University of Tennessee, Knoxville
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Doctoral Dissertations
Graduate School
5-2005
Serial Long Bone Histology: Inter- and Intra-Bone
Age Estimation
Mariateresa Anne Tersigni
University of Tennessee - Knoxville
Recommended Citation
Tersigni, Mariateresa Anne, "Serial Long Bone Histology: Inter- and Intra-Bone Age Estimation. " PhD diss., University of Tennessee,
2005.
http://trace.tennessee.edu/utk_graddiss/2298
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To the Graduate Council:
I am submitting herewith a dissertation written by Mariateresa Anne Tersigni entitled "Serial Long Bone
Histology: Inter- and Intra-Bone Age Estimation." I have examined the final electronic copy of this
dissertation for form and content and recommend that it be accepted in partial fulfillment of the
requirements for the degree of Doctor of Philosophy, with a major in Anthropology.
Murray K. Marks, Major Professor
We have read this dissertation and recommend its acceptance:
William M. Bass, Walter E, Klippel, Darinka Mileusnic
Accepted for the Council:
Dixie L. Thompson
Vice Provost and Dean of the Graduate School
(Original signatures are on file with official student records.)
To the Graduate Council:
I am submitting herewith a dissertation written by Mariateresa Anne Tersigni entitled
“Serial Long Bone Histology: Inter- and Intra-Bone Age Estimation.” I have
examined the final electronic copy of this dissertation for form and content and
recommended that it be accepted in partial fulfillment of the requirements for the
degree of Doctor of Philosophy, with a major in Anthropology.
_Murray K. Marks____________
Major Professor
We have read this dissertation
and recommend its acceptance:
_William M. Bass______________
_Walter E, Klippel_____________
__Darinka Mileusnic___________
Accepted for the Council:
_____Anne Mayhew_______________
Vice Chancellor and Dean of Graduate Studies
(Original signatures are on file with official student records.)
SERIAL LONG BONE HISTOLOGY:
INTER- AND INTRA- BONE AGE ESTIMATION
A Dissertation
Presented for the
Doctor of Philosophy
Degree
The University of Tennessee, Knoxville
Mariateresa Anne Tersigni
May 2005
Dedication
This dissertation is dedicated to my grandmother, Benedetta Tersigni, whose passing was
a shock to all of us. Her love of life and undying devotion to family is the legacy that she
leaves for us. May her spirit live on in each of us. Thank you Grandma Betta for all that
you have taught me, thank you for your wise words and most of all thank you for
listening without judgment and for praying for me no matter how impossible my dreams
seemed. I am glad you were able to be at my defense after all. I love you and will miss
you immensely.
I would like to dedicate this culmination of my collegiate career to my parents: Dr.
Anthony R. Tersigni and Mrs. Flora P. Tersigni. How do I begin to express my deep
gratitude and undying love for these two people who have supported me throughout my
success and trials over this nine year journey and over the past 26 years of my life? By
your example, you have each taught me how to work hard and that I can accomplish
anything that I set my mind to. Thank you for allowing me the freedom to choose my
own road, no matter how scared you were for me. I did it, Mom and Dad! I am finally Dr.
Mariateresa Anne Tersigni, as I always told you I would be. I love you both and cannot
thank you enough for your support.
This work is also dedicated to my academic “Dads”, Dr. Norman J. Sauer and Dr. Murray
K. Marks. The two of you have seen me through this past nine years and pushed me to
achieve my goals. Even through my stubbornness and tears you helped me to strive for
the impossible. You knew what was best for me, and you made sure that I took that road
no matter how hard I kicked and screamed along the way. Your support in both the
academic arena as well as the emotional realm has been the source of strength and
courage for me through even my toughest times. Thank you for never giving up on me
and for believing that I had great things in store for my future. Thank you for helping me
realize my academic dream and by your example, strengthen my passion for teaching and
research. I am lucky to have been your student and honoured to call you both friends. I
love you both and I blame this whole mess on both of you. I could NEVER have done it
without you!
ii
Acknowledgements
I would like to begin by thanking Dr. Norman J. Sauer. It seems so long ago I
wrote you a letter asking to help you in your lab. I was so scared to talk with you, so
afraid to approach “THE” Dr. Norman J. Sauer. You allowed me into your lab and
brought me into the world of Physical Anthropology. And for that I am immensely
grateful! I will never be able to repay you for your continued support and superior
wisdom, but I will never stop trying to find a way to give you even half of what you have
given me.
I would like to thank the members of my dissertation committee who have helped
me down this winding road:
Dr. Murray Marks, my friend and co-chair. Thank you for all of your help during
this research and for being my biggest supporter even when others thought this research
was useless. You saw the opportunity and just did it. Thanks for instilling your spirit
within me. Thank you for every opportunity you have afforded me and thank you for
always making me work hard for those opportunities…you and I both know it somehow
means a lot more when you have to work your butt off to do it, not matter how little
everyone else has to do for a similar opportunity. Thanks for believing in me through it
all and for forcing me not to abandon my dreams. Nobody can EVER take this away from
me…THIS IS MINE! Mostly thanks for being one of the best friends that I have.
Dr. W. Emerson Klippel, my other co-chair, and the protector of my sanity. Each
day as I passed you in the hall I was greeted with a warm smile and treated to a joyful
laugh. I will never forget that day in your office before my prelims, when I cried my eyes
out and wanted to quit and you wouldn’t let me. Your constant support and ne’er a cross
iii
word has helped me through this struggle. Your enthusiasm for each of my crazy ideas
and your willingness to help, even when it required plumbing and electrical work will
never be forgotten. You are truly the kindest man I know. I will forever b in your debt.
Dr. William M. Bass, the one who started it all. Your steadfast excitement about
my research and your devotion to the advancement of this field has been one of the
beacons of light through this ordeal. You are one of the pillars of this Anthropology
department and I appreciate your willingness to allow me to learn from you both in the
classroom and in the lab.
Dr. Darinka Mileusnic: Thank you for your care and concern both in the academic
arena and outside of it. You have taught me so much about forensic pathology,
departmental politics and life in general.. I will miss our Saturday morning conversations
over an autopsy or two. Thank you for agreeing to help me with this process and thanks
so much for glowing smile each time I come to see you. You have helped me in more
ways than you realize.
Dr. Douglas H. Ubelaker, the man behind the scenes. Thank you for the
immesurable amount of time you spent responding to my emails and allowing me to visit
you at the Smithsonian with my countless questions. I appreciate your insight and
especially your fervent support of this project. Thank you for helping me to narrow my
focus into something manageable for ONE dissertation. Your kindness and
encouragement has been evident since my first presentation at AAFS 2001.
iv
The people behind the scenes:
I would like to thank all of my friends who have helped me throughout this long
and winding road. You constant support and willingness to give of yourself in my times
of need has helped me become what I am today. Each and every one of you have helped
me in ways I cannot express. I would like to take this opportunity to specifically thank a
few of those friends who have been the backbone of my support system.
Natalie Langley: Thank you for your forgiveness and your willingess to give me a
second chance. I will never forget your openness and honesty. You are definitely one of
the best things about my time in Knoxville. I truly value our friendship and I am looking
forward to a lifetime of academic collaboration, close friendship and of course the
occasional wardrobe critique. And to George, thank you for always treating me like a
princess and for introducing me to Grainger County, it brings peace to my soul every
time I visit. Thank you for welcoming me into your world. You’re kindness will never be
forgotten. I heard that!
Derinna Kopp and Andrea Rombauer: You have each helped me get through the
past five years. I cannot begin to tell you how much I appreciate each of you. You are
very special to me.
Franklin Damann (Foxtrot Echo Delta): Fronkers, thanks for your support and for
your friendship. Your kindness has been overwhelming. I wish you and Laurel the best of
luck and hope that your love burns eternally for one another.
Dr. Derek “Monkeyboy” Benedix: Brother, where do I start? If you iddn’t come
into my life I am not sure I could’ve made it through the last few years. You came back
to Knoxville to write your dissertation at exactly the right time. You have always been
v
there for me whenever I need you even when I am freaking out on a Sunday at 6am your
time. I love you ,friend, more than you may ever know and I count my self very lucky ot
have you as my good friend.
Dr. Michelle “Miiiiiisssseeeeerrrryyyyyy” Hamilton: Grazie per la
compartecipazione della vostra saggezza di come sopravvivere in questo reparto con me.
Thanks for enthusiasm and for introducing me to Strindberg and 909 and so much more.
Ti amo!
Heather Brewer: Brew, thanks for being a part of my life. I am so glad that I met
you in Italy and I am so glad that we trusted each other enough to become good friends. I
love you so much. Please remember: You will never find happiness with anyone else
unless you first find it within yourself. You are strong and beautiful on your own; you
don’t need anyone to be who you are inside.
Juliet Brophy: Jules- thanks for your friendship and love. Thank you for sharing
beers and tears with me. Thanks for always being there for me and for making sure I
never doubt myself. You are an amazing woman and a true friend. I hope you will always
believe that and understand that YOU are truly a prize! Love ya, babe.
T. James Smith: James, you and I are so much alike it is scary. Your friendship
and compassion has gotten me through this dissertation. Thanks for our sushi dates and
our Sunspot chat sessions. All I want is for you to find your happiness, and I hope that
you are on your road to that end. Thank you for understanding me no matter how hard i
try to hide my feelings. I am grateful our paths crossed in this life and I hope our
friendship continues throughout our lives. I love you.
vi
Lindsey Caldwell- Oh my dearest Ariel! You are the best girlfriend I have on this
earth. Who would have thought that we would have shared so many stories, tears, smiles
and adventures? I am so proud of the woman you are ( and that’s the gospel truth!) and I
know that you will continue to make me proud as you move toward achieving your goals.
Your friendship and unconditional love has helped me through so much. I’m just
throwing that out there do what you want with it! I appreciate your compassion and
concern and I know that we will be best friends until we are old ladies together. I can’t
wait to laugh about these years and how crazy we were from karaoke to 80’s night at
Michael’s and all the other crazy experiences we have shared. Sorry Lord, we didn’t
mean it. I got a fever and the only prescription is Caldwell (or cowbell!) There’s your
sign! I love you girl… eat your ‘tater salad!
To Paco and Peanut: You have shown me that “SOUL MATES” do exist and that
you never know where you will find them. I hope you eventually get that farm house with
the white picket fence and the rocking chairs on the porch. I hope your attic is full of old
handmade quilts and that your dock has a sailboat at the ready whenever you may wish to
drift away from this world for a while.
Thank Dr. Werner U. Spitz, for your unconditional support and for teaching more
in one summer than I have learned in any university course. You have truly inspired me
to pursue what I love. I will be forever indebted to you for sharing your expertise with
me. I will never forget what you have taught me.
I would like to thank brother and sister for their unconditional love and
unwavering belief in me. Andrea, I am so glad that we have become so close throughout
this journey. I am so lucky to have found in you one of my closest companions, my
vii
cheerleader and a lifelong friend. Thank you for understanding my decisions and for
standing firmly beside me through all of this. Your consideration and enthusiasm has
been a godsend. I love you! John, thanks for helping to keep me on this path. I will
never forget our long discussions and countless tears. Your friendship has helped me
through so much. Thank you for your loyalty and for making sure I know that I am not
the smartest in the Tersigni family. I love you! Andrew, thanks for always sticking up for
me and for taking such great care of my sister. You are a welcome addition to this family
and I look forward to calling you “brother”. Jenny, thank you for giving me a shoulder to
cry on during your time in Tennessee and thank you for treating me like your sister. Your
kind words have brought me through some very difficult times and for that I am grateful.
Mom: Thanks for the advice and for trying to understand what I am going through
especially during this past 5 years. Thank you for allowing me the opportunity to make
my own decisions and make my own mistakes. Thank you for listening and for your love
and prayers. I love you more and more everyday! Dad, how can I begin to thank you?
Your drive and determination to achieve your goals taught me how to succeed. I have
learned what is important in life from watching you. I am so glad that you have allowed
me to follow my dreams and my heart. Thank you for your emotional and financial
support. Thank you , Dad, for the best Sunday I have ever had. That day with you in
Arizona meant more than you know. I love you with all I have.
To my grandparents, Andrea and Benedetta Tersigni and Mario and Maria Pelino:
Thank you for your prayers and encouragement throughout my life. Your strong work
ethic and dedication to the family has helped me become the woman I am today. Thank
you for teaching me how to work hard and love deeply. Le amo!
viii
Thank you to all of my aunts, uncles and cousins, who are to numerous to
mention by name. I appreciate your support and prayers over the past nine years. I love
and appreciate you all very much. Thanks for always being interested in what I do. I am
very lucky to call you my family.
To Canis Familiaris Zinjanthropus (Zinj): You have been my one rock and my
one constant through all of this. Your kisses and playful nature help me realize what is
actually important in this world. You have taught me what unconditional love is all about.
I love you Brother Bear!
Thank you to Brandon L. Raile: How do I begin to thank you for your friendship
and your patience? Your love and support came at exactly the right time. Your calm and
gentle manner has brought me out of many a tizzie and your willingness to help
(especially with computer issues) has been overwhelming. Thank you for getting me over
my fear of photoshop, and thanks for being there as the rest of my fears slowly melt
away. Your patience and devotion is evident in everything you do. Thank you from the
bottom of my heart. I love you…Remember Sailor, Love Endures.
Finally, to Muzza: I cannot find the words to express what you mean to me. You
have brought me out of the abyss and have saved me from myself far too many times to
count. You have shown me what it is to be a true friend, no matter what obstacles stand in
the way. Thank you for being there academically and emotionally throughout this very
rough road. Thanks for making sure I never gave up on this dream or myself. I hope that
you know I will be there whenever you need me, no matter what, SAVVY?! You mean
the world to me and I don’t know what I would do without you in my life. You have
helped me to make a friend in knowledge and you have shown me that she will never
ix
leave me no matter how many of my other friends do. You have taught me so much,
including that knowledge will always provide me a safe harbor and for this I am in your
debt for eternity. I can only reminisce with some things that that have brought a smile to
my face even on the darkest of days: F and S, dearest Muzza, F and S! “Black as is my
need, bleeding as is my heart! The agony is intolerable… For You!” It is either genius or
madness…amazing how often the line between the two is blurred. It’s no good friend, no
good! I’m little! I don’t have any shoes on. Muzza, just know that when I find the world
cold, I will crawl under the patchwork and feel the needle’s binding. Thanks for giving
me something to find comfort and solace in for the rest of my life, you have no idea how
the mere idea of a quilt comforts me. Thanks for sharing your faded dreams, so that my
dreams may shine brightly!
x
Abstract
Few publications report on histological differentiation within and between
mammalian long bone cortices. Specifically, are osteon populations unique to proximal,
midshaft or distal segments? First, this study tests Kerley’s (1965) quantification
categories, i.e., number of osteons, fragments, and non-Haversian canals and percent
circumferential lamellae, to estimate age and second, quantifies those micro-structural
differences within and between human femur, tibia and fibula based upon section
location. In an identical attempt to understand within-bone differences, i.e., proximal
from distal, the same Kerley criteria were qualified and quantified within and between
humeral, radial and ulnar diaphyses. However, no age estimation via Kerley was made
on the pectoral limb bones, as the method never intended to assess age using these.
Using a Buehler Isomet 1000 saw, three thin sections were made from numerous
one-centimeter segments cut from each entire diaphysis. Sections were quantified
identical to Kerley’s quadrant (anterior, posterior, medical and lateral) using a Leica
DMRX light microscope at 16, 50 and 100 magnifications. After video-image capture,
morphometric analysis of utilized Image-Pro Express software with a Dell Optiplex
GX270 computer.
Quantification of these structures indicates statistically significant within-bone
differences, i.e., throughout the shaft, which result in significantly different age
estimations. The means for each quantification category were examined independently
across the four fields using repeated measures ANOVA at the .05 level. Regression
equations for age estimation using Kerley based on the femur, tibia and fibula reveal
widely inconsistent age estimations when compared to those derived from midshaft.
xi
Those findings generate two related conclusions: Metabolically and/or
mechanically, bone maintenance and remodeling varies greatly within the shaft. Second,
fragment location must be identified and existing Kerley and Ubelaker (1978) equations
be used only for midshaft. As this research demonstrates, moving proximal or distal from
midshaft will result in spurious age estimation particularly pertinent to fragmentary
remains. Furthermore, this research underscores that fact that ageing methods developed
from fragmentary bone must account for significant difference of microstructural
populations throughout the shaft as well as within one thin section.
xii
Table of Contents
Section
Page
Chapter 1: Introduction
1
Chapter 2: Bone Biology, Embryology and Histology
Embryonic Development of Bone
Bone Maintenance Cells
Bone Formation and Maintenance
Bone Mineral Composition
Types of Human Bone
Mature Bone
6
6
10
13
19
19
21
Chapter 3: Anthropological Uses of Bone Histology
26
Bioarchaeological and Paleopathological Uses of Bone Histology 33
The Future of Bone Histology in Anthropology
37
Chapter 4: Materials and Methods
38
Chapter 5: Results
49
Chapter 6: Discussion
77
Chapter 7: Conclusions
84
References Cited
87
Appendices
93
Appendix A: Osteometric Measurements of Individuals A, B and C
94
Appendix B: Raw Data for Kerley’s Four Quantification Categories
96
Vita
104
xiii
List of Tables
Table
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Factorial ANOVA summary for femur A
Post Hoc Summary for Percent Non-Haversian Bone
Repeated Measures ANOVA Summary for Osteon Count
Repeated Measures ANOVA Summary for Fragment Count
Repeated Measures ANOVA Summary for Percent
Non-Haversian Bone
Differences of Least Square Means for Posterior Radius
Differences of Least Square Means for Lateral Radius
Differences of Least Square Means for Anterior Ulna
Differences of Least Square Means for Lateral Femur
(Osteon Count)
Differences of Least Square Means for Lateral Tibia
Differences of Least Square Means for Ulna (Fragment Count)
Differences of Least Square Means for Fibula (Fragment Count)
Differences of Least Square Means for Humerus (Percent
Non-Haversian)
Differences of Least Square Means for Ulna (Percent
Non-Haversian)
Differences of Least Square Means for Fibula (Percent
Non-Haversian)
xiv
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List of Figures
Figure
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Human Haversian bone compared to deer plexiform bone
Somite alignment along neural tube
Zones of endochondral ossification
Osteoclasts in Howship’s lacunae
Osteoblasts laying down osteoid on bony trabecula
Osteocytes
Cement (reversal) line
Fragmentary osteon associated with secondary osteons
BMU tunneling through old bone
Plywood lamellar architecture
Human bone periosteum and endosteoum
Compact and cancellous bone
Schematic of human bone structure
Microscopic view of Haversian bone
Elements of each sample
Scoring of the bones for sectioning
Macroscopic cross sectional variation of the femur
Buehler Isomet 1000 thin sectioning saw slicing a cross section of fibula
Microscopic slide of sample A
Features measured in the Kerley method of histological age estimation
Capturing four microscopic fields
Superimposition of 2.06mm2 field size on captured image
Labeling of the microstructures within the field
Raw Osteon Counts for Femur "A" Across Kerley's Four Fields
Raw Fragment Counts for Femur "A" Across Kerley's Four Fields
Percentage of Non-Lamellar Bone for Femur "A" Across Kerley's Four
Fields
Raw Osteon Counts for Femur "A" Across Medial and Lateral Fields
Raw Fragment Number for Femur "A" Across Medial and Lateral Fields
Percent Non-Haversian Bone for Femur "A" Across Medial and Lateral
Fields
Age Estimates (Osteons) for Femur (A)
Age Estimates (Fragments) for Femur (A)
Age Estimates (% Non-Haversian) for Femur (A)
Age Estimates (Osteons) for Tibia (A)
Age Estimates (Fragments) for Tibia (A)
Age Estimates (% Non-Haversian) for Tibia (A)
Age Estimates (Osteons) for Fibula (A)
Age Estimates (Fragments) for Fibula (A)
Age Estimates (% Non-Haversian) for Fibula (A)
xv
2
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List of Figures (Continued)
Figure
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Page
Age Estimates (Osteons) for Femur (B)
Age Estimates (Fragments) for Femur (B)
Age Estimates (% Non-Haversian) for Femur (B)
Age Estimates (Osteons) for Tibia (B)
Age Estimates (Fragments) for Tibia (B)
Age Estimates (% Non-Haversian) for Tibia (B)
Age Estimates (Osteons) for Fibula (B)
Age Estimates (Fragments) for Fibula (B)
Age Estimates (% Non-Haversian) for Fibula (B)
Age Estimates (Osteons) for Femur (C)
Age Estimates (Fragments) for Femur (C)
Age Estimates (% Non-Haversian) For Femur (C)
Age Estimates (Osteons) for Tibia (C)
Age Estimates (Fragments) for Tibia (C)
Age Estimates (% Non-Haversian) for Tibia (C)
Age Estimates (Osteons) for Fibula (C)
Age Estimates (Fragments) for Fibula (C)
Age Estimates (% Non-Haversian) for Fibula (C)
Mean Age Predictions For Femur (A)
Average Age Estimations (Individual A)
Average Age Estimations (Individual B)
Average Age Estimations (Individual C)
xvi
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Chapter 1: Introduction
The frequency of fragmentary casework in forensic medicine is on the
increase. Bone fragmentation, depending upon size and location within the skeleton,
challenges the quintessential biological profile of age, sex, ancestry and stature, as the
morphological traits routinely examined to make these estimations are missing. Most
of the techniques that help to estimate the biological profile require mostly whole if
not entire bones of the skeleton. Thus, fragmentary remains prove difficult to identify
as human, not to mention estimating the biological age, sex, ancestry and stature from
those fragments.
Advances in microscopic techniques as well as an increased need for species
identification has led to advances in the study of bone histology. Using bone thin
sections, microscopy can aid in the determination of whether fragments are “human”
or “non-human.” In 1956, researchers where diagnosing bone at the genus and
species level based upon bone histomorphology (see Enlow and coworkers 1956a, b,
c), i.e., differentiation between reptile, mammal, and fish. Within the class
Mammalia, the histomorphological structure can be quite similar among genera. For
example, sheep and goat have similar plexiform, or parallel layered bone. Some
mammals do have distinct histomorphology; humans, for example, are one of the only
species within Mammalia to have full Haversian (osteonal) bone from the periosteal
(outer surface) to endosteal (inner surface), i.e., the full thickness of the cortical layer.
Many dogs can have osteonal bone in the center of the cortical layer while still
maintaining plexiform bone near the endeosteal and periosteal regions. Chimpanzees
1
also have full Haversian bone, though the osteon size and arrangement is quite
distinct from human. When examining fragmentary remains, it can be relatively
simple to recognize non-human from human if the section contains plexiform
organization (Figure 1). Plexiform bone is not found in developing or mature human
bone. And although plexiform is diagnostic of non-human bone, the presence of
Haversian bone does not necessarily confer “human.” Canines, mature bovids, Pan
and several other larger mammals develop some cortical Haversian bone, although
the osteonal arrangement differs from the arrangement in humans at 100x. In nonhuman mammals that possess Haversian bone, osteons tend to be arranged in bands
with 4-6 osteons arranged linearly (see below). As this phenomenon does not occur
in human Haversian bone, it can often allow for species identification.
Figure 1: Human Haversian bone compared to deer plexiform bone.
2
Once a “human” determination of bone has been made from fragments, what
else can be added to a “forensic” investigation? Kerley (1965) published a unique
and ingenious method using histomorphometric evaluation of human femoral
midshaft cross sections for assessing age at death. He discovered a positive
correlation between advancing age and subtle osteonal signatures. However
successful, the primary sanction preventing wholesale utilization of this technique is
that many times there are no macroscopic cortical landmarks to diagnose “femur.”
Hence, is the Kerley method developed from femora appropriate for other long
bones? In short, are the intra-(within) and even, inter- (between bones of the same
skeleton), histomorphometric signatures identical among all long bones in the body or
are there differences in osteon populations that must be considered to most accurately
estimate age? This has not been studied with respect to the major bones of the axial
skeleton. While the rib and clavicle have been successfully utilized (particularly in
Stout’s research designs) out of convenience at autopsy, there demands specific
criteria established for each long bone. Furthermore, Samson and Branigan (1987)
discovered that their histological/ gross morphological age method for fragmentary
remains was highly inaccurate for females, leading one to believe different cortical
regions (between bones) may be gender specific targets.
This study will qualify and quantify the degree of bone microstructural
differentiation, i.e., osteon number, fragmentary osteon number, percent
circumferential lamellar bone and number of non-Haversian canals (per view),
between (inter-element of one skeleton) and within (intra-element) the long bones of
the human skeleton. This is a study whose time has come again and again as every
3
researcher in bone histology has envisioned such a comprehensive study to “once and
for all” lay to rest by comparison and contrasting any inter and intra-skeletal
histomorphometric variation.
Specifically, this study will examine serial diaphyseal and metaphyseal
“shaft” sections from proximal to distal midshaft of each appendicular long bone of
the human skeleton. This study utilizes Kerley’ s (1965) four quantification
categories, i.e., non-Haversian canal number, osteon number, and osteon fragment
number, and percent circumferential lamellae, to estimate age as well as quantifying
the differential structure counts from human femur, tibia and fibula based upon
section location. Each long bone is evaluated metrically following the Chicago
Standards (after Buikstra and Ubelaker, 1994). From each midshaft one-centimeter
increments are marked proximally and distally, which results in twenty-five to forty
segments per bone. Three thin sections are then cut from each one-centimeter
segment using a high-concentration diamond blade mounted on a Buehler Isomet
1000 saw. This technique employs a modification of the methods individually
created by Stout and Ubelaker over their 25 years of bone histology methodology.
The Kerley quantification categories are used to enumerate the same
microstructures in the humerus, radius and ulna to identify structural change from
proximal to distal. Age estimation, via the Kerley method, was not created from
pectoral girdle elements, as the method was not intended for these bones. All sections
are subjected to quantification identical to Kerley’s quadrant (anterior, posterior,
medial and lateral) using a Leica DMRX light microscope at 5x, 10x and 20x power
magnification. Morphometric analysis of the Kerley’s quantification categories was
4
conducted using Image-Pro Express software on the Dell Optiplex GX270 (see
Chapter 4). Differentiation within and between bones was examined statistically.
Once the diagnostic subtleties of human bone microstructure are documented,
a sister project, will determine if various histological aging methods, i.e., Stout and
Paine (1992), Ericksen (1991), Singh and Gunberg (1970) and Thompson (1979), can
accurately estimate age regardless of bone. From this experimental design, a newly
revised aging method will eventually be developed to precisely age fragments
regardless of their point of origination in the skeleton and provide a baseline “gold
standard” to achieve standardization and foster more wholesale
utilization/collaboration. Additionally, in the future, the same method will be
systematically applied to histomorphometric features of plexiform bone in those nonhuman mammals, i.e., dog, deer, cow, pig and bear, most commonly mistaken by law
enforcement as human (and if small enough lacking diagnostic landmarks, confusing
to osteologists). Non-human species will be examined at various ages to discern the
initial appearance and developmental longevity of Haversian bone. Also, conversion
of non-human plexiform to Haversian bone creates diagnostic confusion when
attempting identification of fragmented remains. This study of non-human
histomorphometric signatures from serial sections may alleviate mis-identification.
5
Chapter 2: Bone Biology, Embryology and Histology
Bone is not a static organ within the body; it is always changing, always being
modified by both mechanical and metabolic processes. The study of bone histology or
microstructure has many far reaching uses in clinical, practical and research settings.
It is imperative, therefore, that those who study the skeleton are familiar with the
processes which form the bones that we study. Unless noted, the following account is
derived from a detailed discussions on skeletal embryogenesis by Bronner and
Farach-Carson, 2004; Carter and Beaupré, 2001; Karaplis, 2002; Marks, Jr. and
Odgren, 2002; and Martin et al, 1998.
Embryonic Development of Bone
Within the embryo there are differential cells that lead to the formation of the
skeleton. The neural crest cells contribute to the formation of the craniofacial
skeleton. Somites differentiate into sclerotomes to aid in the formation of the axial
skeleton, while the mesodermal cells originating from the lateral plate aid in the
formation of the appendicular skeleton. All three types of embryonic building blocks
contribute to skeletogenesis through the cellular movement to specific sites. The
interaction between epithelial and mesnechymal cells facilitate the condensation of
cells that further differentiate into osteoblastic and chondroblastic cells.
The axial skeleton forms through a process known as somitogenesis. This is
the process of segmentation of the paraxial mesoderm from the axial and lateral
6
mesoderm forming tissue on either side of the neural tube (Figure 2). The tissue
forms into somites. Somites are paired blocks of mesoderm that align along the neural
tube and the notochord. At twenty days post fertilization the first somites are noted
within the developing embryo and their numbers increase as the embryo develops. At
23 days after fertilization there are ten somites aligned along the neural tube and
notochord. This number jumps to twenty at 26 days post-fertalization and are
associated with upper limb bud formation. Within the next two days of gestation, the
somite population is elevated to thirty and is associated with the condensation of
mesenchymal matter into a rudimentary skeleton and the appearance of lower limb
buds. Schlerotome formation, directed by the notochord, is fueled by a secretory
hormone called Shh (Sonic hedgehog) which initiates formation of these cells.
Figure 2: Somite alignment along neural tube.
Adapted from Larsen (1997).
7
The specific biochemical pathways that causes mesenchymal stem cells to become
osteoprogenitor cells and to further differentiate into osteoblastic cells has not yet
been defined (Aubin et al, 1993).
The axial and appendicular skeleton arises from a cartilaginous framework in
which cartilage grows rapidly through the division of chondrocytes. Growth of bone
within the body occurs in two distinct methods: Intramebranous ossification and
endochondral ossification. The intramembranous method of bone development occurs
within the flat bones of the body, including the bones of the cranial vault, the facial
bones and portions of the clavicle, scapula, sternum and pelvis. During this process
mesechymal cells directly differentiate into osteoblasts which begin to secrete bone
matrix (osteoid) within a membrane covering.
Growth of long bones occurs as a result of endochondral ossification. The
endochondral ossification of long bones at the growth plate is usually split up into a
series of either four or five zones (Figure 3). For this discussion I will be referring to
the four zones termed reserve (resting), proliferative, hypertrophic ,and provisional
mineralization. The reserve (resting) zone is the most distal zone from the ossification
center. It is not actually resting, in fact, it is active in the formation of both length and
width of the bone. In this zone, stem cells (mesenchymal cells) are differentiated into
full chondrocytes. In the proliferative zone these chondrocytes divide repeatedly and
arrange into columns. The cells themselves becoming disk like and produce type II
collagen and proteoglycans. In the hypertrophic zone, the chondrocytes mature (stop
proliferating) and accumulate glycogen within their cytoplasm, which causes them to
secrete their matrix. The chondrocytes are in the early stages of apoptosis
8
Figure 3: Zones of endochondral ossification.
Adapted from Warwick and Williams (1973).
9
(programmed cell death) in this stage. The cartilage is prepared by these chondrocytes
for mineralization. In the zone of provisional mineralization the degenerating
chondrocytes continue to hypertrophy as they reach the metaphysis and die (apoptotic
death). The surrounding cartilage is then calcified.
This process near the metaphysis accounts for the formation of trabeculae at
the metaphysis. As each chondrocyte hypertrophies, it dissolves the cartilage around
it. The large tunnels left by the dead chondrocytes allow invasion by blood vessels.
This creates narrow bars of bone between the tunnels. Blood vessels invade the
tunnels and are separated by trabecular calcified cartilage. Osteoblasts lay down bone
on these trabeculae. Over time the cartilaginous cores of these primary spongiosa are
replaced by bone. These structures are then called secondary spongiosa. Eventually,
most of the trabeculae in the center of the metaphysis are resorbed for the formation
of the medullary cavity.
Bone Maintenance Cells
There are several different types of bone maintenance cells. These include the
osteoprogenitor cell (which eventually give rise to osteoblasts), osteoclast, osteocyte,
and bone lining cell.
Osteoclasts are bone dissolving cells formed by the fusion of monocytes in the
red marrow. This fusion is initiated by parathyroid hormone release when the serum
calcium concentration in the blood is low. The fusion of the monocytes forms this
10
multinuclear and functionally polarized large cell. Osteoclasts resorb bone by using
its ruffled acidic border (ruffled area gives more surface area) to erode away bone. It
first demineralizes bone away with acids and then dissolves the collagen with
enzymes. Osteoclasts can move quickly and erode as much as tens of micrometers of
bone away per day. Osteoclasts live in resorptive bays also called Howship’s lacunae
(Figure 4).
Osteoblasts are mono-nucleic cells that are formed from differentiated
mesenchymal osteoprogenitor cells (Figure 5). The formation is a 2-3 day process
triggered by mechanical or metabolic stress, i.e., serum calcium concentration too
high triggering calcitonin initiation of mesenchymal cell differentiation. Osteoblasts
produce osteoid, which is the organic portion of bone matrix and are responsible for
laying down new bone. Osteoid is comprised of collagen, non-collagenous proteins,
proteoglycans and water, making it a gel-like substance when deposited. The water is
replaced by minerals (hydroxyapatite) as the osteoid mineralizes. Osteoblasts can lay
down osteoid at a rate of 1µm per day during skeletogenesis and fracture repair.
Figure 4: Osteoclasts in Howship’s lacunae.
Adapted from Nanci (2003).
11
Figure 5: Osteoblasts laying down osteoid on a bony trabecula.
Adapted from Yang and Damron 2002.
Figure 6: Osteocytes.
This image exhibits osteocytes within lacunae (left) and
an osteocyte with it’s projections stretching into canaliculi
(right). Adapted from Nanci (2003).
Osteocytes are actually osteoblasts that become surrounded by bone matrix
that the osteoblasts secrete. Their function is then converted to one of bone
maintenance and communication. They reside in pores called lacunae and
communicate with other osteocytes through tentacle-like projections which are
housed in canaliculi (little canals) (Figure 6). The projections communicate through
ion transfer at gap junctions. That is, the tentacles come close enough to each other to
pass ions and small nutrients to one another through and positive/ negative flow. In
this manner, the endosteum is connected to the periosteum and the proximal end of
the bone is connected to the distal end allowing for communication (biochemical
reciprocity) throughout the bone.
12
Bone lining cells are also converted osteoblasts, although they live on the
bone surface (they do not become embedded in the osteoid). These are the cells which
begin bone remodeling in response to mechanical stress (fracturing).
Bone Formation and Maintenance
Formation and maintenance of bone occurs by the processes of modeling and
remodeling during growth and development. Modeling is the term used to describe
the shaping of bones by osteoblastic and osteoclastic activity in different areas. In
modeling, osteoblasts and osteoclasts work independently and change the bone size
and shape. This process is continuous until maturity, then the rate decreases
significantly.
Modeling is necessary because longitudinal growth does not produce bone of
the correct size and shape. Modeling can be resorptive or formative. For example,
modeling at the metaphyses reduces the bone diameter in order to create a diaphysis.
Osteoclasts work on the periosteal surface of the metaphysis to trim the shaft down to
size. Modeling can also occur at the diaphysis to increase diameter or change the
curvature. Long bone shafts increase in diameter by the slow addition of bone to the
periosteal surface by osteoblasts while osteoclasts remove bone on the endosteal
surface.
Modeling also occurs on flat bones, although it is not as easy as it may appear.
For example, in the growth of the cranial vault, bone cannot just be added at the
suture because this would not alter the shape of the vault bones to accommodate the
13
growing brain. Instead the inner surface of the vault bones are resorbed while the
outer surface has bone laid down on it in the appropriate manner to account for
change in size and shape.
The remodeling process can occur in both types of bone in response to
stressors such as tension, applied load, hormonal changes and the amount of activity
or inactivity that a bone undergoes (Bergman et al., 1996; Eroschenko, 1993).
Remodeling refers to the repair of bone at a particular site using BMUs (Basic
Multicellular Units). Harold Frost (1969) first coined the term BMU and was one of
the first to describe the work of osteoclasts and osteoblasts as a combined effort,
rather than independently resorbing and laying down bone. BMUs are comprised of
both osteoblasts and osteoclasts working together. In remodeling, there is no change
of size or shape of the bone, simply maintenance of the current structure. Remodeling
occurs throughout life, although the rate is reduced after growth ceases. It is not a
continuous process but episodic with each episode having a definite beginning and
ending.
Remodeling systematically removes older bone and replaces it with newly
formed bone. This eliminates damage to old bone and prevents fatigue fractures
which can occur in old bone through mechanical stress. This occurs throughout life.
The deposition and resorption of bone is a constant process regulated by a number of
systems within the body revolving around the level of calcium in the blood. Since the
majority of the body’s calcium is stored within the skeleton, there is very little
calcium in the blood itself. This blood calcium aids in the communication between
cells, the contraction of muscle cells within the body as well as the activity of nerve
14
cells. Even slight changes in the blood calcium levels of the body could cause a
disruption in any or all of these functions. Therefore, if the blood calcium levels are
high, hormones are released that stimulate osteoblast activity and inhibit osteoclast
activity. When the blood calcium levels are low, osteoclast activity is stimulated and
osteoblast activity is inhibited (Schinke and Karsenty, 2002). This constant
resorption and osteogenesis process leads to a distinct microscopic signature. When
looking at a section of human bone, one can recognize Haversian systems (osteons) as
concentric circles surrounding an inner circle which would have held a blood vessel
or neurovascular bundle. Osteocytes, osteoblasts and osteoclasts can often be
identified in the section. Within some sections, especially those from older
individuals, it is quite common to see a newer (younger) Haversian system on top of
another. The newer Haversian system is termed a secondary osteon (Figure 7).
Figure 7: Cement (reversal) line.
Digital image (200x) of a cement line around an osteon (arrow) in the femur
midshaft demarcating the area in which the osteoclasts completed breaking down
the bone and where osteoblasts began rebuilding the bone.
15
A clear reversal line distinguishes where the bone resorption ended and the bone
building began. Secondary osteons tend to increase with age. As secondary osteons
are laid down over older primary osteons, there tends to be an increase in fragmentary
primary osteons (Figure 8) (Bergman et al., 1996; Stout, 1992).
Remodeling determines the structure of most of the skeletal tissues, as well as
their mechanical properties and resistance to fatigue or failure. BMUs are responsible
for remodeling. The Basic Multicellular Units consists of about 10 osteocytes and
several hundred osteoblasts. Frost (1969) indicates that the BMUs operate on the
periosteal, endosteal and trabecular surfaces and within the cortical bone. There are
three stages in the lifetime of a BMU: activation, resorption and formation. During
activation, chemical and/or mechanical signals cause the fusion of monocytes to form
osteoclasts. The resorption process is just that: resorption by osteoclasts. Once
Figure 8: Fragmentary osteon associated with secondary osteons.
A digital image (200x) of an osteon fragment (outlined) associated with
an intact secondary osteon (arrow).
16
osteoclasts have resorbed a certain amount of bone, the formation stage begins with
the differentiation of mesenchymal cells into osteoblasts over the next few days. The
newly formed osteoblasts begin replacing the resorbed bone. The process of
formation is much slower than resorption. An osteoclast can resorb tens of
micrometers per day while an osteoblast can only form 1µm of bone per day. The
resorption process takes about three weeks while the formation process in the same
area takes about 3 months.
The BMUs tunnel through compact bone longitudinally creating a secondary
osteon (Figure 9). The tunnel cannot be completely filled in by osteoblasts because
the blood supply needs to be maintained throughout the bone, thus the osteoblasts
leave a new Haversian canal which houses both a return vessel and a supply vessel.
BMUs replace 5% of bone per year on average in adults.
Figure 9: BMU tunneling through old bone.
Adapted from Kessel (1998).
17
Trabecular bone is remodeled in the same way as cortical bone, but the BMUs
work on each of the trabeculae by scooping bone out and then refilling it, much like
the digging of a trench.
One differential form of remodeling is termed the Regional Accelerating
Phenomenon (RAP) (Frost 1983). This is a sudden increase in the rate of remodeling
due to traumatic injury. This can cause a temporary osteoporosis, due to the lag time
between osteoclastic activity and osteoblastic activity. This is resolved within four
months as the osteoblasts eventually catch up to the osteoclasts.
This is all well and good but what does it mean to the lay anthropologist who
just wants to look at osteons and tell a bit about them. Here are the six stages of an
osteon’s lifecycle that summarizes all of these processes as described by Martin et al
(1998):
1. Activation
Differentiated cells are collected from a precursor cell population and are
moved to the area in which the new osteon is to be formed.
2. Resorption
Osteoclasts resorb bone by moving longitudinally through the bone at a rate of 40µm/day. The
BMU has a diameter of 200µm and a resorption area of a few hundred µm long.
3. Reversal
Transition from osteoclast activity to osteoblast activity takes several days and the reversal
area is the lag space between the resorptive and refilling regions. The reversal line will
eventually form the cement line. Process takes about 30 days.
4. Formation
Osteoblasts appear at the periphery of the newly formed tunnel. They close approximately 12µm per day. They leave a central passage (Haversian canal) for blood vessels to maintain the
BMU, bone matrix and to carry calcium and phosphorus back and forth. This phase takes 3
months.
5. Mineralization
In this phase the osteoid is mineralized. There is a lag time of 10 days between the deposition
of osteoid and the mineralization. Thus there is an osteoid front between the osteoblasts and
the mineralized bone. Primary mineralization occurs within the first few days after
mineralization starts. Secondary mineralization occurs as more minerals are added over the
next several months.
6. Quiescence
After the BMUs have completed their process of remodeling the osteoclasts disappear and the
osteoblasts become osteocytes and maintain the bone.
18
Bone Mineral Composition
Bone is comprised of collagen, minerals, ground substance, non-collagenous
proteins and water. The collagen is a structural protein. The mineral component is
mostly hydroxyapatite <Ca10(PO4)6(OH2)>. The ground substance in comprised of
proteoglycans, and the non-collagenous proteins include osteocalcin that is secreted
by osteoblasts and aids in bone mineralization.
Types of Human Bone
There are two types of bone formed and maintained throughout the life.
Young bone is woven and adult, mature bone is lamellar. Woven bone is immature
and is atypical in the body after the age of five except when bone formation occurs as
a result of fracture or surgery. Woven bone is characteristic of the embryonic
skeleton where it develops from calcified cartilage during the process of
endochondral ossification (as described). It is very flexible but it can be easily
deformed as the collagen fibers and minerals are arranged in a haphazard formation.
This type of bone is fairly weak when compared structurally and biochemically to its
“mature” lamellar counterpart. Thus, woven bone is eventually replaced by more
rigid lamellar bone. Lamellar bone forms more slowly than woven. It is highly
organized and contains concentric lamellar layers. There are two types of lamellar
architecture: 1) the classic lamellar structure and 2) heliocoidal structure (Figure 10).
19
Figure 10: Plywood lamellar architecture.
Digital image depicting plywood orientation of lamellar bone
(Adapted from Photographic Atlas of the Body -2004)
The classic lamellar structure has lamella that are built up on layers of collagen and
laid down in one orientation. The next layer is put down at a right angle to the first
layer. The heliocoidal lamellae are laid down collagen fibers in different direction.
This lamellar formation is actually not individual lamellae but it can still show
lamellar structure.
Two layers frame the microscopic structure of bone: periosteum and
endosteum (Figure 11). The periosteum is an external fibrocellular sheath that
contains collagen fibers and fibroblasts, which, in turn, forms an inner
osteogenic/nourishing layer on top of the compact surface. The endosteum lines the
inner marrow cavity surface as well as all Haversian and Volkmann’s canals. It
consists of osteogenic cells and thin reticular fibers (Bergman et al., 1996).
20
Periosteal
Endosteal
Endosteal
Periosteal
Figure 11: Human bone periosteum and endosteum.
Macroscopic view of a human femur sectioned at midshaft showing the periosteal surface and
endosteal surface (left). The microscopic view of human rib (100 X) showing the periosteal
and endosteal surfaces (right).
Mature Bone
Adult bone consists of two forms of bone: trabecular and compact bone
(Figure 12). Trabecular bone, also known as cancellous or spongy bone, is the
internal porous bone found in irregular bones, flat bones, and the articular ends of
long bones. Cancellous bone is the latticework of bony spicules or trabeculae that are
within the shaft and the epiphyses (Ross et al., 1989). The bony spicules or
trabeculae are beams of bone that form inner scaffolding. In life, the pores within
trabecular bone are connected and filled with marrow. The bone matrix is laid down
in the form of bony spicules (trabeculae) that tend to be <200µm thick. The
arrangement of these trabeculae is irregular although it is often in an orthogonal
formation.
21
Figure 12: Compact and cancellous bone.
A 35mm photograph of a proximal humerus sagittally sectioned.
Compact bone, on the other hand, also known as cortical bone, is the dense bone
found in shafts of long bones and forms the outer cortex that covers trabecular bone.
The micro-organization of this bone consists of many small formations that allow the
inside (endosteum) of the bone to communicate with the outside of the bone
(periosteum) (Figure 13). These include Haversian canals, Volkmann’s canals, and
resorptive bays. Haversian canals are narrow trenches aligned on the long axis of the
bone and contain nerves and capillaries that allow nutrients to move throughout the
entire bone matrix (Figure 14). Volkmann’s canals are short transverse canals
connecting Haversian canals to one another and allowing communication to the outer
bone surface through blood vessels. Resorption bays (also known as Howship’s
lacunae) are scoured out areas in which osteoclasts live during initiation of the
remodeling process. The porosity of both types of bone is dynamic and can be
affected by pathologies or as the bone adapts to mechanical or metabolic stimulus.
22
Figure 13: Schematic of human bone structure.
Adapted from Martin et al (1998).
A
B
Figure 14: Microscopic view of Haversian bone.
The image on the left (A) shows a microscopic view of a human tibia
(400X) showing a circular Haversian canal (indicated by arrow). The
image on the right (B) depicts Haversian bone (osteons) as seen in the
same section of human tibia at 100X (encircled).
23
Thus, obviously, trabecular bone can become more compact and compact
bone can become more porous. Any change in the original porosity of the bone can
affect the mechanical/structural properties of the bone.
Compact bone can be further divided into primary and secondary. Primary
bone is on an existing bone surface (periosteal) during growth of the skeleton. There
are two types of primary bone found within different species: circumferential
lamellae and plexiform. Circumferential lamellar bone has lamellae that are laid down
in a parallel orientation to the bone’s surface. Blood vessels are encompassed by these
circular lamellae, creating Haversian canals longitudinally throughout the bone. The
blood vessels are used to move minerals, nutrients and remodeling cells to the section
of bone that needs attention. This type of primary bone is found in humans. Plexiform
bone is a mixture of both woven and lamellar bone. Plexiform is in a brick wall
pattern because the vascular structure is rectilinear, thus creating the image of
individual bricks. This type of bone is typical in large fast growing animals, such as
horses, cows and goats.
Secondary bone is formed by the resorption of existing bone and the laying
down of new lamellar bone in its place. This is a process known as remodeling.
Secondary osteons usually consist of approximately 16 consecutive circumferential
lamellae that have formed surrounding a Haversian canal that houses blood vessels
and nerves. Secondary osteons are surrounded by a cement line which demarcates
where the osteoclasts ended their resorption process. Most of the compact bone in
adult humans consists of secondary bone, leaving a characteristic morphology of
complete osteons and pieces of old osteons (interstitial lamellae) within a thin section
24
of bone. Interestingly, trabecular bone in adult humans is also mostly secondary bone
and it is turned over from primary to secondary bone more quickly than compact
bone. It is quite unusual to see any osteon formation within trabecular bone, even
secondary trabecular bone because the osteons are too large (200µm) to fit within the
thin trabeculae (<200µm thick).
25
Chapter 3: Anthropological Uses of Bone Histology
Historically, there have been numerous attempts to use bone microstructure to
age individuals (see Robling and Stout 2000). While it is by no means a highly
accurate technique, some researchers claim to be able to histologically age individuals
within three years of their actual age (Thompson 1979) with their specific technique.
Histological aging is, however, a way to age individuals when there are no other
macroscopic means to identify age. It is also a great way to corroborate macroscopic
age estimations.
This microscopic methodology is so important in fact that at the most recent
American Academy of Forensic Sciences Meetings (2005), an entire morning
workshop hosted by Drs. Helen Cho, Robert Paine, Sam Stout and Douglas Ubelaker,
was dedicated to “Forensic Bone Histology”. The topics covered included the
determination of human versus non-human at the microscopic level, a review of
histological age estimation techniques, a discussion about the diagenetic changes
associated with the taphonomic effects of environment on bone and how this
diagenesis can effect histological age estimation, and the effects of malnutrition and
starvation on bone microstructure. The standing-room only workshop reinforced
major components in the histological analysis of bone, and while most presenters
suggested that more research should be undertaken using bone micromorphology,
none suggested any new methods of histological aging, or suggested alternative
sampling sites. Currently, forensic anthropologists are left with the old standbys for
histological aging.
26
There are many techniques to estimate age and each is not without its
problems or critics. It is difficult to learn to age remains in this fashion and is even
more difficult to master any one technique. In fact, many of the techniques use
specific microstructures which may or may not be used by the other technique,
making it a confusing process. Below is a discussion of some of the aging techniques
in the literature with a critique of the usefulness and practicality of each.
In 1965, Ellis Kerley pioneered the use of histomorphometry as an age at
death estimator. Using transverse sections from the midshafts of the femur, tibia and
fibula, Kerley quantified the number of osteons, number of fragmented old osteons,
percent of circumferential lamellar bone (primary bone) and number of nonHaversian canals within each section at four fields situated at the anterior, posterior,
medial and lateral portions of the section. For each thin section the quantities
identified in each of the regions was summed across the four regions. Kerley’s results
indicate that 87.3 % were within 5 years of their actual chronological age and that all
of his specimens were within 10 years of their actual age. There are problems with
this technique, including the fact that you cannot use it if you have no pelvic limb
bones or if you have fragmentary bones since you must have a complete transverse
section from the midshaft. Additionally, the definitions of the microstructures defined
are imperative since Kerley defines intact osteons as being be 80% complete without
remodeling evidence. This is different than the definition later suggested by Stout
(1992). Another methodological issue is that the original article did not address field
size which is important since structures are counted even if they are only partially in
the field.
27
Kerley and Ubelaker (1978) tried to standardize this shortcoming of Kerley
(1965) with an estimation of the original field size as 2.06mm2 and added a correction
factor for field size in case the field size differed from this value. Stout and Gehlert
(1982) suggest that in using this method one should use field size as close to 2.06mm2
since the structures are not randomly distributed throughout the section and may skew
results.
In 1969, Ahlqvist and Damsten published a modification to the Kerley
method. To simplify the method as well as avoid the linea aspera, which may skew
results given muscle attachment and mechanical stress. To avoid the linea aspera they
used quadrants equi-distant between the Kerley quadrants. They also employed a
counting reticle at the level of the section which consisted of a one hundred square
grid to gather the percentage of the grid was covered by Haversian bone and then they
averaged counts across the four fields. The problems with this technique include the
fact that their study only looked at the femur and again requires a complete transverse
section.
Stout and Stanley (1992) suggested that the percentage of Haversian bone had
a non-significant relationship with age but osteon counts had a significant relationship
with age. This would indicate that this characteristic should not be used, or at least
not alone as it is in this study. Stout and Gehlert (1982) compared the two methods
and determined that Kerley’s original method, however, was better at age
determination than the Ahlqvist and Damsten method.
In 1992, Stout and Paine published a method of age determination based on
the middle third of 6th rib and clavicle midshaft. These bones were used for a number
28
of reasons including that anthropologists don’t generally like to section long bones
and rib and clavicle are not needed intact for any osteological measurements.
Additionally, the sternal rib end aging technique is not affected since the section is
taken within the rib shaft. Not to mention the fact that ribs are easy to extract at
autopsy for the purpose of study. This technique is also able to be used on a
fragmentary skeleton.
From the two sections, the number of intact and fragmentary osteons per unit
area were identified using a counting reticle. As mentioned above, they define intact
osteon as 90% complete while everything else is considered a fragment. Stout and
Paine (1992) suggest that at least two sections of each bone should be used in order to
minimize sampling error that can exist even in serial sections (Frost 1969). The total
osteon density is then determined by the sum of the intact and fragmentary osteons
and then put into a regression equation. This technique seems to have minimal
methodological and practical problems other than the fact that it only works on
clavicle and rib and requires a complete cross sections.
There are a few methods that do not require a complete transverse section of
bone. One example is Singh and Gunberg’s (1970) method which uses small sections
taken from the anterior femur and tibia and the posterior mandible. They look at two
fields at the periosteal portion of the bone looking for three variables. Their study
claims to give age estimation in males within 6 years of the actual age. The study
does not examine females and suggests that there are no sex differences in females
according to a small comparative study. Besides the lack of females in this sample,
29
the male sample is skewed to the older age ranges with a mean age of approximately
64 years old.
Thompson (1979) describes a method of age assessment that uses tiny cores of
bone taken from the femur, tibia, humerus and ulna. This study uses over 100
individuals, including females. It looks at 19 different variables including
macroscopic (cortical thickness) and microscopic (secondary osteon populations)
details. Thompson suggests, by testing the method on 8 forensic cases that an age
range as low as ± 3years from the known age. In another article, Thompson et al
(1981) test the method against 28 forensic cases and come up with a mean age range
of ±7.6 years and refer to an equation that is not in the 1979 article. Stout (1992)
suggests that he has no idea what equation is being used to come up with these
ranges.
Ericksen (1991) used a sample of over 300 individuals with a broad age range
to examine her method of histological aging. She used a wedge of bone from anterior
femur at midshaft. Using pictures and the microscope simultaneously, she counted up
the secondary osteons, osteon fragments, non-Haversian canals and percentage of
unremodeled bone. Once again, the weakness of this technique is that it has only been
used for the femur and you must have most of the femur (at least the anterior
midshaft) to use it.
Samson and Branigan (1987) developed a method of histological age
estimation using highly fragmentary or weathered bone. They examined
characteristics like mean cortical thickness and number of Haversian canals. This
gives an age range for males of ±8 years, though it’s utilization in the forensic setting
30
may be minimal as there is an error rate of +/- 16 years for females. If it is difficult to
identify which bone it came from how can one assess whether it is male or female.
An initial attempt by Benedix (2004) to rectify these shortcomings in sorting
fragmentary remains explored the differentiation between the human and non-human
species at the microscopic level, with his main focus on the animal species found is
Southeast Asia whose fragmentary remains are often commingled with or mistaken
for those of missing servicemen from past military conflicts. Benedix discovered that
while some animal bones can be distinguished from human at the microscopic level,
it may be very difficult to distinguish between some fragmentary animal remains and
the remains of humans when there are no macroscopic morphologies available to
make the distinction as their histological structure is very similar.
According to Robling and Stout (2000) there are many problems with aging of
humans according to their microstructure. In this article they present a figure that
organizes different bone microstructure characteristics against whether they increase,
decrease or stay the same with age. They list the different publications that indicate
these findings and they looked at some of the most commonly measured
microstructure characteristics such as osteon size and Haversian canal size. Using
Haversian canal size as an example, there are publications that say that Haversian
canal size increases with age, others that say it decreases with age and still others that
say there is no change in the size associated with age. This discrepancy in different
bones of the skeleton and by different researchers could create quite a problem when
trying to discern problems with techniques or identify new techniques.
31
Another key issue with histological aging of human bone is that most methods
utilize only the midshaft. In a cursory look at the numerous methods, the human
skeleton is well represented: femur (Samson and Branigan 1987; Narasaki 1990;
Drusini 1987; Eriksen 1991; Thompson 1979; Singh and Gunberg 1970; Ahlqvist and
Damsten 1969; Kerley 1965; Kerley and Ubelaker 1978), tibia (Kerley 1965; Kerely
and Ubelaker 1978; Singh and Gunberg 1970; Thompson and Galvin 1983), fibula
(Kerley 1965; Kerely and Ubelaker 1978), ulna (Thompson 1979), clavicle (Stout et
al 1996), 4th rib ( Stout et al 1994) and 6th rib ( Stout and Paine 1992), 2nd metacarpal(
Kimura 1992), occipital (Cool et al 1995), mandible (Singh and Gunberg 1970), and
humerus (Thompson 1979; and Yoshino et al 1994) . The majority of the long bone
methods for histological aging require sectioning at midshaft, regardless of whether
the technique requires transverse sections or cores. Obviously, this begs the following
questions: What happens when you do not have a midshaft or when the bone is
fragmentary? Are these methods applicable to every part of the shaft?
After a thorough literature review on the subject, this author can find no
previous research dealing with the osteon population throughout the shaft, whether it
is static through the length of the shaft or whether the population differs depending
upon muscle attachment sites or proximity to articular surfaces and therefore
trabecular bone. It makes intuitive sense that the areas of bone that are subjected to
more mechanical stress throughout life would show more osteonal turnover than other
areas. That is to say, that the pulling of muscles directly on the bone at muscle
attachment sites must cause more bone remodeling than occurs at non-attachments
sites simply due to the strain placed on the bone at that site. Moreover, areas of the
32
shaft where the cortical bone surrounds trabecular bone, near proximal and distal
articulating surfaces, may have a lower osteon population simply due to the thin the
cortical bone.
There are metabolic and mechanical processes which can affect osteonal
remodeling and thus affect the accuracy of histological aging techniques. Some of
these have been enumerated by Frost (1985) which include hormonal response,
regional trauma, usage or non-usage (disuse atrophy), paralysis, and metabolic
diseases. Additionally Regional Accelerating Phenomenon (RAP) (as discussed
earlier) can be produced by fracture, modification to the bone via surgery or by the
inflammatory process (Frost 1983). These areas modified by the RAP can definitely
affect the accuracy of the aging techniques. As discussed in the next section, other
metabolic and systemic disorders can affect the bone microstructure and thus affect
the ability to accurately age using bone histology.
Bioarchaeological and Paleopathological Uses of Bone Histology
The use of bone histology has tremendously affected paleopathology and for
that matter, bioarcheology, as a whole. The microscopic analysis of human bone has
allowed for a more complete analysis of skeletal remains. That is, the use of bone
histology has allowed for the identification of diseases on an individual basis, the
identification of disease distribution and the history of disease within a population
(Schultz 2001). Bone histology can also help to identify disease loads, living
conditions and general lifeways of ancient populations. According to Larsen (1997),
33
histological morphology can indicate stress within a population since remodeling
rates of human bone are affected by disease and nutrition. Stout (1989) suggested that
based on the differing histological signatures maize agriculturalists have greater
remodeling rates than hunters and gatherers. He suggests that this reflects effects of
overproductivity of parathyroid hormone resulting in low calcium and high
phosphorous concentrations, thus (as described earlier) osteoclasts are over stimulated
to break down bone (increasing remodeling). Larsen (1997) even discusses that the
mineralization period (Stage 5 of the osteons lifecycle) can be influenced by stress.
He also gives an example using bone histology as a means of determining modes of
subsistence and compares the osteon structures of Eskimos (primarily meat eaters),
Arikara (foraging and farming), and Pueblo (primarily maize based subsistence). This
research suggests that Eskimos have a higher rate of type II osteons (secondary
(smaller) remodeling sequences) than the other two groups because of their increased
calcium intake from meat consumption.
Schultz has furthered the study of bone histology in paleopathology with the
diagnoses of diseases of the skeleton using histology. This prolific writer has pushed
the differential diagnoses of pathological conditions to new heights through his use of
microstructure analysis of ancient populations. In fact, his contributions are so
important to paleopathology that in Ortner’s 2003 volume on the identification of
pathological diseases (the most extensive descriptive analysis of bone disorders and
the newest bible in human paleopathology) there is an entire chapter devoted to the
differential diagnoses of bone disease by histological analysis. In addition to the
normal uses of bone histology including the identification of fragmentary remains as
34
human or non-human and microscopic aging techniques, Schultz’s research in bone
histology has identified the bony signatures of metabolic diseases as well as physical
/mechanical infirmities (such as atrophy) that cannot necessarily be identified
macroscopically.
Schultz (2001, 2003) indicates that bone histology can also be used for the
identification of preservation state of remains. This can be helpful in the
determination of whether or not further chemical/genetic analysis of these remains
could be successfully undertaken. This can save the needless waste of money on the
analysis of poorly preserved bone.
Schultz (2003) nearly mandates the use of microscopic techniques for
differential diagnosis of disease by paleopathologists. He suggests that the
histological signature could identify bone tumors, lesions, specific and non-specific
bone inflammations, metabolic disorders, age associated changes, and early stages of
bone disease that are not macroscopically identifiable. This is evidenced by his
discussion of a population that was examined twice, first macroscopically then
microscopically. The initial diagnosis concluded that there was high prevalence of
osteomyelitis and anemia and low incidence of meningeal irritation within this
population. The microscopic analysis revealed that the anemia and osteomyelitis
frequency were actually significantly less than originally thought and that the
meningeal irritation frequency increased. Additionally, microscopic analysis
identified rickets in bones that were macroscopically identified as osteomyelitis.
Schultz (2001) argues that the microscopic determination of bone disease is
more reliable than any macroscopic evaluation. In short, any disorder that can cause a
35
bony response can be identified with the use of histology. He indicates that
treponemal diseases, rickets, scurvy, meningitis and leprosy can usually only be
diagnosed with histology and the microscopic changes and stages of bony destruction
cause by venereal syphilis and yaws.
According to Schultz (2001, 2003), the value of bone histology in
bioarchaeology and paleopathology is use as an epidemiological tool to assess disease
effects on ancient populations. In many ways his research may well help to alleviate
one of the major theoretical problems in bioarchaeology today: the osteological
paradox as described in Wood et al (1992). This deals with the hidden heterogeneity
within populations. The individuals in a population are an unknown mixture of people
who each had differing frailty or susceptibility to disease. This article gives an
example of the paradox in a fictitious population with three subgroups. They describe
a major stressor (disease) that differentially affects these three subgroups. The first
subgroup does not encounter the stressor. The second subgroup is moderately affected
by the stressor and it produces bony lesions within the population. The third subgroup
is affected significantly by the stressor, to an end that most of its members die quickly
even before lesions can form. Wood and coworkers conclude that just looking at the
skeletal series (which is all bioarcheologists have to look at) one could probably only
identify two groups of skeletons macroscopically (lesions and no lesions). From two
groups one could not glean the fact that there were three groups experiencing varying
risks of disease and death. It is possible however, that with the use of histological
analysis early bony changes (prior to death) could be identified, thus splitting the
population back into three groups.
36
The Future of Bone Histology in Anthropology
Even given the sanctions preventing wholesale, systematic destruction of bone
from paleontological, bioarchaeological, historical and forensic sites for histological
assessment, there is much to be learned about the microscopic variables that vary with
age, sex, and within and between bones of the same individual. Continued research
will provide the necessary insights and results that enable us to accurately diagnose
disease using clinical exemplars, in addition to identifying victims from fragmentary
remains and bring closure to cold case files that deal with highly fragmented human
remains.
37
Chapter 4: Materials and Methods
This research was performed in the Mineralized Tissue Histology Laboratory
in the Department of Anthropology at the University of Tennessee. This laboratory
houses equipment for thin sectioning, epoxy resin embedding and microscopic
evaluation bone and dental thin sections and this study made full use of these thin
sectioning, embedding, and microscopy materials.
Three sample sets of bone from three individuals were utilized for this blind
study to enumerate intra and inter-skeletal differences. The bone sectioned in this
study was from three sources: 1) processed elements from amputated limbs donated
to the osteological teaching collection in the Department of Anthropology at the
University of Tennessee, 2) an historic burial from upper east Tennessee from the
Tennessee Bureau of Investigation and 3) two forensic cases through the Regional
Forensic Center at the University of Tennessee Medical Center. None of the
demographic information on these individuals was known to this researcher, although
the data does exist within a database. Each sample consisted of the following
complete elements: humerus, radius, ulna, femur, tibia and fibula (Figure 15).
Each bone of the sample was measured using all standard osteometry
established by Chicago Standards (see Buikstra and Ubelaker, 1994). The results of
all measurements are presented in Appendix A. The midshaft of each bone was
marked on the bone and 1 centimeter increments were marked moving proximally
and distally from midshaft. Each midshaft segment was denoted with an “M”. Distal
sections were denoted D1, D2, D3….Dn, where D1 was the first section taken moving 1
38
Figure 15: Elements of each sample.
Digital image of the six appendicular elements used in each of the three sample sets. From
left: Humerus, Ulna, Radius, Femur, Tibia and Fibula.
centimeter distal from midshaft. Proximal sections were denoted P1, P2, P3….Pn,
where P1 was the first section moving 1 centimeter proximal from midshaft (Figure
16).
Each specimen was given a catalog name such as, HS-B-2-D3, where the first
element was the genus and species abbreviation (HS-Homo sapiens), followed by the
specimen letter (A-C), followed by the element type abbreviation (1-Humerus, 2Radius, 3-Ulna, 4-Femur, 5-Tibia, 6-Fibula) and ending with the section letter and
number combination (D3- 3rd distal segment-3 centimeters from midshaft).
The method of analysis required a series of thin sections produced from each
of the shaft sections. To facilitate sectioning, the bones were cut in half using a
Dremel multi tool. Each element is cut halfway between the midshaft marker and
39
Figure 16: Scoring of the bones for sectioning.
A digital image of two sample sets of bones after each element had been scored at
each one centimeter increment from midshaft. Note that at this point the epiphyseal
ends have already been removed and the long bones have been halved near midshaft.
either the P1 or D1 marker, but never exactly at midshaft, in order to preserve the
actual midshaft for thin sectioning (Figure 16). The most proximal and distal portions
(epiphyses) of the bone were removed near the metaphyses. Thin sectioning began at
midshaft for each skeletal element and moved in 1cm increments proximal and distal
down the shaft of specimen A, while moving at 3 cm increments proximal and distal
down the shaft for specimen B, and C. The change in the increments of sectioning
was directed by microscopic review of the first specimen noting little or no change in
histostructural populations within the 3 cm increments (further corroborated by
statistical analyses of the differentiation within the 3cm increments, see discussion
below), as well as the facilitation of completing the three specimen in a timely
manner. Three consecutive 0.8 millimeter sections were removed at each increment
to ensure that one of the three specimens would be appropriate for microscopic
analysis. So, although there are 25 sections for femur A, 75 thin sections were made
40
(Figure 17). The thin sections were made using a Buehler Isomet 1000 saw with a 15
HC diamond-edged blade at the speed of 200 (Figure 18).
Each thin section then attached to a labeled glass slide (which included
catalog number; then anterior, posterior, medial and lateral orientation arrows; and
element side and name) using Permount mounting media and covered with a glass
cover slip (Figure 19).
After the Permount cured by air drying (this can take at least two weeks) they
were viewed using a Leica DMRX research light microscope at 16X, 50X and 100X
magnification. From each 1centimeter segment, the clearest slide was used for this
analysis.
This histostructural analysis employs the protocol adapted from Kerley (1965)
which used transverse sections from the midshafts of the femur, tibia and fibula.
Kerley quantified the number of osteons, number of fragments of old osteons, percent
of circumferential lamellar bone (primary bone) and number of non-Haversian canals
within each section at four fields situated at the anterior, posterior, medial and lateral
portions of the section (Figure 20). An image of each field was examined for
quantification of structures and digitally captured using the Leica DMRX research
microscope equipped with a Sony digital camera link to a Sony mixer with digital
image capture and color manipulation capabilities (Figure 21).
From there, a field size identical to Kerley’s (2.06 mm2) was superimposed
onto the section so that the cutoff area was clear using Image-Pro Express software on
41
Figure 17: Macroscopic Cross Sectional Variation of Femur.
A digital image representing the proximal sections (P1-P12, midshaft (M), and distal
sections (D1-D12) taken from femur A.
42
Figure 18: Buehler Isomet 1000 thin- sectioning saw slicing a
cross section of fibula.
Figure 19: Microscopic Slide of Sample A.
A digital image representing a microscopic slide of the fifth proximal
section (P5) taken from femur A. Note the catalogue number (HS-A-4-P5),
the bone side and name and the orientation grid (top left).
43
50x
100x
50x
Figure 20: Features measured in the Kerley method of histological age estimation.
There are four categories of microscopic structures quantified in Kerley’s aging method.
First, the number of osteons within the field are quantified. This image (top left) has several
(not all) intact osteons circled in red. The next image (top right) depicts Kerley’s second
category of fragmentary osteons (arrow points to fragment). The bottom image depicts the
final two categories in Kerley’s method: number of non-Haversian canals (elipse) and
percentage of the bone in their field covered by circumferential lamellae (arrows).
Figure 21: Capturing four microscopic fields.
This digital image shows the four microscopic images captured from one segment of this
femur. One image is captured from the anterior, medial, posterior and lateral segments of the
thin section of bone.
44
Each of the four categories of the Kerley method was quantified for every thin
section (approximately 130 slides) of the humerus, radius, ulna, femur, tibia and
fibula. Each osteon, fragment and non-Haversian canal was labeled to facilitate
counting (Figure 23). The percent of non-Haversian bone was estimated using a Dell
Optiplex GX270 desktop computer (Figure 22). Morphometric analysis of the four
Kerley quantification categories was conducted.
quartered grid within the field. All four categories were quantified for the anterior,
medial, posterior and lateral fields for each of the thin sections and were recorded in
Microsoft Office Excel 2003.
Additionally, the femur, tibia and fibula quantifications were used to estimate
age after Kerley and Ubelaker (1978), as these are the skeletal elements that are used
for age estimation. These equations include:
Femur
Osteons Y= 2.278+0.187x+ 0.0026x2 +/- 9.19 years
Fragments Y= 5.241+0.509x+0.017x2-0.00015x3 +/- 6.98 years
Lamellar Y= 75.017-1.790x+0.0114x2 +/- 12.52 years
Non-Haversian Y= 58.390-3.184x+0.0628x2-0.00036x3 +/- 12.12 years
Tibia
Osteons Y= -13.4218+0.660x +/- 10.53 years
Fragments Y= -26.997+2.501x-0.014x2 +/- 8.42 years
Lamellar Y= 80.934-2.281x+0.019x2 +/- 14.28 years
Non-Haversian Y= 67.872-9.070x+0.440x2-0.0062x3 +/- 10.19 years
Fibula
Osteons Y= -23.59+0.74511x +/- 8.33 years
Fragments Y= -9.89+1.064x +/- 3.66 years
Lamellar Y= 125.09-10.92x+0.3723x2-0.00412x3 +/- 10.74 years
Non-Haversian Y= 62.33-9.776x+0.5502x-0.00704x3 +/- 14.62 years
The associated age estimates were compared within and between bones of the same
individual. That is, each section (i.e. D3, P6, M) taken from femur A will have
45
Figure 22: Superimposition of 2.06mm2 field size on captured image.
A digital image captured from the femur of Specimen B at segment P3.
The circle is the exact 2.06 mm2 field superimposed onto the image
to ensure that only the microstructures within this area are counted.
Figure 23: Labeling of the microstructures within the field.
A digital image of an enlargement of the field size depicted in Figure 22 with the
osteons and fragments digitally labeled using Image Pro Express 4.5 software. The osteons
are marked with a dot and a corresponding number preceded by an “O” while the fragments
are marked with a line and corresponding number with a preceding “F”.
46
separate age estimations calculated for that section. These estimations were compared
within the femur and also compared to the tibia and fibula of specimen A.
A factorial ANOVA analysis was run on the twenty-five segments of Femur A
to determine if there were significant differences between mean osteon count, mean
fragment number and mean percentage of non-Haversian bone within the 3 cm
segments. This analysis would determine if sectioning could be reduced to every third
centimeter, or whether there was enough differentiation within a three centimeter
segment to warrant sectioning at every centimeter.
The means for osteon number, fragment number and the percentage of nonHaversian bone were statistically compared over the four fields (anterior, medial,
posterior and lateral) at across the common segments for the population (samples
A,B, and C combined) using repeated measures ANOVA in SPSS. Only the common
segments could be compared, that is, only segments that all three samples had in
common could be used to compute a variance for the population. The number of nonHaversian canals was not considered in this analysis since a majority of the sections
did not exhibit non-Haversian canals.
The repeated measures design was utilized to remove individual variation and
specifically look at the differences between the population for mean osteon number,
mean fragment number and mean percentage of circumferential lamellar bone across
the four fields (anterior, medial, posterior, and lateral). The ANOVA repeated
measures model used was the mixed model, which allows for the use of both fixed
effects (examples include gender or in this case bone type) and random effects (which
include location within the bone, as there are an infinite number of places on the bone
47
that could have been selected). Within the mixed model design, the random effects
would then be analyzed to determine their effect on the variance, while taking into
account the fixed effects and their contribution (if any) to variance (Madrigal, 1998
and Bailey, 1995). The means for each quantification category (osteon number,
fragment number and percentage of circumferential lamellae) were examined
independently across the four fields using the entire population. The criterion of
α=0.05 was used in these analyses to determine significance of difference.
When significant differences were found for the means at individual
orientation areas, a post hoc test was performed to determine which segment(s) in
particular were contributing to the overall significant differences for that field. The
tests selected for this additional analysis was the Least Significant Difference (LSD)
test, as it can control for constant alphas (in this case, α=0.05) across a number of
multiple tests. This ensures that the p-value given for each test is a true p-value and
not skewed due to the number of machinations that the LSD test goes through.
48
Chapter 5: Results
The complete osteometric values for the uncut long bones of individuals A, B
and C are listed in appendix A. The raw osteon, fragment, percent non-Haversian
bone, and non-Haversian canal data collected after Kerley (1965) is presented by
anterior, medial, posterior and lateral field size and is additionally grouped by
segment (i.e., P1, M, D3) then bone (i.e., femur or tibia) and finally by individual (A,
B or C) in spreadsheet format in Appendix B. The following figures illustrate the
key results of the raw data.
The raw data from each of the four Kerley fields for femur A is plotted
for osteon count, fragment count and percent non-Haversian bone across individual
A’s 25 shaft segments (P1-P12, M, D1-D12) in figures 24-26. Non-Haversian canals
were not used in any of the figures as these were very rare throughout all three
individuals, and thus were not a good indication of age differentiation throughout any
of the long bones in this study. The graphs show an overlapping of values across the
four fields, but when the anterior and posterior fields are removed from the plots
(Figures 27-29), a very different picture arises. Figure 27 shows that the medial and
lateral osteon values only overlap at one segment out of the 25 segments (P12). Figure
28 identifies only a minimal overlap of values for fragment number at D6 and D7.
Figure 29 shows no overlap of values for percentage of non-Haverisan bone across
the medial and lateral fields. In fact, the plot shows severe differences between the
medial values (between 40% and 60%) and lateral values (between 2% and 5%).
49
70
60
Raw Osteon Counts
50
40
Anterior
Medial
Posterior
Lateral
30
20
10
0
D12 D11 D10 D9 D8 D7 D6 D5 D4 D3 D2 D1
M
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
Segments
Figure 24 : Raw Osteon Counts for Femur "A" Across Kerley's Four Fields
30
25
Raw Fragment Count
20
Anterior
Medial
Posterior
Lateral
15
10
5
0
D12 D11 D10 D9 D8 D7 D6 D5 D4 D3 D2 D1
M
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
Segments
Figure 25: Raw Fragment Counts for Femur "A" Across Kerley's Four Fields
50
70
60
Percent Non-Lamellar Bone
50
40
Anterior
Medial
Posterior
Lateral
30
20
10
0
D12 D11 D10 D9 D8 D7 D6 D5 D4 D3 D2 D1
M
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
Segments
Figure 26: Percentage of Non-Lamellar Bone for Femur "A" Across Kerley's Four Fields
60
50
Osteon Number
40
Medial
Lateral
30
20
10
0
D12 D11 D10 D9 D8 D7 D6 D5 D4 D3 D2 D1
M
P1
P2
P3
P4
P5
P6
P7
P8
P9 P10 P11 P12
Segment
Figure 27: Raw Osteon Counts for Femur "A" Across Medial and Lateral Fields
51
30
25
Fragment Number
20
Medial
Lateral
15
10
5
0
D12 D11 D10 D9 D8 D7 D6 D5 D4 D3 D2 D1
M
P1
P2
P3
P4
P5
P6
P7
P8
P9 P10 P11 P12
Segment
Figure 28: Raw Fragment Number for Femur "A" Across Medial and Lateral Fields
70
60
Percent Non-Haversian Bone
50
40
Medial
Lateral
30
20
10
0
D12 D11 D10 D9 D8 D7 D6 D5 D4 D3 D2 D1
M
P1
P2
P3
P4
P5
P6
P7
P8
P9 P10 P11 P12
Segment
Figure 29: Percent Non-Haversian Bone for Femur "A" Across Medial and Lateral Fields
52
This severe difference in values throughout two of the four positional fields within
one segment is most likely the reason for using a sum of the values across the four
Kerley fields to estimate age.
The summed values across the four fields at each segment for osteon number,
fragment number and percent non-Haversian bone were placed into the appropriate
regression equations for femur, tibia and fibula. A mean value was obtained as well
as, an age range which included the addition and subtraction of the standard error for
the estimate. The mean values (square) and age range (bar) was plotted for each
segment based on summed osteon number, summed fragment number and average
percent non-Haversian bone for each femur, tibia and fibula (Figures 30-56). Figures
30-38 represent age estimates for individual A. This individual was actually 50 years
old at the time of death. Figures 39-47 display age estimates for individual B, with an
actual age at death of 17 years old. Figures 48-56 enumerate the age estimates for
individual C, who was 60 years old at the time of death.
Figure 57 shows the summed value of osteon number at each segment of
femur A, plotted against the summed value of fragment number at each segment of
femur A as well as the average of these two sums at each segment of femur A. Since
it has been determined that the age estimates from the summed osteon number and the
summed fragment number most closely estimate age when using the Kerley method
(Stout and Gelhert 1982), the osteon ages and the fragment ages were averaged at
each section and estimates for each of the three pelvic limb elements. These estimates
were then plotted on one graph for each individual (Figures 58-60).
53
160
140
120
Age (Years)
100
80
60
40
20
0
D12 D11 D10 D9
D8
D7
D6
D5
D4
D3
D2
D1
M
P1
P2
P3
P4
P5
P6
P7
P8
P9 P10 P11 P12
P7
P8
P9 P10 P11 P12
Sections
Figure 30: Age Estimates (Osteons) for Femur (A)
100
90
80
70
Age (Years)
60
50
40
30
20
10
0
D12 D11 D10 D9
D8
D7
D6
D5
D4
D3
D2
D1
M
P1
P2
P3
P4
P5
P6
Sections
Figure 31: Age Estimates (Fragments) for Femur (A)
54
90
80
70
Age (Years)
60
50
40
30
20
10
0
D12 D11 D10 D9
D8
D7
D6
D5
D4
D3
D2
D1
M
P1
P2
P3
P4
P5
P6
P7
P8
P9 P10 P11 P12
Sections
Figure 32: Age Estimates (% Non-Haversian) for Femur (A)
160
140
120
Age (Years)
100
80
60
40
20
0
D9
D6
D3
M
P3
Sections
Figure 33: Age Estimates (Osteons) for Tibia (A)
55
P6
P9
90
80
70
Age (Years)
60
50
40
30
20
10
0
D9
D6
D3
M
P3
P6
P9
Sections
Figure 34: Age Estimates (Fragments) for Tibia (A)
90
80
70
Age (Years)
60
50
40
30
20
10
0
D9
D6
D3
M
P3
P6
Sections
Figure 35: Age Estimates (% Non-Haversian) for Tibia (A)
56
P9
160
140
120
Age (Years)
100
80
60
40
20
0
D12
D9
D6
D3
M
P3
P6
P9
P6
P9
Sections
Figure 36: Age Estimates (Osteons) for Fibula (A)
80
70
60
Age (Years)
50
40
30
20
10
0
D12
D9
D6
D3
M
P3
Sections
Figure 37: Age Estimates (Fragments) for Fibula (A)
57
140
120
100
80
60
40
20
0
D12
D9
D6
D3
M
P3
P6
P9
Figure 38: Age Estimates (% Non-Haversian) for Fibula (A)
100
90
80
70
Age (Years)
60
50
40
30
20
10
0
D9
D6
D3
M
P3
Sections
Figure 39: Age Estimates (Osteons) for Femur (B)
58
P6
P9
40
35
30
Age (Years)
25
20
15
10
5
0
D9
D6
D3
M
P3
P6
P9
Sections
Figure 40: Age Estimates (Fragments) for Femur (B)
80
70
60
Age (Years)
50
40
30
20
10
0
D9
D6
D3
M
P3
P6
Sections
Figure 41: Age Estimates (% Non-Haversian) for Femur (B)
59
P9
90
80
70
Age (Years)
60
50
40
30
20
10
0
D9
D6
D3
M
P3
P6
P9
P6
P9
Sections
Figure 42: Age Estimates (Osteons) for Tibia (B)
30
20
Age (Years)
10
0
D9
D6
D3
M
P3
-10
-20
-30
Sections
Figure 43: Age Estimates (Fragments) for Tibia (B)
60
70
60
Age (Years)
50
40
30
20
10
0
D9
D6
D3
M
P3
P6
P9
Sections
Figure 44: Age Estimates (% Non-Haversian) for Tibia (B)
70
60
Age (Years)
50
40
30
20
10
0
D12
D9
D6
D3
M
P3
P6
Sections
Figure 45: Age Estimates (Osteons) for Fibula (B)
61
P9
P12
35
30
25
Age (Years)
20
15
10
5
0
D12
D9
D6
D3
M
P3
P6
P9
P12
-5
-10
Sections
Figure 46: Age Estimates (Fragments) for Fibula (B)
140
120
Age (Years)
100
80
60
40
20
0
D12
D9
D6
D3
M
P3
P6
Sections
Figure 47: Age Estimates (% Non-Haversian) for Fibula (B)
62
P9
P12
180
160
140
Age (Years)
120
100
80
60
40
20
0
P12
P9
P6
P3
M
D3
D6
D9
Sections
Figure 48: Age Estimates (Osteons) for Femur (C)
80
70
60
Age (Years)
50
40
30
20
10
0
P12
P9
P6
P3
M
D3
Sections
Figure 49: Age Estimates (Fragments) for Femur (C)
63
D6
D9
90
80
70
Age (Years)
60
50
40
30
20
10
0
P12
P9
P6
P3
M
D3
D6
D9
Sections
Figure 50: Age Estimates (% Non-Haversian) For Femur (C)
140
120
Age (Years)
100
80
60
40
20
0
P9
P6
P3
M
D3
Sections
Figure 51: Age Estimates (Osteons) for Tibia (C)
64
D6
D9
80
70
60
Age (Years)
50
40
30
20
10
0
P9
P6
P3
M
D3
D6
D9
Sections
Figure 52: Age Estimates (Fragments) for Tibia (C)
100
90
80
70
Age (Years)
60
50
40
30
20
10
0
P9
P6
P3
M
D3
D6
Sections
Figure 53: Age Estimates (% Non-Haversian) for Tibia (C)
65
D9
100
90
80
70
Age (Years)
60
50
40
30
20
10
0
P9
P6
P3
M
D3
D6
D9
D6
D9
Sections
Figure 54: Age Estimates (Osteons) for Fibula (C)
50
45
40
35
Age (Years)
30
25
20
15
10
5
0
P9
P6
P3
M
D3
Sections
Figure 55: Age Estimates (Fragments) for Fibula (C)
66
90
80
70
Age (Years)
60
50
40
30
20
10
0
P9
P6
P3
M
D3
D6
D9
Sections
Figure 56: Age Estimates (% Non-Haversian) for Fibula (C)
140
120
Age (Years)
100
80
Average
Osteon
Fragment
60
40
20
0
D12 D11 D10 D9 D8 D7 D6 D5 D4 D3 D2 D1
M
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11
Sections
Figure 57: Mean Age Predictions For Femur (A)
67
120
100
Age (Years)
80
Femur
Tibia
Fibula
60
40
20
0
D9
D6
D3
M
P3
P6
P9
Segments
Figure 58: Average Age Estimations (Individual A)
60
50
Age (Years)
40
Femur
TIbia
Fibula
30
20
10
0
D9
D6
D3
M
Sections
P3
P6
Figure 59: Average Age Estimations (Individual B)
68
P9
100
90
80
70
Age (Years)
60
Femur
Tibia
Fibula
50
40
30
20
10
0
P9
P6
P3
M
D3
D6
D9
Sections
Figure 60: Average Age Estimations (Individual C)
A factorial ANOVA was performed on femur A to determine whether the
summed osteon number, summed fragment number and average percent of nonHaversian bone were significantly different from segment to segment. The summary
data is presented in Table 1. The results indicate that osteon number and fragment
number are not significantly different from segment to segment in Femur A,
suggesting that it is not necessary to sample at one centimeter increments. Hence,
three centimeter increments account for enough of the variation throughout the shaft.
Percentage of non-Haversian bone, however, does differ significantly from segment
to segment of femur A. Further post hoc statistics were run to determine where this
significant difference was located throughout the shaft. These statistics are presented
in Table 2.
69
Table 1: Factorial ANOVA Summary for Femur A
Comparison
F-Value
p-Value
Osteon Count by Segment
1.10
0.3628
Fragment Number by Segment
1.04
0.4296
% Non-Haversian Bone by Segment
1.75
0.0350*
Table 2: Post Hoc Summary for Percent Non-Haversian Bone
Segment Comparison
t-Value
p-Value
D1-D11
-2.69
0.0089
D1-D12
-1.86
0.0674
D3-P6
-2.10
0.0391
D9-P6
-2.30
0.0245
D9-P12
-2.15
0.0349
D10-D11
-2.93
0.0045
D10-D12
-2.10
0.0391
D11-D2
3.57
0.0006
D11-D3
3.71
0.0004
D11-D4
3.57
0.0006
D11-D5
2.59
0.0116
D11-D6
3.32
0.0014
D11-D7
3.22
0.0019
D11-D8
3.32
0.0014
D11-D9
3.91
0.0002
D11-M
3.57
0.0006
D11-P1
2.93
0.0045
D11-P2
2.83
0.0059
D11-P3
3.17
0.0022
D11-P5
3.32
0.0014
D11-P7
2.74
0.0078
D11-P8
3.47
0.0009
D11-P10
2.34
0.0217
D11-P11
2.54
0.0132
D12-D2
2.74
0.0078
D12-D3
2.88
0.0052
70
Table 2 (Continued)
Segment Comparison
t-Value
p-Value
D12-D4
2.74
0.0078
D12-D6
2.49
0.0150
D12-D7
2.39
0.0192
D12-D8
2.49
0.0150
D12-D9
3.08
0.0029
D12-M
2.74
0.0078
D12-P1
2.10
0.0391
D12-P2
2.00
0.0488
D12-P3
2.34
0.0217
D12-P4
2.10
0.0391
D12-P5
2.49
0.0150
D12-P8
2.64
0.0101
Repeated measures ANOVA analysis tested the significance of osteon number
(Table 3), fragment number (Table 4) and percent non-Haversian bone (Table 5)
across all segments of each long bone at a specific directional position (anterior,
medial, posterior and lateral). Significance was based on an alpha of 0.05. Only the
posterior and lateral radius, anterior ulna, lateral femur and lateral tibia showed a
significant difference for osteon count. The ulna and fibula showed a significant
differences for fragment count. There were significant differences seen in humerus,
ulna and fibula for percent of non-Haversian bone. If a value was found to be
significant at the 0.05 level, a series of post hoc evaluations including least squared
means model was used to identify the contributing factors to significance. The post
hoc statistics are summarized in Tables 6-15.
71
Table 3: Repeated Measures ANOVA Summary for Osteon Count
Bone/Position
F-Value
p-Value
Humerus/ Anterior
0.68
0.6719
Humerus/ Medial
1.75
0.1999
Humerus/ Posterior
0.23
0.9595
Humerus/Lateral
0.55
0.7631
Radius/Anterior
0.56
0.7309
Radius/Medial
1.45
0.3709
Radius/Posterior
9.76
0.0232*
Radius/Lateral
8.95
0.0271*
Ulna/Anterior
5.76
0.0225*
Ulna/Medial
1.26
0.3689
Ulna/Posterior
1.21
0.3871
Ulna/Lateral
0.88
0.5216
Femur/Anterior
0.42
0.8510
Femur/Medial
0.35
0.8949
Femur/Posterior
0.41
0.8569
Femur/Lateral
3.08
0.0462*
Tibia/Anterior
1.61
0.2263
Tibia/Medial
1.36
0.3060
Tibia/Posterior
1.28
0.3362
Tibia/Lateral
5.42
0.0064*
Fibula/Anterior
0.49
0.8039
Fibula/Medial
1.85
0.1716
Fibula/Posterior
1.10
0.4184
Fibula/Lateral
1.41
0.2869
72
Table 4: Repeated Measures ANOVA Summary for Fragment Count
Bone/Position
F-Value
p-Value
Humerus
1.99
0.0779
Radius
1.36
0.2615
Ulna
5.35
0.0012*
Femur
0.41
0.8691
Tibia
0.16
0.9873
Fibula
2.34
0.0398*
Table 5: Repeated Measures ANOVA Summary for Percent Non-Haversian Bone
Bone/Position
F-Value
p-Value
Humerus
2.49
0.0302*
Radius
1.42
0.2387
Ulna
6.40
0.0003*
Femur
1.10
0.3729
Tibia
0.43
0.8547
Fibula
5.66
<0.001*
Table 6: Differences of Least Square Means for Posterior Radius (Osteon Count)
Segment Comparison
t-Value
p-Value
D3-P3
2.65
0.0572
D3-P6
6.43
0.0030
D3-P9
3.01
0.0397
D6-P6
4.91
0.0080
M-P6
4.91
0.0080
P3-P6
3.78
0.0194
73
Table 7: Differences of Least Square Means for Lateral Radius (Osteon Count)
Segment Comparison
t-Value
p-Value
D3-D6
5.20
0.0065
D3-M
4.29
0.0127
D3-P3
4.29
0.0127
D3-P6
6.10
0.0037
Table 8: Differences of Least Square Means for Anterior Ulna (Osteon Count)
Segment Comparison
t-Value
p-Value
D3-P3
-2.74
0.0289
D6-P3
-4.76
0.0021
M-P3
-2.60
0.0356
P3-P6
2.60
0.0353
Table 9: Differences of Least Square Means for Lateral Femur(Osteon Count)
Segment Comparison
t-Value
p-Value
D3-M
-2.30
0.0400
D3-P3
-2.37
0.0351
D3-P6
-2.95
0.0121
D3-P9
-3.02
0.0106
D9-P6
-2.66
0.0207
D9-P9
-2.73
0.0181
74
Table 10: Differences of Least Square Means for Lateral Tibia (Osteon Count)
Segment Comparison
t-Value
p-Value
D3-P6
-2.39
0.0343
D3-M
2.73
0.0183
D6-D9
3.58
0.0038
D6-M
5.12
0.0003
D6-P3
3.58
0.0038
D6-P6
2.90
0.0133
D6-P9
4.35
0.0009
M-P6
-2.22
0.0466
Table 11: Differences of Least Square Means for Ulna (Fragment Count)
Segment Comparison
t-Value
p-Value
D3-M
-2.79
0.0075
D3-P3
-3.72
0.0005
D6-M
-2.19
0.0333
D6-P3
-3.12
0.0030
M-P6
2.28
0.0272
P3-P6
3.09
0.0033
Table 12: Differences of Least Square Means for Fibula (Fragment Count)
Segment Comparison
t-Value
p-Value
D6-P9
-2.03
0.0460
D9-P6
-2.77
0.0071
D9-P9
-3.01
0.0035
M-P6
-2.03
0.0460
M-P9
-2.28
0.0258
75
Table 13: Differences of Least Square Means for Humerus (Percent Non-Haversian)
Segment Comparison
t-Value
p-Value
D3-M
1.77
0.0804
D6-M
2.47
0.0160
D6-P3
2.28
0.0258
D9-M
2.20
0.0310
D9-P3
2.03
0.0457
D9-P6
2.37
0.0203
Table 14: Differences of Least Square Means for Ulna (Percent Non-Haversian)
Segment Comparison
t-Value
p-Value
D3-P3
2.92
0.0053
D3-P6
2.84
0.0065
D6-M
2.40
0.0204
D6-P3
4.14
0.0001
D6-P6
3.94
0.0003
Table 15: Differences of Least Square Means for Fibula (Percent Non-Haversian)
Segment Comparison
t-Value
p-Value
D3-D6
-2.56
0.0124
D3-D9
-2.04
0.0452
D6-P3
2.94
0.0043
D6-P6
3.80
0.0003
D6-P9
4.50
<0.0001
D9-P3
2.42
0.0180
D9-P6
3.27
0.0016
D9-P9
3.97
0.0002
M-P6
2.52
0.0140
M-P9
3.21
0.0019
76
Chapter 6: Discussion
The results of this research present a number of specific conclusions about the
ability to utilize any part of the shaft of a specific long bone with specific aging
methods. Based on the picture painted by plotting raw counts of osteon number,
fragment number and percent of non-Haversian bone, it is obvious that these values
differ greatly from anterior, medial, posterior and lateral positions within the same
bone (see Figures 24-29). Actually, the difference between medial and lateral values
at the same segment of the same bone is so great that their corresponding lines on the
graph rarely overlap, if they overlap at all. Given this, fragmentary remains will be
very hard to age even within the same bones. To adjust for this severe difference the
Kerley method uses sums of values across the four fields. This “averaging” can
reduce the effect that the differentiation between positions has on the age estimate,
but it may also give a false picture of the lack of significant difference throughout the
shaft (see Table 1). This is what is noted in the factorial ANOVA which indicates the
lack of significance in summed or averaged values of the four fields across each
segment throughout the shaft.
When looking at the age estimates derived from the specific regression
equations (see Figures 30-56), there is much more differentiation shown throughout
the shaft for each age estimate. This suggests that if the age estimate is made on areas
other than the midshaft there is a higher probability that age estimate will be
significantly different than the estimate at midshaft. The accuracy of these estimates
differs according to bone and quatification category. For example, individual A was
77
50 years old at the time of death, yet very few of the prediction equations gave an age
of 50 years within the range of the estimation. None of the segments of femur A
included the age of 50 in the age range based solely on osteon number. Ten of the
twenty-five segments of femur A included 50 in the age range for fragments. The best
quatification category for femur A was the percent non-Haversian bone, where 22 of
25 segments included 50 years in the age range. The tibia for individual A showed a
similar pattern. None of the 7 segments of tibia A included 50 years in the age range
using only osteon number. Only 2 of 7 segments (D9 and D6) included 50 years in
the age range based on fragments, while 5 of 7 segments included 50 years in the age
range based on percent non-Haversian bone.
Individual B was a much younger individual (17 years old at death). This was
evident by the difficulty the regression equations had isolating the age of this
individual. None of the femoral and tibial osteon estimations included 17 years in the
range. Only one of the fibular osteon estimations included 17 years old and that was
the most distal segment of the bone (D12). Tibial and fibular fragment estimates each
had only two segments that included the correct age. In fact, each of the other
estimates given for the tibial segments based on fragments included negative ages in
the range of the estimate. Six of seven femoral fragments included 17 years in the age
estimate. The percentage of non-Haversian bone provided little help for femur and
tibia which each had only one segment that included the appropriate age. Five of the
nine fibular segments included 17 years in the age estimates for percent of nonHaversian bone (all distal segments).
78
Individual C was another older individual (60 years of age at death) which
again posed a problem for the Kerley equations. None of the femoral or fibular
segments included 60 years of age in the estimate based on osteons and only one of
the tibial segments gave an appropriate age range. In fact, all of the femoral and
fibular estimates severely overaged this individual by as much as double the original
age. None of the tibial and fibular segments included 60 years in the age range based
on fragment number, while half of the femoral segments included the appropriate age
in the range. All of the fibular segments underaged this individual based on fragment
number. Age estimates based on percentage of non-Haversian bone seemed to better
estimate the age of this individual, as all of the femoral segments and 7 of 8 tibial
segments included the appropriate age. Only two of the seven fibular segments
estimated the age of this individual correctly based on the percent of non-Haversian
bone, all other segments underaged this individual.
Taking into account this summary of the results as well as the information
presented in the age estimate graphs (Figures 57-60), it is clear that the Kerley
method was hard pressed to accurately estimate the age of the individuals in this
research even when averaging the osteon and fragment age estimates as suggested by
Stout and Gehlert (1982). This may be due to the fact that these three individuals
represent the absolute ends of the age spectrum and beyond what Kerley’s method is
based on. Since most of his data was derived from military casualties between 20 and
40 years of age, his method is from a much different aged population than the
individuals aged in this study. This said, it does not imply that this method should not
79
have been used for this study, instead it reiterates that these are merely estimates and
that one must know the limitations of the aging methods before they are used.
The statistical analysis shows the differentiation within and between the
elements of the skeleton more clearly based on each quantification category (Tables
3-5). It indicates that based on osteon count, the humerus and the fibula have no
significant differences throughout the shaft. Each of the other long bones has at least
one position which shows a significant difference in the ostoen number throughout
the segments. The posterior and lateral radius show significant differences between
the proximal and distal portions of the bone. This may well be an effect of the
functional morphology of the bone at these locations. The proximal end of the
posterior and lateral radius is the site of the supinator and pronator teres muscle
attachements, that rotate and flex the hand, while the distal end of the radius has no
such attachments acting upon it. Similarly the anterior and medial radius are
completely covered (proximal to distal) by muscle attachments (flexor pollicis
longus, that flexes the thumb, and the pronator quadratus, which turns the wrist)
which are equally pulling on these surface of the bone. The anterior ulna is
completely covered by the flexor digitorum profundus, which flex the digits, which
may account for its significiance. The lateral tibia shows severe significant
differences between the proximal and distal end. Morphologically, this may be
explained simply by the attachment of the tibialis anterior and tibialis posterior
muscles that both elevate the foot separated by the interosseous membrane and
ligament that attaches proximally. No such muscle attachment is found at the distal
portion of the lateral tibia. These muscle attachment sites may account for a great deal
80
of the micromorpholoigcal differentiation throughout the bone due to the mechanical
stress and strain placed on the bone by the action of the muscle. The more a muscle
pulls and modifies on a section of bone, the more that area is susceptible to acute or
chronic microfractures and thus would be in need of bone repair and remodeling.
Areas that do not have such a high stress load may not undergo remodeling at the
same rate. The main caveat of thinking about these functional demands should
heavily caution anyone looking to apply a blanket aging method for any portion of the
shaft of a long bone, not to mention, disable the notion that all of the long bones are
similar in terms of micromorphology.
Given the results of this research, it is imperative that future research take the
following into consideration:
1.) While much attention has been paid to the pelvic limb, very little is
understood about the pectoral limb and the change in these elements as the skeleton
ages. Biomechanically, less stressed bones will remodel less and may give a more
clear look at actual microstructural changes due to age of the bone as opposed to
repair of the bone due to stress. Since the pectoral limb elements do not undergo the
same mechanical stress as the pelvic limb elements do, it is possible that the humerus,
radius and ulna may give better age estimations than the highly stressed femur, tibia
and fibula.
2). The data from this research indicates that the humerus and fibula are best
at age estimation of the individuals in this research. More focus should be placed on
older and younger individuals in future studies to identify if these less mechanically
81
stressed bones are more accurate at aging across the age spectrum as well as the old
and the young.
3) Biomechanical stress and strain are often reviewed at the macroscopic level
in anthropology, though little has been done at the microscopic level. It is imperative
that we understand what effect stress and strain caused by habitual action, obesity,
biomechanical loading, and other mechanical stresses have on human bone at the
microscopic level. It seems that our focus is at the macromorphological level and only
when the bone fails under stress. What happens before the bone fails and how does
the micromorphology accommodate these pressures? What effect do specific cultural
and social practices and habits have on the bone at the microscopic level?
4) From the differentiation found within one segment between the anterior,
medial, posterior and lateral positions it is clear that it may be very difficult to age
fragmentary remains when no gross anatomical landmarks are available. This begs
the question: Is there a way to take any fragment of bone and give a reasonable age
estimate? While the current methodology may not allow us to answer this question
there must be micro and macroscopic evidence of age even on small fragments of
bone as it is a living entity which grows and changes throughout the human life cycle.
Other avenues of inquiry must be identified to establish a method of age estimation of
fragmentary human remains. These avenues may include but are not limited to
biochemical signatures of bone, microstructural metrics (measurement of the
microstructures like osteons, fragments, Haversian canals or lacunae),
micromorphological population quantification and natural diagenic process of bone.
82
A plethora of questions still exist about human bone at the microscopic level.
It is clear that if one were to focus on nothing other than bone micromorphology for
an entire career one would still be left with more questions than s/he started with.
This is the nature of an ever changing facet of humankind that we only understand on
a cursory level.
83
Chapter 7: Conclusions
For any histological evaluation of bone to be successful, we must understand
cortical variation throughout the skeleton. Concomitantly, we must understand all
current methods of histological analysis, including the specific variables and criterion
which are required to use the method. Until we ascend to these heights, it must not be
assumed that fragments contain similar histomorphology throughout the bones of the
skeleton, let alone within one bone. This research has demonstrated that the Kerley
Method should only be used to age a femur, tibia or fibula at midshaft as the variation
throughout the shaft of these structures will introduce error into the age estimate. In
developing new methods to analyze fragments, the results of this study must be taken
into account. It demonstrates that the femur exhibits the greatest microstructural
variation, based on Kerley’s four quantification categories, followed closely by the
tibia and radius. The humerus, ulna and fibula have less within-bone variation for
each of Kerley’s criteria. This incongruent nature of this histological design of the
long bones within the skeleton suggest that with the criterion used for this study it
may be impossible to come up with a all encompassing fragmentary aging technique.
This does not suggest however that one or more of these criteria could not be used in
conjunction with other variables to identify a method for the age estimation of highly
fragmentary remains.
At present, sanctions against destructive analysis prohibit even a fundamental
understanding of intra- and inter-bone and intra-skeletal variation. This is unfortunate.
Even though this sample is small, one major goal of this study was to histologically
84
demonstrate diaphyseal variation in both inter- and intra-skeletal elements. Using
current histological aging techniques requires knowledge of the fragment’s origin as
well as a detailed knowledge of the technique itself. It would not be prudent to
employ a stature regression equation from a population not represented the specific
victim to estimate his/her stature. Similarly, one would not age a female pubis using
male standards. In light of this research, it is equally erroneous to apply midshaft
aging techniques and equations carte blanche to each and every long bone fragment.
Because of the impracticality of DNA examination for each fragment due to
economic prohibitions, fragment size or risk of specimen loss due to the total
destructive nature of this analysis, there is a promising future for the continued
development of mineralized tissue techniques in forensic anthropology. To this end
there are three long term goals within physical anthropology that must be achieved:
First, it is necessary for educators to dispel the notion that an expertise is mineralized
tissue histology (whether dental or osseous) is worthless and/or impossible to obtain.
Moreover, there should be training readily available (or at least a knowledge of how
to acquire specific training) for students with an interest in this growing subspecialty.
Second, we need to obliterate the notion that destructive analysis destroys
information. The histological perspective gained here and in future analyses provides
far more information about human skeletal variation than poorly curated, underused,
or fractured bones and teeth from teaching collections. Finally, fragmentary remains
will continue to discourage and inhibit interpretation by forensic anthropologists until
we recognize that we cannot be afraid of micromorphological diagnosis and
interpretation.
85
It is the opinion of this researcher that bone micromophology is a growing
area of forensic and bioarchaeological importance and with the help of researchers
like her, it will see a promising future full of exemplary research and extraordinary
conclusions about the utility of both bone microstructural anatomy, as well as, the
ability for anthropology and anthropologists to delve into a more clinical research
philosophy.
86
References Cited
87
References Cited
Ahlqvist J and Damsten O (1969) A modification of Kerley’s method for the
microscopic determination of age in bone. J Forensic Sci 14:205-212
Aubin JE, Turksen K, Heersche JNM (1993) Osteoblastic Cell Lineage. In: M Noda
(ed) Cellular and Molecular Biology of Bone . USA, Academic Press. Pp.247.
Bailey NTJ (1995) Statistical Methods in Biology. Third Edition. Cambridge,
Cambridge University Press.
Benedix DC (2004) Differentiation of fragmented bone from South East Asia: the
histological evidence. Dissertation. University of Tennessee Electronic
Library.
Bergman RA, Afifi AK, and Heidger Jr. PM (1996) Histology. Philadelphia, W.B.
Saunders Company, Pp.64-75.
Bronner F and Farach-Carson MC (2004) Bone Formation: Volume 1 in the series
Topics in Bone Biology. London, Springer-Verlag London Limited.
Buikstra JE and Ubelaker DH (1994) Standards for Data Collection from Human
Skeletal Remains: Proceedings of a seminar at The Field Museum of Natural
History. Arkansas Archaeological Survey Research Series No. 44.
Carter DR and Beaupré GS (2001) Skeletal Function and Form: Mechanobiology of
Skeletal Development, Aging, and Regeneration. Cambridge, Cambridge
University Press.
Cool SM, Hendrikz JK, Wood WB (1995) Microscopic age changes in the human
occipital bone. J Forensic Sci 40:789-796.
Drusini A (1987) Refinements of two methods for the histomorphometric
determination of age in human bone. Z Morphol Anthropol 77:167-176.
Enlow DH and Brown SO (1956a) A comparative histological study of fossil and
recent bone tissue. Part I. Texas Journal of Science. 8:405-443.
Enlow DH and Brown SO (1956b) A comparative histological study of fossil and
recent bone tissue. Part II. Texas Journal of Science. 9:186-214.
88
Enlow DH and Brown SO (1956c) A comparative histological study of fossil and
recent bone tissue. Part III. Texas Journal of Science. 10:187-230.
Ericksen MF (1991) Histological estimation of age at death using the anterior cortex
of the femur. Am J Phys Anthropol 84:171-179
Eroschenko VP (1993) di Fiore’s Atlas of Histology with functional correlations.
Philadelphia, Lea and Febinger, Pp.37-49.
Frost HM (1969) Tetracycline based histological analysis of bone remodeling. Calcif
Tissue Res 3:211-237
Frost HM (1983) The regional acceleratory phenomenon: a review. Henry Ford Hosp
Med J 31:3-9.
Frost HM (1985) The “new bone”: Some anthropological potentials. Yearb Phys
Anthropol 28:211-226.
Karaplis AC (2002) Embryonic Development of Bone and the Molecular Regulation
of Intramembranous and Endochondral Bone Formation. In: JP Bilezikian, LG
Raisz and GA Rodan (eds.) Principles of Bone Biology: Volume 1. Second
Edition. USA, Academic Press. Pp. 33-58.
Kerley ER (1965) The microscopic determination of age in human bone. Am J Phys
Anthropol 23:149-164
Kerley ER and Ubelaker DH (1978) Revisions in the microscopic method of
estimating age at death in human cortical bone. Am J Phys Anthropol 49: 545546.
Kessel RG (1998) Basic Medical Histology:The Biology of Cells, Tissues and
Organs. New York, Oxford University Press. Pp. 148.
Kimura K (1992) Estimation of age at death from second metacarpals. Z Morph
Anthropol 79:169-181.
Larsen CS (1997) Bioarcheaology: Interpreting Behavior from the Human Skeleton.
Cambridge, Cambridge Univeristy Press.Pp. 6-63.
Larsen WJ (1997) Human Embryology. Second Edition. New York, Churchill
Livingstone Inc. Pp. 83.
Madrigal L (1998) Statistics for Anthropology.Cambridge, Cambridge University
Press. Pp. 113-127.
89
Marks, Jr. SC and Odgren TA (2002) Structure and Development of the Skeleton. In:
JP Bilezikian, LG Raisz and GA Rodan (eds.)Principles of Bone Biology:
Volume 1. Second Edition. USA, Academic Press. Pp. 3-16.
Martin RB, Burr DB and Sharkey NA (1998) Skeletal Tissue Mechanics. London,
Springer-Verlag London Limited.
Nanci A (2003) Ten Cate’s Oral Histology: Development, Structure and Function.
Sixth Edition. USA, Mosby Inc. Pp. 120-125.
Narasaki S (1990) Estimation of age at death by femoral osteon remodeling:
application of Thompson’s core technique to modern Japanese. J Anthropol
Soc Nippon 98:29-38.
Photographic Atlas of the Body (2004) New York, Firefly Books. Pp. 100.
Ross MH, Reith EJ, and Romwell LJ (1989) Histology: A Text and Atlas. Baltimore,
Williams and Wilkins, Pp. 141-160.
Robling AG and Stout SD (2000) Histomorphometry of Human Cortical Bone:
Applications to Age Estimation. In: Katzenberg MA and Saunders SR (eds)
Biological Anthropology of the Human Skeleton.. Wiley-Liss, Inc. Pp. 187213.
Samson C and Branigan K (1987)A new method of estimating age at death from
fragmentary and weathered bone. In Boodington A, Garland AN and Janaway
RC (eds.) Death, decay and reconstruction: Approaches to archaeology and
forensic science. Manchester, Manchester University Press. Pp. 109-126.
Schinke T and Karsenty G (2002) Transcriptional Control of Osteoblast
Differentiantion and Function. In: Bilezikian JP, Raisz LG and Rodan GA
(eds.) Principles of Bone Biology: Volume 1. Second Edition USA, Academic
Press. Pp. 83-92.
Schultz M (2001) Paleohistopathology of bone: A new approach to the study of
ancient diseases. Yearbook of Physical Anthropology 44:106-147.
Schultz M (2003) Light Microscopic Analysis in Skeletal Paleopathology. In:
Ortner DJ (ed). Identification of Pathological Conditions in Human Skeletal
Remains. USA, Elsevier Science. Pp. 73-109
Singh IJ and Gunberg DL (1970): Estimation of age at death in human males from
quantitative histology of bone fragments. Am J Phys Anthropol 33:375-382.
90
Stout SD (1989) Histomorphometric analysis of human skeletal remains. In İşcan MY
and Kennedy KAR (eds): Reconstruction of Life from the Skeleton. New
York: Wiley-Liss, Pp. 41-52.
Stout SD (1992) Methods of determining age at death using bone microstructure. In
Saunders SR and Katzenberg MA (eds): Skeletal Biology of Past Peoples:
Research Methods. New York, Wiley-Liss, 1992, 21-25.
Stout SD, Dietze WH, Işcan MY, and Loth SR (1994) Estimation of age at death
using cortical histomorphometry of the sternal end of the fourth rib. J Forensic
Sci 39:778-784.
Stout SD and Gehlert SJ (1982) Effects of field size when using Kerley’s histological
method for determining age at death. Am J Phys Anthropol 58:123-125.
Stout SD and Paine RR (1992) Brief communication: Histological age estimation
using rib and clavicle. Am J Phys Anthropol 87:111-115.
Stout SD, Porro MA and Perotti B. (1996) Brief Communication: A test and
correction of the clavicle method of Stout and Paine for histological age
estimation of skeletal remains. Am J Phys Anthropol 100:139-142.
Stout SD and Stanley SC (1992) Percent osteonal bone vs osteon counts: The variable
of choice for estimating age at death. Am J Phys Anthropol 86:515-519.
Thompson DD (1979): The core technique in the determination of age at death in
skeletons. J Forensic Sci 24:902-915.
Thompson DD, Salter EM, Laughlin WS (1981) Bone core analysis of Baffin Island
skeletons. Arctic Anthropol 17:87-96.
Thompson DD and Galvin CA (1983) Estimation of age at death by tibial osteon
remodeling in an autopsy series. Forensic Sci Int 22:203-211.
Warwick R and Williams P (1973) Gray’s Anatomy. Philadelphia, WB Saunders.
Wood J Milner G, Harpending H, and Weiss K (1992) The osteological paradox..
Current Anthropology 33:343-370.
Yang YJ and Damron T (2002) Histology of Bone. e-Medicine. 19 August, 2002. eMedicine.com. 1 March 2005 http://www.emedicine.com/orthoped/
topic403.htm
91
Yoshino M, Imaizumi K, Miyasaka S, and Seta S (1994) Histological estimation of
age at death using microradiographs of humeral compact bone. Forensic Sci
Int 64:191-198
92
Appendices
93
Appendix A:
Osteometric Measurements of Individuals A, B, and C
94
Individual A
Humerus:
Max. Length
Epi. Breadth
Max. V. Head Dia.
Max. Dia. Mdsht.
Min. Dia. Mdsht.
Radius:
Max. Length
Sag. Dia. Mdsht.
Trans. Dia. Mdsht.
Ulna:
Max. Length
Dorsovolar Dia.
Trans. Dia.
Physiological Len.
Min. Circum.
Femur:
Max. Length
Bicond. Length
Epicond. Length
Max. Dia. of Head
AP Subtroch. Dia.
Tran. Subtroch.
Dia.
AP Dia. Mdsht.
Trans. Dia. Mdsht.
Circum. at Mdsht.
Tibia:
Cond-Mall. Length
Max. Prox. Epi. Br.
Max. Dist. Epi. Br.
Max. Dia. Nutr.
For.
Tran. Dia. Nutr.
For.
Circ. at Nutr. For.
Fibula:
Max. Length
Max. Dia. Mdsht.
Individual B
Individual C
306
60
46
21
15
312
66
45
23
19
330
67
48
23
22
239
11
12
238
12
15
256
12
15
254
15
11
226
35
254
15
14
228
40
275
18
12
240
36
446
444
77
47
26
30
425
422
88
43
22
32
470
468
89
46
27
33
29
23
89
24
26
85
29
28
95
353
74
51
35
369
80
56
32
391
83
54
37
22
28
23
95
101
100
342
10
360
14
391
16
95
Appendix B:
Raw Data for Kerley’s Four Quantification Categories
Key:
A= Anterior
M= Medial
P=Posterior
L=Lateral
O=Osteon
F=Fragment
%=Percent Non-Haversian Bone
NH=Non-Haversian Canals
96
Catalogue
Number
HS-A-1-D6
HS-A-1-D3
HS-A-1-M
HS-A-1-P3
HS-A-1-P6
HS-A-1-P9
HS-A-3-D6
HS-A-3-D3
HS-A-3-M
HS-A-3-P3
HS-A-4-D12
HS-A-4-D11
HS-A-4-D10
HS-A-4-D9
HS-A-4-D8
HS-A-4-D7
HS-A-4-D6
HS-A-4-D5
HS-A-4-D4
HS-A-4-D3
HS-A-4-D2
HS-A-4-D1
HS-A-4-M
HS-A-4-P1
HS-A-4-P2
HS-A-4-P3
HS-A-4-P4
HS-A-4-P5
HS-A-4-P6
HS-A-4-P7
HS-A-4-P8
HS-A-4-P9
HS-A-4-P10
HS-A-4-P11
HS-A-4-P12
HS-A-5-D9
HS-A-5-D6
HS-A-5-D3
HS-A-5-M
HS-A-5-P3
HS-A-5-P6
HS-A-5-P9
Letter
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
Bone
L. Hum
L. Hum
L. Hum
L. Hum
L. Hum
L. Hum
L. Ulna
L. Ulna
L. Ulna
L. Ulna
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Tibia
L. Tibia
L. Tibia
L. Tibia
L. Tibia
L. Tibia
L. Tibia
AO
52
52
50
52
49
35
22
39
44
55
35
31
38
51
53
42
47
43
40
48
50
41
41
55
49
44
59
50
50
46
54
39
46
43
38
45
55
45
62
46
46
20
Seg
D6
D3
M
P3
P6
P9
D6
D3
M
P3
D12
D11
D10
D9
D8
D7
D6
D5
D4
D3
D2
D1
M
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
P11
P12
D9
D6
D3
M
P3
P6
P9
97
AF
13
23
7
11
9
6
11
13
16
15
8
7
18
21
27
10
18
14
21
13
11
13
28
18
23
17
7
15
13
15
19
14
9
15
16
15
13
14
7
11
9
18
A%
15
5
2
5
5
2
30
40
15
10
25
40
15
2
10
15
2
2
5
2
5
15
2
10
2
5
10
10
5
2
5
20
15
20
10
2
2
25
40
10
10
10
ANH
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
MO
50
59
57
48
33
37
30
46
44
48
35
32
24
38
36
31
38
33
34
33
38
44
44
41
37
34
41
28
31
42
44
31
38
44
48
44
60
58
41
60
47
49
MF
6
16
17
13
12
6
7
9
9
19
13
11
13
15
11
10
12
10
14
12
12
12
7
5
11
6
7
6
8
4
11
2
9
7
12
10
7
11
14
16
19
11
M%
20
5
2
15
5
10
60
30
20
2
15
25
15
20
40
25
40
40
40
40
40
25
40
40
40
40
40
50
50
50
50
60
50
30
10
5
40
5
30
2
20
5
MNH
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
Catalogue
Number
HS-A-6-D12
HS-A-6-D9
HS-A-6-D6
HS-A-6-D3
HS-A-6-M
HS-A-6-P3
HS-A-6-P6
HS-A-6-P9
HS-B-1-P9
HS-B-1-P6
HS-B-1-P3
HS-B-1-M
HS-B-1-D3
HS-B-1-D6
HS-B-1-D9
HS-B-2-P9
HS-B-2-P6
HS-B-2-P3
HS-B-2-M
HS-B-2-D3
HS-B-2-D6
HS-B-3-P6
HS-B-3-P3
HS-B-3-M
HS-B-3-D3
HS-B-3-D6
HS-B-4-D9
HS-B-4-D6
HS-B-4-D3
HS-B-4-M
HS-B-4-P3
HS-B-4-P6
HS-B-4-P9
HS-B-5-D9
HS-B-5-D6
HS-B-5-D3
HS-B-5-M
HS-B-5-P3
HS-B-5-P6
HS-B-5-P9
HS-B-6-D12
HS-B-6-D9
HS-B-6-D6
Letter
A
A
A
A
A
A
A
A
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
Bone
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Hum
L. Hum
L. Hum
L. Hum
L. Hum
L. Hum
L. Hum
L. Radius
L. Radius
L. Radius
L. Radius
L. Radius
L. Radius
L. Ulna
L. Ulna
L. Ulna
L. Ulna
L. Ulna
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Tibia
L. Tibia
L. Tibia
L. Tibia
L. Tibia
L. Tibia
L. Tibia
L. Fibula
L. Fibula
L. Fibula
AO
1
53
51
51
47
43
40
46
30
31
26
44
32
40
34
30
21
28
33
30
24
25
35
17
23
22
21
27
12
24
26
30
27
30
33
24
34
29
30
32
19
27
19
Seg
D12
D9
D6
D3
M
P3
P6
P9
P9
P6
P3
M
D3
D6
D9
P9
P6
P3
M
D3
D6
P6
P3
M
D3
D6
D9
D6
D3
M
P3
P6
P9
D9
D6
D3
M
P3
P6
P9
D12
D9
D6
98
AF
10
10
12
20
10
13
15
15
9
3
5
4
4
4
7
3
0
2
5
6
4
7
6
6
6
5
4
0
2
2
3
5
7
5
3
3
2
4
6
5
2
5
4
A%
2
15
2
2
15
2
2
2
20
2
5
2
2
5
5
2
2
5
10
10
5
2
20
25
10
20
30
50
60
10
10
10
2
15
5
2
2
2
5
2
40
5
10
ANH
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
MO
35
53
48
47
51
31
47
54
35
33
28
27
39
36
35
21
20
31
29
32
26
21
18
26
21
19
28
28
31
24
26
35
35
23
25
23
18
24
27
23
21
14
25
MF
9
13
12
15
11
12
19
20
6
4
3
3
2
4
6
2
5
4
4
6
8
4
9
6
3
2
5
4
4
3
6
7
2
8
0
0
0
0
0
4
4
9
6
M%
2
25
10
10
15
2
5
2
5
5
40
10
5
10
2
2
2
2
10
10
10
2
2
10
10
30
25
20
40
5
10
10
20
15
30
50
60
50
40
20
2
40
10
MNH
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
2
0
0
0
0
0
1
0
0
0
0
0
0
0
0
Catalogue
Number
HS-B-6-D3
HS-B-6-M
HS-B-6-P3
HS-B-6-P6
HS-B-6-P9
HS-B-6-P12
HS-C-1-P9
HS-C-1-P6
HS-C-1-P3
HS-C-1-M
HS-C-1-D3
HS-C-1-D6
HS-C-1-D9
HS-C-2-P6
HS-C-2-P3
HS-C-2-M
HS-C-2-D3
HS-C-2-D6
HS-C-3-P6
HS-C-3-P3
HS-C-3-M
HS-C-3-D3
HS-C-3-D6
HS-C-4-P12
HS-C-4-P9
HS-C-4-P6
HS-C-4-P3
HS-C-4-M
HS-C-4-D3
HS-C-4-D6
HS-C-4-D9
HS-C-5-P9
HS-C-5-P6
HS-C-5-P3
HS-C-5-M
HS-C-5-D3
HS-C-5-D6
HS-C-5-D9
Letter
B
B
B
B
B
B
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
Bone
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Hum
L. Hum
L. Hum
L. Hum
L. Hum
L. Hum
L. Hum
L. Radius
L. Radius
L. Radius
L. Radius
L. Radius
L. Ulna
L. Ulna
L. Ulna
L. Ulna
L. Ulna
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Tibia
L. Tibia
L. Tibia
L. Tibia
L. Tibia
L. Tibia
L. Tibia
AO
23
24
26
31
29
23
39
49
43
28
36
42
50
37
35
39
35
43
26
39
32
29
19
43
41
37
38
41
36
28
31
46
27
45
57
48
47
39
Seg
D3
M
P3
P6
P9
P12
P9
P6
P3
M
D3
D6
D9
P6
P3
M
D3
D6
P6
P3
M
D3
D6
P12
P9
P6
P3
M
D3
D6
D9
P9
P6
P3
M
D3
D6
D9
99
AF
7
4
5
5
6
7
9
9
9
7
9
9
15
9
12
13
9
12
1
12
13
8
8
17
12
15
13
12
13
5
4
10
9
10
9
11
14
9
A%
10
10
2
2
2
10
2
10
2
2
2
2
5
5
2
2
2
10
20
2
10
10
15
2
2
2
10
2
15
30
25
2
2
0
2
2
5
0
ANH
0
2
0
0
1
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
MO
19
16
21
25
31
25
29
39
42
32
46
47
43
32
26
42
48
44
30
37
31
41
34
57
48
45
47
54
49
42
44
35
28
33
47
52
49
37
MF
5
2
3
7
11
6
6
13
11
11
14
5
13
12
11
12
13
11
10
15
7
9
12
13
15
8
15
16
12
9
11
5
5
4
8
9
11
11
M%
10
40
10
2
2
25
5
2
5
2
2
2
2
2
2
2
2
5
2
5
20
25
10
2
2
2
2
2
2
5
2
10
10
25
2
2
5
2
MNH
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Catalogue
Number
HS-C-6-P9
HS-C-6-P6
HS-C-6-P3
HS-C-6-M
HS-C-6-D3
HS-C-6-D6
HS-C-6-D9
Catalogue
Number
HS-A-1-D6
HS-A-1-D3
HS-A-1-M
HS-A-1-P3
HS-A-1-P6
HS-A-1-P9
HS-A-3-D6
HS-A-3-D3
HS-A-3-M
HS-A-3-P3
HS-A-4-D12
HS-A-4-D11
HS-A-4-D10
HS-A-4-D9
HS-A-4-D8
HS-A-4-D7
HS-A-4-D6
HS-A-4-D5
HS-A-4-D4
HS-A-4-D3
HS-A-4-D2
HS-A-4-D1
HS-A-4-M
HS-A-4-P1
HS-A-4-P2
HS-A-4-P3
HS-A-4-P4
HS-A-4-P5
HS-A-4-P6
HS-A-4-P7
HS-A-4-P8
HS-A-4-P9
Letter
C
C
C
C
C
C
C
Letter
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
Bone
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Fibula
Bone
L. Hum
L. Hum
L. Hum
L. Hum
L. Hum
L. Hum
L. Ulna
L. Ulna
L. Ulna
L. Ulna
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
Seg
P9
P6
P3
M
D3
D6
D9
Seg
D6
D3
M
P3
P6
P9
D6
D3
M
P3
D12
D11
D10
D9
D8
D7
D6
D5
D4
D3
D2
D1
M
P1
P2
P3
P4
P5
P6
P7
P8
P9
100
AO
50
46
43
36
27
33
28
AF
11
11
15
12
5
8
6
A%
2
2
2
10
15
30
15
ANH
0
0
0
0
0
0
3
MO
44
36
37
36
33
38
36
MF
12
10
16
9
10
15
7
M%
2
15
10
15
5
10
5
MNH
0
0
0
0
0
0
0
PO
51
50
46
49
54
41
42
52
57
50
35
37
31
39
34
40
32
31
47
52
52
47
43
47
50
45
45
39
32
41
34
29
PF
7
6
13
3
7
8
13
7
22
23
5
8
16
13
21
9
10
15
15
11
13
8
15
17
12
9
11
5
10
11
16
7
P%
10
10
10
5
2
2
30
5
2
2
40
40
10
2
2
2
10
25
2
2
2
10
5
10
20
10
10
2
40
20
2
20
PNH
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
LO
65
57
54
42
50
45
49
38
52
45
50
46
52
41
46
38
45
47
47
52
56
46
52
50
53
47
49
49
51
55
51
51
LF
7
13
20
9
10
8
14
12
21
18
20
19
20
26
24
8
11
14
21
18
15
18
21
20
20
25
20
17
21
24
19
21
L%
10
20
5
5
5
10
20
2
2
5
5
5
2
2
2
2
2
2
2
2
2
5
2
2
2
2
2
0
2
2
2
2
LNH
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Catalogue
Number
HS-A-4-P10
HS-A-4-P11
HS-A-4-P12
HS-A-5-D9
HS-A-5-D6
HS-A-5-D3
HS-A-5-M
HS-A-5-P3
HS-A-5-P6
HS-A-5-P9
HS-A-6-D12
HS-A-6-D9
HS-A-6-D6
HS-A-6-D3
HS-A-6-M
HS-A-6-P3
HS-A-6-P6
HS-A-6-P9
HS-B-1-P9
HS-B-1-P6
HS-B-1-P3
HS-B-1-M
HS-B-1-D3
HS-B-1-D6
HS-B-1-D9
HS-B-2-P9
HS-B-2-P6
HS-B-2-P3
HS-B-2-M
HS-B-2-D3
HS-B-2-D6
HS-B-3-P6
HS-B-3-P3
HS-B-3-M
HS-B-3-D3
HS-B-3-D6
HS-B-4-D9
HS-B-4-D6
HS-B-4-D3
HS-B-4-M
HS-B-4-P3
HS-B-4-P6
HS-B-4-P9
HS-B-5-D9
Letter
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
B
Bone
L. Femur
L. Femur
L. Femur
L. Tibia
L. Tibia
L. Tibia
L. Tibia
L. Tibia
L. Tibia
L. Tibia
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Hum
L. Hum
L. Hum
L. Hum
L. Hum
L. Hum
L. Hum
L. Radius
L. Radius
L. Radius
L. Radius
L. Radius
L. Radius
L. Ulna
L. Ulna
L. Ulna
L. Ulna
L. Ulna
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Tibia
Seg
PO
50
49
36
47
37
61
62
57
49
47
26
41
54
51
57
49
57
43
38
26
32
32
25
18
22
25
21
28
28
30
27
16
22
20
18
25
33
26
26
33
31
40
34
30
P10
P11
P12
D9
D6
D3
M
P3
P6
P9
D12
D9
D6
D3
M
P3
P6
P9
P9
P6
P3
M
D3
D6
D9
P9
P6
P3
M
D3
D6
P6
P3
M
D3
D6
D9
D6
D3
M
P3
P6
P9
D9
101
PF
13
14
10
5
9
9
17
17
16
22
6
12
10
16
20
16
19
17
2
3
3
3
4
3
4
1
2
4
0
3
0
5
4
9
6
5
8
7
2
3
5
9
6
1
P%
15
10
40
40
50
25
2
5
2
5
20
10
2
2
2
5
2
2
5
2
2
10
20
40
50
40
50
2
30
10
30
2
5
2
2
10
10
20
30
50
20
5
10
40
PNH
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
LO
52
52
45
49
69
57
42
60
55
52
35
53
53
56
54
44
46
47
30
30
47
38
30
26
39
24
23
26
26
34
21
17
22
29
26
22
22
29
28
36
34
44
40
31
LF
16
12
27
14
11
16
17
14
18
9
10
7
13
9
17
15
20
13
3
3
2
3
4
4
5
4
4
6
8
7
4
3
5
7
4
4
4
9
4
5
4
6
5
3
L%
2
2
2
20
25
10
10
20
40
5
10
2
2
5
2
2
2
2
5
2
2
2
15
15
2
5
5
2
2
15
15
5
10
20
40
30
40
5
20
15
2
2
2
15
LNH
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
Catalogue
Number
HS-B-5-D3
HS-B-5-M
HS-B-5-P3
HS-B-5-P6
HS-B-5-P9
HS-B-6-D12
HS-B-6-D9
HS-B-6-D6
HS-B-6-D3
HS-B-6-M
HS-B-6-P3
HS-B-6-P6
HS-B-6-P9
HS-B-6-P12
HS-C-1-P9
HS-C-1-P6
HS-C-1-P3
HS-C-1-M
HS-C-1-D3
HS-C-1-D6
HS-C-1-D9
HS-C-2-P6
HS-C-2-P3
HS-C-2-M
HS-C-2-D3
HS-C-2-D6
HS-C-3-P6
HS-C-3-P3
HS-C-3-M
HS-C-3-D3
HS-C-3-D6
HS-C-4-P12
HS-C-4-P9
HS-C-4-P6
HS-C-4-P3
HS-C-4-M
HS-C-4-D3
HS-C-4-D6
HS-C-4-D9
HS-C-5-P9
HS-C-5-P6
HS-C-5-P3
HS-C-5-M
HS-C-5-D3
Letter
B
B
B
B
B
B
B
B
B
B
B
B
B
B
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
Bone
L. Tibia
L. Tibia
L. Tibia
L. Tibia
L. Tibia
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Fibula
L. Hum
L. Hum
L. Hum
L. Hum
L. Hum
L. Hum
L. Hum
L. Radius
L. Radius
L. Radius
L. Radius
L. Radius
L. Ulna
L. Ulna
L. Ulna
L. Ulna
L. Ulna
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Femur
L. Tibia
L. Tibia
L. Tibia
L. Tibia
L. Tibia
Seg
PO
30
28
31
24
20
4
17
15
22
16
22
12
25
22
44
35
34
35
39
39
54
34
37
40
42
41
34
37
42
30
33
53
46
41
34
45
42
41
34
31
25
36
34
39
D3
M
P3
P6
P9
D12
D9
D6
D3
M
P3
P6
P9
P12
P9
P6
P3
M
D3
D6
D9
P6
P3
M
D3
D6
P6
P3
M
D3
D6
P12
P9
P6
P3
M
D3
D6
D9
P9
P6
P3
M
D3
102
PF
3
8
5
5
8
0
3
0
2
4
4
5
3
6
10
16
9
9
12
7
8
12
12
10
15
11
9
15
10
10
9
9
13
11
10
12
9
7
6
9
8
4
11
11
P%
10
2
2
10
20
70
20
40
25
25
25
5
2
25
2
2
5
2
2
2
2
2
2
2
2
20
2
2
25
30
20
2
2
2
2
10
5
10
2
5
10
2
2
5
PNH
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
LO
30
16
25
31
22
7
29
15
20
20
25
21
25
37
34
35
34
39
40
31
44
30
35
35
46
36
38
40
37
40
38
60
50
45
51
43
46
42
40
31
36
29
38
41
LF
0
0
0
2
3
0
7
5
13
3
3
5
16
9
4
15
8
10
5
5
7
12
14
12
14
14
11
11
12
9
15
15
16
14
22
11
14
15
4
4
8
10
9
9
L%
50
70
40
20
30
60
30
50
20
10
5
10
2
2
2
2
2
2
10
2
5
2
2
2
2
10
2
2
10
10
10
2
2
2
0
2
0
5
5
20
15
2
2
2
LNH
2
2
1
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Catalogue
Number
HS-C-5-D6
HS-C-5-D9
HS-C-6-P9
HS-C-6-P6
HS-C-6-P3
HS-C-6-M
Letter
C
C
C
C
C
C
Bone
L. Tibia
L. Tibia
L. Fibula
L. Fibula
L. Fibula
L. Fibula
Seg
PO
44
43
36
47
37
42
D6
D9
P9
P6
P3
M
103
PF
15
8
13
14
9
7
P%
2
15
5
2
10
20
PNH
0
0
0
0
0
0
LO
47
34
21
19
26
29
LF
15
11
5
8
5
6
L%
2
2
15
25
25
25
LNH
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0
0
0
0
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Vita
Mariateresa Anne Tersigni was born on July 27, 1978 to Anthony and Flora Tersigni.
She is the granddaughter of Andrea and Benedetta Tersigni and Mario and Maria
Pelino. She is the sister of John and Andrea. She graduated from Regina High School
in Harper Woods, Michigan in 1996. She attended Michigan State University, where
she received a Bachelor of Science in Anthropology and a Bachelor of Science in
Microbiology in May 2000. She was accepted to the Master’s program in the
department of Anthropology at The University of Tennessee-Knoxville where she
received her Masters of Arts in August 2002. She became a PhD student in physical
Anthropology at The University of Tennessee-Knoxville in August 2002, and
advanced to candidacy in December of 2003. She received her Doctorate of
Philosophy in May of 2005.
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