CURRICULUM VITAE
LISA M. (SEMANICK) REIDER
[email protected]
EDUCATION AND TRAINING
Degrees
PhD/2014
University of Maryland
Baltimore, Gerontology
(Epidemiology track)
MHS/2007
Johns Hopkins Bloomberg
School of Public Health,
Epidemiology
B.S./2002
Washington College, Biology
Certificates
Gerontology/2007
Chronic Disease Self Management /2006
Fundamentals for the Research Coordinator/2005
Johns Hopkins Bloomberg
School of Public Health
Stanford University, CA
Johns Hopkins University
PROFESSIONAL EXPERIENCE
Assistant Scientist, November 2009-present, Johns Hopkins Bloomberg School of Public
Health, Department of Health Policy and Management. Principal responsibilities include
serving as the Director of the Protocol Development, Implementation and Monitoring
Core within the Coordinating Center of the Major Extremity Trauma Research
Consortium (METRC), a multicenter orthopaedic trauma research consortium. The
overall goal of the Consortium is to produce the evidence needed to establish treatment
guidelines for the optimal care of the wounded warrior and ultimately improve the
clinical, functional and quality of life outcomes of both service members and civilians
who sustain high energy trauma to the extremities.
Research Associate, June 2007 – October 2009, Johns Hopkins Bloomberg School of
Public Health, Department of Health Policy and Management. Principal responsibilities
included directing the Guided Care study, a cluster randomized controlled trial of a model
of primary care for high risk older adults with multiple chronic conditions. This involved
oversight of all aspects of the trial including study design, data management, delivery of
results and preparation necessary to disseminate the model in addition to maintaining
subcontracts and institutional review board agreements with partner agencies.
Project Coordinator, June 2005 – June 2007, Johns Hopkins Bloomberg School of Public
Health, Department of Health Policy and Management. Primary responsibilities included
data management and analysis as well as assisting the project director of the Guided Care
study on all aspects of the study.
Research Assistant, November 2004- June, 2005, Johns Hopkins School of Medicine,
Division of Geriatrics. Facilitated early-stage research project development related to
home health care delivery and nursing home services. Conducted secondary data analysis
of the National Home Health and Hospice Care and Nursing Home survey data.
Project Coordinator, September 2002-November 2004, Johns Hopkins University School
of Medicine, Department of Radiology. Primary responsibilities included managing and
organizing large data sets of x ray scans from multiple research studies and clinical trials
worldwide for bone strength analysis using a program developed by the laboratory.
Participated in the statistical analysis and interpretation of study results examining the
effects of osteoporosis treatment, diet, hormonal influences and exercise on the structural
mechanics of the hip in different ethnic, gender and age groups. Managed IRB
applications and subcontract agreements with other universities and pharmaceutical
companies.
PROFESSIONAL ACTIVITIES
Society Membership
The Gerontological Society of America (GSA), 2009Society for Clinical Trials (SCT), 2009HONORS AND AWARDS
Person in Training Award from the Health Sciences Section of the Gerontological
Society of America, 2011.
PUBLICATIONS
Journal Articles
Boyd CM, Wolff JL, Giovannetti E, Reider L, Weiss C, Xue QL, Leff B, Boult C,
Hughes T, Rand C. (2014). Healthcare task difficulty among older adults with
multimorbidity. Med Care. 52 Suppl 3:S118-25.
Giovannetti ER, Reider L, Wolff JL, Frick KD, Boult C, Steinwachs D, Boyd CM.
(2013) Do older patients and their family caregivers agree about the quality of chronic
illness care? Int J Qual Health Care. 25(5):515-24.
Marsteller JA, Hsu YJ, Wen M, Wolff J, Frick K, Reider L, Scharfstein D, Boyd C, Leff
B, Schwartz L, Karm L, Boult C. (2013). Effects of Guided Care on Providers’
Satisfaction with Care: A Three-Year Matched-Pair Cluster-Randomized Trial.
Population Health Management. 16(5):317-25.
Boult C, Leff B, Boyd CM, Wolff JL, Marsteller JA, Frick KD, Wegener S, Reider, L,
Frey K, Mroz TM, Karm L, Scharfstine, DO (2013). A matched cluster-randomized trial
of guided care for high risk older patients. J Gen Intern Med. 28(5):612-21.
Reider L, Hawkes W, Hebel JR, D’Adamo C, Magaziner J, Miller R, Orwig D, Alley
DE. (2013). The association between body mass index, weight loss and physical function
in the year following a hip fracture. J Nutr Health Aging. 17(1):91-5.
Dattalo M, Giovannetti ER, Scharfstein D, Boult C, Wegener S, Wolff JL, Leff B, Frick
KD, Reider L, Frey K, Noronha G, Boyd C. (2012). Who Participates in Chronic Disease
Self-management (CDSM) Programs? Differences Between Participants and
Nonparticipants in Population of Multimorbid Older Adults. Med Care. 50(12):1071-5.
Boult C, Reider L, Leff B, Frick KD, Boyd CM, Wolff JL, Frey K, Karm L, Wegener
ST, Mroz T, Scharfstein DO. (2011) The Effect of Guided Care Teams on the Use of
Health Services: Results from a Cluster-Randomized Controlled Trial. Arch Intern Med.
171(5):460-6.
Skolasky RL, Green AF, Scharfstein D, Boult C, Reider L, Wegener ST. (2011).
Psychometric Properties of the Patient Activation Measure Among Multi-morbid Older
Adults. Health Serv Res. 46(2):457-78.
Boult C, Reider L, Leff B, Frick K, Boyd CM, Wolff JL, Frey K, Karm L, Wegener ST,
Mroz T, Scharfstein DO. (2011). The Effect of Guided Care Teams on the Use of Health
Services: Results from a Cluster-Randomized Controlled Trial. Arch Intern Med.
14;1717(5):460-6.
Marsteller J, Hsu YJ, Reider L, Frey K, Wolff JL, Boyd CM, Leff B, Karm L,
Scharfstein D, Boult C. (2010). Physician Satisfaction with Chronic Care: A Randomized
Trial of Guided Care. Ann Fam Med. 8(4):308-315.
Boyd CM, Reider L, Frey K, Scharfstein D, Leff B, Wolff J, Groves C, Karm L,
Wegener S, Marsteller J, Boult C. (2009). The Effects of Guided Care on the Perceived
Quality of Health Care for Multi-morbid Older Persons: 18 month Outcomes from A
Cluster-Randomized Controlled Trial. Journal of General Internal Medicine. 25(3):23542.
Wolff JL, Giovannetti ER, Boyd CM, Reider L, Palmer S, Scharfstein D, Marsteller J,
Wegener S, Frey K, Leff B, Frick KD, Boult C. (2010). Effects of Guided Care on
Family Caregivers. Gerontologist. 50(4):459-470.
Giddens JF, Frey K, Reider L, Boult C. (2009). Expanding the Gerontological Nursing
Role in Guided Care. Geriatric Nursing. 30(5):358-364.
Reider L, Beck T. J., Hochberg M.C., Hawkes W.G., Orwig D., YuYahiro J.A., Hebel
R., Magaziner J. (2010). Women with Hip Fracture Experience Greater Loss of
Geometric Strength in the Contralateral Hip during the Year Following Fracture
Compared to Age-Matched Controls. Osteoporosis International. 21:741-750.
Leff B, Reider L, Frick KD, Scharfstein D, Boyd CM, Frey K, Karm L, Boult C. (2009)
“Guided Care” and the cost of complex health care. American Journal of Managed Care.
15(8):555-559.
Woff JL, Rand-Giovannetti E, Palmer S, Wegener S, Reider L, Frey K, Scharfstein D,
Boult C. (2009). Caregiving and chronic care: The Guided Care Program for Families
and Friends. J Gerontol A Biol Sci Med Sci. 64A (7):785-791.
Boult C, Reider L, Frey K, Leff B, Boyd CM, Wolff J, Wegener S, Marsteller J, Karm L,
Scharfstein D. (2008) The early effects of “Guided Care” on the quality of health care for
multimorbid older persons: a cluster-randomized controlled trial. J Gerontol A Biol Sci
Med Sci. 63A (3):321-327.
McNabney MK, Wolff JL, Semanick LM, Kasper JD, Boult C. (2007). Care needs of
higher-functioning nursing home residents. Journal of the American Medical Directors
Association. 8(6):409-12.
Uusi-Rasi K, Beck TJ, Semanick LM, Daphtary M, Crans GG, Desaiah D, Harper KD.
(2006).
Structural Effects of Raloxifene on the Proximal Femur: Results from the Multiple
Outcomes of Raloxifene Evaluation Trial. Osteoporosis Int. 4; 1-12.
Semanick LM, Beck TJ, Wheeler V, Patirck A, Bunker C, Zmuda J. (2005). Association
of Body Composition and Physical Activity with Proximal Femur Geometry in MiddleAged and Elderly African Men: The Tobago Bone Health Study. Calcified Tissue
International. 77(3):160-6.
Uusi-Rasi, K., Semanick LM, Zanchetta JR, Bogado CE, Eriksen EF, Sato M, Beck TJ.
(2005).
Effects of teriparatide [rhPTH(1-34)] treatment on structural geometry of the proximal
femur in elderly osteoporotic women. Bone. 36(6):948-58.
Khoo B, Beck TJ, Qiao Q, Parakh Q, Semanick L, Prince R, Singer K, Price R. (2005).
In vivo short-term reproducibility of hip structure analysis variables in comparison with
bone mineral density Using paired dual-energy x-ray absorptiometery scans from multicenter clinical trials. Bone. 37(1):112-21.
Berger VW, Semanick L. (2005). Refining the Assessment of the sensitivity and
specificity of diagnostic tests, with applications to prostate cancer screening and nonsmall-cell lung cancer staging. Rev Panam Salud Pbulica. 18(1):64-70.
Books
Boult, C., Giddens, J., Frey, K., Reider, L., Novak, T. Guided Care A New NursePhysician Partnership in Chronic Care. Springer Publishing. New York, NY, 2009.
CURRICULUM VITAE
LISA REIDER
PART II
TEACHING:
Annual METRC Research Coordinator Trainings (2010, 2011, 2012, 2013)
Training Sessions: Guided Care Nurse Training (2006)
Workshops: “Measuring Bone Structural Geometry with DXA Data” at the 25th
American Society of Bone Mineral Research Annual meeting (2003)
RESEARCH GRANT PARTICIPATION:
“Guided Care: Integrating High Tech and High Touch”, July 1, 2005- June 30, 2010;
funded by the AHRQ/NIA/Hartford Foundation, Chad Boult (PI); $2,421,196
A randomized-controlled trial designed to evaluate the effect of a specially trained
Guided Care Nurse on the quality and outcomes of care for high-risk older persons, their
voluntary caregivers, and their primary care physicians. My role on the grant is to serve
as the Project Director.
“Treatment Burden in Complex Older Patients as a Target for Intervention”, December 1,
2008- July 31, 2010; funded by the AHRQ, Cynthia Boyd (PI); $154,404
The purpose of this work is to develop instruments and strategies to assist clinicians and
older patients integrate multiple therapeutic regimens in a patient-centric manner to
improve clinical outcomes. My role on the grant is to serve as the data analyst.
“Extremity Trauma Research Consortium”, September 15, 2009-August 31, 2014; funded
by the Department of Defense, Ellen MacKenzie (PI); $18,000,000
The purpose of this multi-center consortium is to address the critical research issues
challenging recover from severe extremity trauma. My role on this grant is to serve as
Associate Director for the core within the Coordinating Center tasked with protocol
development, implementation and monitoring.
“The Major Extremity Trauma Research Consortium”, September 29, 2010-September
28, 2015; funded by the Department of Defense, Ellen MacKenzie (PI); $38,657,995
This grant provides the opportunity to add clinical sites to the existing consortium and to
conduct additional studies that address the critical research issues challenging recover
from severe extremity trauma. My role on this grant is to serve as Associate Director for
the core within the Coordinating Center tasked with protocol development,
implementation and monitoring.
ACADEMIC SERVICE:
Johns Hopkins Bloomberg School of Public Health
Department of Health Policy and Management
Served: member, Research Committee, 2009-
PRESENTATIONS:
Reider L, Hawkes W, Hebel JR, D’Adamo C, Magaziner J, Miller R, Orwig D, Alley
DE. The Association Between Body Mass Index, Weight Loss and Physical Function in
the Year Following a Hip Fracture. Paper presentation at the Gerontological Society of
America Meeting, Boston, MA, November 2011.
Giovannetti E, Reider L, Xue QL, Wolff J, Hughes T, Weiss CO, Leff B, Rand C, Boult
C, Boyd C. Factors Associated with Change in Health Care Task Difficulty Among
Multimorbid Older Adults. Poster Presentation at American Geriatric Society Annual
Research Meeting. Washington D.C. May 2011.
Yerges-Armstrong L.M., Hochberg M.C., Hawkes W., Reider L., Beck T., Hebel R.,
Orwig D., Magaziner J. Bone Mineral Density in Men with and without Hip Fracture.
Poster Presentation at American Society of Bone Mineral Research Annual Research
Meeting. Toronto, Canada. October 2010.
Marsteller J, Hsu YJ, Reider L, Frey K, Wolff JL, Boyd CM, Leff B, Karm L, Boult C.
Physician Satisfaction with Chronic Care: Results from a Three-Year Randomized Trial
of Guided Care. Poster Presentation at AcademyHealth Annual Research Meeting.
Boston, MA: June 2010.
Marsteller J, Wen M, Hsu YJ, Reider L, Frey K, Wolff JL, Boyd CM, Leff B, Wegener
S, Karm L, Boult C. Nurse Satisfaction with the Guided Care Role over Time. Poster
Presentation at Academy Health Annual Research Meeting. Boston, MA: June 2010.
Reider L. Improving the Quality and Efficiency of Health Care for Older Adults.
Community Forum on Health, cosponsored by CIGNA and Johns Hopkins HealthCare.
Baltimore, MD; March 2010.
Marsteller J, Hsu YJ, Reider L, Frey K, Wolff JL, Boyd CM, Leff B, Karm L,
Scharfstein DO, Boult C. Physician Satisfaction with Chronic Care: A Randomized Trial
of Guided Care. Academy Health Annual Research Meeting. Chicago, IL; June 2009.
L. Reider. Guided Care: Challenges of Implementing an RCT of a Health Service
Intervention in the Primary Care Setting. The Gerontological Society of America 61st
Annual Scientific Meeting National Harbor, MD. November 2008.
Symposium. Preliminary Outcomes of a Cluster Randomized Controlled Trial of Guided
Care. The Gerontological Society of America 61st Annual Scientific Meeting.
Washington, DC; November 2008. Reider L.Preliminary Outcomes of the RCT of
Guided Care. Wolff J. and Rand-Giovannetti E. The Guided Care Program for Families
and Friends. Marsteller J. Satisfaction among Physicians Working with a Guided Care
Nurse. Frick K. Early Effects of Guided Care on Annualized Use of Services. Frey K.
Specialization of the Guided Care Nurse.
Dattalo M, Boult C, Wegener S, Rand-Giovannetti E, Reider L, Frey K. Individual and
Contextual Factors that Influence Multi-morbid Older Adults' Participation in Chronic
Disease Self-Management Programs. 136th Annual Meeting of the American Public
Health Association. San Diego, CA; October 2008.
L. Reider, Beck T. J, Hochberg M, Hawkes W, Orwig W. YuYahiro J, Hebel R,
Magaziner, J. Women who Fractured their Hips Experience Greater Loss of Geometric
Strength in the Contralateral Hip During the Year Following Fracture Compared to AgeMatched Controls. Plenary Poster Presentation at the American Society for Bone Mineral
Research, Montreal. September 2008.
Marsteller JA, Fagan P, Schuster A, Hsu YJ, Dunbar L, Rosenthal L, Millman A, Boyd
C, Reider L, Frey K, Boult C. Integration of Care Management into Primary Care
Practice: Care Manager Perspectives. Academy Health 2008 Annual Research Meeting.
Washington, DC; June 2008.
Boyd CM, Leff B, Marsteller J, Reider L, Frey K, Karm L, Boult C. Effects of Guided
Care on Chronically Ill Patient's Utilization of Inpatient Services. Society of General
Internal Medicine Annual Meeting. Pittsburgh, PA; April 2008.
Dunbar L, Karm L, Reider L, Frey K, Marsteller J, Boult C. Guided Care for Members
with Chronic Conditions. 26th National Meeting of the VA Health Services Research and
Development Service. Baltimore, Maryland; February 2008.
Dunbar L, Karm L, Reider L, Frey K, Marsteller J, Boult C. Guided Care for Members
with Chronic Conditions. State of the Military Health System 2008 Annual Conference.
Washington, DC; January 2008.
Symposium. Guided Care: Implementing the Chronic Care Model for Frail Older
Persons. The Gerontological Society of America 60th Annual Scientific Meeting "The
Era of Global Aging: Challenges & Opportunities." San Francisco, CA; November 2007.
Frey K. Implementing the Chronic Care Model for Frail Older Persons. Boyd C. The
Clinical Model. Wolff J. Education and Support for Informal Caregivers. Wegener S.
Developing and Maintaining Health Behaviors.
Reider L. Design and Ongoing Randomized Controlled Trial. Boult C. Diffusion into
Practice.
Boyd C, Leff B, Wolff J, Reider L, Noronha G, Karm L, Boult C. Predictors of SelfRated Quality of Primary Care for Multimorbid Older Adults. Annual Scientific Meeting
of the Society of General Internal Medicine; 2007.
Boult C, Leff B, Boyd C, Wolff J, Wegener S, Reider L, Frey K, Rand-Giovannetti E. A
Cluster-Randomized Controlled Trial of Guided Care: Baseline Data and Initial
Experiences. Annual Scientific Meeting of the American Geriatrics Society; 2007.
Boult C, Leff B, Boyd C, Reider L, Brager R, Frey KP. Guided Care for Multi-Morbid
Older Adults. Annual Meeting of the American Public Health Association; 2006.
L. Semanick. Teriparatide [rhPTH (1-34] Treatment Improves the Structure of the
Proximal Femur in Women with Osteoporosis. 25th American Society of Bone Mineral
Research Annual Meeting: Minneapolis, Minnesota, September 22, 2003
Abstract
Title of Dissertation: Evaluating the Relationship between Muscle and Bone Modeling
Response in Older Adults
Lisa Reider, Doctor of Philosophy, 2014
Dissertation Directed by: Jay Magaziner, PhD, Department of Epidemiology
Background: Bone modeling, the process that continually adjusts bone strength in
response to prevalent muscle-loading forces throughout an individual’s lifespan, may
play an important role in bone fragility with age. Femoral stress, an index of bone
modeling response can be estimated using measurements of DXA derived bone geometry
and loading information incorporated into an engineering model. Assuming that
individuals have adapted to habitual muscle loading forces, greater stresses indicate a
diminished response and a weaker bone
Aims/Methods: The aims of this dissertation were to 1) evaluate the association of
femoral stress with measures of lean mass and muscle strength among healthy older
adults participating in the Health ABC study using linear regression; 2) determine
whether femoral stress predicts incident fracture among the same cohort of older adults
using cox proportional hazards models; and 3) evaluate the association of femoral stress
with measures of lean mass and muscle strength in women after hip fracture participating
in the 3rd and 4th cohort of the Baltimore Hip Studies using linear regression and to
determine whether femoral stress changes the year following fracture using longitudinal
data analysis.
Results: Lean mass explained more of the variation in femoral stress than measures of
muscle strength among healthy older men and women as well as in women with hip
fracture. Remaining variability in femoral stress may reflect individual variation in
modeling response. After adjusting for measures of lean mass and strength, women in
the highest tertile of femoral stress had 77% higher hazard of fracture and men in the
highest tertile of femoral stress had 84% higher hazard of fracture relative to women and
men in the lowest tertile, respectively. This suggests that deficiencies in bone modeling
response may be an important predictor of fracture. Femoral stress did not appear to
change the year following fracture in older women.
Conclusion: Future studies should focus on refining measures of bone modeling
response by incorporating better measures of muscle force. While femoral stress does not
have clinical applications per se, it allows us to investigate a potentially important
mechanism underlying bone fragility and provides a framework for thinking about
treatments that could improve the interaction between muscle and bone.
Evaluating the Relationship between Muscle and Bone Modeling Response in Older
Adults
By
Lisa Semanick Reider
Dissertation submitted to the Faculty of the Graduate School of the
University of Maryland, Baltimore in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
2014
Acknowledgements
I’d like to acknowledge the support and mentorship from my dissertation committee: Jay
Magaziner, Dawn Alley, Thomas Beck, Michelle Shardell, Ram Miller, and John
Schumacher. Your engagement, collaborative approach and thoughtful reviews made this
an extremely valuable experience for me.
I’d like to give special thanks to Thomas Beck who gave me my first “real” job- thank
you for being a great boss, teacher, mentor and friend. I feel incredibly fortunate for all
you have done; I would not be where I am without your guidance and encouragement.
I’d like to thank my colleagues; I am lucky to work with a wonderful, smart, and
ambitious team of people. Thank you for inspiring me to work hard every day.
I’d like to thank my family and friends who have supported and encouraged me through
many, many years of school.
And finally I’d like to give a special acknowledgement to my husband, Nick, for sticking
by me every step of the way, for reminding me what is truly important and for forcing me
to take breaks when I needed them the most; for not putting our life on hold.
iii
Table of Contents
Chapter
Page
Acknowledgements………………………………………………………………...
iii
List of Tables……………………………………………………………………….
iv
List of Figures………………………………………………………………………
v
Chapter 1: Overview……………………………………………………………...
1
Why is Bone Adaptation Important? ...................................................................
1
Specific Aims…………………………………………………………………
3
Organization of the Dissertation………………………………………………...
3
Chapter 2: Review of Previous Research on the Bone-Muscle Relationship….
6
Changes in Bone and Muscle with Age…………………………………………
6
Theoretical Support for Skeletal Adaptation……………………………………
9
Measuring Bone Modeling Response…………………………………………...
15
Chapter 3: Methods……………………………………………………………….
29
Overview of Methods…………………………………………………………...
29
Overview of Data Sets…………………………………………………………..
31
Measures………………………………………………………………………...
34
Study Samples…………………………………………………………………
42
Analysis Plan……………………………………………………………………
44
Limitations………………………………………………………………………
47
Strengths………………………………………………………………………...
48
Power Calculation………………………………………………………………
50
iv
Chapter 4: Evaluating the Relationship between Muscle and Bone Modeling
Response in Older Adults………………………………………………………
59
Chapter 5: An Estimate of Bone Modeling Response Predicts Incident
Fracture in Older Adults…………………………………………………………
78
Chapter 6: Evaluating the Relationship between Muscle and Bone Modeling
Response in Older Women after a Hip Fracture………………………………..
96
Chapter 7: Discussion……………………………………………………………..
112
Appendix…………………………………………………………………………...
120
Glossary…………………………………………………………………………….
126
References………………………………………………………………………….
127
v
List of Tables
Table
Page
Table 4.1. Baseline Characteristics for Men and Women by Quartile of Femoral
Stress………………………………………………………………………………..
72
Table 4.2. Partial Correlation of Femoral Stress with Percent Lean Mass and
Body Adjusted Knee Strength Adjusted for Site…………………………………..
74
Table 4.3. Association between Percent Total Body Lean Mass, Total Body
Adjusted Knee Strength and Femoral Stress among Men and Women……………
74
Table 5.1. Baseline Characteristics for Men and Women with and without a
Fracture……………………………………………………………………………..
90
Table 5.2. Incidence Rate (per 1000 person years) of Fracture by Tertile of
Femoral Stress……………………………………………………………………...
91
Table 5.3. Cox Proportional HR and 95% CIs by Tertile of Femoral Stress………
91
Table 6.1. Study Sample Characteristics by Quartile of Femoral Stress…………...
107
Table 6.2. Partial Correlation of Femoral Stress with Percent Lean Mass and
Body Adjusted Grip Strength Adjusted for Site……………………………………
107
Table 6.3. Association of Femoral Stress with Percent Total Body Lean Mass,
Total Body Adjusted Grip Strength among Women after Hip Fracture (n=138)…..
108
vi
List of Figures
Figure
Page
Figure 1a. The mechanostat framework for bone adaptation……………………..
4
Figure 1b. Dissertation aims. These aims will focus on the component of the
mechanostat theory that addresses the relationship between mechanical load,
load generated stresses, and bone structural geometry……………………………
4
Figure 3.1. HSA pixel profiles of bone cross sections measured at the narrow
neck (NN), intertrochanteric (IT) and shaft regions of the femur. Only measures
at the NN are used in the femoral stress calculation………………………………
34
Figure 3.2. Dimensions from the HSA profile at the narrow neck region used to
calculate femoral stress……………………………………………………………
35
Figure 3.3. Estimating Femoral Stress. W1 refers to body weight, Fm to the
abductor muscle force, and Fj to the joint force at the femoral head. NL= Neck
Length; d= distance from the center of mass; TL= distance from the weight
vector to the outer surface of the greater trochanter; SL= shaft length is
computed from forensic formula on height: α = neck shaft angle from HSA ; β=
10 degrees; θ= 19 degrees. A detailed description of the stress calculation is
included in the Appendix………………………………………………………….
36
Figure 4.1. Estimating Femoral Stress. W1 refers to body weight, Fm to the
abductor muscle force, and Fj to the joint force at the femoral head. NL= Neck
Length; d= distance from the center of mass; TL= distance from the weight
vector to the outer surface of the greater trochanter; SL= shaft length is
computed from forensic formula on height: α = neck shaft angle from HSA ; β=
10 degrees; θ= 19 degrees. A detailed description of the stress calculation is
included in the Appendix………………………………………………………….
63
Figure 5.1. Estimating Femoral Stress. W1 refers to body weight, Fm to the
abductor muscle force, and Fj to the joint force at the femoral head. NL= Neck
Length; d= distance from the center of mass; TL= distance from the weight
vector to the outer surface of the greater trochanter; SL= shaft length is
computed from forensic formula on height: α = neck shaft angle from HSA ; β=
10 degrees; θ= 19 degrees. A detailed description of the stress calculation is
included in the Appendix………………………………………………………….
82
Fig 5.2. Time to fracture in men and women by tertile of femoral stress (p value
comparing groups using the log rank test)………………………………………...
92
Figure 6.1. Estimating Femoral Stress. W1 refers to body weight, Fm to the
vii
abductor muscle force, and Fj to the joint force at the femoral head. NL= Neck
Length; d= distance from the center of mass; TL= distance from the weight
vector to the outer surface of the greater trochanter; SL= shaft length is
computed from forensic formula on height: α = neck shaft angle from HSA ; β=
10 degrees; θ= 19 degrees. A detailed description of the stress calculation is
included in the Appendix………………………………………………………….
99
Figure 6.2 Change in Femoral Stress over the 12 months following hip fracture
(n=138)……………………………………………………………………………
108
viii
Chapter 1: Overview
Why is Bone Adaptation Important?
One of the central functions of the skeleton is to form the levers that enable
muscle-actuated body motions. An important evolutionary aspect of doing so is the
ability of the bone to continually adjust the amount, the distribution, and to some extent
the properties of its material. This ensures that bone has the necessary rigidity to provide
functional motion but is not so heavy as to restrict evolutionary survival 1. Functionally,
bone adapts to changes in mechanical load generated by muscle forces throughout life
by adding material where it is needed to resist increased loads and by removing bone
from where it is no longer needed when loads diminish.
Muscle loading forces on bone have important clinical relevance because they
influence the physiologic regulatory mechanisms that govern bone adaptation and
therefore affect bone strength 2. The underlying mechanism for bone adaptation and the
conceptual framework guiding this dissertation are described by Frost’s Mechanostat
theory (Figure1a). According to the Mechanostat theory, bone constantly evaluates its
mechanical environment and compares it with what is expected under normal habitual
loading conditions in order to optimize bone architecture and maintain bone strains via
bone modeling 1.
The bone adaptive process has implications for the ability of bone to resist
fractures as trauma occurs throughout life. This is particularly important in older adults
with osteoporosis, a skeletal disorder characterized by bone fragility and an increased risk
of fracture. Osteoporosis results in high health care costs, poor functional outcomes,
chronic disability and even death 3. Since changes in muscle load drive bone adaptation
1
and therefore bone strength, it is important to evaluate the ability of bone to respond to
these loading forces. While bone adaptation should continue throughout life, older
individuals with a deficient modeling response may build weaker, more fragile bones.
Declines in bone strength that often occur with age may be attributed to concurrent
declines in muscle load including reduced physical activity, loss of muscle mass and
diminished muscle strength. However it is also possible that declines in bone strength are
a result of deficiencies in bones response to muscle load stimuli. While many studies
have examined the loading forces that cause bones to fracture, few have evaluated the
physiologic loading forces that influence bone strength and bones adaptive response to
these forces. Evaluating these relationships may help understand the mechanisms
underlying bone fragility and help identify older adults at risk of future fracture.
The purpose of this dissertation is to evaluate the relationship between measures
of muscle load and an estimate of bone modeling response among older adults. In order
to assess bone modeling response, it would be ideal to measure the strains (distortions of
the bone architecture) caused by loading forces. Load generated strains cannot be
measured non-invasively however they are proportional to stresses which quantify the
concentration of loading force at a specific point within the bone. Stresses can be
estimated using bone geometry from DXA image data and information about loading
forces applied to a defined region of the bone. An estimate of the stresses generated at the
medial cortex of the femoral neck in a one legged stance will be used to make inferences
about bone modeling response in order to address the specific aims of this dissertation.
These aims are addressed in three separate papers (Figure 1b). The first paper
evaluates how different measures of muscle load are related to femoral stress among
2
healthy older adults. The second paper determines whether femoral stress is associated
with incident fracture. The third paper evaluates how different measures of muscle load
are related to femoral stress among older women with a hip fracture and will determine
whether femoral stress changes during the year following fracture.
Specific Aims
Aim 1: To evaluate the association of measures of muscle load (lean mass and muscle
strength) with femoral stress among healthy older adults.
Aim 2: To determine whether femoral stress predicts incident fracture among older
adults.
Aim 3: To evaluate the association of measures of muscle load (lean mass and muscle
strength) with femoral stress among women with a hip fracture; and to describe the
change in femoral stress during the year following hip fracture.
Organization of the Dissertation
The second chapter of the dissertation provides a review of prior research that has
contributed to our understanding of bone adaptation and why bone response to muscle
loading forces including lean mass and muscle strength is an important indicator of the
mechanisms underlying bone fragility. The third chapter provides a detailed description
of the femoral stress calculation as well as the analysis plan for each of the three papers.
The fourth chapter includes a final draft of the three papers. The fifth chapter integrates
the results from all three papers and provides a discussion of the implications of these
results for future research.
3
Figure 1a. The mechanostat framework for bone adaptation
Figure 1b. Dissertation aims. These aims will focus on the component of the
mechanostat theory that addresses the relationship between mechanical load, load
generated stresses, and bone structural geometry.
4
References
1. Skerry, TM 2006 One mechanostat or many? Modifications of the site-specific
response of bone to mechanical loading by nature and nurture. J Musculoskelet Neuronal
Interact 6:122-127.
2. Forwood, MR 2001 Mechanical effects on the skeleton: are there clinical implications?
Osteoporos Int 12:77-83.
3. Ensrud, KE 2013 Epidemiology of fracture risk with advancing age. J Gerontol A Biol
Sci Med Sci 68:1236-1242.
5
Chapter 2: Review of Previous Research on the Bone-Muscle Relationship
This chapter will provide an overview of the research that has contributed to our
understanding of bone adaptation and why bone response to muscle loading forces
including lean mass, muscle strength and physical activity is an important mechanism
underlying bone fragility. The first section of this chapter will describe the physiological
changes in bone and muscle that occur with age. The second section will provide
theoretical support for bone functional adaptation highlighting the importance of muscle
loading effects on bone. This section will also discuss how bone functional adaptation is
related to osteoporosis. The final section will discuss methods for measuring bone
modeling response using DXA image data and how this information can be used to
make inferences about deficiencies in the modeling response in older adults.
Changes in Bone and Muscle with Age
Changes in Bone with Age
Throughout life, bone is continuously regenerated through remodeling, a tightly
controlled process which involves the resorption of old bone via osteoclast cells and the
formation of new bone via osteoblast cells. With age, this process is thought to become
unbalanced and the amount of resorbed bone surpasses the amount of bone that is
replaced. This is thought to be a result of age-related cellular changes including
apoptosis of bone cells, decline in osteoblast generation, and hormonal changes that
affect the cell signaling pathways that stimulate bone modeling 1.
Changes in bone across the human lifespan are most commonly examined in
terms of the corresponding decline in bone mineral density (BMD). In conventional terms
BMD is clinically equated with bone strength because the age patterns in BMD
6
correspond reasonably well to epidemiological observations in the incidence of
osteoporosis, osteoporotic fracture and even to laboratory measurements of bone strength
where bones are experimentally fractured. However, BMD is highly dependent on bone
size and shape and is not a measure of strength per se. Bone strength depends on bone
material properties defined by the stiffness and porosity of bone tissue and bone
architecture (i.e. the distribution of tissue within the bone.
Tissue properties are difficult to measure non-invasively but bone architecture can
be measured using two and three dimensional imaging technology. Bone architecture is
also referred to as bone geometry because the dimensions used to describe geometry are
those used in engineering calculations. Bone geometry has provided important
information about age related changes in bone strength and their biomechanical
implications for bone fragility and fracture. It is well documented that bone diameter
increases with age 2-5. In the presence of loss of bone mass, wider bones are structurally
stronger than bones with narrow in diameter because the bone is distributed further from
the center of mass, conferring a more stable structure. Bone strength is reflected by
section modulus (Z), a measure of bone strength in bending and cross sectional area
(CSA), a measure of bone strength in compression. Bone weakness can occur if bone
resorption from within the bone exceeds formation of new bone on the outer surfaces
because it results in thinner bone cortices. Studies have shown that older adults with hip
fracture have wider bones, lower CSA, lower Z and thinner cortices compared to older
adults without a fracture 6,7.
Previous studies comparing BMD to bone structural geometry found that some
indices of bone strength, when included in fracture prediction models with BMD, have
7
independent associations with fracture. However, compared with BMD, key bone
strength outputs did not do any better at predicting hip fracture 7,8. The value of
measuring bone geometry is that it provides an explanation for why low BMD is a useful
marker for fracture 9. People with the same BMD can have different structural strength
based on how the mass is distributed throughout the bone. Furthermore, indices of bone
structural strength are more strongly associated with physical activity than BMD and may
be better measures for evaluating the relationship between muscle and bone 10.
Changes in Muscle with Age
A number of factors are associated with age related changes in muscle including
1) reduction in the number of motor neurons which are necessary to activate muscle
fibers in contraction; 2) muscle fiber atrophy; 3) change in muscle quality ie: fatty
infiltration in muscle; 4) imbalance of protein synthesis which leads to muscle loss; and
5) loss of tendon stiffness 11-13. These changes lead to a loss of muscle function,
specifically the loss of power necessary to perform mobility related tasks such as
walking, rising from a chair, climbing steps or maintaining balance. While muscle
decline varies by skeletal site and gender, studies have estimated that adults experience a
40% loss in lean mass and muscle cross sectional area between the ages of 20 and 60. In
addition, cross sectional and longitudinal studies show a 20-40% reduction in upper and
lower limb muscle contractile power during this period 11
While age related declines in both muscle mass and muscle strength often occur
together, research shows that these changes aren’t necessarily correlated 1415. For
example, studies of the effects of resistance training on muscle mass and muscle strength
have shown that changes in strength were observed before changes in mass 14 Other cross
8
sectional studies have shown that muscle mass explains only 35% of the variability in
muscle strength 14. This is important because many studies use muscle mass as a proxy
for muscle strength, when in fact they may have independent effects. Since the theoretical
basis for bone adaptation is supported by changes in muscle loading forces, it may be
important to consider both muscle mass and muscle strength in evaluating this
relationship.
Theoretical Support for Skeletal Adaptation
Bone Functional Adaptation and the Mechanostat Theory
In the late 1900’s, Julius Wolff, an orthopedic surgeon, first described the
bioengineering model of bone adaptation stating that mechanical load influences the
structure of bone tissue over the course of a lifetime. Since then and particularly over the
last several decades, “Wolff’s Law” has evolved into our current understanding of bone
functional adaptation which is governed by two important principals: 1) organisms are
able to adapt their structure to new living conditions and 2) bone cells respond to
mechanical strain 16. Underlying bone functional adaptation is a dynamic regulatory
system that operates through a series of feedback loops where strain (the deformation of
bone tissue) is the controlling influence on bone.
This regulatory system is triggered by mechanical stimulation of the bone tissue
which elicits a cellular response. Although the exact mechanisms are not fully
understood, numerous studies show that osteocyte cells which are aligned on the surface
of bone sense changes in load generated by compression, bending or torsion of cortical
bone and subsequently produce signaling molecules that orchestrate the activities of
osteoblast and osteoclast cells 17. Higher levels of strain from loading events such as
9
vigorous physical activity result in the addition of bone tissue and lower levels of strain
from inactivity lead to bone resorption. In both cases, the purpose of bone adaptation is to
restore strain to original levels. 16.
In the 1970s, Lanyon and colleagues made the first in vivo assessments of strain
and showed that peak strain magnitudes were similar in weight bearing long bones across
many different species 18-21 Despite the uniformity of peak strains across species, strain
magnitudes are not the same in all bones and the strain threshold that produce change in
bone structure may also differ. This is because different skeletal locations are not
habitually loaded in the same manner. For example, the skull has lower peak strains than
the tibia which experiences more load from every day activity. In addition to peak strain
magnitude, the rate of strain change and its direction, as well as the number of cycles, the
duration of loading and the frequency of repetition are also important factors that
influence bone adaptation 22-26. Studies have shown that high rates of strain on the order
of 100-200,000 microstrains per second generated by activities of jumping or running
have a strong bone forming effect while microstrains less than 4,000 microstrains per
second have no effect27.
Harold Frosts’s mechanostat theory refined the bone functional adaptation model
for load bearing bones by focusing attention on muscles as the primary determinant of
mechanical load. His work identified the “minimum effective strain” thresholds that
would evoke a positive and negative adaptive response to muscle forces. Positive
responses would induce bone modeling and increase bone mass and strength, while
negative responses would result in bone resporption and a decrease in bone mass and
strength 28,29.
10
This model is known as the mechanostat theory since the mechanism underlying
the feedback loop is similar to a room with a thermostat that senses temperature change,
activating heat when the room becomes too cold and turning the heat down when the
room becomes too hot. The bone mechanostat set points are influenced by muscle forces
and are likely mediated by non mechanical factors including drugs, diet, hormones,
genetics and disease. According to the mechanostat theory, bone constantly evaluates its
mechanical environment and compares it with what is expected under habitual loading
conditions in order to optimize bone architecture. “Normal” loading conditions are highly
individualized and therefore disuse and overloading are relative to these individual set
points 30.
Muscle forces: Drivers of Bone Functional Adaptation
The field of pediatric and adolescent bone research developed the concept of the
“functional muscle-bone unit,” which suggests that bone fragility and bone disease result
from the mechanostats failure to regulate bone strength. Muscle development precedes
bone development, providing evidence for making causal inferences about the
relationship between muscle and bone strength observed in children and young adults 31.
Studies haves shown that bone mineral content (BMC) and an index of bone strength
based on bone dimensions obtained from three dimensional pQCT data have a strong
linear association with age and muscle size during childhood and early adulthood. This
suggests that BMC and bone strength are a function of muscle development in both
children and young adults 32. The relationship between muscle and bone shifts in girls
after puberty with girls having larger bones than can be explained by their muscle
strength. It is possible that estrogen effects the mechanostat in girls by lowering the
11
remodeling threshold so that bone mass can increase more rapidly than in boys. This
would allow girls to build more bone for extra calcium necessary for gestation later in life
32
.
In clinical practice, the relationship between muscle size or force and BMC can be
used to diagnose bone development problems in children. For example, bone defects can
be identified if BMC is lower than expected for muscle force or size or if muscle force or
size is too low for height using a standard “normal” reference population. Similarly,
reference curves of the BMC/lean mass ratio in older adults by gender and menopausal
status can be used to identify osteopenia 33. We currently do not employ these methods in
practice when investigating bone disease in older adults although many studies have
recognized the importance of accounting for muscle mass when measuring bone strength.
Studies in older men and women have shown that lean muscle mass is a stronger
predictor of bone strength as measured by the geometric attributes of the bone, than fat
mass and body weight. 34-37 This is likely attributed to the forces generated by muscles to
which bones respond, and indeed recent studies show that muscle forces are significantly
associated with bone strength 38,39. Studies have shown that among older adults, declines
in muscle mass and strength and bone fragility are closely related. For example, one
study found that sarcopenia, a condition characterized by elevated muscle loss resulting
in loss of muscle strength, and function is associated with osteoporosis. This study
showed that sarcopenic older women with a hip fracture were 80% more likely to have
osteoporosis than non-sarcopenic older women 40. The close relationship between muscle
and bone is also observed after hip fracture when bone mass, bone strength and lean mass
continue to significantly decline 41-43
12
Since muscle size and muscle strength are positively associated with bone
strength and because muscle size and strength are influenced by physical activity,
physical activity should therefore have positive effects on bone. Physical activity effects
on bone are stronger in children than they are in adults 44This is because bone and muscle
continue to grow during childhood and children and adolescents are more physically
active than older adults. According to the mechanostat theory, bones that have adapted to
strong muscle forces as a result of vigorous activity in young adulthood would adapt to
declines in physical activity in older age by lowering the strain threshold for bone
formation. In order to produce an osteogenic response in these individuals, physical
activity would have to be vigorous enough to raise the strain threshold to induce bone
formation. The activities necessary to induce bone modeling may not be achievable in
frail older adults 44. Among older adults that do remain physically active, it is possible to
obtain a positive effect on bone, but the gains from activity tend to be small. It could be
the case that some individuals have a deficient modeling response to muscle loading
forces which may explain why their bones become fragile.
How the Mechanostat Theory Explains Bone Fragility and Osteoporotic Fracture in
Older Age
Lanyon and Skerry proposed a hypothesis that suggests bone fragility in older
adults is the result of failure of bone to adapt to normal loading conditions rather than a
“systemic disorder” defined by hormone regulated bone loss. They made four arguments
in support of this hypothesis based on the principle that the objective of functionally
adaptive bone modeling and remodeling is to establish and maintain bone architecture to
sufficiently reduce fracture risk. First, they provided evidence in support of the
13
mechanostat theory from studies demonstrating that changes in strain environment effect
changes in bone architecture. Bone cells respond to strain related stimuli to turn on either
bone modeling or remodeling which results in targeted changes to bone architecture 45.
Second, the authors draw parallels between individuals with apparent modeling
deficiencies and individuals with osteoporosis. There is some evidence from animal and
human studies that suggest similarities in bone architecture changes indicating
structurally weaker bones 45. Individuals with a reduced modeling response will not be
able to adapt appropriately to changes in functional load bearing and thus will have
higher internal bone strains requiring more force to illicit a positive bone response (ie:
bone formation). Higher bone strains are indicative of weaker bones and are likely
associated with higher risk for fracture. 46. Since fragility fractures account for a sizable
component of annual health care expenditures in the United States, which is expected to
increase as the population ages 47it is important to identify those at highest risk for
fracture. Detecting bone modeling deficiencies may be another important aspect of
defining fracture risk.
Third, the authors suggest that the decline in estrogen receptors may contribute to
the impairment of bone’s adaptive response in both men and women. Several studies
have shown that low or absent estrogen receptor numbers are associated with osteopenia.
There is also evidence that bone cells’ response to strain is modulated through a response
pathway that requires estrogen receptors. Finally, the authors briefly discuss results from
exercise studies that show that bone loss can be reversed from participation in vigorous
exercise. However, there are no studies evaluating the outcomes of continued loading of
bones with diminished modeling response. If active individuals are unable to build bone,
14
this may be indicative of an underlying pathology related to the bone adaptive response
as opposed to the ability to generate force on bone. The authors advocate for other ways
to affect the adaptive response including supplementing exercise with estrogen, providing
strain stimuli through other mechanisms like vibrating platforms and by targeting the
cellular pathways that control bone cell response to strain 45.
Identifying the cellular pathways that control bone modeling response to muscle
load induced strain is critical for understanding the mechanisms that result in bone
fragility. However, we can also glean important information about bone adaptation by
better characterizing physiologic muscle load in older age. The section below describes
how bone modeling response can be estimated from readily available image data and how
this information can be used to further our knowledge about the relationship between
muscle and bone.
Measuring Bone Modeling Response
Animal models
Animal models have been used extensively to study bone response to mechanical
loading. A common experimental model uses a loading apparatus to produce bending
loads on the ulna of mature rats. In one such study, strain in the loaded ulna increased by
70-100% compared to the unloaded control limb. This stimulated an increase in bone
diameter in regions under highest bone strain in the direction of the bending load,
resulting in a 64-165% increase in bone strength measured directly in mechanical tests
after rats were sacrificed 48. Load induced strain on bone can also be measured in vivo
using strain gauges attached directly to the muscle. Studies conducted in monkeys and
dogs showed that maximum bone strains increased when going from a walk to a trot or
15
gallop, demonstrating that bone strains increased when the intensity of activity increased.
Results also indicated that bone structural strength was higher in regions that bore the
highest strain during vigorous physical activity. These studies demonstrate that increases
in bone strain are associated with positive changes in bone structure resulting in stronger
bones.
The bone response to load induced strain observed in animal studies should be
evident in humans; however, extrapolating results from animal studies to make inferences
about human bone adaptation should be done with caution for several reasons. First,
animal studies conducted in laboratories control for the variability in movement and are
typically measured during walking or running on flat surfaces like treadmills. Under
“normal” loading conditions movement is more heterogeneous in load direction and
therefore in where the bone strains are highest. Second, the direction and location of the
strain is also influenced by muscle position and patterns of habitual (and non-habitual)
activity which differ across species 16. Finally, age related changes in mechanical and non
mechanical factors that influence bone adaptation will differ across species as a result of
differences in physiologic aging.
Measuring bone modeling response in humans
A direct measure of bone modeling response to load would require a measure of
in vivo strains (ie: deformation of bone tissue) or their cellular stimuli; however it is not
currently possible to do this in a non invasive manner. Stresses, which quantify the
concentration of loading force at a specific location in the bone, can be computed using
measurements of bone geometry and information about the magnitude and direction of
16
loading forces. Stress is proportional to strain and therefore provides an index of
modeling response.
Previous studies have employed models using three dimensional data and finite
element modeling techniques in order to measure bone stresses at the proximal femur
during gait and from the forces generated by a fall. A study by Lotz et al. evaluating
femoral stress showed that during gait and falls, the distribution of load shifted from
trabecular bone to cortical bone. During gait, compressive stresses were concentrated at
the inferior region of the femoral neck whereas during falls, stresses were concentrated in
the superior-posterior neck region 49. This study also showed that femoral stresses were
higher in osteoporotic compared to non osteoporotic bone. Subsequent studies have
supported these findings by demonstrating that cortices in the superoposterior region of
the femur, which experiences the highest stresses in a sideways fall, get significantly
thinner with age and are thinner in older adults who fracture compared to those who do
not 50-52. A recent case control study of older men and women with and without hip
fracture showed that the supero-posterior region of the femur best predicted fracture and
that the cortical thickness in this area was much lower in fracture cases compared to non
fracture controls 53.
These studies demonstrate that stresses increase in areas of the bone that are
structurally weak. This is important for explaining why bones fracture in certain regions
rather than others but it doesn’t explain why the bone became structurally weakened in
the first place. It is possible that the under-loading of these regions with age contributes
to structurally weaker bones; bone strength is adjusted downward to adapt to low activity
or disuse. However bone fragility may also be explained by modeling deficiencies which
17
adjusts the bone to a weaker condition than expected for a given load. Therefore, in order
to interpret femoral stress as an index of bone modeling response, we must control for
maximum physiologic forces generated by normal loading conditions; higher stresses
relative to individual load may indicate a deficient response.
One way to do this is by estimating femoral stresses in a single legged stance.
Stresses are estimated in the medial cortex, an area of the femoral neck that experiences
the highest forces from standing, gait and other load bearing activity. This region is
therefore structurally stronger compared to other areas along the cortex to bear these
loads and the cortex tends to remain thick even in old age 52. Bone structural strength in
this area of the bone will have adapted to loads generated under normal loading
conditions and should therefore tell us something about how the bone has adapted to
these loads.
An underlying assumption of this model is that at steady-state when loads are not
drastically changing, bones in an individual can be considered to be adapted to the
prevalent loads that they experience. If all individuals responded identically to loads, the
stresses produced in any bone by the adaptive load should be identical. However, given
variation in response to load, those with reduced response should produce bones that are
weaker and generate higher stresses. To compute the stress, it is necessary to know the
bone geometry at the narrow neck and the maximal loading forces that are applied to that
area. Bone geometry can be estimated from two or three dimensional images. The
advantage of using two dimensional image data like dual energy x-ray absorptiometry
(DXA) scans is that they are widely available on individuals participating in large cohort
studies and clinical trials. The method most frequently used for measuring bone
18
dimensions from DXA data is the Hip Structural Analysis program. The program
converts raw DXA images into a bone mass format so that pixel profiles of the bone
mineral distribution and bone structural properties can be estimated in a cross sectional
slice at three different regions - at the narrowest section of the neck, the intertrochanteric
region and the femoral shaft. HSA has been employed in numerous studies including
studies evaluating bone structural strength in children 54-62, studies describing racial and
gender differences in bone strength 63-70, studies evaluating the relationship between bone
strength and fracture 7,43, studies evaluating treatment of osteoporosis 71-77, studies of
genetic determinants of bone structural strength 78-80, and studies describing the
association between body composition and bone strength 6,34,35,81-83. From the HSA output
at the femoral neck, cross sectional moment of inertia (CSMI), a measure of the forces on
the bone in bending, and CSA, a measure of the axial forces along the narrow neck, can
be derived. Femoral stress can be estimated based on these dimensions along with
information about loading forces in a single leg stance 46.
While measuring femoral stress is an indirect method of evaluating the effects of
muscle load on bone, it may be able to provide us with important information about bone
adaptation in older adults and advance our knowledge about the relationship between
bone and muscle. Currently, bone strength assessments require the interpretation of a
multitude of measures that must be interpreted together. Femoral stress may provide one
useful measure of bone strength that is based on the mechanical loading forces that
influence them.
Using this model, a previous study showed that women with a history of fracture
had higher femoral stress compared to women with no fracture 46. Building upon that
19
study, this dissertation will employ the femoral stress model to further investigate the
relationship between muscle load and bone. In order to detect deficiencies in modeling
response, we need to measure stress generated under maximum load. In this model,
stresses are estimated under a single body weight load as the maximal forces on bone
can’t be directly measured. However we know that forces are muscle driven and therefore
are some multiple of body weight. If everyone experienced the same forces, then we
could say higher femoral stress indicates deficiencies in load using the current model. But
since muscle varies across individuals, we need to account for that variability. The first
aim of this dissertation will examine the extent to which measures of muscle load account
for variation in femoral stress among healthy older adults. The remaining heterogeneity
after accounting for muscle load may be an index of modeling response where higher
stresses indicate modeling deficiencies and weaker bones.
Since weaker bones are more likely to fracture, the second aim of this dissertation
will determine whether femoral stress, as an index of bone modeling response, predicts
incident fracture among men and women. The third aim of the dissertation will evaluate
the association of muscle load with femoral stress among women with a hip fracture and
will determine whether femoral stress changes the year following fracture, a time when
significant changes to muscle mass and strength occur.
20
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34. Travison, TG, Araujo, AB, Esche, GR, Beck, TJ, McKinlay, JB 2008 Lean mass and
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Zmuda, JM 2005 Association of body composition and physical activity with proximal
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36. Kaptoge, S, Dalzell, N, Loveridge, N, Beck, TJ, Khaw, KT, Reeve, J 2003 Effects of
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23
37. Capozza, RF, Cointry, GR, Cure-Ramirez, P, Ferretti, JL, Cure-Cure, C 2004 A DXA
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JM, Cawthon, PM, Ensrud, KE, Osteoporotic Fractures in Men (MrOS) Study Group
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JR, Magaziner, J, Study of Osteoporotic Fractures Research Group 2010 Women with hip
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44. Frost, HM 1999 Why do bone strength and "mass" in aging adults become
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45. Lanyon, L, Skerry, T 2001 Postmenopausal osteoporosis as a failure of bone's
adaptation to functional loading: a hypothesis. J Bone Miner Res 16:1937-1947.
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48. Robling, AG, Hinant, FM, Burr, DB, Turner, CH 2002 Improved bone structure and
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bouts. J Bone Miner Res 17:1545-1554.
49. Lotz, JC, Cheal, EJ, Hayes, WC 1995 Stress distributions within the proximal femur
during gait and falls: implications for osteoporotic fracture. Osteoporos Int 5:252-261.
50. Bell, KL, Loveridge, N, Power, J, Garrahan, N, Stanton, M, Lunt, M, Meggitt, BF,
Reeve, J 1999 Structure of the femoral neck in hip fracture: cortical bone loss in the
inferoanterior to superoposterior axis. J Bone Miner Res 14:111-119.
51. Crabtree, N, Loveridge, N, Parker, M, Rushton, N, Power, J, Bell, KL, Beck, TJ,
Reeve, J 2001 Intracapsular hip fracture and the region-specific loss of cortical bone:
analysis by peripheral quantitative computed tomography. J Bone Miner Res 16:13181328.
52. Mayhew, PM, Thomas, CD, Clement, JG, Loveridge, N, Beck, TJ, Bonfield, W,
Burgoyne, CJ, Reeve, J 2005 Relation between age, femoral neck cortical stability, and
hip fracture risk. Lancet 366:129-135.
53. Johannesdottir, F, Poole, KE, Reeve, J, Siggeirsdottir, K, Aspelund, T, Mogensen, B,
Jonsson, BY, Sigurdsson, S, Harris, TB, Gudnason, VG, Sigurdsson, G 2011 Distribution
of cortical bone in the femoral neck and hip fracture: a prospective case-control analysis
of 143 incident hip fractures; the AGES-REYKJAVIK Study. Bone 48:1268-1276.
54. Jackowski, SA, Faulkner, RA, Farthing, JP, Kontulainen, SA, Beck, TJ, Baxter-Jones,
AD 2009 Peak lean tissue mass accrual precedes changes in bone strength indices at the
proximal femur during the pubertal growth spurt. Bone 44:1186-1190.
55. Wetzsteon, RJ, Petit, MA, Macdonald, HM, Hughes, JM, Beck, TJ, McKay, HA 2008
Bone structure and volumetric BMD in overweight children: a longitudinal study. J Bone
Miner Res 23:1946-1953.
56. Macdonald, HM, Kontulainen, SA, Petit, MA, Beck, TJ, Khan, KM, McKay, HA
2008 Does a novel school-based physical activity model benefit femoral neck bone
strength in pre- and early pubertal children? Osteoporos Int 19:1445-1456.
57. Petit, MA, Beck, TJ, Hughes, JM, Lin, HM, Bentley, C, Lloyd, T 2008 Proximal
femur mechanical adaptation to weight gain in late adolescence: a six-year longitudinal
study. J Bone Miner Res 23:180-188.
58. Petit, MA, McKay, HA, MacKelvie, KJ, Heinonen, A, Khan, KM, Beck, TJ 2002 A
randomized school-based jumping intervention confers site and maturity-specific benefits
on bone structural properties in girls: a hip structural analysis study. J Bone Miner Res
17:363-372.
25
59. Petit, MA, Beck, TJ, Kontulainen, SA 2005 Examining the developing bone: What do
we measure and how do we do it? J Musculoskelet Neuronal Interact 5:213-224.
60. Janz, KF, Gilmore, JM, Levy, SM, Letuchy, EM, Burns, TL, Beck, TJ 2007 Physical
activity and femoral neck bone strength during childhood: the Iowa Bone Development
Study. Bone 41:216-222.
61. Janz, KF, Burns, TL, Levy, SM, Torner, JC, Willing, MC, Beck, TJ, Gilmore, JM,
Marshall, TA 2004 Everyday activity predicts bone geometry in children: the iowa bone
development study. Med Sci Sports Exerc 36:1124-1131.
62. Forwood, MR, Bailey, DA, Beck, TJ, Mirwald, RL, Baxter-Jones, AD, Uusi-Rasi, K
2004 Sexual dimorphism of the femoral neck during the adolescent growth spurt: a
structural analysis. Bone 35:973-981.
63. Chen, Z, Qi, L, Beck, TJ, Robbins, J, Wu, G, Lewis, CE, Cauley, JA, Wright, NC,
Seldin, MF 2011 Stronger bone correlates with African admixture in African-American
women. J Bone Miner Res 26:2307-2316.
64. Nelson, DA, Beck, TJ, Wu, G, Lewis, CE, Bassford, T, Cauley, JA, LeBoff, MS,
Going, SB, Chen, Z 2011 Ethnic differences in femur geometry in the women's health
initiative observational study. Osteoporos Int 22:1377-1388.
65. Nelson, DA, Pettifor, JM, Barondess, DA, Cody, DD, Uusi-Rasi, K, Beck, TJ 2004
Comparison of cross-sectional geometry of the proximal femur in white and black
women from Detroit and Johannesburg. J Bone Miner Res 19:560-565.
66. Nelson, DA, Barondess, DA, Hendrix, SL, Beck, TJ 2000 Cross-sectional geometry,
bone strength, and bone mass in the proximal femur in black and white postmenopausal
women. J Bone Miner Res 15:1992-1997.
67. Wang, XF, Duan, Y, Beck, TJ, Seeman, E 2005 Varying contributions of growth and
ageing to racial and sex differences in femoral neck structure and strength in old age.
Bone 36:978-986.
68. Cauley, JA, Lui, LY, Stone, KL, Hillier, TA, Zmuda, JM, Hochberg, M, Beck, TJ,
Ensrud, KE 2005 Longitudinal study of changes in hip bone mineral density in Caucasian
and African-American women. J Am Geriatr Soc 53:183-189.
69. Travison, TG, Beck, TJ, Esche, GR, Araujo, AB, McKinlay, JB 2008 Age trends in
proximal femur geometry in men: variation by race and ethnicity. Osteoporos Int 19:277287.
26
70. Yates, LB, Karasik, D, Beck, TJ, Cupples, LA, Kiel, DP 2007 Hip structural
geometry in old and old-old age: similarities and differences between men and women.
Bone 41:722-732.
71. Takada, J, Miki, T, Imanishi, Y, Nakatsuka, K, Wada, H, Naka, H, Yoshizaki, T, Iba,
K, Beck, TJ, Yamashita, T 2010 Effects of raloxifene treatment on the structural
geometry of the proximal femur in Japanese women with osteoporosis. J Bone Miner
Metab 28:561-567.
72. Chen, Z, Beck, TJ, Cauley, JA, Lewis, CE, LaCroix, A, Bassford, T, Wu, G, Sherrill,
D, Going, S 2008 Hormone therapy improves femur geometry among ethnically diverse
postmenopausal participants in the Women's Health Initiative hormone intervention
trials. J Bone Miner Res 23:1935-1945.
73. Beck, TJ, Lewiecki, EM, Miller, PD, Felsenberg, D, Liu, Y, Ding, B, Libanati, C
2008 Effects of denosumab on the geometry of the proximal femur in postmenopausal
women in comparison with alendronate. J Clin Densitom 11:351-359.
74. Uusi-Rasi, K, Beck, TJ, Semanick, LM, Daphtary, MM, Crans, GG, Desaiah, D,
Harper, KD 2006 Structural effects of raloxifene on the proximal femur: results from the
multiple outcomes of raloxifene evaluation trial. Osteoporos Int 17:575-586.
75. Uusi-Rasi, K, Semanick, LM, Zanchetta, JR, Bogado, CE, Eriksen, EF, Sato, M,
Beck, TJ 2005 Effects of teriparatide [rhPTH (1-34)] treatment on structural geometry of
the proximal femur in elderly osteoporotic women. Bone 36:948-958.
76. Greenspan, SL, Beck, TJ, Resnick, NM, Bhattacharya, R, Parker, RA 2005 Effect of
hormone replacement, alendronate, or combination therapy on hip structural geometry: a
3-year, double-blind, placebo-controlled clinical trial. J Bone Miner Res 20:1525-1532.
77. Bonnick, SL, Beck, TJ, Cosman, F, Hochberg, MC, Wang, H, de Papp, AE 2009
DXA-based hip structural analysis of once-weekly bisphosphonate-treated
postmenopausal women with low bone mass. Osteoporos Int 20:911-921.
78. Streeten, EA, Beck, TJ, O'Connell, JR, Rampersand, E, McBride, DJ, Takala, SL,
Pollin, TI, Uusi-Rasi, K, Mitchell, BD, Shuldiner, AR 2008 Autosome-wide linkage
analysis of hip structural phenotypes in the Old Order Amish. Bone 43:607-612.
79. Karasik, D, Dupuis, J, Cupples, LA, Beck, TJ, Mahaney, MC, Havill, LM, Kiel, DP,
Demissie, S 2007 Bivariate linkage study of proximal hip geometry and body size
indices: the Framingham study. Calcif Tissue Int 81:162-173.
80. Rivadeneira, F, van Meurs, JB, Kant, J, Zillikens, MC, Stolk, L, Beck, TJ, Arp, P,
Schuit, SC, Hofman, A, Houwing-Duistermaat, JJ, van Duijn, CM, van Leeuwen, JP,
Pols, HA, Uitterlinden, AG 2006 Estrogen receptor beta (ESR2) polymorphisms in
27
interaction with estrogen receptor alpha (ESR1) and insulin-like growth factor I (IGF1)
variants influence the risk of fracture in postmenopausal women. J Bone Miner Res
21:1443-1456.
81. Beck, TJ, Petit, MA, Wu, G, LeBoff, MS, Cauley, JA, Chen, Z 2009 Does obesity
really make the femur stronger? BMD, geometry, and fracture incidence in the women's
health initiative-observational study. J Bone Miner Res 24:1369-1379.
82. Szulc, P, Uusi-Rasi, K, Claustrat, B, Marchand, F, Beck, TJ, Delmas, PD 2004 Role
of sex steroids in the regulation of bone morphology in men. The MINOS study.
Osteoporos Int 15:909-917.
83. Looker, AC, Beck, TJ, Orwoll, ES 2001 Does body size account for gender
differences in femur bone density and geometry? J Bone Miner Res 16:1291-1299.
28
Chapter 3: Methods
Overview of Methods
The purpose of this dissertation is to examine the relationship between measures
of muscle load and bone modeling response among older adults. Bone modeling, the
process that continually adjusts bone strength in response to prevalent loading forces
throughout an individual’s lifespan, may play an important role in bone fragility with age.
As discussed in Chapter 2, bone modeling has been described in terms of the Frost
Mechanostat, in which bone tissue is added or removed to ensure that minute
deformations (strains) of bone tissue remain within some ‘normal’ range 1. Individuals
with a deficient modeling response would be expected to add less bone in response to
load stimuli and thus have reduced skeletal strength and greater susceptibility to fracture.
Since strains cannot be directly measured in vivo, we can evaluate modeling
response by looking at stresses, which are proportional to strains. The femoral stress
estimate used in the analyses described below is based on a calculation of the stress
generated at the medial cortex of the proximal femoral neck in a one legged stance. The
advantage of measuring stresses in this region is that the medial cortex experiences the
highest forces from standing, gait and other load bearing activities and is therefore
structurally stronger than other areas along the cortex. Bone structural strength in this
area of the bone will have adapted to forces generated under normal loading conditions
and should therefore tell us something about how the bone has adapted to these loads. We
can get a measure of femoral stress using body weight, but unless we use the right force
(or correct for it) we can’t tell from that measure alone if higher stress is due to reduced
muscle load or a deficiency in response to load. To differentiate the reasons for a high
29
stress measure, we would either need 1) an invasive way to confirm deficiencies in
cellular response to load, or 2) a direct measure of muscle forces to see if one has higher
stress than expected given the level of force experienced. As a result, the best we can do
is account for measures of muscle load (approximations of force) in our assessment of
femoral stress. The analyses proposed below will evaluate the associations of multiple
measures of muscle loading forces with femoral stress. The aims of this study will be
addressed in three separate papers using data from the Health ABC study and the 3rd and
4th Baltimore Hip Study.
Aim 1 (Health ABC visit 1)
The first paper will provide a cross sectional evaluation of the association of measures of
muscle load with femoral stress among healthy older adults ages 70-79 participating in
the Health ABC study at visit 1. The purpose of this aim is to determine the amount of
variation in stress explained by different sources of muscle load including lean mass and
muscle strength. Linear regression will be used to model these associations separately for
men and women.
Aim 2 (Health ABC all visits)
In the second paper, continuous time survival models will be used to determine whether
femoral stress at visit 1 among older adults participating in Health ABC is associated
with time to first fracture. If higher stresses indicate a reduced response to load and
therefore a weaker bone, individuals with higher stresses at visit 1 may be more likely to
fracture compared to individuals with lower stresses.
30
Aim 3 (BHS 3 & 4)
The third paper will provide a cross sectional evaluation of the association of measures of
muscle load with femoral stress among older women with a hip fracture participating in
the 3rd and 4th waves of the Baltimore Hip Studies. Linear regression will be used to
model these associations. This paper will also address whether femoral stress changes
the year following fracture. Mixed effect models will be used to evaluate the change in
femoral stress.
Overview of Data Sets
Health Aging and Body Composition (Health ABC)
The Health Aging and Body Composition (Health ABC) study cohort includes
3,075 black men and women recruited from Pittsburgh, Pennsylvania and Memphis
Tennessee in the period between March 1997 through July 1998. White participants were
recruited from a random sample of Medicare beneficiaries using zip codes from these two
cites and black participants were recruited from all age eligible residents in the same
geographic areas. To be eligible for the study, older adults had to be between 70-79 years
of age during the recruitment period and have no self reported disability. This included
self report of no difficulty with walking one quarter mile or climbing 10 stairs without
resting; no difficulty performing activities of daily living; and no reported use of an
ambulatory aid including a cane, walker, crutches or other special equipment. Eligibility
criteria also included no history of active treatment for cancer in the prior 3 years, no
enrollment in a lifestyle intervention trial and no plan to move out of the area in the
following 3 years 2.
31
The baseline visit included a home interview to collect information on self
reported health status, weight history, physical function and activity, work and volunteer
activities, appetite and eating behavior, smoking and alcohol use, sleep habits, pain,
chronic conditions, osteoporosis and falls, medical conditions, health care and social
support. Home interviews were followed by a clinic visit during which participants were
asked to complete physical performance tests, provide a fasting blood sample, and
undergo a bone density scan for measures of body composition and BMD. An inventory
of medications currently being taken along with height, weight and cognitive status were
also assessed during the baseline clinic visit. Participants were followed annually for a
total of 13 years. Follow-up clinic visits included similar measures of function, health and
behavior. Home exams were offered to participants who were unable or unwilling to
return. If home exams were refused, a telephone interview was obtained using a proxy
informant identified by the participant during previous visits if necessary 2.
The Health ABC study data is ideal for addressing Aims 1 and 2 because the data
include DXA scans previously analyzed using the HSA software which is necessary for
computing femoral stress. Further, this data set also contains robust measures of lean
mass and muscle strength necessary to evaluate the associations described in Aim 1.
Finally, this study objectively measured occurrences of incident fracture from hospital
data allowing us to evaluate the association between femoral stress and time to fracture
described in Aim 2.
Baltimore Hip Studies (BHS) 3 & 4: Hip Fracture in Women
Participants in BHS3, a prospective cohort study of hip fracture recovery, were
recruited from two area hospitals from 1992-1995. Participants were recruited within 48
32
hours of admission and included 205 Caucasian women age 65 years or older with a new
hip fracture of the proximal femur. Women were excluded if they had a pathologic or
distal femur fracture, or if they were admitted more than 48 hours following fracture 3.
Participants were evaluated at 3 and/or 10 days after hospital admission and at 2, 6 and
12 months later 3. Participants in BHS4, a prospective randomized trial of a year-long
home-based exercise program following usual care for hip fracture, were recruited from
three area hospitals from 1998-2004. Participants were recruited within 15 days of
fracture and included 180 community dwelling women 65 years or older admitted within
72 hours of a nonpathological hip fracture. Women were excluded if they had a
pathologic fracture, cardiovascular, neurologic and respiratory diseases, bone disease,
metastatic cancer, cirrhosis, end stage renal disease, hardware in the contralateral hip and
conditions that increased risk of falling while exercising. Women were also excluded if
they could not walk without assistance from another person prior to fracture or if they
scored lower than 20 on the Mini Mental State Examination. Participants were evaluated
at 10 days after enrollment and at 2, 6, and 12 months later 4. Measures were collected at
the clinical site or, when this was not possible, in the participant’s home.
The BHS-3 & 4 study data is ideal for addressing Aim 3 because the data include
DXA scans previously analyzed using the HSA software which is necessary for
computing femoral stress as well as robust measures of lean mass and muscle strength.
The BHS-3 & 4 also captures these measures at multiple points in time during the year
following a hip fracture allowing us to investigate how femoral stress changes during a
time period when significant changes in bone and muscle occur 3,5-7
33
Measures
Central to all three study aims is femoral stress which can be computed at the hip
from DXA derived geometry combined with information about forces in a simple
engineering model using a single body weight force 8,9. Bone geometry was measured
using the Hip Structural Analysis (HSA) program developed at The Johns Hopkins
University 10 which employs conventional DXA image data to derive geometric
properties of bone cross-sections using principals first described by Martin and Burr
11
.The HSA software generates geometry from profiles of pixel values traversing the
proximal femur at its narrowest point when viewed in a frontal plane DXA image (Fig
3.1 & 3.2).
Figure 3.1. HSA pixel profiles of bone cross sections measured at the narrow neck (NN),
intertrochanteric (IT) and shaft regions of the femur. Only measures at the NN are used in
the femoral stress calculation.
34
Figure 3.2. Dimensions from the HSA profile at the narrow neck region used to calculate
femoral stress
The software also provides information to locate the center of the femoral head in order
to estimate the weight vector, the neck-shaft angle, the vector distances to the femoral
neck cross-section, the abductor force and the ground reaction force vectors. Femoral
stress was estimated on the medial aspect of the neck cross-section using a body weight
load as follows:
σ =My + F
I
A
where M represents the net bending moment orthogonal to the neck axis, I is the HSAderived cross sectional moment of inertia (CSMI) for bending in the frontal plane; A
represents the bone surface in the cross section (CSA); y represents the displacement of
the medial surface from the neutral axis and F represents axial component of loading
force. Forces were computed at the medial cortex of the femoral neck in a one legged
stance using body weight (in Newtons) and femur length estimated from height using
forensic formulas 12. Using the formalism employed by McLeish et al, the gravitational
load on the femoral head (joint force) is assumed to be 5/6 body weight, the ground
35
reaction forces at the knee is 8/9 of body weight and the abductor force is oriented at an
angle of 19 degrees to the horizontal with the magnitude computed to achieve static
equilibrium 13. Forces in the frontal plane are represented in the left diagram in Figure 1,
and are resolved to their x and y components in the right (i.e., FMx, FMy, Fjx,Fjy). Forces
are then balanced to ensure that all components in the x and y directions sum to zero
(Figure 3.3).
Figure 3.3: Estimating Femoral Stress. W1 refers to body weight, Fm to the abductor
muscle force, and Fj to the joint force at the femoral head. NL= Neck Length; d= distance
from the center of mass; TL= distance from the weight vector to the outer surface of the
greater trochanter; SL= shaft length is computed from forensic formula on height: α =
neck shaft angle from HSA ; β= 10 degrees; θ= 19 degrees. A detailed description of the
stress calculation is included in the Appendix.
36
The main dependent and independent variables and the potential confounders are
described in detail below organized by study aim. The description provides a brief
justification for each variable and how each variable will be operationalized.
Acknowledging the gender differences in bone strength and body composition, all
analyses will be performed separately for men and women 14-16.
Aim 1
Main Dependent variable:
Femoral stress will be estimated using the methodology described above from DXA data
collected during visit 1.
Main Independent variables:
Muscle strength Isokinetic knee extension strength was measured using a KinCom 125
AP dynamometer (Cattanooga, TN) at 60o per second. The average strength from three
reproducible trials was used. Participants with a medical condition including a systolic
blood pressure >=200 mmHG, diastolic blood pressure >=110 mmHG or who reported a
history of cerebral aneurysm, cerebral bleeding, bilateral total knee replacement, or
severe bilateral knee pain were excluded from testing 2.
Lean mass was measured using whole body DXA scans conducted on Hologic 4500A
machines at both study centers (Hologic, Waltham, MA) 17.
Covariates:
Age in years will be treated as a continuous variable in final analyses. Age is included as
a covariate because it is associated with declines in bone strength, muscle mass, and
muscle strength 18-21. Age is also associated with declines in physical activity 22,23, most
likely as a result of declines in muscle mass and/or strength 2,24. Individuals should adapt
37
to changes in load over time and therefore stresses should change little with increasing
age. However if age is associated with a reduced response to load, age may explain why
older individuals have higher femoral stress compared to younger individuals.
Race categories will include Caucasian and African Americans. Race is included as a
covariate because previous studies have shown that bone strength and muscle mass are
higher in older African Americans compared to Caucasians which may explain
differences in femoral stress observed among individuals 25-27 .
Health status will be measured using the number of self reported co-morbidities from the
following conditions: osteoarthritis, coronary heart disease including a history of angina,
coronary artery disease or myocardial infarction, congestive heart failure, stroke,
dementia, diabetes, chronic obstructive pulmonary disease, and peripheral vascular
disease. These conditions are associated with weaker bones, lower lean mass and/or
weaker muscle strength 28-35. Health status will also be measured using self rated health
which will be categorized as excellent/very good, good, or fair/poor. Self rated health
status is associated with a number of health related outcomes including disability and
mortality 36-39
Medications will be measured as number of all medications taken at the time of the visit 1
interview and will serve as another measure of morbidity and health.
Site will be included as a covariate in order to control for differences across the two data
collection locations.
Aim 2
Main Dependent variable:
38
Time to first fracture will be calculated as the time from visit 1 to date of first fracture
during the 13 year follow-up period. Incident fractures were assessed every 6 months by
self report during the study follow-up period. All fractures were validated by radiology
reports and adjudicated as confirmed, uncertain or no fracture. Adjudication was
complete for fractures reported through 12/31/08 for both Memphis and Pittsburgh
clinical sites. For this analysis, fractures were counted if they were adjudicated as
“confirmed” and if they were defined as fragility, traumatic or stress fractures.
Main Independent variables:
Femoral Stress will be estimated using the methodology described above from DXA data
collected during visit 1.
Covariates:
Age in years will be treated as a continuous variable in final analyses. Age is included as
a covariate because it is associated with increased risk of hip fracture 40-42 and may also be
associated with differences in femoral stress.
Race categories will include Caucasian and African Americans. Race is included as a
covariate because previous studies have shown that hip fracture rates are lower in African
Americans than Caucasians 43 and may also be associated with differences in femoral
stress.
Health status will be measured using the number of self reported co-morbidities from the
following conditions: osteoarthritis, coronary heart disease including a history of angina,
coronary artery disease or myocardial infarction, congestive heart failure, stroke,
dementia, diabetes, chronic obstructive pulmonary disease, and peripheral vascular
disease. These conditions are associated with weaker bones, lower lean mass and/or
39
weaker muscle strength 28-35. Health status will also be measured using self rated health
which will be categorized as excellent/very good, good, or fair/poor. Self rated health
status is associated with a number of health related outcomes including disability and
mortality 36-39.
Physical Function will be measured using the short physical performance battery (SPPB)
at visit 1. Individuals were scored on a scale of 0 (worst performance) to 12 (best
performance) based on their walking speed, ability to do chair stands and standing
balance 44. Walking speed scores were based on the fastest trial (m/sec) over 3, 4 or 6
meters. A score of 0 was assigned if unable to complete, and among those who did
complete the walk, scores were based on walking speed where 1 represented the slowest
and 4 the fastest pace. Chair stands were scored based on ability to complete 5
consecutive stands. A score of 0 was assigned if unable to complete, and among those
who did complete 5 stands, scores were based on time where 1 was the slowest and 4 the
fastest time to complete. Standing balance scores were assigned based on ability to
balance in semi-tandem and tandem stands. A score of 0 was assigned if unable to hold
semi-tandem balance for at least 1 second, and among those who could balance, scores
were assigned based on balance time. Similarly, a score of 0 was assigned if unable to
hold tandem balance for at least 3 seconds, and among those who could balance, scores
were assigned based on balance time 44.
Aim 3
Data will be pooled across BHS3 &4 studies.
Main Dependent Variable:
40
Femoral Stress will be estimated using the methodology described above from DXA data
collected during baseline and at 2, 6 and 12 months following fracture. In BHS4, scans
were taken on both Hologic and Lunar prodigy scanners. Only Hologic scans were used
in these analyses; the HSA program for analyzing lunar data requires additional
calibration algorithms which may affect comparability of the data.
Main Independent Variables:
Muscle strength was measured using grip strength at the time of baseline assessment.
Grip strength was measured using a JAMAR dynamometer using the average of two
trials. The maximum measure will be used. Since grip strength measurement was not
available in many women within 10 days following hip fracture, grip strength measured
at 2 months will be used as the baseline measure.
Lean mass will be measured using whole body DXA scans taken at the baseline visit.
Whole body DXA scans were conducted on Hologic QDR 1000 machines.
Covariates:
Age in years will be treated as a continuous variable in final analyses. Age is included as
a covariate because it is associated with declines in bone strength, muscle mass, and
muscle strength 18-21. Age is also associated with declines in physical activity 22,23, which
may result in declines in muscle mass and/or strength 2,24 and may be associated with
femoral stress.
Health status will be measured using the number of self reported co-morbidities from the
following conditions: osteoarthritis, coronary heart disease including a history of angina,
coronary artery disease or myocardial infarction, congestive heart failure, stroke,
dementia, diabetes, chronic obstructive pulmonary disease, and peripheral vascular
41
disease. These conditions are associated with weaker bones, lower lean mass and/or
weaker muscle strength 28-35. Health status will also be measured using self rated health
which will be categorized as excellent/very good, good, or fair/poor. Self rated health
status is associated with a number of health related outcomes including disability and
mortality 36-39.
Study Samples
A DXA scan is required in order to calculate femoral stress. Therefore, the analytic
samples from each of the studies will be restricted to those individuals who have a DXA
scan in addition to measures of lean mass, muscle strength and physical activity and
complete covariate data. Figures 1-3 below present the sample available for each analysis
by study aim.
Study Sample for Aim 1
3,075 Enrolled (Health ABC visit 1)
3042 with DXA Scans
16 without enough information to
estimate femoral stress
3,026 with a femoral stress calculation
88 missing lean mass
376 missing grip strength
4 missing general health
19 missing medication
2,539 with complete data
42
Main independent variables include lean mass and isokinetic knee strength. Covariates
include age, race, co-morbidities, general health status, and number of medications
known to effect bone.
Study Sample for Aim 2
3,075 Enrolled (Health ABC visit 1)
3,042 with DXA Scans
16 without enough information to
estimate femoral stress
3,026 with a femoral stress calculation
88 missing lean mass
376 missing grip strength
4 missing general health
78 missing SPPB
2,480 complete data
Covariates include age, race, co-morbidities, general health status, and physical
performance as measured by the SPPB.
43
Study Sample for Aim 3
205 Enrolled in BHS3
180 Enrolled in BHS4
176 with at least one DXA
scan
105 with at least one
DXA scan*
174 with a femoral stress
calculation at baseline
67 with a femoral stress
calculation at baseline
87 with complete data
51 with complete data
138 pooled with
complete data
Main independent variables include lean mass and grip strength. Covariates include age,
co-morbidities, and general health status; the BHS4 sample was restricted to white
women consistent with BHS3. *Only scans taken on Hologic QDR scanners were
analyzed; scans taken on prodigy and lunar scanners were not.
Analysis Plan
Aim 1
Differences in participant characteristics will be compared across quartiles of
femoral stress separately for men and women using the p test for trend. To account for
the confounding effects of body size on lean mass and knee strength, total body lean mass
will be divided by total body mass to compute percent lean mass; and isokinetic knee
44
strength will be divided by total body mass to compute isokinetic knee strength
proportion, a measure knee strength adjusted for body mass. To quantify the strength of
linear relationship, we will compute partial correlations of femoral stress with percent
lean mass and isokinetic knee strength proportion adjusted for study site. Linear
regression will be used to model the association of femoral stress with percent lean mass
and isokinetic knee strength proportion. Four sets of gender stratified models will be
generated. The first set of models will include percent lean mass and study site. The
second set of models will include isokinetic knee strength proportion and site. The third
set of models will include both percent lean mass and isokinetic knee strength proportion,
and site. The fourth set of models will include both percent lean mass, isokinetic knee
strength proportion, site and all covariates described above. All analyses will be
conducted using STATA statistical software version 9 (Stata, College Station, TX, USA).
Aim 2
Differences in participant characteristics will be compared by fracture status using t-tests
for continuous variables and chi square tests for categorical variables. To best illustrate
the relationship between femoral stress and fracture, femoral stress will be categorized
into tertiles and treated as a categorical variable in final analyses. The proportion of
individuals without a fracture over time will be compared across tertiles using the log
rank test. Poisson regression will be used to compute and compare incidence rates of
fracture (per 1000 person years) across tertiles of femoral stress. Cox proportional
hazards regression will be used to model the association between tertile of femoral stress
and time to first fracture. Models will be run separately for men and women. Participants
who did not experience a fracture before 12/31/08 will be censored at the date of last
45
follow or 12/31/08, whichever came first. Fully adjusted models will include the
covariates described above. Total body lean mass and isokinetic knee strength will be
scaled by body size consistent with a previous study evaluating the relationship between
lean mass, grip strength and femoral stress (Reider paper 1). All analysis will be
conducted using STATA statistical software version 9.0 (Stata, College Station, TX,
USA).
Aim 3
Differences in participant characteristics will be compared across quartiles of
femoral stress using the p test for trend. To account for the confounding effects of body
size on lean mass and grip strength, total body lean mass will be divided by total body
mass to compute percent lean mass; and grip strength will be divided by total body mass
to compute grip strength proportion. Partial pearson correlation coefficients will be used
to quantify the correlation of femoral strength with percent lean mass, and grip strength
proportion after adjustment for study group (BHS3 or BHS4). Linear regression will be
used to model the associations of femoral stress with percent lean mass and grip strength
proportion at baseline. Four models will be fit. The first model will include percent lean
mass and study group. The second model will include grip strength proportion and study
group. The third model will include both percent lean mass and grip strength proportion,
and group. The fourth model will include both percent lean mass, grip strength
proportion, group and all covariates described above. Mixed effect models with random
intercepts will be fit to evaluate the change in femoral stress over the 12 months
following fracture where time will be included as an indicator variable for 2, 6, and 12
46
months. Models will be adjusted for covariates described above. All analyses will be
conducted using STATA-V9.
Limitations
While DXA scans can be used to measure bone structural dimensions, they were
not designed to do so. Forces in bending (CSMI) and compression (CSA) can only be
measured in the two dimensional plane of the image. Therefore accuracy of the measures
depends on the consistency of femur positioning on the scanner which can be difficult in
older adults who may not be very flexible 10. Measurement accuracy also depends on the
quality of the image. Noisy or blurred images make it difficult to detect bone edges. This
problem tends to be worse in heavy individuals and images from some scanner models
that use faster scanner modes 10. Bias in measures of the femur dimensions from DXA as
well as bias in the assumptions made regarding the forces in a one legged stance could
over or underestimate femoral stress. Forces to which the individual adapts are only
crudely estimated for the stress calculation. This introduces an uncertainty which can
limit the ability to distinguish individuals with abnormally high or low responses to
maximal loading forces that this study will attempt to address.
HSA results are more consistent when applied to studies with a large number of
DXA scans because of the issues with poor image quality. Effects may not be detectable
in these analyses particularly in the hip fracture population because the samples are small.
Despite these measurement limitations, HSA has been widely used to provide insight into
the structural changes in different populations and under different conditions.
47
Strengths
This study will evaluate a novel measure of bone adaptation using data that has
previously been analyzed using the Hip Structural Analysis program. The interpretation
of HSA output is complicated by the fact that the structural dimensions are not
independent therefore requiring the interpretation of multiple single parameters that
interact in complex ways to influence bone strength. The femoral stress calculation
provides a composite measure that provides a more easily interpreted single parameter. It
should also be recognized that the stress is computed at one of the femur’s strongest
points under conditions where it is very unlikely to fail. This however is an advantage of
the method since the goal is mainly to evaluate the response of bone to load stimuli under
“normal” physiologic condition and not under those very different modes where the
femur is likely to fracture. This is also advantageous from a technical perspective
because physiological loading concentrates load on the thicker medial cortex rather than
on the thinned lateral region where local buckling failure is likely. Local buckling failure
is far more difficult to predict and requires data not available in 2D DXA.
There are other technologically more sophisticated methods of measuring bone
strength dimensions non-invasively including finite element analysis and three
dimensional CT. However these methods are not as easy to apply as the HSA and require
three dimensional scans which are not obtained as often in the clinical setting. DXA
scans from both clinical practice and research are widely available and the HSA software
can be used with minimal training.
Data from the Health ABC study and the Baltimore Hip Study allows us to
evaluate the relationship between different indices of mechanical load and femoral stress
48
in diverse populations ranging from healthy, well functioning older men and women to
women following hip fracture. These studies include well validated and widely used
measures of body composition and muscle strength. While many studies have used lean
mass as a proxy for mechanical load, it is unclear how to best characterize load and
therefore the loads to which bones adapt and this study will explore the independent and
joint effects of muscle mass and muscle strength. Further, these studies include data
capture over multiple points in time providing the opportunity to determine if femoral
stress predicts incident fracture (aim 2) and whether femoral stress changes the year
following fracture (aim 3).
49
Power Calculation
Aim 1
Regression- for men and women pooled
N
0.2
100% (n=3035)
1.0
90% (n=2,732)
1.0
80% (n=2,428)
1.0
70% (2,125)
1.0
60% (n=1,821)
1.0
50% (n=1,518)
1.0
Slope (Effect Size)
0.5
1.0
1.0
1.0
1.0
1.0
1.0
0.8
1.0
1.0
1.0
1.0
1.0
1.0
The table provides the power (%) for detecting an association between lean mass and
femoral stress assuming different sample sizes for small, medium and large effect sizes.
If all participants who had a DXA scan analyzed using HSA have no missing covariate
data, there will be 3,035 participants. Even assuming that half of these participants have
missing data, we will still be able to detect a significant association between lean mass
and femoral stress. The detectable association is interpreted as a standardized slope: the
mean difference in femoral stress (in standard deviations) per standard deviation of lean
mass. Therefore, if the true slope of the line obtained by regressing standardized femoral
stress against standardized lean mass is 0.2 (small effect, in other words there is a mean
difference of 0.2 standard deviations in femoral stress for every standard deviation
difference in lean mass), we will be able to reject the null hypothesis that this slope
equals zero (ie: there is no relationship between femoral stress and lean mass) with 100%
power. The type I error probability associated with this test of this null hypothesis is 0.05.
Power is also calculated for standardized grip strength. Therefore the power estimates
will be the same.
50
Regression- for men and women separately
Men
N
100% (n=1475)
90% (n= 1328)
80% (n= 1180)
70% (n= 1033)
60% (n= 885)
50% (n=738)
0.2
1.00
1.00
1.00
1.00
1.00
1.00
Slope (Effect Size)
0.5
1.00
1.00
1.00
1.00
1.00
1.00
0.8
1.00
1.00
1.00
1.00
1.00
1.00
0.2
1.00
1.00
1.00
1.00
1.00
1.00
Slope (Effect Size)
0.5
1.00
1.00
1.00
1.00
1.00
1.00
0.8
1.00
1.00
1.00
1.00
1.00
1.00
Women
N
100% (n=1567)
90% (n= 1410)
80% (n= 1254)
70% (n= 1097)
60% (n= 940)
50% (n= 784)
Even when analyzing the association between mechanical load and femoral stress
separately for men and women, we are adequately powered to detect a significant
association for small, medium and large effect sizes.
Aim 2
Survival Analysis
% high femoral stress
10%
30%
50%
HR
1.2
0.778
0.986
0.995
1.5
1.00
1.00
1.00
1.8
1.00
1.00
1.00
2.0
1.00
1.00
1.00
The table provides the power to reject the null hypothesis that the survival curves for
individuals with high versus low femoral stress are equal for different hazard ratios for
51
non vertebral fracture assuming no missing data (n=3035). These estimates are based on
an accrual interval of 1 year (patients were enrolled between 1997-1998), and a follow up
time of 9 years (fracture status was ascertained and adjudicated through 2009). We are
assuming a median time of 3.5 years to first non vertebral fracture based on a previous
study looking at the association between inflammatory cytokines at visit 1 and incident
fracture (Cauley et al, 2007).
If 10% of the population has high femoral stress and the true hazard ratio is 1.2, we will
be able to reject the null hypothesis that the survival curves for low and high femoral
stress are equal with 78% power. The Type I error probability associated with this test of
this null hypothesis is 0.05.
Aim 3
Regression
N
100% (n=241)
90% (n=217)
80% (n=193)
70% (n=169)
60% (n=145)
50% (n=121)
Slope (Effect Size)
0.5
1.00
1.00
1.00
1.00
1.00
1.00
0.2
0.872
0.835
0.790
0.734
0.667
0.587
0.8
1.00
1.00
1.00
1.00
1.00
1.00
The table provides the power (%) for detecting an association between lean mass and
femoral stress assuming different sample sizes for small, medium and large effect sizes.
If all participants who had a DXA scan analyzed using HSA have no missing covariate
data, there will be 241 participants. The detectable association is interpreted as a
standardized slope: the mean difference in femoral stress (in standard deviations) per
standard deviation of lean mass. Therefore, if the true slope of the line obtained by
52
regressing standardized femoral stress against standardized lean mass is 0.2 (small effect,
in other words there is a mean difference of 0.2 standard deviations in femoral stress for
every standard deviation difference in lean mass), we will be able to reject the null
hypothesis that this slope equals zero (ie: there is no relationship between femoral stress
and lean mass) with 66% power. The type I error probability associated with this test of
this null hypothesis is 0.05.
Power decreases with decreasing sample size for detecting small effects, however there is
adequate power to detect larger effects. Power is also calculated for standardized grip
strength. Therefore the power estimates will be the same.
53
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58
Chapter 4: Evaluating the Relationship between Muscle and Bone Modeling
Response in Older Adults1
Introduction
Bone fragility in older adults leads to osteoporotic fracture, resulting in high health
care costs, poor health outcomes, disability and death 1. Most investigations of bone
fragility have focused on the apparent remodeling imbalance between bone formation and
resorption that reduces bone mineral density (BMD) with age 2-5. However, anomalies in
bone modeling may also play a critical role in the skeletal fragility of aging. Modeling is
the process that continually adjusts skeletal strength to resist prevalent forces (loads)
from physical activity throughout the human lifespan 6,7. The mechanism has been
described in terms of the Frost Mechanostat, where bone tissue is added or removed to
ensure that minute deformations (strains) of bone tissue remain within some ‘normal’
range 8. Bones may become weak because they either adapted to reduced loads or
because they have a diminished response to load. Individuals with a normal response to
load as a result of disuse (e.g. becoming sedentary) will have strains that remain just
under the upper threshold and will have weaker bones in absolute terms. Individuals with
a diminished response to load (ie: abnormal bone modeling) will require higher strains to
cause bone formation. In other words, the upper threshold has increased which means
less bone tissue and higher strains relative to typical loading forces. Measuring bone’s
modeling response may better capture aspects of bone homeostasis that determine bone
strength than a simple measure like BMD cannot.
1
Lisa Reider, Jay Magaziner, Thomas Beck, Dawn Alley, Michelle Shardell, Ram Miller, John
Schumacher, others TBD. Manuscript in preparation.
59
The forces that stimulate bone modeling are generally believed to be muscle load
induced strains or minute deformations of loaded tissue detected by osteocytes distributed
within the lacunar spaces 9. Assuming that the skeleton is fully adapted to prevalent
loading forces, the strains generated by those loads should be an index of bone modeling
response. We expect that individuals with a deficient response have less bone mass
relative to the forces the bone typically experiences which will result in higher than
average strains. Currently, skeletal tissue strains cannot be measured non-invasively.
Fortunately, stresses, which are proportional to strain, quantify the concentration of
loading force at a specific location on the bone and can be computed using measurements
of bone geometry with loading information incorporated into an engineering model.
In this paper we use an engineering model to compute stress generated at the
medial cortex of the femoral neck in a one-legged stance. Stress is concentrated at this
site under stance loads and the bone tissue remains physiologically preserved in older age
. Stress (‘force concentration’) is a function of bone geometry (dimensions) and the
10
direction and magnitudes of loading forces. Our model employed DXA derived hip
geometry combined with information about forces at the hip under a single body weight
force 11,12. We know from biomechanical studies that hip forces are a multiplicative
function of body weight that increases with the type and intensity of physical activity that
the individual normally experiences. Unless we use the right force (or correct for it) in
our current model, we cannot tell whether higher stresses are due to reduced muscle load
or a deficiency in response to load. To differentiate the reasons for a high stress measure,
we would either need an invasive way to confirm deficiencies in cellular response to load
or a direct measure of muscle forces to see if one has higher stress than expected given
60
the level of force. As a result, the best we can do is account for measures of muscle load
in our assessment of femoral stress.
The purpose of this paper is to evaluate the associations of lean mass and muscle
strength with the femoral stress measure generated from the engineering model and to
examine the extent to which lean mass and muscle strength account for variation in
femoral stress among a cohort of healthy older adults. The remaining heterogeneity after
accounting for indicators of muscle load should be an index of modeling response.
Results from this study will provide context for future studies of bone modeling response
and provide a method for evaluating the important role that muscle plays in this
relationship.
Methods
Study Population
The Health Aging and Body Composition (Health ABC) study cohort includes
1,491 men (37% black) and 1,584 women (46% black) aged 70-79 at time of enrollment.
Participants were recruited using Medicare beneficiary listings from Pittsburgh,
Pennsylvania and Memphis Tennessee between May 1997 and July 1998. Eligibility
criteria included no self reported difficulty with walking one quarter mile or climbing 10
stairs without resting; no difficulty performing activities of daily living; and no reported
use of an ambulatory aid including a cane, walker, crutches or other special equipment.
Eligibility criteria also included no history of active treatment for cancer in the prior 3
years, no enrollment in a lifestyle intervention trial and no plan to move out of the area in
the following 3 years 13
61
The analytic sample for this analysis included 1,252 men and 1,287 women that
had a DXA scan analyzed using the Hip Structural Analysis (HSA) program and all
measured covariates described below. Compared to the analytic sample, men and women
without all measured covariates were significantly older and reported more co-morbidity
and poorer health, and took more medications. Compared to the analytic sample, men
without all measured covariates were significantly shorter and more likely to be black.
Femoral Stress Estimation
Bone geometry was measured using the Hip Structural Analysis program from
dual energy x-ray absorptiometry data 14. The HSA software generates geometry from
profiles of pixel values traversing the proximal femur at its narrowest point when viewed
in a frontal plane DXA image. The software also provides information to locate the
center of the femoral head in order to estimate the weight vector, the neck-shaft angle, the
vector distances to the femoral neck cross-section, the abductor force and the ground
reaction force vectors. Femoral stress was estimated on the medial aspect of the neck
cross-section using a body weight load as follows:
σ =My + F
I
A
where M represents the net bending moment orthogonal to the neck axis, I is the HSAderived cross sectional moment of inertia (CSMI) for bending in the frontal plane; A
represents the bone surface in the cross section (CSA); y represents the displacement of
the medial surface from the neutral axis and F represents axial component of loading
force. Forces were computed at the medial cortex of the femoral neck in a one legged
stance using body weight (in Newtons) and femur length estimated from height using
62
forensic formulas 15. Using the formalism employed by McLeish et al, the gravitational
load on the femoral head (joint force) is assumed to be 5/6 body weight, the ground
reaction forces at the knee is 8/9 of body weight and the abductor force is oriented at an
angle of 19 degrees to the horizontal with the magnitude computed to achieve static
equilibrium 16. Forces in the frontal plane are represented in the left diagram in Figure
4.1, and are resolved to their x and y components in the right (i.e., FMx, FMy, Fjx,Fjy).
Forces are then balanced to ensure that all components in the x and y directions sum to
zero.
Figure 4.1: Estimating Femoral Stress. W1 refers to body weight, Fm to the abductor
muscle force, and Fj to the joint force at the femoral head. NL= Neck Length; d= distance
from the center of mass; TL= distance from the weight vector to the outer surface of the
greater trochanter; SL= shaft length is computed from forensic formula on height: α =
63
neck shaft angle from HSA ; β= 10 degrees; θ= 19 degrees. A detailed description of the
stress calculation is included in the Appendix.
Muscle Loading Forces
Knee Muscle strength: Isokinetic knee extension strength was measured using a KinCom
125 AP dynamometer (Cattanooga, TN) at 60o per second. The average strength from
three reproducible trials was used. Participants with a medical condition including a
systolic blood pressure >=200 mmHG, diastolic blood pressure >=110 mmHG or who
reported a history of cerebral aneurysm, cerebral bleeding, bilateral total knee
replacement, or severe bilateral knee pain were excluded from testing 13,17.
Total body lean mass was measured from whole body DXA scans conducted on Hologic
4500A machines at both study centers (Hologic, Waltham, MA). 17.
Covariates
Age, race, number of co-morbidities, general health status, and number of medications
were included in final analyses. Age was recorded in years and participants’ race was
categorized as either white or black. Number of co-morbidities was measured via selfreport of the following conditions: osteoarthritis, coronary heart disease including a
history of angina, myocardial infarction, congestive heart failure, stroke, diabetes, and
chronic obstructive pulmonary disease. Health status was measured using self rated
health and categorized as excellent/very good, good, or fair/poor. Medication use was
assessed at the time of the first clinic visit and was treated as a continuous variable
(number of medications taken). Regression analysis also controlled for study site.
Statistical Analysis
64
Differences in participant characteristics were compared across quartiles of
femoral stress separately for men and women using a non parametric test for trend across
ordered groups. To account for the confounding effects of body size on lean mass and
knee strength, total body lean mass was divided by total body mass to compute percent
lean mass; and isokinetic knee strength was divided by total body mass to compute
isokinetic knee strength proportion, a measure knee strength adjusted for body mass. To
quantify the strength of linear relationship, we computed partial correlations of femoral
stress with percent lean mass and isokinetic knee strength proportion adjusted for study
site. Linear regression was used to model the association of femoral stress with percent
lean mass and isokinetic knee strength proportion. Four sets of gender stratified models
were generated. The first set of models included percent lean mass and study site. The
second set of models included isokinetic knee strength proportion and site. The third set
of models included both percent lean mass and isokinetic knee strength proportion, and
site. The fourth set of models included both percent lean mass, isokinetic knee strength
proportion, site and all covariates described above. All analyses were conducted using
STATA statistical software version 9 (Stata, College Station, TX, USA).
Results
Descriptive Characteristics
Demographic information, health status, body composition and bone geometry for
men and women are described in Table 1 by quartile of femoral stress. Mean femoral
stress was higher in women (9.51; SD=1.85 MPas) than in men (8.02; SD=1.43 MPa),
where higher stress indicates weaker bones. For the purposes of illustration, mean BMD,
CSA and CSMI at the femoral neck are also shown by stress quartile. Note that CSA and
65
CSMI, which are used in the calculations of stress, are expectedly lower in higher
quartiles of femoral stress, with an analogous inverse pattern for BMD and femoral stress
(Table 4.1).
Femoral stress was not significantly associated with age, race, health status or
medication use in men; but in women, Whites had lower femoral stress compared with
Blacks (p<0.001) (Table 4.1).
Relationship of Femoral Stress with Body Size and Muscle Loading Forces
Weight is positively associated with femoral stress, owing to the use of weight to
approximate the forces in measuring femoral stress. In both men and women, average
weight increased across quartiles of femoral stress. However, individuals with a higher
proportion of lean mass relative to their total mass had lower femoral stress. Average
percent lean mass was 65% (SD=4.1) in men in the highest quartile of femoral stress
compared with 70% (SD=4.7) in men in the lowest quartile (p<0.001). Similarly, the
average percent lean mass was 53.5% (SD=4.4) in women in the highest quartile of
femoral stress compared with 60% (SD=5.7) in women in the lowest quartile (p<0.001).
Likewise, men and women with higher knee strength relative to their total body mass had
significantly lower femoral stress.
Correlations of femoral stress with percent total body lean mass and isokinetic
knee strength proportion were stronger in women than in men. In both men and women,
the correlation between femoral stress and percent lean mass was stronger (-0.40 in men
and -0.45 in women) than the correlation between isokinetic knee strength proportion (0.16 in men -0.24 in women) (Table 4.2).
66
Table 4.3 provides coefficients from linear regression models of femoral stress
on percent lean mass and isokinetic knee strength proportion. In men, results from model
1 showed that for every percent increase in lean mass, mean femoral stress is 0.122 MPa
lower (95% CI: -0.138, -0.107; p<0.001) and model 2 showed that for every kg-for-kg
increase in isokinetic knee strength proportion, mean femoral stress was 0.62 MPa lower
(95%CI: -0.833, -0.403; p<0.001). Percent lean mass explained more of the variability in
femoral stress (R2=0.1867) than isokinetic knee strength proportion (R2=0.0547). Results
from model 3 showed that when both percent lean mass and isokinetic knee strength
proportion were included in the regression model, only percent lean mass was
significantly associated with femoral stress. The relationship between percent lean mass
and femoral stress did not change after including other covariates in model 4. In women,
results from model 1 showed that for every percent increase in lean mass, mean femoral
stress was 0.152 MPa lower (95% CI: -0.168, -0.135; p<0.001) and model 2 showed that
for every unit increase in isokinetic knee strength proportion, mean femoral stress was
1.49 MPa lower (95% CI: -1.82, -1.16; p<0.001). As in men, percent lean mass explained
more of the variability in femoral stress (R2=0.2374) than isokinetic knee strength
proportion (R2=0.0946). Results from model 3 showed that when both percent lean mass
and isokinetic knee strength proportion were included in the regression model, both were
significantly associated with femoral stress, but the strength of the association between
isokinetic knee strength proportion and femoral stress was substantially diminished (B=0.339 95%CI: -0.0676, -0.001vs. -1.49 95% CI: -1.82, -1.16) after accounting for lean
mass. Results did not change substantially after including other covariates in model 4.
Discussion
67
The purpose of this paper was to evaluate the relations of muscle mass and muscle
strength with a measure of bone modeling response (femoral stress) among older men
and women. Differences in femoral stress may indicate variations in adaptation to load
that lead to weaker, more fracture susceptible bones. In order to effectively compare
individuals, we need to account for variability in modeling response that come from
differences in muscle loading forces. Current methods for estimating femoral stress do
not directly incorporate information about these forces. Here, we accounted for muscle
load by evaluating the relative independent contributions of both lean mass and muscle
strength to femoral stress.
Our data show stronger correlations between percent lean mass and femoral stress
than the correlations between isokinetic knee strength proportion and femoral stress. This
finding is consistent with results from a recent study that examined the cross sectional
relationship between muscle mass, muscle strength and bone strength parameters among
older men and women 18 . Results from that study showed that correlations between
muscle mass and bone strength were twofold higher than the correlations between knee
strength and bone strength parameters. We found that in both men and women,
controlling for percent lean mass accounted for more variability in femoral stress than
controlling for isokinetic knee strength proportion, as measured by the R2. In women,
percent lean mass and isokinetic knee strength proportion were significantly associated
with femoral stress independent of each other and after adjusting for potential
confounders. In men, however, only percent lean mass was significantly associated with
femoral stress in fully adjusted models.
68
In both men and women, 75-80% of the variation in femoral stress remains
unexplained even after accounting for indicators of muscle load and a range of
demographic and health variables. The remaining variability likely reflects individual
variation in bone modeling response to load. Although the focus of this study was on
muscle effects, hormonal and genetic factors also play an important role in the regulation
of this physiologic process and may explain differences in femoral stress among
individuals as well as differences in femoral stress between men and women 7. For
example, it is likely that postmenopausal estrogen deficiency contributes to alterations in
the modeling response resulting in weaker bones and higher stresses in older women
compared to older men.7,19.
This study has several strengths. First, it is novel in the sense that while the
relationship between muscle mass, muscle strength and bone is well established in
children and adolescents 20, these relationships have not been well defined in older adults.
Previous studies in adults have employed models using three dimensional data and finite
element modeling techniques in order to measure bone stresses at the proximal femur
during gait and from the forces generated by a fall 10,21,22,22. These studies demonstrate that
stresses increase in areas of the bone that are structurally weak, but they do not explain
why bones become weak in the first place. Femoral stress, an index of modeling
response, may provide some insight into why some older adults have weaker bones. Our
study builds on previous research by attempting to identify individuals with a reduced
modeling response based on an algorithm using widely available data. The engineering
model we used for these analyses relies on DXA data that is widely available on
individuals participating in large cohort studies and clinical trials using HSA, a technique
69
for deriving bone structural properties from two dimensional data that has been employed
in numerous studies 23-30.
Despite these strengths, there are several limitations. First our measure of femoral
stress involves assumptions about the magnitude and direction of the forces generated in
a one legged stance. In addition, the engineering model of the hip was restricted to the
frontal plane, and the forces on the hip in ambulation are frequently directed out of the
frontal plane. However, current methods for a more complex three dimensional model
require computed tomography, which was only available for a small subset of individuals
in this study. Second, we may not have adequately captured all aspects of load in our
attempt to approximate muscle loading forces. A direct measure of physical activity
would be a valuable addition to this analysis, but the self-report measure of physical
activity available was minimally correlated with femoral stress, percent lean mass and
body mass adjusted knee strength, so it appeared insufficient to assess these relationships.
Third, we assumed that whole body lean mass and leg muscle strength are proportional to
the forces generated at the hip. Our measure of muscle strength relied on knee extension
strength, and although knee extension strength involves thigh muscles that also function
in ambulation, it is not a direct evaluation of hip forces per se. We utilized total lean mass
based on whole body DXA scans, which should be greater in more active individuals.
However, lean mass may not be evenly distributed throughout the body, and we were
unable to measure the muscle regions that most directly determine maximal muscle force
at the hip. Finally, despite our best efforts to ensure that the association between our lean
mass and muscle strength measure with femoral stress was not merely due to their
70
associations with weight (by dividing lean mass and strength by body mass), there is
always the chance of residual confounding due to model misspecification.
In conclusion, results from this study provide insight into bone modeling
differences as measured by femoral stress. Lean mass is an important contributor to
femoral stress, indicating that the measure captures elements of bone’s response to load.
Variation in femoral stress after accounting for lean mass and knee strength, which was
independently associated with femoral stress in women, may provide an indicator of
heterogeneity in the modeling response to load. Future research on femoral stress should
consider incorporating an assessment of muscle loading forces.
71
72
Table 4.1 Continued
73
Table 4.2 Partial correlation of Femoral Stress with percent lean mass and body
adjusted knee strength adjusted for site
Men
Women
Percent lean mass
-0.4014
-0.4534
Body Mass Adjusted Knee
-0.1577
-0.2384
Strength
Table 4.3 Association between Percent Total Body Lean Mass, Total Body Adjusted
Knee Strength and Femoral Stress among Men and Women
Model
Percent Lean
Mass
1
-0.122
(-0.138, -0.107)
---
2
3
4
-0.123
(-0.139, -0.106)
-0.123
(-0.141, -0.106)
Men
(n=1252)
B (95%CI)
Body Mass
Adjusted Knee
Strength
---0.618
(-0.833, -0.403)
0.018
(-0.200, 0.235)
-0.040
(-0.266, 0.483)
R2
0.1867
0.0547
0.1868
0.1916
Percent Lean
Mass
-0.152
(-0.168, -0.135)
---0.144
(-0.162, -0.123)
-0.141
(-0.159, -0.123)
Women
(n=1287)
B (95%CI)
Body Mass
Adjusted Knee
Strength
---1.49
(-1.82, -1.16)
-0.339
(-0.676, -0.001)
-0.365
(-0.711, -0.019)
R2
0.2374
0.0946
0.2397
0.2410
Percent total body lean mass= total body lean mass (kg)/total body mass (kg); Total leg
mass adjusted knee strength= isokinetic knee strength/total body mass (kg)
Model1: Linear regression models adjusted for site only
Model2: Linear regression models adjusted for site only
Model3: Linear regression; models include percent lean mass, body mass adjusted knee
strength and site
Model 4: Linear regression; models include percent lean mass, body mass adjusted knee
strength, site and the following covariates: age, race, number of comorbidities, health
status and number of medications taken
74
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77
Chapter 5: An Estimate of Bone Modeling Response Predicts Incident Fracture in
Older Adults2
Introduction
More than 2 million fragility fractures occur per year accounting for 20 billion
dollars in annual health care costs in the United States. The majority of these fractures
occur among older adults, and the number of fractures is expected to increase by half
over the next decade as the population ages 1. Fracture prevention strategies rely on
effectively identifying older adults at highest risk for fracture. The majority of existing
research on fracture prediction has focused on reduced bone mineral density (BMD)
across the adult lifespan resulting from the apparent remodeling imbalance between bone
formation and resorption 2-5. The remodeling imbalance is thought to influence fragility
by causing a net loss of bone mass when rates of bone resorption outpace rates of
formation 6.
Anomalies in bone modeling, which have been less extensively studied, may also
play a critical role in the skeletal fragility of aging 7. Modeling is the process that
continually adjusts skeletal strength to resist prevalent forces (loads) from physical
activity throughout the human lifespan 8,9. The mechanism has been described in terms of
the Frost Mechanostat, where bone tissue is added or removed to ensure that minute
deformations (strains) of bone tissue remain within some ‘normal’ range 10. Individuals
with a deficient modeling response would be expected to add less bone in response to
load stimuli and thus have reduced skeletal strength and greater susceptibility to fracture
when traumas occur.
2
Lisa Reider, Jay Magaziner, Thomas Beck, Dawn Alley, Michelle Shardell, Ram Miller, John
Schumacher, others TBD. Manuscript in preparation.
78
Assuming that the skeleton is fully adapted to prevalent loading forces, the strains
generated by those loads should be an index of modeling response. We expect that
individuals with a deficient response will have higher-than-average strains than those
with a normal response under equivalent loads, but skeletal tissue strains cannot currently
be measured non-invasively. Fortunately, stresses, which are proportional to strain,
quantify the concentration of loading force at a specific location on the bone and can be
computed using measurements of bone geometry with information on prevalent loads
incorporated into an engineering model.
In this paper, we examine the association between stress and incident fracture. We
use an engineering model to compute stress generated at the medial cortex of the femoral
neck in a one-legged stance. Stress is concentrated at this site under stance loads, and the
bone tissue remains physiologically preserved in older age 11. Stress (‘force
concentration’) is a function of bone geometry (dimensions) and the directions and
magnitudes of loading forces. Our model employed DXA derived hip geometry
combined with information about forces at the hip under a single body weight force 12,13.
A previous study demonstrated that femoral stress was higher among older
women with a history of fracture compared to those with no fracture 12. Building upon
that study, this paper evaluates whether femoral stress predicts incident fracture among
both older women and older men. This paper also examines whether femoral stress
independently predicts fracture after controlling for characteristics associated with both
femoral stress and fracture – most notably, lean mass and strength. Loading forces are
predominantly muscle-generated, and previous research has shown that lean mass and
muscle strength are associated with femoral stress 14. In addition, there is growing
79
recognition that muscle mass and quality play a role in fracture, though these
relationships have not been clearly defined. In order to test the independent association
between femoral stress and fracture, we must account for the main drivers of femoral
stress and adjust for muscle mass and strength. We hypothesize that higher femoral
stress is associated with higher risk of fracture in both men and women independent of
muscle mass and strength.
Methods
Study Population
The Health Aging and Body Composition (Health ABC) study cohort includes
1,491 men (37% black) and 1,584 women (46% black) aged 70-79 years at time of
enrollment. Participants were recruited using Medicare beneficiary listings from
Pittsburgh, Pennsylvania and Memphis Tennessee between May 1997 and July 1998.
Eligibility criteria included no self reported difficulty with walking one quarter mile or
climbing 10 stairs without resting; no difficulty performing activities of daily living; and
no reported use of an ambulatory aid including a cane, walker, crutches or other special
equipment. Eligibility criteria also included no history of active treatment for cancer in
the prior 3 years, no enrollment in a lifestyle intervention trial and no plan to move out of
the area in the following 3 years 15
The analytic sample for this analysis included 1,225 men and 1,255 women that
had a DXA scan at the baseline visit analyzed using the Hip Structural Analysis (HSA)
program and all measured covariates described below. Compared to the analytic sample,
men and women without all measured covariates reported more co-morbidities and
poorer health and had poorer balance and slower walking speed. Men without all
80
measured covariates were more likely to be black and women without all measured
covariates were older, heavier and had weaker knee strength.
Femoral Stress Estimation
Bone geometry was measured using the Hip Structural Analysis program from
dual energy x-ray absorptiometry data 16. The HSA software generates geometry from
profiles of pixel values traversing the proximal femur at its narrowest point when viewed
in a frontal plane DXA image. The software also provides information to locate the
center of the femoral head in order to estimate the weight vector, the neck-shaft angle, the
vector distances to the femoral neck cross-section, the abductor force and the ground
reaction force vectors. Femoral stress was estimated on the medial aspect of the neck
cross-section using a body weight load as follows:
σ =My + F
I
A
where M represents the net bending moment orthogonal to the neck axis, I is the HSAderived cross sectional moment of inertia (CSMI) for bending in the frontal plane; A
represents the bone surface in the cross section (CSA); y represents the displacement of
the medial surface from the neutral axis and F represents axial component of loading
force. Forces were computed at the medial cortex of the femoral neck in a one legged
stance using body weight (in Newtons) and femur length estimated from height using
forensic formulas 17. Using the formalism employed by McLeish et al, the gravitational
load on the femoral head (joint force) is assumed to be 5/6 body weight, the ground
reaction forces at the knee is 8/9 of body weight and the abductor force is oriented at an
angle of 19 degrees to the horizontal with the magnitude computed to achieve static
81
equilibrium 18. Forces in the frontal plane are represented in the left diagram in Figure
5.1, and are resolved to their x and y components in the right (i.e., FMx, FMy, Fjx,Fjy).
Forces are then balanced to ensure that all components in the x and y directions sum to
zero.
Figure 5.1: Estimating Femoral Stress. W1 refers to body weight, Fm to the abductor
muscle force, and Fj to the joint force at the femoral head. NL= Neck Length; d= distance
from the center of mass; TL= distance from the weight vector to the outer surface of the
greater trochanter; SL= shaft length is computed from forensic formula on height: α =
neck shaft angle from HSA ; β= 10 degrees; θ= 19 degrees. A detailed description of the
stress calculation is included in the Appendix.
Muscle Loading Forces
Isokinetic knee extension strength and percent total body lean mass were included
as measures of muscle load. Isokinetic knee extension strength was measured using a
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KinCom 125 AP dynamometer (Cattanooga, TN) at 60o per second. The average strength
from three reproducible trials was used. Participants with a medical condition including a
systolic blood pressure >=200 mmHG, diastolic blood pressure >=110 mmHG or who
reported a history of cerebral aneurysm, cerebral bleeding, bilateral total knee
replacement, or severe bilateral knee pain were excluded from testing 15,19. Total body
lean mass was measured from whole body DXA scans conducted on Hologic 4500A
machines at both study centers (Hologic, Waltham, MA). 19.
Incident Fracture
Incident fractures were assessed every 6 months by self report during the study
follow-up period. All fractures, including date of fractures, were validated by radiology
reports and adjudicated as confirmed, uncertain or no fracture. Adjudication was
complete for fractures reported through 12/31/08 for both Memphis and Pittsburgh
clinical sites. For this analysis, fractures were counted if they were adjudicated as
“confirmed” and if they were defined as fragility, traumatic or stress fractures. A total of
392 fractures were counted and included elbow (2.6%), foot (8.0%), hand (1.3%), knee
(2.8%), lower arm (15.3%), lower leg (6.9%), hip (22.5%), pelvis (5.7%), spine/back
(18.4%), tailbone (2.1%), upper arm (13.0%), and upper leg (1.3%) fractures.
Covariates
Covariates included age, race, number of co-morbidities, general health status,
and physical function. Age was recorded in years and participants’ race was categorized
as either white or black. Number of co-morbidities was measured via self-report of the
following conditions: osteoarthritis, coronary heart disease including a history of angina,
myocardial infarction, congestive heart failure, stroke, diabetes, and chronic obstructive
83
pulmonary disease. Health status was measured using self rated health and categorized as
excellent/very good, good, or fair/poor. Physical function was measured using the short
physical performance battery (SPPB). Individuals were scored on a scale of 0 (worst
performance) to 12 (best performance) based on their walking speed, ability to do chair
stands and standing balance 20. Walking speed scores were based on the fastest trial
(m/sec) over 3, 4 or 6 meters. A score of 0 was assigned if unable to complete and among
those who did complete the walk, scores were based on walking speed where 1
represented the slowest and 4 the fastest pace. Chair stands were scored based on ability
to complete 5 consecutive stands. A score of 0 was assigned if unable to complete and
among those who did complete 5 stands, scores were based on time where 1 was the
slowest and 4 the fastest time to complete. Standing balance scores were assigned based
on ability to balance in semi-tandem and tandem stands. A score of 0 was assigned if
unable to hold semi-tandem balance for at least 1 second and among those who could
balance scores were assigned based on balance time. Similarly, a score of 0 was
assigned if unable to hold tandem balance for at least 3 second and among those who
could balance scores were assigned based on balance time 20. Analysis also controlled for
study site.
Statistical Analysis
Differences in participant characteristics were compared by fracture status using ttests for continuous variables and chi square tests for categorical variables. To best
illustrate the relationship between femoral stress and fracture, femoral stress was
categorized into tertiles and treated as a categorical variable in final analyses. The
proportion of individuals without a fracture over time was compared across tertiles using
84
the log rank test. Poisson regression was used to compute and compare incidence rates of
fracture (per 1000 person years) across tertiles of femoral stress. Cox proportional
hazards regression was used to model the association between tertile of femoral stress
and time to first fracture. Models were run separately for men and women. Participants
who did not experience a fracture before 12/31/08 were censored at the date of last follow
or 12/31/08, whichever came first. Fully adjusted models included the covariates
described above. Total body lean mass and isokinetic knee strength were scaled by body
size consistent with a previous study evaluating the relationship between lean mass, grip
strength and femoral stress (Reider paper 1). All analysis was conducted using STATA
statistical software version 9.0 (Stata, College Station, TX, USA).
Results
Over an 11.7 year period (4,264 days), 131 men and 255 women experienced at
least one incident fracture. Table 5.1 describes femoral stress and its components,
demographic information, health status, muscle mass and muscle strength for men and
women by fracture status. Mean femoral stress was significantly higher in those who
fracture in both men (8.5, SD= 1.5 vs. 7.9, SD=1.4) and women (9.7, SD= 1.8 vs. 9.4,
SD=1.8) where higher stress indicates weaker bones. Cross sectional area (CSA) and
cross sectional moments of inertia (CSMI) at the femoral neck, which were used in the
calculation of stress, were significantly lower in men and women who fracture, as was
BMD. Men who fracture were more likely to be white (p<0.001) and were significantly
older (p=0.034), and women who fracture were more likely to be white (p<0.001) and
had significantly higher percent lean mass (p=0.034) and lower weight (p<0.001).
85
Isokinetic knee strength proportion, health status, standing balance and gait speed were
not significantly associated with fracture in men or women.
The incidence rate of fracture increased with increasing level of femoral stress in
both men and women (Table 5.2). The incidence rate comparing the lowest to highest
tertile was 9.8 vs. 16.0 per 1000 person years in men (p=0.021) and 19.2 vs. 27.1 per
1000 person years in women (p=0.045). Kaplan Meier curves suggest that men and
women in the highest tertile of femoral stress experience a fracture earlier than men in the
middle and lowest tertile (p=0.056 in men; p=0.089 in women) (Figure 5.2).
In unadjusted proportional hazards models, the risk for fracture was greater in the
highest compared to the lowest tertile of femoral stress in women (hazard ratio HR=1.37,
95%CI: 1.00-1.89) and in men (HR=1.56, 95%CI: 1.01-2.42) (Table 5.3). After adjusting
for measures of muscle load estimated by percent total body lean mass and body size
adjusted knee strength, the hazard ratio increased from 1.37 to 1.77 in women and 1.56 to
1.84 in men. Adjusting for additional covariates further strengthened the association
between femoral stress and incident fracture in women (HR= 1.98 95% CI: 1.39-2.83)
though not in men (HR= 1.84, 95% CI: 1.16-2.92). There was no significant difference in
fracture risk between the medium and low tertiles in men and women.
Discussion
The purpose of this paper was to determine whether femoral stress, as an index of
bone modeling response, predicts fracture among older men and women. We
hypothesized that higher femoral stress indicating a deficiency in bone modeling
response would be associated with higher fracture rates; we found that men and women
in the highest tertile of femoral stress had a significantly greater risk of fracture compared
86
to men and women in the lowest tertile of femoral stress, even after additional adjustment
to capture multiple measures of muscle load and other confounding factors.
Prior studies have shown that the forces which generate strains in bone that evoke
the bone modeling response are muscle driven 9,10, and indeed, they account for some of
the variability in femoral stress 14. There are two ways in which bones can become weak.
In the first, an individual with a normal modeling response adjusts bone strength
downward to adapt to low activity or disuse. In the second, an individual with a deficient
modeling response adjusts the bone to a weaker condition than it should for the load it
experiences. In order to detect deficiencies in modeling response we need to measure
stresses generated under maximum load but we are unable to directly measure the
maximal forces on bone. Femoral stress is estimated under a single body weight load but
we know forces are muscle driven and therefore are some multiple of body weight. If
everyone experienced the same forces, then we could say higher femoral stress indicates
deficiencies in load using the current model. But since muscle varies across individuals,
we need to account for that variability. We approximated muscle loading effects by using
measures of lean mass and knee extension strength. We found that the association
between femoral stress and fracture was strengthened after adjusting for muscle mass and
knee strength in both men and women. The relative rate for fracture, comparing the
highest versus the lowest tertile of femoral stress, increased from 56% to 84% in men and
increased from 37% to 77% in women after adjusting for lean mass and strength.
Our study builds upon previous work by demonstrating that (1) incident fractures are
more common among those with higher femoral stress, which may reflect deficiency in
modeling response; (2) the association between higher femoral stress and fracture are
87
significant in men as well as in women; and (3) the association between higher femoral
stress and fracture is stronger after adjusting for variation in muscle loading forces. These
results provide evidence to suggest that some individuals do not respond properly to load
stimuli and therefore build a weaker bone. Measuring bone modeling response and
improving on our ability to do so may have important clinical implications for how we
identify and treat individuals at risk for fracture. A measure of bone modeling response
may enhance other widely used measures of fracture risk such as bone mineral density
(BMD) because it examines the geometric distribution of bone important to evaluating
bone strength. Though BMD serves as an important clinical tool for determining fracture
risk, it does not adequately capture the underlying heterogeneity in skeletal strength. The
purpose of this paper was not to compare the predictive ability of femoral stress versus
BMD, but rather present a new measure of bone strength based on the loading forces that
influence it. Bone modeling response provides a framework for bone fragility different
from the BMD paradigm which may influence the way we think about prevention and
treatment. Most research on pharmacologic treatment to date has focused on drugs that
minimize bone resorption 21-25. However if the mechanism underlying bone fragility is
related to bone modeling deficiencies, future research should aim to develop treatments
that would improve response to load, thereby making weight-bearing activities such as
exercise more efficient.
This study had several strengths, including longitudinal data collection on a large
cohort of older men and women, prospective capture and adjudication of fracture events
and the provision of DXA data from which femoral stress could be estimated. Despite
these strengths, it is important to note limitations of the femoral stress measure. Our
88
measure of femoral stress involves assumptions about the magnitude and direction of the
forces generated in a one legged stance. In addition, the engineering model of the hip was
restricted to the frontal plane, and the forces on the hip in ambulation are frequently
directed out of the frontal plane. However, current methods for a more complex three
dimensional model require computed tomography, which was only available for a small
subset of individuals in this study. Single body weight load likely underestimates the total
force acting on the bone. Ideally, a measure of modeling response would incorporate
muscle forces and future measures should consider how best to do this more directly.
In conclusion, results from this study found that higher femoral stress, which
provides an indicator of bone modeling response, is associated with increased risk of
fracture in older men and women. Bone modeling response may be an important
mechanism underlying bone fragility and fracture susceptibility because it drives the
adaptation of bone strength to withstand the forces the bone experiences. Future research
should focus on refining measurements of bone modeling response that better incorporate
individual variations in muscle load. Results from these studies will provide important
insight into the relationship between bone and muscle that will in turn have important
implications for how we identify those at risk for fracture and for how best to prevent
fractures from occurring.
89
Table 5.1. Baseline Characteristics for Men and Women with and without a
Fracture
Fracture
(n=131)
Men
(n=1225)
No
Fracture
(n=1094)
p
Fracture
(n=255)
Women
(n=1255)
No
Fracture
(n=1000)
p
Femoral Stress and its
components
Femoral Stress MPa,
8.5 (1.5)
7.9 (1.4)
<0.001
9.7 (1.8)
9.4 (1.8)
0.046
mean, (SD)
Weight, kg mean, (SD)
79.4 (11.9)
81.6 (13.3)
0.0704 67.1 (13.7) 70.8 (14.3) <0.001
NN BMD, g/cm2 mean
0.76 (0.13)
0.87 (0.17)
<0.001 0.67 (0.12) 0.76 (0.15) <0.001
(SD)
NN CSA, cm2 mean
2.60 (0.44)
2.94 (0.58)
<0.001 2.02 (0.37) 2.26 (0.44) <0.001
(SD)
NN CSMI (cm4), mean,
2.87 (0.67)
3.16 (0.92)
0.0006 1.67 (0.43) 1.81 (0.50) <0.001
(SD)
Covariates
Percent total body lean
67.7 (4.6)
67.4 (4.7)
0.536
57.4 (5.8)
56.6 (5.3)
0.034
mass, mean % (SD)
Isokinetic knee strength
1.40 (0.34)
1.43 (0.36)
0.354
1.04 (0.30) 1.04 (0.28)
0.995
proportion, mean (SD)
Age, y, mean (SD)
74.2 (2.8)
73.7 (2.9)
0.034
73.6 (2.9)
73.3 (2.8)
0.134
% White
79.4
62.8
<0.001
76.8
49.4
<0.001
Co-morbidities, mean
0.46 (0.75)
0.60 (0.82)
0.084
0.53 (0.73) 0.51 (0.75)
0.695
(SD)
General Health Status
(%)
49.6
47.8
45.9
44.1
Excellent/VG
38.2
36.5
41.9
41.4
Good
12.2
15.7
12.2
14.5
Fair/Poor
SPPB Score, mean (SD)
10.4 (1.4)
10.4 (1.3)
0.853
9.9 (1.5)
9.9 (1.5)
0.626
MPa= mega pascals; NN=narrow neck; BMD= bone mineral density; CSA=cross sectional area; CSMI=
cross sectional moment of inertia. Co-morbidities- number of conditions from the following: osteoarthritis,
angina, MI, CHF, stroke, diabetes, and COPD. Isokinetic knee strength proportion= isokinetic knee
strength/total body mass (g); SPPB= short physical performance battery.
90
Table 5.2. Incidence Rate (per 1000 person years) of Fracture by Tertile of Femoral
Stress
Tertile
Low
P
Medium
P
High
IR (95% CI) value IR (95% CI) value IR (95% CI)
9.8
0.021
11.3
0.129
16.0
Men
(6.5-13.2)
(7.8-14.8)
(11.8-20.2)
19.3
0.045
23.0
0.337
27.1
Women
(14.9-23.7)
(18.1-27.9)
(21.7-32.4)
P value comparing incidence rates in low vs high and medium vs high tertile
Table 5.3. Cox Proportional HR and 95% CIs by Tertile of Femoral Stress
Men
Women
M1
M2
M3
M1
M2
M3
HR
HR
HR
HR
HR
HR
(95% CI)
(95% CI)
(95% CI)
(95% CI)
(95% CI)
(95% CI)
Low
----Med
1.10
1.23
1.25
1.17
1.32
1.33
(0.70-1.75)
(0.77-1.95)
(0.78-2.00)
(0.86-1.61)
(0.95-1.83)
(0.96-1.85)
High
1.57
1.84
1.84
1.37
1.77
1.98
(1.02-2.42)
(1.16-2.92)
(1.16-2.92)
(1.00-1.87)
(1.25-2.50)
(1.39-2.83)
M1=adjusted for site only; M2= adjusted for percent total body lean mass, body size adjusted knee strength
and site; M3= adjusted for total body lean mass, body size adjusted knee strength, age, race, co-morbidities,
general health status, standing balance and walking speed.
In men: Low (3.41-7.36 Mpa); med (7.36-8.49Mpa); high (8.50-14.89 Mpa). In women: Low (3.88-8.57
Mpa); med (8.57-10.13 Mpa); High (10.13-17.50).
91
92
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Chapter 6: Evaluating the Relationship between Muscle and Bone Modeling
Response in Older Women after a Hip Fracture3
Introduction
Bone modeling, the process that continually adjusts bone strength in response to
prevalent loading forces throughout an individual’s lifespan, may play an important role
in bone fragility with age. Bone modeling has been described in terms of the Frost
Mechanostat, in which bone tissue is added or removed to ensure that minute
deformations (strains) of bone tissue remain within some ‘normal’ range 1. Individuals
with a deficient modeling response would be expected to add less bone in response to
load stimuli and thus have reduced skeletal strength and greater susceptibility to fracture.
Forces which generate strains in bone that initiate the modeling originate with
muscle activity 1-3. While these forces cannot be measured directly, measures of lean
mass and muscle strength have been shown to account for some of the variability in
femoral stress, an index of bone modeling response4. Femoral stress (which is
proportional to strain) can be computed at the medial cortex of the femoral neck, a site
where stress is concentrated and bone tissue is physiologically preserved in older age,
using DXA derived bone geometry and information about forces at the hip under a single
body weight load 5. Bone modeling response provides a framework for bone fragility that
examines bone structural strength and incorporates the important loading effects that
come from muscle. Residual heterogeneity in femoral stress after accounting for
measures of muscle load may indicate individual variation in modeling response. These
relationships have been previously examined among a cohort of healthy older adults 4,
3
Lisa Reider, Jay Magaziner, Thomas Beck, Dawn Alley, Michelle Shardell, Ram Miller, John
Schumacher. Manuscript in preparation.
96
and femoral stress has been shown to predict fracture 6. The purpose of this paper is to
evaluate these associations among women with a recent hip fracture. Specifically, we will
evaluate the association of lean mass and muscle strength with femoral stress. This paper
also examines whether femoral stress changes during the year following a fracture, a time
during which significant changes to muscle and bone strength occur 7-12. While bone
becomes structurally weak following fracture, we don’t expect that modeling response to
muscle load changes. This is important for thinking about recovery in the post fracture
period and the impact of building muscle mass and strength on bone.
Methods
The study sample included participants from the third and fourth cohorts of the
Baltimore Hip Studies (BHS-3 & BHS-4). Participants in BHS3, a prospective study of
hip fracture recovery, were recruited from two area hospitals from 1992-1995.
Participants were recruited within 48 hours of admission and included 205 Caucasian
women age 65 years or older with a new hip fracture of the proximal femur. Participants
were evaluated at 3 and/or 10 days after hospital admission and at 2, 6 and 12 months
later 13. Participants in BHS4, a prospective randomized trial of a year-long home-based
exercise program following usual care for hip fracture, were recruited from three area
hospitals from 1998-2004. Participants were recruited within 15 days of fracture and
included 180 community dwelling women 65 years or older admitted within 72 hours of a
nonpathological hip fracture. Participants were evaluated at 10 days after enrollment and
at 2, 6, and 12 months later 14. Both white and black women were enrolled in BHS4;
however, this analysis was restricted to white women because the number of enrolled
black women was small to be consistent with BHS3.
97
The final sample for this analysis included 87 BHS3 participants and 51 BHS4
participants who had at least a baseline Dual Energy X-ray Absorptiometry (DXA) scan
analyzed using the Hip Structural Analysis (HSA) program and all measured covariates
described below. Compared to the analytic sample, BHS3 participants without measured
covariates were younger, had lower total body mass, but higher percent lean mass, and
lower grip strength. Compared to the analytic sample, BHS4 participants without
measured covariates had significantly lower percent lean mass.
Femoral Stress Estimation
Bone geometry was measured using the Hip Structural Analysis program from
dual energy x-ray absorptiometry data 15. The HSA software generates geometry from
profiles of pixel values traversing the proximal femur at its narrowest point when viewed
in a frontal plane DXA image. The software also provides information to locate the
center of the femoral head in order to estimate the weight vector, the neck-shaft angle, the
vector distances to the femoral neck cross-section, the abductor force and the ground
reaction force vectors. Femoral stress was estimated on the medial aspect of the neck
cross-section using a body weight load as follows:
σ =My + F
I
A
where M represents the net bending moment orthogonal to the neck axis, I is the HSAderived cross sectional moment of inertia (CSMI) for bending in the frontal plane; A
represents the bone surface in the cross section (CSA); y represents the displacement of
the medial surface from the neutral axis and F represents axial component of loading
force. Forces were computed at the medial cortex of the femoral neck in a one legged
98
stance using body weight (in Newtons) and femur length estimated from height using
forensic formulas 16. Using the formalism employed by McLeish et al, the gravitational
load on the femoral head (joint force) is assumed to be 5/6 body weight, the ground
reaction forces at the knee is 8/9 of body weight and the abductor force is oriented at an
angle of 19 degrees to the horizontal with the magnitude computed to achieve static
equilibrium 17. Forces in the frontal plane are represented in the left diagram in Figure
6.1, and are resolved to their x and y components in the right (i.e., FMx, FMy, Fjx,Fjy).
Forces are then balanced to ensure that all components in the x and y directions sum to
zero.
Figure 6.1: Estimating Femoral Stress. W1 refers to body weight, Fm to the abductor
muscle force, and Fj to the joint force at the femoral head. NL= Neck Length; d= distance
from the center of mass; TL= distance from the weight vector to the outer surface of the
greater trochanter; SL= shaft length is computed from forensic formula on height: α =
99
neck shaft angle from HSA ; β= 10 degrees; θ= 19 degrees. A detailed description of the
stress calculation is included in the Appendix.
Muscle Loading Forces
Grip strength: Maximum grip strength was measured using a hand-held dynamometer
(Jamar, Clifton NJ) at 2 months.
Total body lean mass was measured during hospital admission for treatment of hip
fracture from whole body DXA scans conducted on Hologic QDR 1000 or QDR 1500
machines at study centers (Hologic, Waltham, MA) 14
Covariates
Age, race, number of co-morbidities, and general health status were included in final
analyses. Age was recorded in years and participants’ race was categorized as either
white or black. Number of co-morbidities at time of fracture was measured via self-report
of the following conditions: osteoarthritis, coronary heart disease including a history of
angina, myocardial infarction, congestive heart failure, stroke, diabetes, and chronic
obstructive pulmonary disease. Health status prior to injury was measured using self rated
health and categorized as excellent/very good, good, or fair/poor. Final analysis also
controlled for study group (BHS3, BHS4- intervention group, and BHS4- control group).
Analysis
Differences in participant characteristics were compared across quartiles of
femoral stress using the p test for trend. To account for the confounding effects of body
size on lean mass and grip strength, total body lean mass was divided by total body mass
to compute percent lean mass; and grip strength was divided by total body mass to
compute grip strength proportion. Partial pearson correlation coefficients were used to
100
quantify the correlation of femoral strength with percent lean mass, and grip strength
proportion after adjustment for study group (BHS3 or BHS4). Linear regression was used
to model the associations of femoral stress with percent lean mass and grip strength
proportion at baseline. Four models were fit. The first model included percent lean mass
and study group. The second model included grip strength proportion and study group.
The third model included both percent lean mass and grip strength proportion, and group.
The fourth model included both percent lean mass, grip strength proportion, group and all
covariates described above. Mixed effect models with random intercepts were fit to
evaluate the change in femoral stress over the 12 months following fracture where time
was included as an indicator variable for 2, 6, and 12 months. Models were adjusted for
covariates described above. All analyses were conducted using STATA-V9.
Results
Demographic information, health status, body composition and bone geometry
measured at baseline are described in Table 6.1 by quartile of femoral stress where higher
stress indicates weaker bones. For the purposes of illustration, mean BMD, CSA and
CSMI at the femoral neck are also shown by stress quartile. Note that CSA and CSMI,
which are used in the calculations of stress, are expectedly lower in higher quartiles of
femoral stress, with an analogous inverse pattern for BMD and femoral stress. Total body
mass is positively associated with femoral stress, owing to the use of total mass to
approximate the forces in measuring femoral stress. In both men and women, average
body mass increased across quartiles of femoral stress. Femoral stress was not
significantly associated with age or health status (Table 6.1). Individuals with a higher
proportion of lean mass relative to their total mass had lower femoral stress. Average
101
percent lean mass was 64.7% (SD=6.2) in women in the highest quartile of femoral stress
compared with 72.6% (SD=6.6) in women in the lowest quartile (p<0.001). Likewise,
women with higher grip strength relative to their total body mass had significantly lower
femoral stress.
Table 6.2 shows the correlation of femoral stress with percent lean mass and grip
strength proportion. Femoral stress was more strongly (negatively) correlated with
percent lean mass than with grip strength proportion (-0.44 vs -0.23) (Table 6.2).
Table 6.3 provides coefficients from linear regression models of the association of
femoral stress with percent lean mass and grip strength proportion. Results from model 1
showed that for every percent increase in lean mass, average femoral stress was 0.112
MPa lower (95% CI: -0.150, -0.073; p<0.001) and model 2 showed that for every 1-unit
increase in grip strength proportion, average femoral stress was 3.88 MPa lower (95%CI:
-6.72, -1.04; p=0.008). Percent lean mass explained more of the variability in femoral
stress (R2=0.1981) than grip strength proportion (R2=0.0537). Results from model 3
showed that when both percent lean mass and isokinetic knee strength proportion were
included in the regression model, only percent lean mass was significantly associated
with femoral stress. The results did not change substantially after including other
covariates in model 4.
Femoral stress decreased by 0.07 MPa the year following fracture but this change
was not significant (p=0.403) (Figure 6.2).
Discussion
The purpose of this paper was to evaluate the relative independent contributions
of both lean mass and muscle strength to femoral stress, a measure of bone modeling
102
response, among older women with a hip fracture at the time of fracture. In addition, we
also examined whether femoral stress changed the year following fracture. Our data show
that femoral stress was more strongly related to percent lean mass than to grip strength
proportion. We found that only percent lean mass was significantly associated with
femoral stress after adjusting for potential confounders. Controlling for percent lean mass
accounted for more of the variability in femoral stress than controlling for grip strength
proportion as measured by the R2.
These results are consistent with results from a previous study examining the
relationship between lean mass and muscle strength in healthy older adults (Reider paper
1). However, both lean mass and muscle strength (as measured by isokinetic knee
strength proportion) were significantly associated with femoral stress independent of each
other after adjusting for similar covariates among women in the previous study. In both
studies, lean mass appears to be an important predictor of femoral stress, indicating that
the measure captures elements of bone’s response to load in healthy women as well as in
women with a hip fracture. Variation in femoral stress after accounting for lean mass
indicates heterogeneity in the modeling response to load. Interestingly, there was wide
variation in femoral stress among women with a hip fracture, in addition to higher
femoral stress compared to healthy older women (Reider paper 1). Differences in femoral
stress may reflect hormonal and genetic factors which play an important role in
regulating the modeling process.
Previous studies have shown that bone strength and body composition
significantly change the year following fracture 7,11,12. We would expect that if bone was
appropriately adapting to reduced load over time, than bone would become structurally
103
weaker in an absolute sense but this would not necessarily change bone’s ability to
respond to load. Therefore, individuals with a normal modeling response would likely
build bone in response to increased muscle load during recovery. In contrast, individuals
with a deficient modeling response (ie: relatively higher femoral stress) will require more
muscle force in order to build bone. These individuals have less bone tissue relative to
the muscle forces they typically encounter and so stresses are abnormally high. In this
study, we see that average femoral stress did not change the year following fracture.
The strengths of this study include the longitudinal data collection on women after
hip fracture and the availability of DXA data from which femoral stress could be
estimated. The engineering model we used for these analyses relies on DXA data using
HSA, a technique for deriving bone structural properties from two dimensional data that
has been employed in numerous studies 18-25.
Despite these strengths, there are several limitations. First our measure of femoral
stress involves assumptions about the magnitude and direction of the forces generated in
a one legged stance. In addition, the engineering model of the hip was restricted to the
frontal plane, and the forces on the hip in ambulation are frequently directed out of the
frontal plane. Second, we may not have adequately captured all aspects of load in our
attempt to approximate muscle loading forces. A direct measure of physical activity
would be a valuable addition to this analysis, but a previous study showed that self-report
measure of physical activity appeared insufficient to assess these relationships as it was
minimally correlated with femoral stress, percent lean mass and muscle strength (Reider
paper 1). Third, we assumed that lean mass and muscle strength are proportional to the
forces generated at the hip. Our measure of muscle strength relied on grip strength, which
104
is not a direct evaluation of hip forces. Since grip strength measurement was not
available in many women within 10 days following hip fracture, grip strength measured
at 2 months was used as the baseline measure. We utilized total lean mass based on
whole body DXA scans, which should be greater in more active individuals. However,
lean mass may not be evenly distributed throughout the body, and we were unable to
measure the muscle regions that most directly determine maximal muscle force at the hip.
Fourth, despite our best efforts to mitigate the confounding effect of weight on the
association between our lean mass and muscle strength measure with femoral stress by
using percent lean mass and grip strength proportion, there is always the chance of
residual confounding due to model misspecification. Finally, the analytic sample for this
paper included women from two different studies; the BHS3 was a prospective
observational study conducted in the early 1990s with few exclusion criteria, and BHS4
was an intervention study conducted 15 years later in which only 20% of identified hip
fracture patients met the inclusion criteria. We included study group in statistical models
to adjust for these differences and their potential effect on the relationship between
measures of muscle load and femoral stress.
In conclusion this study demonstrates that the relationship between measures of
muscle load and femoral stress in women with a hip fracture is similar to those observed
in healthy older women. Bone modeling response may be an important mechanism
underlying bone fragility and because it is relatively stable over short periods, it may be
most useful in identifying those at risk of future fracture (Reider paper 2). Future research
should focus on refining measurements of bone modeling response that better incorporate
individual variations in muscle load. Results from these studies will provide important
105
insight into the relationship between muscle and bone.
106
Table 6.1: Study Sample Characteristics by Quartile of Femoral Stress
Q1
(n=35)
Q2
(n=34)
Q3
(n=35)
Q4
(n=34)
p
Femoral Stress and Its
Components
6.09-7.82
7.87-8.88
8.90-10.18
10.21-15.37
Femoral stress (range) MPa
Total body mass, kg (mean, SD)
53.3 (9.7)
57.0 (8.2)
60.4 (41.9)
66.5 (12.2)
Narrow Neck BMD (g./cm2)
0.59 (0.09)
0.61 (0.08)
0.59 (0.12)
0.56 (0.09)
0.037
Narrow Neck CSA (g/cm )
1.93 (0.35)
1.90 (0.27)
1.78 (0.36)
1.74 (0.27)
0.003
Narrow Neck CSMI
1.81 (0.52)
1.64 (0.36)
1.52 (0.43)
1.49 (0.29)
0.001
Covariates
Age, years, mean (SD)
81.7 (7.2)
80.4 (6.5)
78.9 (7.3)
79.1 (7.0)
0.085
Health Status
Co-morbidities (n; range)
0.86 (0-3)
1.0 (0-3)
1.17 (0-4)
1.0 (0-4)
0.431
General Health Status
Excellent/Very Good
34.3
26.5
40.0
38.2
0.445
Good
37.1
50.0
40.0
38.2
Fair/Poor
28.6
23.5
20.0
23.5
Mechanical Load
Percent Lean Mass (mean %,
72.6 (6.6)
69.0 (6.4)
66.1 (6.3)
64.7 (6.2)
<0.00
SD)
1
Grip Strength Proportion (mean,
0.29 (0.10)
0.28 (0.11)
0.27 (0.11)
0.24 (0.09) 0.017
SD)
MPa=pascals; BMD= bone mineral density; CSA= cross sectional area; CSMI= cross sectional moment of
inertia; comorbidities=number of from the following: osteoarthritis, angina, MI, CHF, stroke, diabetes, and
COPD; total body mass adjusted grip strength measurement 2 months following fracture
Table 6.2. Partial Correlation of Femoral Stress with Percent Lean Mass and Body
Adjusted Grip Strength Adjusted for Site
Correlation coefficient
Percent lean mass
-0.4427
Body Mass Adjusted Grip Strength
-0.2264
107
Table 6.3 Association of Femoral Stress with Percent Total Body Lean Mass, Total
Body Adjusted Grip Strength among Women after Hip Fracture (n=138)
Model Percent Lean Mass
Beta (95% CI)
1
2
3
4
P
value
-0.112 (-0.120, -0.073)
---0.104 (-0.143, -0.065)
-0.097 (-0.138, -0.056)
Body Mass
Adjusted Grip
Strength Beta
(95% CI)
<0.001
---3.88 (-6.72, -1.04)
<0.001 -2.27 (-4.94, 0.394)
<0.001 -2.39 (-5.22, 0.435)
P
value
--0.008
0.094
0.096
R2
19.8%
5.4%
21.5%
23.4%
Percent total body lean mass= total body lean mass (kg)/total body mass (kg); Total body mass adjusted
grip strength= dynometer strength/total body mass (kg)
Model 1: Linear regression models adjusted for group only
Model 2: Linear regression models adjusted for group only
Model 3: Linear regression; models include percent lean mass, body mass adjusted knee strength and group
Model 4: Linear regression; models include percent lean mass, body mass adjusted knee strength, group
and the following covariates: age, race, number of comorbidities, and health status
P=0.403
9.26
9.11
BL
2
9.12
6
9.19
12
Month
Figure 6.2. Change in Femoral Stress over the 12 months following hip fracture
(n=138)
108
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2. Skerry, TM 2008 The response of bone to mechanical loading and disuse: fundamental
principles and influences on osteoblast/osteocyte homeostasis. Arch Biochem Biophys
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Response in Older Adults. (in preparation).
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Canadian Multicentre Osteoporosis Study (CaMos) Research Group 2013 Heterogeneity
in Skeletal Load Adaptation Points to a Role for Modeling in the Pathogenesis of
Osteoporotic Fracture. J Clin Densitom.
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Fractures in Older Adults. (in preparation).
7. Reider, L, Beck, TJ, Hochberg, MC, Hawkes, WG, Orwig, D, YuYahiro, JA, Hebel,
JR, Magaziner, J, Study of Osteoporotic Fractures Research Group 2010 Women with hip
fracture experience greater loss of geometric strength in the contralateral hip during the
year following fracture than age-matched controls. Osteoporos Int 21:741-750.
8. Magaziner, J, Wehren, L, Hawkes, WG, Orwig, D, Hebel, JR, Fredman, L, Stone, K,
Zimmerman, S, Hochberg, MC 2006 Women with hip fracture have a greater rate of
decline in bone mineral density than expected: another significant consequence of a
common geriatric problem. Osteoporos Int 17:971-977.
9. Visser, M, Harris, TB, Fox, KM, Hawkes, W, Hebel, JR, Yahiro, JY, Michael, R,
Zimmerman, SI, Magaziner, J 2000 Change in muscle mass and muscle strength after a
hip fracture: relationship to mobility recovery. J Gerontol A Biol Sci Med Sci 55:M43440.
10. Karlsson, MK, Obrant, KJ, Nilsson, BE, Johnell, O 2000 Changes in bone mineral,
lean body mass and fat content as measured by dual energy X-ray absorptiometry: a
longitudinal study. Calcif Tissue Int 66:97-99.
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Hebel, JR, Magaziner, J 2014 Short-term changes in body composition after surgical
repair of hip fracture. Age Ageing 43:275-280.
13. Fox, KM, Magaziner, J, Hawkes, WG, Yu-Yahiro, J, Hebel, JR, Zimmerman, SI,
Holder, L, Michael, R 2000 Loss of bone density and lean body mass after hip fracture.
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14. Orwig, DL, Hochberg, M, Yu-Yahiro, J, Resnick, B, Hawkes, WG, Shardell, M,
Hebel, JR, Colvin, P, Miller, RR, Golden, J, Zimmerman, S, Magaziner, J 2011 Delivery
and outcomes of a yearlong home exercise program after hip fracture: a randomized
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15. Beck, TJ 2007 Extending DXA beyond bone mineral density: understanding hip
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111
Chapter 7: Discussion
The purpose of this dissertation was to evaluate the associations of femoral stress,
an index of bone modeling response, with measures of muscle load among older adults.
Bone modeling, the process that continually adjusts bone strength in response to
prevalent loading forces throughout an individual’s lifespan, may play an important role
in bone fragility with age. Bone modeling has been described in terms of the Frost
Mechanostat, in which bone tissue is added or removed to ensure that minute
deformations (strains) of bone tissue remain within some ‘normal’ range 1. While in vivo
strains cannot be easily measured, stresses, which quantify the concentration of loading
force at a specific location on the bone, can be estimated using measurements of DXA
derived bone geometry and loading information incorporated into an engineering model 2.
The model employed in these analyses calculated stress at the medial cortex of the
femoral neck under a single body weight load. The model assumes that the femur should
be adapted to habitual loads; therefore if all individuals responded equally, maximum
stress at the femur generated by those loads should be equal. Thus greater stresses under
equivalent load indicate a diminished response and a weaker bone. One drawback to the
femoral stress model is that loading forces are only computed from body weight in a
single leg stance likely underestimate the true physiologic forces acting on bone.
Biomechanical studies indicate that hip forces are a multiplicative function of body
weight that increases with the type and intensity of physical activity that the individual
normally experiences, but these forces are difficult to determine directly. The best we can
do with the current model is account for approximations of these forces in our assessment
of femoral stress.
112
In this dissertation, associations of femoral stress with measures of muscle load
that should approximate muscle forces were evaluated in a cohort of healthy older men
and women as well as in older women after hip fracture. The first paper evaluated the
associations of femoral stress with measures of total body lean mass and isokinetic
muscle strength among healthy older men and women participating in the Health ABC
study. The second paper determined whether femoral stress was associated with incident
fracture among the same cohort of older men and women. The third paper evaluated the
associations of femoral stress with measures of total body lean mass and maximum grip
strength. In addition, this paper evaluated change in femoral stress the year following
fracture.
Results from the first paper showed that femoral stress was more strongly related
to percent lean mass than isokinetic knee strength proportion and that controlling for
percent lean mass accounted for more variability in femoral stress than controlling for
isokinetic knee strength. Even after accounting for measures of muscle load, there was
wide variation in femoral stress among both men and women likely reflecting individual
variation in bone modeling response. Interestingly, percent lean mass and isokinetic knee
strength proportion were significantly associated with femoral stress independent of each
other in women, but only percent lean mass was significantly associated with femoral
stress in men. These differences may be due to gender specific hormonal interactions
with muscle tissue that result in changes to muscle mass and strength thereby affecting
the load on bone 3.
If deficiencies in modeling response as evidenced by higher stress indicate weaker
bones, then femoral stress should be an indicator of fracture risk. A previous study
113
demonstrated that femoral stress was higher among older women with a history of
fracture compared to those with no fracture 2. Building upon that study, the second
dissertation paper showed that over a 12 year observation period, incident fractures were
more common among men and women in the highest compared to lowest tertile of
femoral stress. The association between high femoral stress (i.e.: deficient modeling
response) and fracture are significant in men as well as in women and the association is
stronger after adjusting for variation in muscle loading forces.
Results from the third paper examining the association of femoral stress with
measures of muscle load among women with a recent hip fracture showed that femoral
stress was more strongly related to percent lean mass than grip strength proportion. These
results were consistent with the results from paper 1 examining the relationship between
lean mass and muscle strength in healthy older adults. Lean mass appears to be an
important predictor of femoral stress in healthy women as well as in women with a hip
fracture. Interestingly, there was wide variation in femoral stress among women with a
hip fracture, in addition to higher femoral stress compared to healthy older women. The
third paper also examined whether femoral stress changes during the year following a hip
fracture, a time during which significant changes to muscle and bone strength occur, and
despite those changes femoral stress did not change significantly. Previous studies have
shown that while bone structural strength declines following fracture 4 it remains in
equilibrium with the declining loads over this period. This suggests that bone’s ability to
respond to load does not change as a result of a hip fracture. Individuals with a normal
modeling response would likely build bone in response to increased muscle load during
recovery. In contrast, individuals with a deficient modeling response (i.e.: relatively
114
higher femoral stress) will require more muscle force in order to build bone. These
individuals have less bone tissue relative to the muscle forces they typically encounter
and so stresses are abnormally high.
Bone modeling response provides a framework for bone fragility that is not
always consistent with current interpretations of the BMD paradigm. Though BMD
serves as an important clinical tool for diagnosing osteoporosis and determining fracture
risk, it does not adequately capture the underlying heterogeneity in skeletal strength. A
measure of bone modeling response may add value to BMD and other measures of
fracture risk because it examines the geometric distribution of bone important to
evaluating bone strength. Furthermore, current bone strength assessments require the
evaluation of multiple measures that must be interpreted together. Femoral stress
provides a single measure of bone strength that accounts for the mechanical relationship
between muscle and bone.
The muscle-bone relationship has been defined previously as the bone mineral
content (BMC) per cross sectional muscle area (CSA) to help conceptualize pediatric
skeletal disorders, and, more recently, was used to evaluate the muscle-bone interactions
at the spine among young and old women 5. This study found no significant difference in
muscle mass among normal women, women with osteopenia and women with
osteoporosis. However, women with osteopenia and osteoporosis had lower BMC to
muscle area ratio compared to normal women; and the BMC to muscle area ratio was
comparable among older normal women and young women. The authors suggest that the
low BMC per muscle area in women with osteoporosis may be a result of reduced
mechanosensitivity (i.e.: deficient modeling response). This approach to assessing
115
modeling response assumes a linear relationship between strain and bone mass. Since
strain is dependent on how bone mass is distributed, femoral stress provides a more
sophisticated and potentially more accurate depiction of modeling response since it
accounts for the distribution of mass in the bone.
Femoral stress as it is currently estimated does not have clinical applications, per
se, but it allows us to investigate a potentially important mechanism of bone fragility
using existing data from large prospective studies and provides a platform for future
research into the important relationship between muscle and bone. Previous studies have
shown that muscle is positively associated with bone and therefore building or
maintaining muscle should have positive effects on bone. However if individuals have a
deficient modeling response to muscle load, then it is unclear whether interventions
aimed at building muscle, like physical activity, will have a normal impact. This is not to
say that physical activity isn’t important. It serves many benefits beyond those that affect
bone. These benefits include maintaining balance and mobility which enable older adults
to continue living in the community and contribute to over-all well being. Since
individuals with higher stresses require more force to ellicit a positive bone response,
future studies should aim to determine how much force is necessary and what activities
produce these forces. Further, studies also should investigate whether there is a point
when these activities are no longer feasible or beneficial in terms of bone health. Results
from these studies may support the need for pharmaceutical treatments that improve
response to load making weight bearing activities more efficient. This will require a
better understanding of the biomechanical and molecular interactions between bone and
muscle tissue.
116
Ideally, muscle forces would need to be incorporated more directly into the
femoral stress estimate. However, to do this, we would need to determine how best to
measure muscle force, ideally the forces from normal activities that generate the stresses
we have computed. In both studies employed in this thesis, the muscle force was
measured at a location remote from the proximal femur where stress was computed.
While we would expect correlation between the forces where we measured and those at
the hip it is not surprising that correlations were poorer than an objective measure of lean
mass, especially with regard to grip strength. A previous study showed that grip strength
and lower limb strength are weakly correlated 6. If modeling response is region specific,
it is likely that measures of lean mass and muscle strength at the hip would have a
stronger association with femoral stress. Although total body lean mass and knee strength
may be indirect measures of the forces which we’d like to capture, we did see that knee
strength and lean mass were independently associated with femoral stress in healthy
women while only lean mass was associated with femoral stress in men. Hormonal and
genetic factors may also play an important role in this relationship and may explain
differences in femoral stress between men and women.
We were unable to evaluate the direct effects of physical activity on femoral
stress. The measures of self-reported physical activity available in the elderly populations
studied were minimally correlated with femoral stress so appeared inadequate for
assessing these relationships. However, it is likely that physical activity has both direct
and indirect effects on bone. In our sample we may not have been able to detect the direct
effects of activity on bone because the range in values was small with a larger proportion
of very lowvalues; very few people reported high levels of vigorous activity. Physical
117
activity should indirectly affect bone via muscle strength. When we adjusted for muscle
strength, this may have eliminated any existing activity effects. Future studies evaluating
the forces that correspond to self reported activity as well as to objective measures of
activity such as accelerometry, may improve our understanding of activity effects on the
modeling response. In addition to investigating how best to incorporate these forces into
the femoral stress model, future models should be validated using bone and muscle
parameters obtained from CT data.
While muscle forces are an important driver of bone adaptation, they are not the
only input that influences modeling response. Systemic hormones such as IGF-1,
androgens and estrogens affect both muscle and bone. Future research on the role they
play in muscle-bone interactions may provide insight into how modeling deficiencies
originate. In summary, measuring bone modeling response and improving on our ability
to do so may help us better understand the mechanism underlying bone fragility which
may have important clinical implications in the future.
118
References
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119
Appendix
Reproduced with permission from C. Hamilton
The stress analysis at the femoral neck employs a mechanical model of the femur
incorporating the forces acting on it and the parameters needed to define the geometry as
shown in Table 1 and Figure 1. Note that this analysis is restricted to the frontal plane
defined by the axes of the femoral neck and shaft, due to the limitations of the 2D DXA
data. Resolved forces and geometry were calculated as follows:
All forces and moments were then balanced to achieve static equilibrium using equations
(i) to (iii) (i.e
.
,
,
):
(i)
(ii)
(iii)
Note that the magnitude of the weight vector is assumed to be 8/9 body weight (1).
120
After computing FJx, FJy and FM, FJ and its angle
and (v):
were computed using equations (iv)
(iv)
(v)
The internal forces in the neck resulting from the external forces FM, FJ and W1 are
shown in a femoral neck free-body diagram (Figures 2 and 3), where Ms, PS and V are
bending moment, axial load and shear force on the neck cross-section respectively. The
small shear force was neglected in our calculations.
The bending moment MS and axial force PS were calculated using equations (vi) and
(vii):
(vi)
(vii)
Axial stress on the medial surface of the femoral neck was computed using a formula
from simple engineering beam theory using equation (viii):
(viii)
Where MS and PS are the bending moment and axial load on the neck cross-section I and
A are the cross-sectional moment of inertia, and the cross-sectional area of the crosssection, and yMedial is the perpendicular distance between the medial surface, and the
121
neutral axis. At the medial surface, the bending moment and axial load produce
compressive stresses which are represented by a positive sign.
Tables
Table 1. Parameters used in the derivation of medial femoral neck stance stress.
Parameter Name
Description
Symbol
Unit
Derivation
Neck shaft angle
The angle between the
axes of the neck and
shaft.
α
Radians
Computed
in HSA
Theta
Angle of the resultant
muscle force vector
with the horizontal.
θ
Radians
Assumed
to equal
70 o
Null
Angle of the joint
force vector with the
horizontal.
Ø
Radians
Derived
Beta
Angle of the femoral
shaft with the vertical.
β
Radians
Assumed
to equal
10o
Neck length
Distance from the user
defined centre of the
femoral head to the
intersection of the
neck and shaft axes.
NL
Metres
Computed
in HSA
SL
Metres
Computed
using a
standard
forensic
formula
(2)
TL
Metres
Computed
in HSA
Metres
Computed
Shaft length
Neck to trochanter
length
Neck to trochanter
length (assumed to be
horizontal).
Neck distance
Neck to studied cross- d
section length (at the
122
narrowest point in the
neck).
in HSA
Body weight
Body weight
W
Newtons
Computed
from
DXA scan
Cross Sectional
Area
the total mineralized
bone surface in the
cross section which is
proportional to
standard bone mineral
content. CSA is a
measure of the axial
forces along the
narrow neck.
CSA
g/cm2
Computed
from HSA
Cross Sectional
Moment of Inertia
also called the “second CSMI
moment area” is
derived as the integral
of pixel thickness
weighted by the square
of distance from the
center of mass. This is
the distance between
the center of the
femoral head and the
outer edge of the bone
from where the cross
section is measured.
This is a measure of
the forces on bone in
bending.
123
Computed
from HSA
Figure 1 Free body diagram of the femur.
In (a) forces are shown in the original
directions. In (b) forces are resolved in the
x-y directions. α: Neck-Shaft Angle
(NSA);
θ : Angle of the resultant muscle force
vector with the horizontal; Ø: Angle of the
joint force vector with the horizontal; β:
Angle of the femoral shaft with the
vertical; NL: Neck Length; SL : Shaft
Length; TL : Center of femoral head to
Trochanter Length; d: Center of femoral
head to neck cross-section (at the
narrowest point).
Figure 2. Femur internal forces resulting
from the external forces FM, FJ and W1.
Ms, PS and V are bending moment, axial
load and shear force on the cortical
cross-section respectively.
124
Figure 3. Free-body diagram of the femoral neck. Bending moment =
and axial force =
.
125
Glossary of Terms
Stress- force per unit area, usually in a cross section, from externally applied forces on an
object.
Compressive stress- result of opposing forces directed inward toward the same
point (eg. Pushing inward on the ends of the bone).
Bending stress- tensile stress on the outer curvature and compressive stress on the
inner curvature of bending. The stresses are greatest on the outer surface
Strain- the deformation in shape or dimensions of bone subjected to loading forces.
Strain is proportional to stress by the “elastic modulus”, a proportionality constant
characteristic of the material used in engineering analysis
Bone adaptation- refers to the process that drives changes in bone strength in order to
support loading forces.
Response to load- refers to the bones’ ability to adapt to changes in mechanical load. It
is thought that this response mechanism is controlled at the cellular level and can
be influenced by non mechanical factors including hormones, genetics, and
disease.
Mechanical load- refers to the loading forces on the bone; specifically muscle forces and
the physical activities that influence them.
Bone dimensions- also referred to as structural geometry which describe the dimensions
of the bone that are used in an engineering calculation of load stress and bone
structural strength.
126
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