20: 57-72.

Frost HM. 1958. Preparation of thin, undecaleified bone sections by a rapid manual method. Stain Technol 38: 272-276.

Frost HM. 1969. Tetracycline-based histological analysis of bone remodeling. Calc Tiss Res 3: 211-237.

Frost HM. 1987. Secondary Osteon Populations: An Algorithm for Determining Mean Bone Tissue Age. Yearb Phys Anthropol 30:221-238.

Gibson VA, Stover SM, Gibeling JC, Hazelwood SJ, Martin RB. 2006. Osteonal effects on elastic modulus and fatigue line in equine bone. J Biomech 39: 217-225.

Havill LM. 2004. Osteon Remolding Dynamics in Macaca mulatta: Normal Variation with Regard to Age, Sex, and Skeletal Maturity. Calcified Tissue Int 74: 95-102.

Jowsey J. 1968 Age and Species Differences in Bone. Cornell Vet 58:74-94.

Kaptoge S, Dalzell N, Loveridge N, Beck TJ, Khaw KT, Reeve J. 2003. Effects of gender, anthropometric variables, and aging on the evolution of hip strength in men and women aged over 65. Bone 32: 561-570.

Kerley ER. 1965. The Microscopic Determination of Age in Human Bone. Am J Phys Anthropol 23: 149-163.

Kerley ER, Ubelaker DH. 1978. Revisions in the Microscopic Method of Estimating Age at Death in Human Cortical Bone. Am J Phys Anthropol 49: 545-546.

Kimmel DB, Jee WSS. 1983. Measurements of area, perimeter, and distance: Details of data collection in bone histomorphometry. In Recker RR, editor. Bone Histomorphometry: Techniques and Interpretations.Boca Raton, FL: CRC Press, pp. 89-108.

Kohrt WM. 2001. Aging and the osteogenic response to mechanical loading. Int J Sport Nutr Exerc Metab 11: S137-142

28

Kumar V, Abbas AK, Fausto N. 2005. Robbins and Cotran’s Pathological Basis of Disease, 7th ed. W.B. Saunders Co.

Landeros O, Frost HM. 1964. The cross section size of the osteon. Henry Ford Hosp Med Bull 12: 517-525.

Lipson SF, Katz JL.1984. The Relationship Between Elastic Properties and Microstructure of Bovine Cortical Bone. J. Biomech 17: 231-240.

Lovejoy CO, Burstein AH, Heiple KG. 1976. The biomechanical analysis of bone strength. A method and its application to platycnemia. Am J Phys Anthropol 44: 489-506.

Martin RB, Burr DB. 1982. A Hypothetical Mechanism for the Stimulation of Osteonal Remodelling by Fatigue Damage. J Biomech 15: 137-139.

Martin RB, Burr DB. 1989. Structure, function, and adaptation of compact bone. New York: Raven Press.

Martin RB, Burr DB, Sharkey NA. 2004. Skeletal Tissue Mechanics. New York: Springer.

Martin RB, Pickett JC, Zinaich S. 1980. Studies of skeletal remodeling in aging men. Clin Orthop Relat Res 268-282.

Moyle DD, Welborn III JW, Cooke FW. 1978. Work to fracture of canine femoral bone. J Biomech 11: 435-440.

O’Brien FJ, Taylor D, Clive Lee T. 2005. The effect of bone microstructure on the initiation and growth of microcracks. J Orthop Res 23: 475-480.

Ortner DJ. 1975. Aging Effects on Osteon Remodeling. Calc Tiss Res 18: 27-36.

Paine RR, Brenton BP. 2006. Dietary Health Does Affect Histological Age Assessment: An Evaluation of the Stout and Paine (1992) Age Estimation Equation Using Secondary Osteons from the Rib. J Forensic Sci 51: 489-492.

29 Parfitt AM. 1983. The physiological and clinical significance of bone histomorphometric data. In: Recker RR, editor. Bone histomorphometry: techniques and interpretation. Boca Raton: CRC Press Inc.

Parfitt AM. 1990. Bone-forming cells in clinical conditions. In: Hall BK, editor. Bone: A Treatise, Vol. 1. Caldwell: Telford Press. pp. 351-429.

Parfitt AM. 1994. Osteonal and hemi-osteonal remodeling: The spatial and temporal framework for signal traffic in adult human bone. J Cell Biochem 55: 273-286.

Parfitt AM. 1997. Genetic effects on bone mass and turnover-relevance to black/white differences. J Amer Coll Nutr 16: 325-333.

Parfitt AM, Drezner MK, Glorieux FH, Kanis JA, Malluche H, Meunier PJ, Ott SM, Recker RR. 1987. Bone Histomorpometry: Standardization of Nomenclature, Symbols, and Units. J Bone Miner Res 2: 595-610.

Pearson OM, Lieberman DE. 2004. The Aging of Wolff’s Law Ontogeny and Responses to Mechanical Loading in Cortical Bone. Yearb Phys Anthropol 47: 63-99.

Pfeiffer S. 1998. Variability in Osteon Size in Recent Human Populations. Am J Phys Anthropol 106: 219-227.

Pirok, D. J., Ramser, J. R., Takahashi, H., Villanueva, A. R., Frost, HM. 1966. Normal histological, tetracycline and dynamic parameters in human mineralized bone sections. Henry Ford Hosp Med Bull 14: 195-218.

Pollitzer ME, Anderson JJB. 1989. Ethnic and genetic differences in bone mass. Am J Clin Nutr 50: 1244-1259.

Qiu S, Fyhrie DP, Palnitkar S, Rao DS. 2003. Histomorphometric Assessment of Haversian Canal and Osteocyte Lacunae in Different-Sized Osteons in Human Rib. Anat Rec 272A: 520-525.

Raab DM, Crenshaw TD, Kimmel DB, Smith EL. 1991. A histomorphometric study of cortical bone activity during increased weight bearing exercise. J Bone Miner Res 6: 741-749.

30

Robling AG, Stout SD. 2000. Histomorphometry of Human Cortical Bone: Applications to Age Estimation. In Katzenburg MA, Saunders SR, editors. Biological Anthropology of the . New York: Wiley-Liss, Inc. pp. 187-213.

Rothschild BM, Panza RK. 2007. Lack of bone stiffness/strength contribution to osteoarthritis-evidence for primary role of damage. Rheumatology 46: 246-249.

Russ JC. 1990. Computer Assisted Microscopy: The Measurement and Analysis of Images. New York: Plenum Press.

Russo CR, Lauretani F, Seeman E, Bartali B, Bandinelli S, Di Iorio A, et al. 2006. Structural adaptations to bone loss in aging men and women. Bone 38: 112-118.

Saha S, Hayes WC. 1977. Relations Between Tensile Impact Properties and Microstructure of Compact Bone. Calcif Tiss Res 24: 65-72.

Schaffler MB, Choi K, Milgrom C.1995. Aging and matrix microdamage accumulation in human compact bone. Bone 17: 521-525.

Singh IJ, Gunberg DL. 1970. Estimation of Age at Death in Human Males From Quantitative Histology of Bone Fragments. Am J Phys Anthropol 33: 373-381.

Skedros JG, Mason MW, Bloebaum RD. 1994. Differences in Osteonal Micromorphology Between Tensile and Compressive Cortices of a Bending Skeletal System: Indications of Potential Strain-Specific Differences in Bone Microstructure. Anat Rec 239: 405-413.

Stout SD. 1982. The effects of long-term immobilization on the histomorphology of human cortical bone. Calcified Tissue Int 34: 337-342.

Stout SD, Gehlert SJ. 1979. Histomorphological Identification of Individuals among Mixed Skeletons. Curr Anthropol 20: 803-805.

Stout SD, Lueck R. 1995. Bone remodeling rates and maturation in three archaeological skeletal populations. Am J Phys Anthropol 98: 161-171.

Stout SD, Paine RR. 1992. Histological Age Estimation Using Rib and Clavicle. Am J Phys Anthropol 87: 111-115.

31

Stout SD, Simmons DJ. 1979. Use of histology in ancient bone research. Yrbk Phys Anthropol 44: 263-270.

Stout SD, Teitelbaum SL. 1976. Histomorphometric determination of formation rates of archaeological bone. Calc Tiss Res 21: 163-169.

Streeter M, Stout SD. 2003. The histomorphometry of the subadult rib: age associated changes in bone mass and the creation of peak bone mass. In: Agarwal SC, Stout SD, editors. Bone Loss and Osteoporosis: An Anthropological Perspective. New York: Springer, pp. 91-101.

Takahashi H, Epker B, Frost HM. 1965. Relation between age and size of osteon in man. Henry Ford Hosp Med Bull 13: 25-31.

Takahashi H, Frost HM. 1965. The presence of frequency maxima in histograms of resportion space sizes in human rib cortex. Henry Ford Hosp Med Bull 13: 41-50.

Tappen NC. 1977. Three-dimensional studies on resorption spaces and developing osteons. Am J Anat 149: 301-317.

Thompson DD. 1980. Age Changes in Bone Mineralization, Cortical Thickness, and Haversian Canal Area. Calcified Tissue Int 31: 5-11.

Tommerup LJ, Raab DM, Crenshaw TD, Checovich MM, Smith EL. 1993. Does weight bearing exercise affect non-weight-bearing bone? J Bone Miner Res 8: 1053- 1058. van Oers RF, Ruimerman R, Tanck E, Hilbers PA, Huiskes R. 2008a A unified theory for osteonal and hemi-osteonal remodeling. Bone 42: 250-259. van Oers RF, Ruimerman R, van Rietbergen B, Hilbers PA, Huiskes R. 2008b. Relating osteon diameter to size. Bone 43: 476-482.

Widmaier EP, Raff H, Strang KT. 2001. Vanders, Sherman, and Luciano’s Human Physiology, 8th ed. Mcgraw-Hill.

Wolff J. 1892. The Law of Bone Remodeling. [Translation of Wolff’s Das Gesetz der Transformation der Knochen Maquet P and Furlong R]. Berlin: Springer.

32

Woo SL, Kuei SC, Amiel D, Gomez MA, Hayes WC, White FC, Akeson WH. 1981. The effect of prolonged physical training on the properties of : a study of Wolff’s law. J Bone Joint Surg Am 63: 780-787.

Wu K, Schubeck KE, Frost HM, Villanueva A. 1970. Haversian Bone Formation Rates Determined by a New Method in a Mastodon, and in Human Diabetes Mellitus and Osteoporosis. Calc Tiss Res 6: 204-219.

33