Florida State University Libraries
Electronic Theses, Treatises and Dissertations
The Graduate School
2014
Body Composition, Functional, and
Nutritional Characteristics of Patients with
Hip or Knee Osteoarthritis
Sarah Purcell
Follow this and additional works at the FSU Digital Library. For more information, please contact [email protected]
FLORIDA STATE UNIVERSITY
COLLEGE OF HUMAN SCIENCES
BODY COMPOSITION, FUNCTIONAL, AND NUTRITIONAL CHARACTERISTICS
OF PATIENTS WITH HIP OR KNEE OSTEOARTHRITIS
By
SARAH PURCELL
A Thesis submitted to the
Department of Nutrition, Food, and Exercise Sciences
in partial fulfillment of the
requirements for the degree of
Master of Science
Degree Awarded:
Spring Semester, 2014
Sarah Purcell defended this thesis on March 24, 2014.
The members of the supervisory committee were:
Carla M. M. Prado
Professor Directing Thesis
Jeong-Su Kim
Committee Member
Dan McGee
Committee Member
The Graduate School has verified and approved the above-named committee members, and
certifies that the thesis has been approved in accordance with university requirements
ii
ACKNOWLEDGMENTS
There are numerous people who have helped accomplish everything I have done for this thesis
and all of my academic endeavors. This would not have been possible without my wonderful
major professor, Dr. Carla Prado. Your patience, guidance, and ideals parallel no other. I am
thankful every day that our paths have crossed and I have had the chance to work with you.
I would also like to thank my committee members, Dr. Jeong-Su Kim and Dr. Dan McGee for
taking the time to be on my committee and offering invaluable advice.
Dr. Robert Thornberry, Dr. Andrew Wong and all of the employees at Tallahassee Orthopedic
Clinic are superb examples of hard-working people who truly want to help others. Your patience
and eagerness to help are what made this project happen- thank you!
A huge thanks goes out to my colleague, Jingjie Xiao. Thank you for always cheering me up. I
am so grateful that we have become such good friends.
To my family, I owe everything. Without your endearing reminders of the importance of higher
education and constant support, I would not be who I am today.
Most importantly, I thank my best friend and future husband, Chase Pennell. Your unconditional
love and support has guided me to accomplish everything I ever dreamed of and I am a better
person because of you.
iii
TABLE OF CONTENTS
List of Tables ................................................................................................................................. vi
List of Figures ............................................................................................................................... vii
List of Abbreviations ................................................................................................................... viii
Abstract .......................................................................................................................................... ix
1.
CHAPTER ONE: INTRODUCTION .....................................................................................1
1.1
1.2
1.3
2.
CHAPTER TWO: LITERATURE REVIEW .........................................................................5
2.1
2.2
3.
Abnormal Body Composition ........................................................................................5
2.1.1 Sarcopenia ..........................................................................................................7
2.1.1.1 Prevalence ....................................................................................................8
2.1.1.2 Mechanisms .................................................................................................8
2.1.1.3 Health outcomes.........................................................................................11
2.1.2 Obesity .............................................................................................................12
2.1.2.1 Prevalence ..................................................................................................13
2.1.2.2 Mechanisms ...............................................................................................13
2.1.2.3 Health outcomes.........................................................................................14
2.1.2 Sarcopenic Obesity ..........................................................................................14
2.1.3.1 Prevalence ..................................................................................................15
2.1.3.2 Mechanisms ...............................................................................................15
2.1.3.3 Health outcomes.........................................................................................15
Osteoarthritis ................................................................................................................15
2.2.1 Hip Replacements ............................................................................................18
2.2.2 Knee Replacements ..........................................................................................20
CHAPTER THREE: MATERIALS AND METHODS ........................................................22
3.1
3.2
3.3
4.
Background ....................................................................................................................1
Purpose...........................................................................................................................3
Specific Aims and Hypotheses ......................................................................................3
Study Cohort ................................................................................................................22
Data Collection Procedures..........................................................................................22
3.2.1 Demographics and Clinical History .................................................................22
3.2.2 Anthropometrics and Body Composition ........................................................22
3.2.3 Functional Characteristics ................................................................................23
3.2.3.1 Handgrip strength.......................................................................................23
3.2.3.2 Questionnaires............................................................................................23
3.2.4 Nutrient Intake .................................................................................................24
3.2.5 Prognostication: Surgical Outcomes ................................................................25
Statistical Analysis .......................................................................................................26
CHAPTER FOUR: RESULTS ..............................................................................................28
4.1
Demographic Characteristics .......................................................................................28
iv
4.2
4.3
4.4
4.5
4.6
5.
Anthropometric and Body Composition Characteristics .............................................28
Comorbidities...............................................................................................................29
Functional Outcomes ...................................................................................................29
Nutrient Intake .............................................................................................................31
Surgical Outcomes .......................................................................................................32
CHAPTER FIVE: DISCUSSION .........................................................................................45
5.1
5.2
5.3
5.4
Review of Hypotheses and Conclusions .....................................................................45
Summary ......................................................................................................................46
Discussion of Results ...................................................................................................46
Limitations and Future Research .................................................................................55
APPENDICES ...............................................................................................................................57
A.
HUMAN SUBJECTS APPROVAL ......................................................................................57
B.
INFORMED CONSENT FORM ..........................................................................................59
C.
PATIENT DATA COLLECTION FORM ............................................................................62
D.
DISTRIBUTION OF BODY COMPOSITON STRATIFIED BY GENDER ......................73
REFERENCES ..............................................................................................................................75
BIOGRAPHICAL SKETCH .........................................................................................................90
v
LIST OF TABLES
Table 4.1 Overall Characteristics of Patients with Hip or Knee Osteoarthritis ............................33
Table 4.2 Body Composition Associations with Functional Variables in Patients with Hip or
Knee Osteoarthritis ............................................................................................................36
Table 4.3 Body Composition Characteristics of Patients with Osteoarthritis According to Median
WOMAC Score ..................................................................................................................37
Table 4.4 Frequencies of Surgical Complications in Patients Undergoing Hip or Knee
Replacement Surgery .........................................................................................................38
vi
LIST OF FIGURES
Figure 2.1 Description of Common Body Composition Terms ......................................................5
Figure 2.2 Proposed Relationship between Osteoarthritis, Muscle, and Fat Mass .......................18
Figure 3.1 Timeline of Data Collection ........................................................................................25
Figure 4.1 Flow Diagram Showing an Overview of the Study Selection Process........................39
Figure 4.2 Relationship between Body Mass Index (BMI) and A. Fat Mass (FM) and B. Fat Free
Mass (FFM) ......................................................................................................................40
Figure 4.3 Difference between A. Fat Mass Index (FMI) and Fat Free Mass Index (FFMI) and
B. Body Fat Percentage for Patients with and without Low Back Pain ...........................41
Figure 4.4 Distribution of A. Body Composition Phenotypes by Fat Mass (FM) and Fat Free
Mass (FFM) and B. HGS, C. WOMAC Scores, and D. FES-I Scores ..............................42
Figure A.1 Distribution of Fat Mass (FM) and Fat Free Mass (FFM) and A. HGS, B. WOMAC
Scores, and C. FES-I Scores Stratified by Gender ............................................................73
vii
LIST OF ABBREVIATIONS
25(OH)D: 25-Hydroxyvitamin D
ADL: Activities of Daily Living
AMDR: Acceptable Macronutrient Dietary Range
ASM: Appendicular Skeletal Mass
BIA: Bioelectrical Impedance Analysis
CI: Confidence Interval
BMI: Body Mass Index
DXA: Dual X-Ray Absorptiometry
FES-I: Falls Efficacy Scale, International
FFM: Fat Free Mass
FFMI: Fat Free Mass Index
FM: Fat Mass
FMI: Fat Mass Index
GH: Growth Hormone
HGS: Handgrip Strength
HR: Hazard Ratio
IGF-1: Insulin-Like Growth Factor
IMAT: Intra-Muscular Adipose Tissue
InCHIANTI study: ‘Aging in Chianti Area’ Study
METs: Metabolic Equivalents of Tasks
NHANES: National Health and Nutrition Examination Survey
OA: Osteoarthritis
OR: Odds Ratio
pQCT: Peripheral Quantitative Computed Tomography
RDA: Recommended Dietary Allowance
RMR: Resting Metabolic Rate
SMI: Skeletal Mass Index
TBW: Total Body Water
TJA: Total Joint Arthroplasty
TJR: Total Joint Replacement
THA: Total Hip Arthroplasty
TKA: Total Knee Arthroplasty
TOC: Tallahassee Orthopedic Clinic
WHO: World Health Organization
WOMAC: Western Ontario and McMaster Universities Osteoarthritis Index
viii
ABSTRACT
Background: Body composition refers to the amounts of fat and lean tissues in the body. It is a
superior measurement compared to simple assessments of body weight and other
anthropometrics. Osteoarthritis (OA) is an important public health problem and one of the most
common causes of disability among American adults. An estimated 67 million Americans will
develop OA, many of whom will require surgical intervention. The majority of patients with hip
or knee OA are obese, older and sedentary. These factors would make them prone to a body
composition phenotype of concurrent excess fat and low lean tissue that has been associated with
unfavorable health outcomes in other cohorts of patients. Although obesity (assessed by overall
body weight or its derivatives) has been extensively studied in patients with OA, only a handful
of these studies have investigated fat versus lean tissue contributions to OA outcomes.
Furthermore, there is evidence to suggest that body composition abnormalities may be caused
and perpetuated by abnormal nutrition and physical function.
Objectives: The overall purpose of this study was to describe body composition, functional and
nutritional characteristics of patients with hip or knee OA undergoing total hip arthroplasty
(THA) or total knee arthroplasty (TKA) surgery and to investigate the relationship between
abnormal body composition and surgical-related outcomes.
Methods: In this prospective pilot study, patients scheduled for THA or TKA due to OA were
recruited from August 2013 until February 2014 from the Tallahassee Orthopedic Clinic (TOC).
Patients underwent body composition assessment using bioelectrical impedance analysis.
Functional measurements included handgrip strength testing and questionnaires that comprised
of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Activities
of Daily Living (ADL), Aerobics Center Longitudinal Study Questionnaire, and the Falls
Efficacy Scale, International (FES-I). Dietary data were collected through a 24-hour dietary
recall. In a subset of patients, medical discharge summaries were analyzed to quantify surgical
outcomes.
Results: A total of 42 patients (66.7% females) with a mean age of 66 ± 10 years were included
in this study. The body mass index (BMI) ranged from 21.5 to 55.0 kg/m2 with 69% of patients
being classified as obese. A wide variability of fat mass (FM) and fat-free mass (FFM) was
observed across the BMI spectrum. Patients reporting low back pain presented with lower FFM
ix
index (FFMI), p=0.026, as well as with significantly higher body fat percentage (p=0.049).
Handgrip strength was positively correlated with FFMI (r=0.44, p=0.008), but not with other
functional assessments. Total WOMAC scores were significantly correlated to FMI (r=0.34,
p=0.039), while BMI trended towards significance. An overall low ADL score was observed and
physical activity levels were most strongly negatively correlated with FMI (r=-0.46, p=0.006).
FES-I was positively associated with numerous body composition compartments, the strongest of
which was body fat percentage (r=0.48, p=0.024). In order to further explore the relationship
between body composition and functional variables, individual assessments were dichotomized
in function of both FM and FFM, on the basis of the idea that higher FM and lower FFM values
would be associated with poorer physical function. Using this approach, different patterns of
unfavorable physical function emerged among different body composition phenotypes. Overall,
patterns of higher FM and lower FFM were associated with adverse outcomes. Average caloric
intake was approximately 1700 kcals/day, with a mean protein consumption of 0.81 g/kg body
weight/day. Protein intake was not associated with body composition variables. Conversely, iron
intake was positively associated with FFMI (r=0.43, p=0.019) and average handgrip strength
(r=0.43, p=0.020). Surgical outcomes were available for a small number of patients (n=16) and
was not associated with any body composition, functional or nutritional characteristics.
Nonetheless, pre-surgical blood creatinine (mg/dL) was positively associated with FFMI (r=0.60,
p=0.040).
Conclusion: The major findings of this study reveal a wide variability of body composition (FM
and FFM) in patients with hip or knee OA despite BMI. Overall, FM and FFM were more
strongly associated with functional assessments and low back pain compared to simple measures
of body weight. We conclude that the assessment of body composition may be advantageous
compared to simple anthropometric measurements when predicting poor functional status in
patients with hip or knee OA. This remains to be tested in future larger studies with more indepth and accurate body composition assessments.
x
CHAPTER ONE
INTRODUCTION
1.1 Background
Body composition refers to the amounts of fat and lean tissues in our body; it is a science
that looks beyond a unit of body weight, evaluating the proportion of different tissues and their
relationship to health. Simple measurements of body weight and other anthropometrics are often
inadequate as they can conceal the proportions of body composition components. Our
understanding of the prevalence and significance of abnormal human body composition has been
greatly enhanced by the advance of new in vivo technology.
Sarcopenia, the age-associated loss of muscle mass and strength, is an abnormal body
composition phenotype that typically begins in early adulthood but does not manifest
consequences until later in life. The prevalence of sarcopenia in non-Hispanic Caucasians has
been estimated to be as high as 13.5% in men and 23.1% in women between the ages of 65-70,
increasing to 52.6% in men and 43.2% in women over 80 years of age (1). There are numerous
postulated mechanisms of sarcopenia including a decrease in physical activity, nutritional factors
(especially decreased protein and energy intake), oxidative stress, neuromuscular changes, and
endocrine alterations. Sarcopenia alone has the potential to lead to disability which can further
aggravate the development of muscle loss by limiting activity, creating an infinite cycle (2).
Other consequences of sarcopenia include disability (3), falls (1), poor functional status (4), and
a higher rate of mortality (5). Because of its association with severe disability in the geriatric
population, healthcare costs of sarcopenia can be astounding. In fact, in 2000, Janssen and
colleagues estimated that sarcopenia was accountable for approximately 18.5 billion dollars in
healthcare costs in the United States alone (6).
Another abnormality in body composition is obesity, which is the excess of adipose
tissue. Using common cut points from the World Health Organization (WHO), nearly one-third
of Americans are overweight or obese (7). The list of etiologies of obesity is vast and includes
excessive macronutrient intake (particularly excess overall energy intake with a high percentage
of fat consumption), low physical activity, environmental, demographic, genetic, and biological
factors. The associations of obesity to poor health are well documented and include heart disease,
1
Type II diabetes, osteoarthritis, sleep apnea, reproductive abnormalities, certain cancers,
high blood pressure, dyslipidemia, stroke, and liver/gallbladder disease, among others (8).
Recently, the concurrent appearance of sarcopenia and obesity has been identified and
deemed sarcopenic obesity (9). Prevalence rates have been estimated to be 4 to 9% in older
adults (10). In sarcopenic obesity, the consequences of sarcopenia and obesity are often
observed together and may lead to a poorer health profile. The double burden of excess adiposity
and compromised muscle mass may lead to repercussions such as higher risks for metabolic
syndrome, insulin resistance, cardiovascular disease risks (11) and disability (12) as well as
decreases in physical functioning (13, 14), extended hospital stays (15), and heightened mortality
rates (16, 17) in comparison to those with normal body composition or sarcopenia or obesity
alone.
Although the incidence of abnormal body composition may occur in several conditions or
clinical situations (such as aging, diabetes, cancer, among others), the prevalence and
significance of abnormal body composition, particularly sarcopenic obesity, in patients with
osteoarthritis (OA) is not clearly defined. Osteoarthritis is a painful degenerative musculoskeletal
disease that most frequently occurs in the hips and knees and is one of the most disabling
conditions in developed countries (18). For the past 15 years, arthritis and other rheumatic
conditions have been the most common causes of disability among adults, affecting an estimated
27 million Americans (19).
While there are multiple causes of OA, the most publicized risk factors are advancing age
and obesity; the first due to deterioration of the joints and the second due to increase pressure on
hips and knees (20, 21). Furthermore, there is evidence of an array of obesity-related
mechanical, biochemical, and endocrine derangements, which may influence the prevalence and
prognostication of OA (22). Approximately 87% of individuals with this condition are aged ≥ 45
years (53% of which being age ≥ 65 years), with a 66% overall prevalence of obesity (23, 24).
As mentioned above, there is relatively limited information on body composition of patients with
OA, especially on those undergoing surgery, but given the incidence of this condition in obese
and older adults, and the decrease in physical function associated with this condition, one would
predict that these patients are at risk for abnormal body composition phenotypes, particularly
sarcopenic obesity, and that this phenotype would be associated with poorer functional and
nutritional characteristics.
2
1.2 Purpose
The overall purpose of this study was to describe body composition, functional and
nutritional characteristics of patients with hip or knee OA undergoing total hip arthroplasty
(THA) or total knee arthroplasty (TKA) surgery (pre-surgery), and to investigate the relationship
between abnormal body composition and surgical-related outcomes.
1.3 Specific Aims and Hypotheses
Specific aim 1: To investigate the variability of body composition, particularly fat free mass
(FFM) by unit of body mass index (BMI).
Hypothesis 1: In a sample of patients with hip or knee OA, a high variability of body
composition [fat mass (FM) and FFM] will be observed across the BMI spectrum.
a) FFM variability will be greater than two fold regardless of BMI.
Specific aim 2: To examine the relationships between body composition (FM and FFM),
functional, and nutritional characteristics of patients with OA.
Hypothesis 2: In a sample of patients with hip or knee OA, an association between body
composition (FM and FFM), functional [handgrip strength, Activities of Daily Living (ADL),
Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and Falls Efficacy
Scale, International (FES-I)] and nutritional (protein, fat, energy) variables will be observed.
More specifically:
a) Lower FFM will be significantly associated with:
-
higher scores on the ADL, WOMAC and the FES-I questionnaires (indicators of poorer
function), and lower values on the handgrip strength test.
-
lower protein and energy intakes.
b) Higher FM will be significantly associated with:
-
higher scores on the ADL, WOMAC and the FES-I questionnaires (indicators of poorer
function), and lower values on the handgrip strength test.
-
higher fat and energy intake.
Specific aim 3: To investigate the association between body composition (FM and FFM) on THA
or TKA surgical outcomes.
3
Hypothesis 3: In a sample of patients with hip or knee OA, abnormal body composition (higher
FM and/or lower FFM) will be associated with greater surgical-related complications. More
specifically:
-
longer length of hospital stay.
-
greater incidence of thromboembolic disease.
-
greater incidence of infections.
-
delayed wound healing.
-
altered laboratory values.
4
CHAPTER TWO
LITERATURE REVIEW
2.1 Abnormal Body Composition
Despite the importance of assessing body weight, these simple evaluations can conceal
underlying aberrations in lean and adipose tissues. Numerous phenotypes of abnormal body
composition have been characterized and a short overview of pertinent terms is provided in
Figure 2.1.
Fat Free Mass (FFM): the sum of skeletal and non-skeletal muscle, organs, connective
tissue and bone.
Lean Body Mass (LBM): also called lean soft tissue, includes total body water, total
body protein, carbohydrates, nonfat lipids and soft tissue mineral, therefore, all fat and
bone mineral are excluded.
Skeletal Muscle Mass: comprises the largest part of the FFM (~ 53-54%) and hence of
the LBM compartment.
Appendicular Skeletal Muscle (ASM): sum of lean tissues from arms and legs.
Adipose Tissue: a tissue level component that can be divided into subcutaneous, visceral,
yellow marrow, and interstitial components. Approximately 80% of adipose tissue is fat
mass (FM).
Sarcopenia: low skeletal muscle mass associated with advancing age.
Figure 2.1 Description of Common Body Composition Terms
Assessing body composition in the elderly (age ≥ 65 years) is particularly important as
there is a natural age-associated decline in muscle mass and an increase in FM even in weightstable elders (25). This degeneration of muscle mass is deemed sarcopenia and is associated with
functional impairment (26) increased risks of falls (and thus heightened risk of fractures) (27),
and disability (1). Another type of abnormal body composition phenotype, obesity (excess
adipose tissue), has been extensively researched and is associated with a plethora of
5
comorbidities (8). Sarcopenia can also occur concurrently with obesity in a condition called
‘sarcopenic obesity’. Extreme alterations in body composition can slowly lead to numerous
unfavorable health outcomes while a higher proportion of lean mass to fat mass is protective
against physical limitations and disability (28).
While there is a myriad of measurement methods that can assess bodily components,
bioelectric impedance analysis (BIA) is a quick, non-invasive method to quantify FM and FFM.
This method may be applied to identify those with deviations in body composition and may be of
prognostic relevance in some clinical populations (29).
The mechanistic principle behind BIA is based off of the rate electric current can move
through bodily compartments. Much of the human body is composed of water that is highly
conductive of electricity; of this total body water (TBW), approximately 45% is extracellular and
55% is intracellular (30). Adipose tissue has much less conductivity than does muscle or bone.
BIA takes advantage of the different rates at which electrical current passes through the body in
order to estimate body composition through determining impedance.
Impedance is the opposition an electric current faces through a conductor; it is a
combination of reactance and resistance. Resistance is purely the opposition of the conductor to
the current (31) and arises from intra and extracellular fluid (32). Reactance is caused by the
capacitant effect (capacitance defined here as the ability of bodily tissue to store an electrical
charge) of cell membranes, connective tissue, and non-ionic tissues that slow some of the electric
current. Reactance is highly dependent on electrical frequencies as it will increase with rising
frequencies, but will eventually reach a maximum and will begin to fall (31). The association
between resistance and reactance reflects the differing electrical values of tissues that can be
affected by various disease states, hydration, and nutritional status; thus, phase angle, a measure
of this relationship, has been used to forecast clinical outcomes (32).
In clinical use, single frequency BIA typically operates at 50kHz. Impedance
measurements can calculate body fat content through use of an empirical formula usually
obtained from linear regression analysis (33). Though BIA offers many clinical advantages, it is
also sensitive to changes in intracellular water; therefore, in conditions such as protein
malnutrition in which water balance is disturbed, BIA would not be an accurate measurement as
many of its assumptions are deemed invalid. It is also highly specific to the white race, as several
of the regression equations are intended for white persons only. When applied in non-Hispanic
6
blacks, TBW and FFM are under predicted. In healthy adults with a typical body fat distribution,
BIA can be a useful method to estimate body fat percentage. However, in obese individuals, BIA
may underestimate body fat percentage and most simple BIA machines do not account for the
variety of anatomic locations of body fat depositions (such as central and peripheral) (34).
As BIA relies on regression equations specific to different populations, the reliability and
validity of this technique has been questioned. Demura et al measured body composition using
three different BIA methods (foot-to-foot, hand-to-foot, and hand-to-hand) and validated the
results of body fat against hydro-densitometry estimates of body fat. Results showed that handto-foot BIA machines had the highest correlation (r = 0.88) with hydrodensitometry compared to
foot-to-foot (r = 0.75) and hand-to-hand measurements (r = 0.71) (35). In a sample of over 200
adults, Jebb et al (36) tested the validity foot-to-foot BIA using dual-energy X-ray
absorptiometry (DXA) as a reference. The reported agreement of FM between these two
measures was ± 7.9 kg.
2.1.1 Sarcopenia
A body composition abnormality that has piqued contemporary research interest is
sarcopenia. The Greek translation of the word equates to sarx meaning flesh and penia meaning
loss, a term formally recognized in 1989 by Rosenberg (37).
Defining sarcopenia is both challenging and controversial and varies according to the
measurement method and assessed population. A generally accepted criterion is a level of muscle
mass lower than two standard deviations below that of a healthy, young population. Studies by
Baumgartner et al (1), Melton et al (38), and Gillete-Goyonette et al (39) used DXA to inspect
appendicular skeletal muscle (ASM, Figure 2.1) of study participants in relation to the Rosetta
study, which consisted of healthy males and females, age 18-40 years (40). Alternatively, BIA
measurements of body composition have been used to define sarcopenia (41, 42). Popular cutpoints are those ascertained by Janssen et al (26, 43) which were established using BIA-derived
resistance combined with patient anthropometric and demographic characteristics to estimate
skeletal muscle mass. Additionally, publications from the Health, Aging, and Body Composition
Study have defined sarcopenia as a level of ASM in the sex-specific lowest 20% of the sample
(44, 45). Of note, their cut point values were similar to those used by Baumgartner et al (1).
More recently, the European Working Group on Sarcopenia suggested that the loss of strength
and function should also be taken into consideration (46).
7
2.1.1.1 Prevalence. The prevalence of sarcopenia varies greatly, in part due to the
absence of concrete defining criteria as discussed above. The prevalence of sarcopenia was
investigated in 4504 elderly subjects (aged ≥ 60) participants of the National Health and
Nutrition Examination Survey (NHANES) III study (26) . Skeletal muscle index (SMI= skeletal
muscle/body weight × 100) values were used to differentiate lower muscularity into two
categories: class I sarcopenia (SMI values within one to two standard deviations below young
adult reference values) and class II sarcopenia (SMI values below two standard deviations of a
young reference group). This sample presented with sarcopenia rates up to 59% in class I and
10% in class II. Iannuzzi-Sucich and colleagues (47) assessed sarcopenia prevalence using ASM
assessed by DXA and found a prevalence rate of up to 26.8% in a sample of 336 otherwise
healthy older adults. In a large assessment of participants in the Rancho-Bernado study, Castillo
and colleagues used BIA-derived FFM below two standard deviations below a gender specific
healthy reference population (42). Prevalence rates for sarcopenia were as high as 16% in the
oldest (85 years or older) group (41). Bijlsma et al utilized seven different diagnostic criteria and
applied these definitions to the same sample and demonstrated the wide variability of sarcopenia
prevalence. Rates of muscle loss varied from 0-45.2% and increased with advancing age (48).
Sarcopenia knows no ethnic boundaries, as was demonstrated in a study done by
Hedayati and colleagues (49). While the primary aim of this particular study was to determine
the prevalence of sarcopenia using two different measurement models, the authors also compiled
worldwide data on the occurrence of sarcopenia in over six different ethnicities. Their findings
indicated class II sarcopenia prevalence ranged from 0.0 to 57.6% in men and from 0.0 to 60.0%
in women, with much of the variability attributed to inconsistent analysis techniques and
participant diversity among studies; as findings above also suggested, those with the highest rates
of sarcopenia were oldest old (80 years of age and older). Clearly, there are numerous factors
that affect the prevalence rates of this condition.
2.1.1.2 Mechanisms. There are several postulated mechanisms that contribute to the
etiology of muscle atrophy with aging. Extrinsically, physical inactivity certainly wreaks havoc
on the integrity of muscle mass and strength in a variety of different age groups (50, 51). This
reduced activity will inevitably cause loss of strength which in turns makes daily activities
progressively more difficult. There is often a subsequent reduction in any activities that cause
discomfort, further worsening muscle loss as a part of a vicious cycle (9). Nutrition is an
8
extrinsic factor that can prominently alter the development of sarcopenia. Protein consumption is
particularly important in the geriatric population as the basic components of protein, amino
acids, are essential in muscle tissue synthesis, and hence anabolism. Inadequate protein intake,
(especially insufficient animal protein, a source of high biological value protein) often observed
in elderly, would therefore negatively affect muscle genesis and repair (52). To compound this
problem, there may also be an attenuation of muscle protein synthesis in response to amino acid
supplementation in older individuals (53). One longitudinal cohort study by Houston et al (54)
analyzed lean mass and protein intake in a large group of elderly participants (N = 2066, mean
age 74.5). After three years, those in the highest quintile of energy-adjusted protein intake lost
43% less lean mass than those in the lowest quintile. The results remained significant after
adjusting for FM, suggesting that low protein intake may indeed be a risk factor for the
development of sarcopenia.
Because of the increased need of amino acids and the possible reduction of protein
synthesis in the elderly, the current recommended dietary allowance (RDA) of 0.8 g/ kg body
weight/day have been criticized as being too low for a geriatric population (55, 56). Despite the
importance of protein consumption in this cohort, one finding suggests that an alarming 32-41%
of women and 22-38% of men over 50 years of age consume less than the RDA (57).
Total energy intakes are also important to consider in the aging muscle. The stages of
amino acid transport, protein synthesis, and proteolysis are all energy dependent. In times of
need, amino acids may be utilized as cellular fuel, especially in hepatic, renal, and skeletal
muscle systems. Thus, energy from carbohydrates and fat is crucial to the supply of energy
demands sparing the use of amino acids as fuel (58). Hence, the expression of protein as a ratio
to energy is useful when discussing dietary requirements and intakes in population groups. In
advancing age, energy requirements decrease to a greater extent than do protein requirements; as
such, protein dense foods are more critical in the geriatric population (59).
Vitamin D may also interact with muscle and influence the state of skeletal muscle
health. This vitamin may affect skeletal muscle strength and mass indirectly through its
metabolic effects on phosphorus and calcium or directly through 1,25-dihydroxyvitamin D
receptors found in many biological tissues including muscle. Vitamin D is necessary for normal
growth and development of muscle fibers and deficiency may manifest as muscle pain and
weakness (60). Tieland and colleagues (61) assessed serum 25-hydroxyvitamin D [25(OH)D]
9
levels as well as dietary vitamin D intake levels in community dwelling older adults (mean age
79 years). In a multiple linear regression analysis adjusted for numerous confounding factors,
serum 25(OH)D levels were positively associated with ASM and trended toward a significant
positive association with leg lean mass. Both 25(OH)D status and vitamin D intake were both
associated with at least one measure of function. Another micronutrient, calcium, has been
investigated as a link to sarcopenia. In a sample of non-obese Koreans, an inverse relationship
between sarcopenia (defined here as a weight-adjusted ASM less than two standard deviations
below the sex-specific mean of a large sample of healthy young adults) and calcium intake was
observed independently of numerous factors including total energy intake and serum 25(OH)D
status (62).
Sarcopenia is likely at least somewhat due to augmented oxidative stress that is a part of
the senescence process. An accumulation of reactive oxygen species damages DNA, lipids, and
proteins that can further harm cellular integrity. In response, cells have altered growth rates and
life cycles and increased defense mechanisms through antioxidant systems (63). Antioxidants
such as vitamin E, vitamin C, glutathione, carotenoids, flavonoids, copper, iron, manganese,
selenium, and zinc are essential cofactors for many antioxidant systems. In conjunction with
amino acids (principally leucine), a recent study in rats indicated that antioxidants may improve
muscle protein anabolism (64). In humans, women who show blood indices of heightened
oxidative damage have lower grip strength (65) and, in a large sample of elderly (N = 986, mean
age 75.3 years), low blood levels of numerous antioxidant vitamins and minerals were associated
with poor function and strength (66).
Clearly, diet and physical activity have a paramount ability to influence the development
and progression of sarcopenia, but other microscopic stimuli such as neuromuscular changes,
endocrine alterations, and cellular dysfunction/apoptosis are important to consider when
discussing the etiology of muscle degeneration. Motor neurons innervate muscle fibers; a single
motor neuron and the fibers it controls are called a ‘motor unit’. As the number of motor units is
lost to denervation with age, an increased burden of work is thrust upon the remaining motor
units. These surviving units will recruit denervated fibers which will then change the fiber type
to that of the motor unit. The net outcome is a conversion of type II fibers to type I fibers (slow
motor units), which further agitates muscle coordination and strength (67).
10
The endocrine system undergoes changing levels of hormones in senescence which can
affect the quality of muscle. Sex steroid hormones (testosterone and estrogen) along with insulin,
growth hormone (GH) and insulin-like growth factor-1 (IGF-1) particularly impact muscle
metabolism. Testosterone has anabolic, anticatabolic and possibly anti-inflammatory effects on
muscle and unfortunately declines with age (68). Testosterone replacement in men with
hormonal deficiency has been shown to increase muscle mass, strength, and function, though the
effects on muscle strength are attenuated in eugonadal individuals. Additionally, this therapy is
associated with numerous undesirable side effects (69). Estrogen has a direct effect on muscle
anabolism and the onset of menopause in women alongside reduced energy expenditure results
in increases in FM and losses of FFM (70). Estrogen and testosterone are doubly important as
they hinder the production of muscle destroying catabolic cytokines such as interleukin (IL)-1
and IL-6 (69). Another hormone, IGF-1, has also been implicated in sarcopenia development as
it activates satellite cell proliferation and differentiation (71) and mediates many of GH’s effect
on tissues; it also has a positive correlation with grip strength, especially in men (72). Growth
hormone is most pertinent to sarcopenia and strength because of its ability to increase the
number of Type II muscle fibers (73). Unfortunately, IGF-1 and GH levels decline with
senescence and muscle may become less sensitive to IGF-1 (71).
2.1.1.3 Health outcomes. The health outcomes of sarcopenia are typically grim. When
observing muscle loss alone, Baumgartner and colleagues found that both men and women with
sarcopenia had much higher odds of reporting three or more disabilities than those without
sarcopenia (1). One smaller follow-up study followed participants for 5.5 years and measured
body composition, disability, and health status. The probability of having poor disability scores
increased in those who lost the most FFM (3). However, many findings suggest that FFM or
muscle in relation to FM is a more powerful predictor of function and/or disability (74-76).
Those with sarcopenia may be at greater risk of falls and fractures, which can in turn lead to
compromised ability to perform daily activities (77), negatively altered quality of life (78), and
high mortality rates (79). In a cohort of elderly men, lower scores of height-adjusted ASM was
associated with escalated odds of preceding year falls, as well as impaired measures of static and
dynamic balance (27). However, retrospective findings of falls must be interpreted with care, as
the fear of falling often follows a fall, leading to decreased activity, and thus worsening the
11
sarcopenia condition. In a prospective study, elderly with sarcopenia were over three times more
likely to fall in a two-year period compared to those with normal muscle mass (80).
Sarcopenic individuals may also be at increased risk of shorter survival. Studies relying
on surrogate measurements of body composition such as mid-arm circumference and arm muscle
area reported higher mortality in those with lower values of corrected arm muscle area (HR for
those below pre-established cut points=1.94; 95% CI: 1.25, 3.00; p = 0.003) (81) and arm muscle
area (HR for those in lowest tertile=1.94; 95% CI: 1.36, 3.02; p < 0.001) (81, 82). Importantly,
the rate of muscle loss may be a significant factor to consider in assessing mortality. Szulc and
colleagues measured longitudinal changes in body composition by DXA in 715 men every 18
months for a total of 7.5 years. Those who did not survive had lower lean and FM; importantly,
the lowest tertile of muscle loss (participants who had the most rapid muscle loss) was most
predictive of death (OR 3.80; 99% CI: 1.73, 8.35; p ˂ 0.001) in a model that also included
central lean mass, appendicular FM, and central FM (83). The association between sarcopenia
and mortality was also confirmed in a subset analysis of 398 participants from the Framingham
Heart Study who underwent BIA measurements (84). Greater declines in height-adjusted FFM
(FFMI, kg/m2) were associated with higher risk of mortality (HR per unit decline in FFMI= 1.9;
95% CI: 1.3, 2.6; p ˂ 0.0001). The InCHIANTI (‘Invecchiare nel Chianti’ or ‘Aging in the
Chianti area’) study, on the other hand, reported different results than the ones above. Muscle
mass and density were assessed using peripheral quantitative computed tomography (pQCT) of
the right calf in 934 men and women age ≥65 years in order to assess sarcopenia’s effect on
mortality after a 6 year follow-up. After adjusting for numerous confounders, none of the body
composition parameters (muscle density, muscle area, and fat area) were associated with hazard
ratios significantly elevated to suggest these components predicted mortality (85).
Regardless of mortality, the potential debilitating effects of sarcopenia can be costly to
the healthcare system. A 2004 estimate predicted that 18.5 billion US healthcare dollars are spent
on sarcopenia-related disability (6).
2.1.2 Obesity
Obesity is commonly defined by BMI, which only takes into account height adjusted
weight. The most widely used classification system for BMI is that established by WHO as
follows: 18.5-24.9 kg/m2 as normal weight, 25.0-29.9 kg/m2 as overweight, 30.0-34.9 kg/m2 as
class I obese, 35.0-39.9 kg/m2 as class II obese, and ≥40.0 kg/m2 as morbid obese (86). Though
12
BMI does not take into account body fat, a relationship between the two seems to be present,
even across multiple ethnicities (87). In fact, body fat percentage can be used as an important
tool to identify obesity; women with body fat above 30% and men over 20% body fat tend to
have the most health problems, likely due to the metabolic abnormalities associated with
excessive adiposity (88) .
2.1.2.1 Prevalence. Using the WHO BMI cut points, the prevalence of obesity in the
United States is alarming. Over one third of adult Americans were classified as overweight or
obese in 2010, with even higher prevalence in the elderly population (7).
2.1.2.2 Mechanisms. Much like any disease, the etiology of obesity is multifactorial and
discoveries about its mechanisms continue to be revealed. An imbalance in the energy balance
equation (specifically excess caloric intake coupled with inadequate energy expenditure) will
lead to excess adiposity over time, though the interactions between components of energy
balance are complex and the exact mechanisms have yet to be elucidated (89). The macronutrient
composition of the diet may also influence the development of obesity. In a large twin study,
those who were severely obese not only consumed more total kilocalories as postulated, but also
had a higher percentage of fat intake compared to normal weight siblings (90).
Genetic, environmental, demographic, and biological factors also strongly influence the
development and severity of obesity. While there is great variability in findings concerning the
impact of genetic predisposition on obesity, the consensus usually states that 30-40% of BMI
variance can be attributed to genetics. Twin and ethnic cohort studies have confirmed that an
obesogenic environment (one in which activity is discouraged and excess eating is effortless) can
increase expression of the obese genotype. In other words, some people with a predisposition to
become obese may not actually gain as much fat in a “health-friendly” environment (91). Resting
metabolic rate (RMR) is partially genetically determined and makes up a large percentage of
total energy expenditure. In obese patients, RMR may be lower than that of their normal weight
counterparts, even after weight loss (92). However, lower RMR may be a side effect of the
multiple causes of obesity and not directly necessarily a cause of excess adiposity. In addition to
these factors that influence the development of obesity, numerous hormones affect weight gain
and these effects differ in men and women. For example, there is a slew of gut hormones that
regulate appetite, energy expenditure, and therefore weight. Of those, peptide YY (which
increases satiety and energy expenditure), ghrelin (which initiates signaling of eating and
13
regulates long term weight), amylin (which increases satiation), and cholecystokinin (which also
influences increased satiety) have been the most extensively studied (93). Sex steroid hormones
have also been the focus of recent research effort, especially since receptors for estrogens,
androgens, and progesterone have been found in adipose tissue. In general, these hormones
regulate leptin and lipoprotein lipase, which, through a serious of metabolic cascades, can
increase lipolysis (94). Unfortunately, there is a natural decline in these hormones with age,
particularly in women. After menopause, a decrease in estrogen is often touted as the cause of
the increase in adiposity in women, most likely through the phosphatidylinositol-3-kinase/protein
kinase B (PI3K/Akt) cascade pathway (95). Interestingly, many of the hormones that alter FM
also negatively modulate muscle metabolism, as discussed in Section 2.1.1.2.
2.1.2.3 Health outcomes. The negative associations of obesity are perhaps the most
comprehensively studied topics in modern health research. Thousands of results indicate that
excess FM is related to heart disease, Type II diabetes, osteoarthritis, sleep apnea, reproductive
abnormalities, certain cancers, high blood pressure, dyslipidemia, stroke, and liver/gallbladder
disease, among others (8). Additionally, hospitalized obese patients may be more susceptible to
infections, principally nosocomial, periodontal, post-surgical, and respiratory infections (96).
Moreover, those who are obese may experience greater mobility-related functional impairments,
(97) as well as walking restrictions (98), which is particularly important in the development OA
as well as the recovery from arthroplasty surgery.
2.1.3 Sarcopenic Obesity
Age associated loss of muscle mass can occur simultaneously with an increase in FM in a
condition known as sarcopenic obesity. Currently there is not a concrete consensus definition of
this body composition abnormality, thus making its definition challenging. Some studies have
used percentiles of a reference population compared to the current sample to define sarcopenic
obesity. For example, Baumgartner et al characterized this condition as having a height adjusted
ASM lower than two standard deviations of a healthy reference group and a body fat percentage
higher than the 60th percentile of the sample (12). In another study by Davison and colleagues,
participants in NHANES III were classified as sarcopenic obese if their body fat percentage was
in the upper two quintiles and muscle mass in the lower two quintiles of the distribution (97).
Other possible methods to define this phenotype include assessing the ratio of muscle to fat mass
(99) or documenting the appearance of low strength coupled with a high BMI (100).
14
2.1.3.1 Prevalence. Much like sarcopenia, the prevalence rates of sarcopenic obesity are
highly dependent on the measurement method selected and how this condition is defined. In the
Baumgartner et al study referenced above, 5.8% of the sample were considered sarcopenic
obese, of which 61.5% were male (12). Davison and colleagues reported rates as high as 9.6% of
this body composition abnormality (97). The variance of the prevalence of sarcopenic obesity
solely due to the defining criteria used was demonstrated in an analysis by Batsis et al (101).
Eight definitions of sarcopenic obesity were applied to participants from NHANES 1999-2004
who had DXA scans available. Prevalence rates ranged from 3.6% to 94.0% and all but one
definition revealed an increased rate of sarcopenic obesity with age.
2.1.3.2 Mechanisms. In healthy individuals, an increase in body weight occurs alongside
an escalation in muscle and bone mass. This balance is sustained by gravitational forces that
stimulate mechanoreceptors in bones and muscle that control the production of growth factors.
However, this process may be compromised in older adults who become obese without the
simultaneous growth of muscle mass and strength, thus giving rise to a body composition
abnormality of high body fat relative to muscle (10). The sarcopenic obese phenotype can also
originate from a significant loss of body weight, particularly FFM. In those who remain obese
despite weight loss, excessive muscle loss can determine the onset of sarcopenic obesity (102).
2.1.3.3 Health outcomes. The presence of low muscle mass or strength coupled with
high body fat is associated with numerous health consequences. In a sample of 607 postmenopausal women, those with sarcopenic obesity presented with low indices of physical fitness,
function, and strength (103). Of importance is the decrement in functional ability observed in
those with this body composition phenotype (1, 12, 13), though another study found no
association between sarcopenic obesity and functional capacity (104). Furthermore, sarcopenic
obesity has been associated with frailty, poor quality of life (105), extended hospital stays (15),
and high mortality rates (16, 17, 106).
2.2. Osteoarthritis
Osteoarthritis is a painful degenerative disease that most commonly occurs in the hips
and knees. It is an inflammatory condition that affects the whole joint and can include damage to
articular cartilage, bone thickening, osteophyte formation, deterioration of ligaments, and
synovial inflammation. Age associated changes in the musculoskeletal system are important
15
predictors of OA (20). Osteoarthritis is commonly thought to be a disease of “wear and tear” in
which obesity aggravates. This theory stems from the fact that the knee undergoes forces
approximately three times body weight during walking and up to six times body weight for
activities such as stair climbing (107). In fact, one study of 3750 elderly women showed that the
risk of developing OA was significantly higher with increasing BMI categories, suggesting that a
high body weight magnifies forces in the knee joint with activity (21). An early finding from the
Framingham Study investigated the effect of weight loss on OA and indicated that weight loss of
approximately 5 kg was associated with over a 50% reduction in the risk of developing knee OA
(108). However, there is another theory as to why OA develops that considers very little in the
way of adiposity, one of which is the muscle dysfunction theory. Like the wear and tear theory,
the knee joint has been studied comprehensively. During normal gait, the knee must overcome
external adduction movement, or else succumb to excessive force to the medial tibiofemoral
joint. Eccentric contraction of the quadriceps muscle in conjunction with adequate hip muscle
strength will stabilize and protect the knee. Therefore, a loss in strength (and/or faulty muscle
activation patterns and proprioception) may precipitate the pathophysiology of OA, at least in the
knees (109). Occupational status can cause OA, especially in floor/brick layers and healthcare
assistants (110), though it appears as though only farmers and fisherman have increased odds at
receiving a THA or TKA due to OA (111).
Osteoarthritis patients may be at an increased risk of falls, which are dangerous events in
the frail elderly, as many succumb to poor mobility and heightened fear of certain activities after
the incident. In a laboratory setting, Kim et al (112) divided patients into those with mild OA,
moderate to severe OA, and age matched controls to objectively assess balance control using the
timed up and go test, the Berg balance scale, and the Tetrax human balance system. Those with
moderate to severe OA had significantly worse scores on the Berg balance scale compared to
mild OA and control participants. From the extensive results of the Tetrax test, the researchers
concluded that OA patients had poorer balance control and relied more on their eyesight to
regulate balance. In a directly applicable setting, further findings indicate that musculoskeletal
pain does indeed influence the risk of falling. A recent comprehensive study by Scott et al (113)
examined body composition (by use of DXA), strength, falls risk, pain, and OA severity in 709
older adults; follow up in this sample was an average of 2.6 years. Those reporting knee pain,
stiffness, and dysfunction also presented with lower muscle quality and a higher risk of falls
16
compared to those with normal knees. These results were not confirmed in men. Other studies
have established a link between OA or general musculoskeletal pain and falls risk (114, 115).
While there are numerous bodily structures such as the vestibular, optical, and muscular systems
that work to prevent falls, the findings above suggest osteoarthritis pain may be an important
factor worsening its incidence risk.
Body composition’s influence on the risk of OA and total joint arthroplasty is
multifactorial. Roubenoff (9) proposed that the effect of excess weight (obesity) on the joints
coupled with a loss of the vital muscle (sarcopenia) that typically supports and protects the joints
plays an important role in the relationship between sarcopenic obesity and OA. The result is a
rise in inflammatory markers that cause excessive joint damage and persist in a failed attempt to
protect the body. When OA progresses to severe joint damage that is not responsive to therapies,
THA and TKA are often the only cure for persistent pain.
Aging combined with the decrease in physical function (mostly due to pain) in patients
with OA may create a vicious cycle of progressive loss of muscle mass with gains in FM giving
rise and perpetuating the sarcopenic obese phenotype, Figure 2.2. Additionally, low protein
intake, hormonal imbalances and inflammation can also lead to the loss of muscle mass and
strength (116), leading to physical disability (1) as well as increased risk of falls and resultant
fractures (27). The decrease in the highly metabolically active muscle compartment decreases
energy expenditure predisposing to weight gain, primarily adipose tissue gain (9). Obesity may
worsen the sarcopenic condition as findings indicate that lipid oxidation is depressed in the
muscle tissue of obese individuals. This waning of oxidation will partition lipids toward storage
in skeletal muscle (intra-muscular adipose tissue,) (117), a sign of adverse muscle quality that
can be predict sarcopenia (118). Obesity can also be perpetuated by a high caloric intake
(especially high fat intake). The inflammatory milieu associated with sarcopenia and obesity can
also increase cartilage degradation (119), worsening or even causing OA, Figure 2.2.
Using the intrinsic relationships between OA, muscle, and adipose tissue described
above, it is reasonable to conclude that abnormal body composition (particularly sarcopenic
obesity) is common in patients with hip or knee OA and is associated with specific physiological,
behavioral and functional profiles such as reduced protein intake, poorer physical function, and
surgical-related outcomes.
17
Osteoarthritis
Inflammation
↓ Physical
Activity
↑ Caloric intake
(especially high fat
intake)
↓ Protein intake
(especially high
quality protein)
↑ Fat mass
Inflammation
Insulin Resistance
↓ Muscle mass
↓ Muscle strength
↑ Physical disability
↑ Risk falls and
fractures
↓ Anabolic hormones
↓ Energy Expenditure
↓ Physical Activity
Hypertension
Dislipidemia
Diabetes
↑ Cardiovascular
disease risk
Figure 2.2 Proposed Relationships between Osteoarthritis, Muscle, and Fat Mass
Adapted from Ormsbee MJ, Prado CM, Ilich JZ, Purcell S, Siervo M, Folsom A, Panton L.
Osteosarcopenic obesity: the role of bone, muscle and fat on health. In Press: Journal of Cachexia,
Sarcopenia and Muscle.
IMAT=intramuscular adipose tissue.
2.2.1 Hip Replacements
Hip arthroplasties are carried out in lesser numbers than knee replacements with an
approximate total of 262,000 procedures in 2010 due to osteoarthritis alone. Those with a hip
fracture constituted slightly over 100,000 THAs and revision rates were around 44,000 in 2010
in the United States alone. Primary hip replacement for any reason costs an average of $17,500
with an extensive 3.3 day average length of stay after surgery (120). Those who undergo this
surgery are usually over the age of 50, female, and Caucasian (121).
Events that lead up to hip arthroplasty are in many ways similar to those related to knee
arthroplasty. Osteoarthritis remains the most common reason for undergoing hip replacement.
18
However, the prevalence of hip fracture in the elderly, frail, osteoporotic population lends itself
to more hip arthroplasties due to trauma. Hip fractures often occur in the femoral neck and the
intertrochanteric and subtrochanteric regions of the bone. Surgical management through a THA
will depend on the severity and placement of the fracture as well as patient and clinical factors
(121). Osteonecrosis, or the death of bone tissue, may also require a consequent THA. Many
clinical and lifestyle factors can cause osteonecrosis of a joint including regular corticosteroid
use, smoking, alcohol use, HIV, and organ transplantations. Unless rare circumstances intervene,
osteonecrosis patients do not face increased complication rates after surgery (122). Like the
mechanisms described in TKA, rheumatoid arthritis can also generate the need for THA, as this
is a systematic disease that can affect any joint of the body.
Total hip arthroplasties can improve quality of life from the patient’s viewpoint. One
study found that most patients (~75%) were satisfied with their decision for surgery an average
of 18 months post-operatively using a 20 point questionnaire (123). In a long term follow up (512 years), only 4.1% of patients were dissatisfied with their surgery and 79% reported the
complete absence or little pain on a daily basis, suggesting that the overwhelming majority of hip
replacements are successful according to the patient’s perspective (124). In a four-year follow up
study, Vissers and colleagues reported lower physical activity but some improvement in certain
aspects of functional status as measured by standard function assessments. Patients also
perceived their function and activity as much higher than baseline (125).
Although gait function typically improves after arthroplasty, many patients exert less
force on the operated leg as a type of compensation for surgery (126). In fact, muscle loss after
hip arthroplasty has also been observed. Rasch and colleagues used computerized tomography to
assess muscle quality (cross sectional area and density) before surgery and again six months and
two years after surgery. In the two year follow up, patients presented with persistent muscle
atrophy in almost all muscles of the operated side. Though there were improvements in muscle
quality two years post-operatively, the operated leg had showed signs of severe atrophy
compared to the healthy side (127).
There has already been a large body of research focusing on body weight’s impact on the
outcomes of THA. Observations of THA outcomes typically endorse the idea that obesity is
associated with negative surgical outcomes (128), though other studies express no significant
differences between obese and non-obese groups (129, 130). In one large review, Vincent et al.
19
concluded that the majority of patients experience improvements in pain and function, even in
obese subjects. However, those who are obese fail to reach the same level of function and
reduced pain as their non-obese counterparts, as measured by the most commonly utilized
assessments such as the Oxford Hip Score, WOMAC, Harris Hip Score, and more (131).
Replacement of the hip joint is a common surgery with a multitude of etiologies. Though
the majority of patients are satisfied with their surgery, long term results of function do not
usually accurately affect these optimistic patient opinions. Muscle atrophy is still persistent in the
operated hip, and those that are obese could possibly face worse long term outcomes.
2.2.2 Knee Replacements
In 2010, approximately 600,000 American adults underwent primary TKA due to OA
with an estimated hospital cost of $16,000. Another 54,000 knee surgeries were revisions of
previous implants with a steeper price tag of $21,500 (120).
Rheumatoid arthritis is another type of arthritis that, like OA, can destroy the joint, but it
is instead a more inflammatory disease. New developments in pharmaceutical management of
the condition have resulted in lower rates of joint replacement surgery for this condition, but it
still remains a cause of TKA. Knee replacement patients with rheumatoid arthritis tend to be
younger and thus need more reliable components to avoid later revision (132).
The outcomes of knee replacement are usually positive. There is a very small percentage
of knee replacement patients who must undergo a revision surgery; in fact, only 4.4% of these
patients face this problem after a ten year follow up. This rate is higher for younger patients and
those with rheumatoid arthritis (133). Though recovery from surgery is challenging for many
patients, long term satisfaction rates are high. A study by Mizner and colleagues utilized three
different questionnaires to assess patient satisfaction with surgery as well as numerous tests to
appraise functional outcomes, strength, and range of motion 1 month and 12 months postsurgery. While patients stated that they were satisfied and their knee was functioning
appropriately, there was a net loss of strength and function in the long term (134). Despite faulty
patient perception of outcomes, TKA, on average, can improve function of more than 30% using
numerous clinical function assessments (133).
The ultimate goal of knee replacement is to diminish discomfort radiating from the joint
and eventually improve quality of life through pain-free activity. However, patients may not
radically change their lifestyle after a primary joint replacement. A recent study utilized data
20
from two large databases to observe weight gain after TKA. After five years, 30.0% of TKA
patients had gained ≥ 5% of baseline body weight, compared to 19.7% of the control sample who
gained this weight (OR=1.6; CI: 1.2,2.2), thus suggesting that TKA patients had a greater risk of
weight gain than their counterparts with healthy joints (135).
Long term differences between obese and non-obese patients’ outcomes may exist. One
study (136) of 48 patients undergoing TKA measured clinical and radiological outcomes
preoperatively and at one year post-operatively found that those with a BMI of ≥ 30 presented
with worse knee and function scores and range of motion when compared to their non-obese
counterparts. Additionally, mean WOMAC score for this group was also significantly higher,
indicating greater pain, stiffness, and difficulty accomplishing daily activities. A nine year follow
up showed a significant increase in function across all BMI categories compared to baseline
measurements, suggesting TKA may improve quality of life regardless of weight categories.
However, when compared to non-obese participants, obese patients showed significantly lower
function scores (137). Baker and colleagues enforced this finding by proposing that though obese
patients present with lower physical functional and pain indexes and also have lower satisfaction
levels, they do indeed benefit from TKA (138).
As discussed previously, commonly performed knee replacement surgeries have
numerous etiologies, of which OA is the most common cause. Though outcomes of this surgery
are typically positive, care should be used when interpreting these findings in relation to the
influence of patient perceived outcomes and body weight on clinical and functional results, as
mostly studies differ vastly in methodologies and do not account for measures of body
composition or strength.
21
CHAPTER THREE
MATERIALS AND METHODS
3.1 Study Cohort
This study was approved by the Florida State University Human Subjects Committee,
Appendix A. Patients planning to undergo THA or TKA due to OA were recruited from August
2013 until February 2014 from the Tallahassee Orthopedic Clinic (TOC). Patients 18 years of
age and older, able to communicate freely in English, and able to give informed consent were
eligible to participate. Exclusion criteria comprised of conditions that could potentially alter
body composition including oral steroid therapy, inflammatory digestive disease, HIV+, or
recent major surgery. Those with a pace-maker were also excluded. After their scheduled visit
with the physician, patients were approached and given a brief explanation of the study;
informed consent (Appendix B) was obtained from all participants who agreed to participate.
3.2 Data Collection Procedures
The components of this study included body composition analysis and completing
additional questionnaires and optional assessments (handgrip strength assessment and 24-hour
dietary recall).
3.2.1 Demographics and Clinical History
In order to gather patients’ self-reported information, a study specific questionnaire was
utilized, Appendix C. The initial pages collected data on demographics, medical, surgical and
psychiatric comorbidities, medications, symptoms, social history, as well as gynecological
history for women. From that point, individual questionnaires were inserted, as outlined in
Section 3.2.2.2.
3.2.2 Anthropometrics and Body Composition
Height was ascertained using a Seca 769 scale with height rod (Hamburg, Germany) and
rounded to the nearest 0.5 inch and body weight was obtained from the results according to the
BIA measurement. From this, BMI was calculated as the ratio of weight (kg)/height (m2) and
classified according to WHO categories as follows: 18.5-24.9 kg/m2 as normal, 25.0-29.9 kg/m2
as overweight, 30.0-34.9 kg/m2 as class I obese, 35.0-39.9 kg/m2 as class II obese, and ≥40.0
kg/m2 as morbid obese (86). Body composition was assessed using the TBF-300A BIA from
22
Tanita (Arlington Heights, Illinois, USA). Patients were asked to remove shoes and socks as well
as any heavy jackets prior to testing. Clothing weight was input as “0” in order to maintain
equality among patients. All participants were considered “standard” male or female as opposed
to “athletic”. Readings on the BIA printout included height, weight, BMI, fat percentage, basal
metabolic rate, impedance, FM, FFM, and TBW. From this, FM and FFM were normalized for
height (FMI and FFMI, measured in kg/m2) and FM:FFM ratio (which considers proportional
differences in body composition components) (102) was calculated.
3.2.3 Functional Characteristics
3.2.3.1 Handgrip strength. For the handgrip strength test, patients were asked to sit on a
stool with a 50 kg mechanical hand dynamometer (Model 78011, Lafayette Instruments,
Lafayette, Indiana, USA) positioned on a table in front of them. Participants used their dominant
hand and kept their elbow on the table during testing. The test consisted of three short efforts
with a 20-30 second pause in between attempts. Results were measured in kilograms and the
average of the attempts was recorded. Handgrip strength was evaluated as a continuous or
categorical variable with the gender-specific median used to dichotomize strength.
Handgrip strength measurement is extremely easy to use, portable, and does not require
trained technicians. Additionally, it demonstrates high test-retest reliability in middle age adults
(139) and in elderly (140). Importantly, test-rest reliability appears to be highest when the
average of three trials is used (141).
3.2.3.2 Questionnaires.
The Western Ontario and McMaster Universities Index of Osteoarthritis (WOMAC): The
WOMAC (Appendix C) subjectively estimates the severity of OA in the hips and knees. There
is a total of twenty-four items, five of which assess pain, two assess stiffness, and seventeen
assess functional limitation. The 5-point Likert-type scale version of this test is the form that was
administered. Scores are evaluated by the sum total number the patient ranks on a scale of 0
(none) to 4 (extreme), with a score of 96 being the maximum amount possible. The final score
can be presented as a total score or divided into individual sections (142). This questionnaire is a
very common clinical tool used to assess the impact that OA has on quality of life and determine
improvement after THA or TKA (143); it has been validated previously (142).
Activities of Daily Living Questionnaire (ADL): This assessment is divided into six sections that
address different areas of disability: self-care activities, household care, employment and
23
recreation, shopping and money, travel, and communication, Appendix C. There are three to six
items in each section and, similar to WOMAC, each are graded on a 4 point scale (0=No
problem to 3 =No longer capable of performing activity). An alternative rating of 9 is provided
for patients in the case that they have never performed that activity or have experienced other
circumstances that equate to the question being not applicable (144).
Aerobics Center Longitudinal Study Questionnaire: Exercise habits were recorded using the
Aerobics Center Longitudinal Study Questionnaire, Appendix C (145, 146). Patients were asked
to recall moderate to vigorous physical activity in the past three months. If appropriate, the
patient was asked to elaborate on the duration and frequency of the exercise. From this
questionnaire, total weekly metabolic equivalent of tasks (METs) were calculated and summed
using the 2011 Compendium of Physical Activities (147).
Falls Efficacy Scale, International (FES-I): The Falls Efficacy Scale, International (Appendix
C) was administered to patients to determine fear of falling. This questionnaire is made up of 16
different activities and patients are asked to rate fear of falling on a scale of 1-4 (1=Not
concerned at all; 4=Very concerned). Scores range from 16-64 with higher scores indicating a
very high concern of falling. This test has been shown to have excellent internal and test-retest
reliability in geriatric patients with and without cognitive impairment (148). This questionnaire
was implemented as part of collaborations with Florida International University (Dr. Edgar
Vieira).
3.2.4 Nutrient Intake
Nutrient intakes were obtained using the multiple pass method from the United States
Department of Agriculture (149, 150). Recall began with a ‘quick list’ of what the patient
consumed in the previous day. The researcher then inquired for any forgotten foods that might
have been missed in the quick list before moving on to discuss the time and occasion of
consumption. Food details such as sauces or added dressings were collected. Lastly, the final
probe included reviewing the complete list of food once more with the patient to ensure that no
food or beverage was forgotten. All data were collected in a non-judgmental manner and whether
or not the day was ‘normal’ for the patient is also noted.
The foods from the recall were input into FoodProcessor Software (Version 10.11, 2012
Esha Research, Salem, Oregon, USA). Resulting protein intakes of this sample were compared to
the RDA value of 0.8g/kg body weight/day (151). All macronutrient percentages were compared
24
to the Acceptable Macronutrient Distribution Ranges (AMDR). Micronutrients that have an
impact on muscle metabolism or have antioxidant roles (calcium, selenium, beta-carotene,
copper, iron, manganese, selenium, zinc, vitamin C, and vitamin D) were also assessed, as
oxidative stress has been indicated as a factor of muscle loss with age as discussed in Chapter
Two.
3.2.5 Prognostication: Surgical Outcomes
Patients’ medical records were reviewed for surgical endpoints such as length of hospital
stay, thromboembolic disease, infections, wound healing complications and laboratory values. A
timeline of the data collection is presented in Figure 3.1.
Pre-surgical outcomes at
pre-operation visit
1-7 days before surgery
-
Post-surgical outcomes
(chart review 2 weeks 6 months after surgery)
Surgery and
hospital stay
-
anthropometry and body composition
functional characteristics
nutrient intake
questionnaires (demographic, ADL,
WOMAC, physical activity, FES-I)
length of hospital stay
thromboembolic disease
infections
wound healing complications
laboratory values
Figure 3.1 Timeline of Data Collection
ADL=Activities of Daily Living; WOMAC=Western Ontario and McMaster Universities
Osteoarthritis Index; FES-I=Falls Efficacy Scale, International
Laboratory values that were recorded in medical charts included results from basic
metabolic panel and complete blood count typically conducted at Tallahassee Memorial
Hospital. These included:
Basic metabolic panel:
-
Serum sodium [milliequivalents per liter (mEq/L)]
-
Serum potassium (mEq/L)
-
Serum chloride [millimoles per liter (mmol/L)]
25
-
Blood Urea Nitrogen [milligrams per deciliter (mg/dL])
-
Carbon Dioxide (mmol/L)
-
Creatinine (mg/dL)
-
Glucose (mg/dL)
Complete blood count:
-
Total white blood cells
-
Total red blood cells
-
Hemoglobin (mg/dL)
-
Hematocrit
-
Mean cell volume
-
Mean corpuscular hemoglobin
-
Mean corpuscular hemoglobin concentration
-
Red blood cell distribution width
3.3 Statistical Analysis
As the primary goal of this pilot study was to profile body composition, functional and
nutritional characteristics of patients undergoing THA or TKA surgery, the initial sample size
was estimated based on a number which would provide a small margin of error and a small
confidence interval. Though this is an ongoing study, the current number of patients assessed is
42 yielding a margin of error of 1/√42 = 0.15.
The Kolmogrov-Smirnov test was utilized to test for normality of data. Data are
expressed as mean ± standard deviation (SD) and range. Comparisons among groups were
conducted using Chi-Square test, Fisher’s exact test, Independent samples t-test, or test for
independent proportions, as appropriate. Body composition and functional variables were further
dichotomized according to the gender-specific median or tertiles.
Correlations between body composition components and collected variables were
calculated with Pearson’s correlation coefficient. Correlations of <0.3 were considered small,
values between ≥ 0.3 and < 0.5 were classified as medium, and correlations of ≥ 5 were large.
Only those variables with medium and large correlations are presented.
26
To determine significance, p-values of ≤ 0.05 were considered significant and all tests are
two-tailed. All statistical analyses were carried out using PASW 21.0 for Windows [Polar
Engineering and Consulting (Nikishi, Alaska), formerly known as SPSS.
27
CHAPTER FOUR
RESULTS
4.1 Demographic Characteristics
Of those eligible patients who were approached (N=60), 18 refused to participate due to
lack of interest or time (many patients had other appointments scheduled after the pre-operation
visit at TOC). To date, 42 (28 females) patients have participated in this ongoing study, Figure
4.1.
Overall, 40.5% (n=17) of patients had been diagnosed with hip OA and the remaining
presented with knee OA. Thirty-eight (90.5%) participants were Caucasian and the remaining 4
(9.5%) were African American. Of those who completed the demographic and background
questionnaire in full (n=38), 18.4% were single, 21.1% claimed to have a partner, 15.8% were
divorced, 13.2% were widowed, and 31.6% were married. In this sample, 2.5% reported having
an education of a grade school level, 38.1% reported completing high school, 42.5% reported
graduating college, and 15% had a post-graduate education. Recorded annual household income
were as follows: 38.6% lower than $30,000, 26.2% between $30,000-50,000, 9.5% between
$50,000-75,000, 11.9% between $75,000-100,000, and 9.5% had income above $100,000. No
differences in demographic characteristics were observed between patients with hip or knee OA.
4.2 Anthropometric and Body Composition Characteristics
Table 4.1 displays the characteristics of study participants. The mean age was 66 ± 10
years (range 43-89); 59.5% were 65 years of age and older. According to BMI classification, 1
patient presented with a normal BMI, 12 (28.6%) were overweight, and 29 (69%) were obese.
Out of the obese subjects, the majority were classified as obese class I. Females presented with a
significantly higher BMI, body fat percentage, FMI, and FM:FFM ratio while males exhibited
higher FFMI. A wide range of FM and FFM across BMI values was observed, Figure 4.2. As
illustrated in this figure, a patient with a BMI of approximately 30.5 kg/m2 may present with a
FM anywhere from 26.8 kg to 51.6 kg; likewise, a patient with a BMI of 35.9 kg/m2 may have a
FFM of 46.4 kg to 79.3 kg. One patient presented with a BMI >2SD above the mean (outlier),
yet had a FM and FFM within 2SD of the mean.
28
Age was negatively correlated with BMI (r=-0.35; p=0.024) and FFMI (r=-0.33,
p=0.035) but age was not significantly correlated with FMI (r=-0.19, p=0.218) or body fat
percentage (r=-0.088, p=0.580). When patients were dichotomized as younger versus 65 years of
age and older, no differences in body composition variables were observed. However, patients
who were older presented with lower average handgrip scores (p=0.016).
4.3 Comorbidities
This cohort reported low rates of comorbid diseases and mental conditions; in fact, 20
patients did not report presence of any comorbidity. No differences in body composition,
functional, or nutritional characteristics were observed between patients with chronic diseases
(cancer, diabetes, heart disease, and osteoporosis) and patients without chronic disease.
However, patients reporting low back pain (n=11) presented with lower FFMI as well as with
significantly higher body fat percentage, Figure 4.3 A and B.
4.4 Functional Outcomes
Handgrip strength and WOMAC, ADL, FES-I, and physical activity scores are reported
in Table 4.1. Compared to females, males presented with higher handgrip strength and lower
FES-I score. Correlations between body composition measurements and functional variables are
presented in Table 4.2; all correlations were of medium (r=0.3-0.5) strength.
A total of 38 patients completed the WOMAC questionnaire with a mean total score of
52.4 (out of a possible 96 points), Table 4.1. Total WOMAC scores were significantly correlated
to FMI and FM:FFM ratio, while body fat percentage and BMI trended towards significance,
Table 4.2. The median values of total WOMAC scores (52) were used to categorize individuals
as low versus high scores (below versus above median, respectively). Patients with lower total
WOMAC scores had lower body fat percentage, FMI, and FM:FFM ratio, while BMI differences
trended towards significance (p=0.055), Table 4.3. WOMAC score was correlated with ADL
(r=0.70, p<0.001), physical activity (r=-0.47, p=0.005), and FES-I (r=0.46, p=0.030) scores.
Thirty-six patients completed the ADL questionnaire; of these, 4 patients reported no
disability at all (score of “0”) and an overall low score was observed among other participants.
Significant correlations were observed between ADL and other functional measurements such as
29
the WOMAC (r=0.70, p<0.001) and physical activity (r=-0.43, p=0.012), but not with FES-I
score and handgrip strength. ADL score was only significantly correlated with FMI, Table 4.2.
Of those who recorded their physical activity (n=35), 5 patients reported not being
physically active. Physical activity levels were negatively correlated with BMI, body fat
percentage and FMI, Table 4.2. Values of physical activity were also negatively correlated with
ADL scores (r=-0.43, p=0.012).
A lower number of patients completed the FES-I questionnaire (n=2; 13 females). FES-I
score was positively associated with BMI as well as with body fat percentage, FMI, and
FM:FFM ratio, Table 4.2. Importantly, FES-I score was positively correlated with the WOMAC
pain score (r=0.49, p=0.022), but not with scores of stiffness or difficulty. Median values of the
handgrip strength, ADL score, physical activity, and FES-I were not able to depict differences in
body composition phenotypes. Overall, body composition components were more strongly
correlated with functional measures compared to BMI alone, Table 4.2.
In order to further explore the relationship between body composition and functional
variables, we dichotomized individual functional assessments in function of both FM and FFM,
on the basis of the idea that higher FM and lower FFM values (Figure 4.4 A) would be
associated with poorer physical function, Figure 4.4 B, C and D. Therefore, functional sores
were categorized by the median (i.e. patients above or below the median scores) and poorer
functional outcomes were interpreted as values below the median for handgrip strength and
scores above the median for WOMAC and FES-I. Clusters of variables were observed in specific
quadrants of body composition phenotype and were in general closer to the median or tertile
values of FM and FFM, as described below.
In regards to median handgrip strength values, patients with a phenotype of higher FM
and lower FFM presented with a higher prevalence of lower strength (7/10, 70%) compared to
those with higher FM and higher FFM (1/7, 14%), p=0.023, Figure 4.4 B. The horizontal line
represents a FM of 38.8 kg and the vertical line represents a FFM of 53.0 kg, both of which
represent the median values for the entire cohort. No other differences were observed among the
other body composition phenotypes.
When the relationship between FM, FFM and median WOMAC score was explored,
patients with a higher FM and lower FFM phenotype presented with a higher prevalence of
WOMAC scores above the median (13/19, 68%) compared with patients with a lower FM and
30
higher FFM phenotype, (2/8, 25%), p=0.038, Figure 4.4 C. The horizontal line represents a FM
of 36 kg, and the vertical line represents a FFM of 62 kg, both of which are close to the median
and within one standard deviation of the mean for the entire cohort. No other differences were
observed among the other body composition phenotypes.
Lastly, median FES-I scores in function of FM and FFM are displayed in Figure 4.4 D.
Patients with a lower FM and lower FFM phenotype demonstrated a higher proportion of higher
FES-I score (4/5, 80%) compared to patients with a lower FM and higher FFM (1/8, 13%),
p=0.015. Furthermore, those with a lower FM and higher FFM had a more favorable score
compared to patients with a higher FM and higher FFM (3/3, 100%), p=0.007. Both horizontal
and vertical lines corresponded respectively to the median values of FM and FFM for the entire
cohort. No differences were observed in the prevalence of higher ADL or lower physical activity
in function of FM and FFM distribution, data not shown. Although the cutoffs of FM and FFM
associated with poorer scores were non-gender specific, a similar pattern emerged when the data
was distributed by gender. Nonetheless, the limited number of patients within each quadrant of
body composition phenotype hindered a meaningful analysis, Appendix D.
4.5 Nutrient Intake
A total of 29 patients (20 females) completed the 24-hour dietary recall. The combined
average energy intake was approximately 1700 kilocalories (kcal)/day with a wide range of 4613798 kcals/day. Males presented with a significantly greater caloric intake compared to females,
Table 4.1. Those with a fat intake above the AMDR (n=11; ≥ 35% of total kcals) presented with
higher overall FM compared to those with lower fat intake (43.6 kg vs. 34.2 kg, respectively,
p=0.047). Fat mass was significantly correlated with the protein:energy ratio (r=0.42, p=0.022)
while FFM was positively associated with grams of carbohydrate consumed (r=0.47, p=0.010)
and total caloric intake (r=0.39, p=0.035). Mean protein intake was 0.81g/kg body weight/day,
although a wide variability was also observed, Table 4.1. Of these, half of the patients (n=15; 11
females) did not meet the RDA for protein (0.8 g/kg/day). No differences emerged in body
composition or functional variables when patients were categorized as either meeting or not
meeting the RDA for protein intake (“lower protein intake” versus “normal/higher protein
intake”). However, those who presented with a protein intake lower than the RDA also presented
31
with a lower energy (p=0.004), and carbohydrate intake (p=0.033), but not necessarily with a
lower fat intake (p=0.083), though the later trended towards significance.
Iron intake was positively associated with FFMI (R=0.43, p=0.019) and average handgrip
strength (R=0.43, p=0.020). No other meaningful differences were found among other
micronutrients and body composition or functional variables.
4.6 Surgical Outcomes
To date, sixteen patients (38%) have consented for the review of their medical record for
surgical-related outcomes. The mean length of hospital stay was 3.13 days for the sample with
little variation between hip and knee replacements (hip=3.14 days versus knee=3.11 days).
Surgical notes were not available for 3 patients at the time of data collection, therefore notes
(discharge summaries) were assessed from the remaining 13 patients. Frequencies of surgical
complications are shown in Table 4.4. All patients presented with altered laboratory values
defined as any value above or below normal reference ranges. Furthermore, all patients presented
with low levels of hemoglobin and hematocrit concentrations after surgery. Six patients (46%)
were prescribed Arixtra® (fondaparinux sodium) for deep vein thrombosis prophylaxis (defined
as a ‘thromboembolic complication’ in Table 4.4). Another common complication was discharge
to a rehabilitation clinic as opposed to a discharge to a home environment (5 patients, 38%).
Out of the pre-surgical laboratory values assessed, a positive correlation was observed
between blood creatinine (mg/dL) and FFMI (r=0.60, p=0.040). No other significant
relationships were observed between pre-surgical laboratorial values and body composition,
functional or nutritional characteristics.
32
Table 4.1 Overall Characteristics of Patients with Hip or Knee Osteoarthritis
Total (N=42)
Males (n=14)
Females (n=28)
Mean ± SD
Mean ± SD
Mean ± SD
(Range)
(Range)
(Range)
Age (yr)
66 ± 10
66 ± 12
66 ± 9
(43-89)
(43-89)
(50-83)
Body Composition Variables
BMI (kg/m2)
33.3 ± 6.1
29.8 ± 4.1
35.0 ± 6.3
(21.5-55.0)
(21.5-35.9)
(26.0-55.0)
BMI categories (%)
Normal/Overweight
31.0%
50%
21.4%
Obese Class 1
33.3%
42.9%
28.6%
p-valuea
0.824
0.007
0.067
Obese Class 2
23.8%
7.1%
32.1%
Obese Class 3
11.9%
0.0%
17.9%
BF (%)
40.3 ± 9.3
(18.8-53.1)
30.1 ± 7.7
(18.8-47.7)
45.4 ± 4.8
(32.8-53.1)
˂0.0001
FM (kg)
38.5 ± 12.6
(11.7-67.0)
30.2 ±12.1
(11.7-54.4)
42.6 ± 10.9
(21.6-67.0)
<0.002
FMI (kg/m2)
14.0 ± 5.1
(4.0-27.6)
9.7 ± 4.3
(4.0-20.8)
16.1 ± 4.1
(9.8-27.5)
˂0.0001
FFM (kg)
55.7 ± 10.8
(41.6-79.3)
66.9 ± 8.7
(50.4-79.3)
50.2 ± 6.6
(41.6-66.2)
<0.0001
FFMI (kg/m2)
19.7 ± 2.6
(15.1-27.4)
21.3 ± 1.9
(17.4-23.5)
18.9 ± 2.6
(15.1-27.4)
0.006
33
Table 4.1 - Continued
FM:FFM ratio (kg/m2)
Functional Variables
Handgrip highest value
(kg)
Total (N=42)
Mean ± SD
(Range)
0.71 ± 0.25
(0.23-1.13)
Males (n=14)
Mean ± SD
(Range)
0.45 ± 0.18
(0.23-0.91)
Females (n=28)
Mean ± SD
(Range)
0.84 ± 0.16
(0.59-1.13)
p-valuea
26.8 ± 8.3
(13.0-50.0)
34.5 ± 8.1
(24.0-50.0)
22.8 ± 5.0
(13.0-34.0)
˂0.0001
25.5 ± 7.9
(12.3-47.3)
32.9 ± 7.6
(22.3-47.3)
21.6 ± 4.6
(12.3-33.0)
˂0.0001
52.4 ± 18.8
(8-96)
44.8 ± 23.0
(8-96)
56.3 ± 15.2
(20-91)
0.071
14.3 ± 13.6
(0-58.3)
27.1 ± 26.4
(0-120)
12.2 ± 11.4
(0-38)
39.1 ± 38.1
(0-120)
15.3 ± 14.6
(0-58.3)
21.3 ± 16.6
(0-64.4)
0.524
27.2 ± 9.7
(17-54)
21.6 ± 3.0
(17-26)
31.1 ± 10.8
(20-54)
0.009
1733 ± 680
(461-3798)
2207 ± 768
(1363-3798)
1520 ± 528
(461-2558)
0.006
19.2 ± 7.4
(5.5-35.6)
24.2 ± 7.1
16.9 ± 6.5
(5.5-28.8)
0.011
73.2 ± 23.8
(27.4-112.0)
77.1 ± 22.9
(46.0-107.8)
71.5 ± 24.6
(27.3-112.0)
0.565
˂0.0001
(12M, 23F)b
Handgrip average value
(kg)
(12M, 23F)b
WOMAC score
(13M, 25F)b
ADL score (%)
(12M, 24F)b
Physical Activity
(METs/week)
0.163
(11M, 23F)b
FES-1 score
(9M, 13F)b
Dietary Variables
Total energy intake
(kcal/day)
(9M, 18F)b
Total energy intake/
Body weight (kcal/kg)
(9M, 18F)b
Protein intake (g/d)
(9M, 20F)b
34
Table 4.1 - Continued
Protein intake/body
weight (g/kg)
Total (N=42)
Mean ± SD
(Range)
0.81 ± 0.27
(0.33-1.34)
Males (n=14)
Mean ± SD
(Range)
0.88 ± 0.24
(0.55-1.22)
Females (n=28)
Mean ± SD
(Range)
0.79 ± 0.29
(0.33-1.34)
p-valuea
293 ± 95
(110-448)
308 ± 92
(184-431)
286 ± 98
(110-448)
0.565
855 ± 419
(177-2216)
1214 ± 503
(597-2216)
693 ± 253
(177-1103)
0.015
585 ± 341
(155-1814)
684 ± 454
(316-1814)
541 ± 278
(155-1038)
0.303
0.583
(9M, 20F)b
Energy from protein
(kcal/day)
(9M, 20F)b
Energy from
carbohydrate (kcal/day)
(9M, 20F)b
Energy from fat
(kcal/day)
(9M, 20F)b
Data are described as mean ± standard deviation or range (continuous variables) and percentage (categorical
variables).a Independent samples t-test or Chi-Square test as appropriate; b Numbers that differ from the whole
group are shown.
BMI= Body Mass Index; BF%= Body Fat Percentage; FM=Fat Mass; FMI= Fat Mass Index; FFM=Fat Free Mass;
FFMI=Fat Free Mass Index; FM:FFM=Fat Mass to Fat Free Mass Ratio; WOMAC=Western Ontario and
McMaster Universities Osteoarthritis Index; ADL=Activities of Daily Living; METs=Metabolic Equivalencies of
Tasks; FES-I= Falls Efficacy Scale, International; Kcal=Kilocalories.
35
Table 4.2 Body Composition Associations with Functional
Variables in Patients with Hip or Knee Osteoarthritis
r
p-valuea
BMI (kg/m2)
WOMAC score
0.30
0.070
ADL percent
0.30
0.070
Physical Activity (METs/week)
-0.46
0.008 **
FES-I score
0.44
0.042 *
BF %
Handgrip, highest (kg)
Handgrip, average (kg)
WOMAC score
Physical Activity (METs/week)
FES-I score
-0.41
-0.42
0.31
-0.44
0.49
0.015
0.013
0.056
0.009
0.020
*
*
FMI (kg/m2)
WOMAC score
ADL percent
Physical Activity (METs/week)
FES-I score
0.34
0.34
-0.46
0.48
0.039
0.041
0.006
0.024
*
*
**
*
FFMI (kg/m2)
Handgrip, highest (kg)
Handgrip, average (kg)
0.44
0.44
0.008 **
0.008 **
FM:FFM ratio (kg/m2)
Handgrip, highest (kg)
Handgrip, average (kg)
WOMAC score
ADL percent
Physical Activity (METs/week)
FES-I score
-0.41
-0.42
0.34
0.30
-0.42
0.47
0.015
0.012
0.039
0.076
0.014
0.029
**
*
*
*
*
*
*
Pearson’s correlation
* p ≤0.05; ** p ≤ 0.01
r=Pearson’s Product Moment Correlation Coefficient; BMI=Body Mass Index;
BF%=Body Fat Percentage; FMI=Fat Mass Index; FFMI=Fat Free Mass Index;
FM:FFM=Fat Mass to Fat Free Mass Ratio.
a
36
Table 4.3 Body Composition Characteristics of Patients with Osteoarthritis
According to Median WOMAC Score
Low total
Normal/high total
WOMAC score
WOMAC score
Total (n=38)
(n=18)
(n=20)
p-valuea
Mean ± SD
Mean ± SD
Mean ± SD
(Range)
(Range)
(Range)
2
BMI (kg/m )
32.7 ± 5.3
31.0 ± 4.7
34.3 ± 5.4
0.055
(21.5-42.3)
(21.5-39.4)
(25.2-42.3)
BF (%)
39.9 ± 9.5
(18.8-53.1)
36.7 ± 9.6
(18.8-50.4)
42.8 ± 8.6
(24.3-53.1)
0.046
FMI (kg/m2)
13.6 ± 4.9
(4.0-22.5)
11.7 ± 4.3
(4.0-19.9)
15.3 ± 4.9
(6.1-22.5)
0.022
FFMI (kg/m2)
19.5 ± 2.2
(15.6-23.9)
19.4 ± 2.6
(15.6-23.2)
19.6 ± 2.0
(16.6-23.9)
0.822
FM:FFM (kg/m2)
0.70 ± 0.25
(0.23-1.13)
0.61 ± 0.24
(0.20-1.0)
0.78 ± 0.24
(0.30-1.10)
0.037
Data are described as mean ± standard deviation and range
a
Independent samples t-test; b Numbers that differ from the whole group are shown.
BMI= Body Mass Index; BF%=Body Fat Percentage; FMI=Fat Mass Index; FFMI=Fat Free Mass
Index; FM:FFM=Fat Mass to Fat Free Mass Ratio; WOMAC=Western Ontario and McMaster
Universities Osteoarthritis Index; ADL=Activities of Daily Living; METs=Metabolic Equivalencies
of Tasks; FES-I=Falls Efficacy Scale, International; Kcal=Kilocalories
37
Table 4.4 Frequencies of Surgical Complications in Patients Undergoing Hip or Knee
Replacement Surgery
Complication
Surgery Type
Hip (n=6)
Knee (n=7)
Laboratory abnormalities
6
7
Thromboembolic
complications
1
5
Rehabilitation discharge
2
3
Excessive pain
2
Physical therapy
abnormalities
1
Transfusion
1
1
Fatigue
1
Bowel abnormalities
1
1
Wound complications
1
Note: Patients could have presented with more than one complication. Physical therapy complications
were defined as any notes on delayed start of physical therapy or unsatisfactory progression of physical
therapy while in the hospital.
n=13; Three patients only had laboratory values available. Laboratory abnormalities are defined as values
above or below laboratorial reference values.
38
Eligible approached patients:
N=60
Did not participate:
N=18
Total patients:
N=42
Completed all preoperation assessments:
n=29
Only completed BIA:
n=2
Completed BIA and part or
all questionnaires:
n=5
Completed BIA and part or
all questionnaires and HGS:
n=5
Figure 4.1 Flow Diagram Showing an Overview of the Study Selection Process
Questionnaires include demographics, the Western Ontario and McMaster Universities Osteoarthritis
Index (WOMAC), Activities of Daily Living (ADL), Physical Activity and Falls Efficacy Scale,
International (FES-I). BIA=Bioelectrical Impedance Analysis. HGS=Handgrip Strength
39
80
A.
70
FM (kg)
60
50
40
30
20
10
0
20
25
30
35
40
BMI
45
50
55
60
45
50
55
60
(kg/m2)
90
B.
FFM (kg)
80
70
60
50
40
30
20
25
30
35
40
BMI
(kg/m2)
Figure 4.2 Relationship between Body Mass Index (BMI) and A. Fat Mass (FM) and B. Fat
Free Mass (FFM)
A. The red box highlights the variability in FM. For example, in patients with a BMI of approximately
30.5 kg/m2, FM ranges from 26.8 kg to 51.6 kg.
B. The red box highlights the variability in FFM. For example, in patients with a BMI of exactly 35.9
kg/m2 FFM ranges from 46.4 kg to 79.3 kg.
40
22
A.
p=0.026
20
18
p=0.257
16
kg/m2
14
12
With low back pain
10
Without low back pain
8
6
4
2
0
FMI (kg/m2)
FFMI (kg/m2)
.
50
B.
p=0.049
45
Percentage of body fat
40
35
30
25
With low back pain
Without low back pain
20
15
10
5
0
Body fat percentage
Figure 4.3 Difference between A. Fat Mass Index (FMI), Fat Free Mass Index (FFMI), and
B. Body Fat Percentage for Patients with and without Low Back Pain
A. Mean (SE) of FMI and FFMI in patients with low back pain compared to those without low back pain.
B. Mean (SE) of body fat percentage in patients with low back pain compared to those without low back
pain.
n with low back pain=11; n without low back pain=28; p-values calculated using independent samples ttest
41
A.
70
B.
7/10
1/7
4/8
5/10
60
FM (kg)
50
40
30
20
10
0
40
50
60
70
80
FFM (kg)
Figure 4.4 Distribution of A. Body Composition Phenotypes by Fat Mass (FM) and Fat
Free Mass (FFM) and B. HGS, C. WOMAC Scores, and D. FES-I Scores
Functional sores were categorized by the median (i.e. patients above or below the median scores).
Symbols represent individual patient scores; circles with no fill indicate worse functional status (below
42
70
C.
1/2
13/19
60
FM (kg)
50
40
30
20
10
2/8
4/9
0
40
50
60
70
80
FFM (kg)
70
D.
3/6
3/3
60
FM (kg)
50
40
30
20
10
1/8
4/5
0
40
50
60
70
80
FFM (kg)
Figure 4.4 - continued
median values for HGS and above median scores for WOMAC and FES-I) while filled circles represent
more favorable functional status (above median values for HGS and below median scores for WOMAC
43
Figure 4.4 - continued
and FES-I). Numbers in each quadrant represent the proportion of unfavorable to total outcomes in that
quadrant.
A. Quadrant analysis to depict four body composition phenotypes based on FM and FFM.
Functional scores were dichotomized and assessed in a function of FM and FFM. Higher FM and lower
FFM mass closely mirrors the sarcopenic obese phenotype. As such, three other phenotypes
could be identified: higher FM and higher FFM (“purely obese”), lower FM and lower FFM, and
lower FM and higher FFM
Adapted from: Baumgartner RN. Body composition in healthy aging. Annals of the New York Academy
of Sciences. 2000;904:437-448 and Prado et al. A population-based approach to define body composition
phenotypes. In press: American Journal of Clinical Nutrition.
B. The vertical line represents the median of FFM (53.0 kg) and the horizontal line represents the median
of FM (38.8 kg). Patients with a higher FM and lower FFM phenotype presented with a significantly
higher proportion of lower handgrip strength compared to those in the higher FM and higher FFM
category (p=0.023). No other differences were observed among the other body composition phenotypes.
C. Both lines for the WOMAC scores were placed based on clear visual clusters of cases. The vertical
line was at 62.0 kg of FFM and the horizontal line was at 36.0 kg of FM. Patients in the higher FM and
lower FFM category presented with a much greater proportion of higher WOMAC scores compared to
those with a lower FM and higher FFM phenotype, (p=0.038).
D. The vertical line in this figure assessing FES-I score represents the median of FFM (53.0 kg) and the
horizontal line represents the median of FM (38.8 kg). In those with lower FM, patients with lower FFM
had a higher probability of having higher FES-I scores , compared to those with lower FM and higher
FFM, p=0.015. In patients with a higher FFM, those with lower FM had a lesser proportion of high FES-I
score compared to those with high FM and high FFM (p=0.007).
HGS=Handgrip Strength; WOMAC=Western Ontario and McMaster Universities Osteoarthritis Index;
FES-I= Falls Efficacy Scale, International. p-values calculated using tests for independent proportions.
44
CHAPTER FIVE
DISCUSSION
5.1 Review of Hypotheses and Conclusions
Hypothesis 1: In a sample of patients with hip or knee OA, a high variability of body
composition [FM and FFM] will be observed across the BMI spectrum.
a) FFM variability will be greater than two fold regardless of BMI.
Hypothesis 1 was partially accepted. Though a wide variability in body composition was
observed, the variance did not reach an amount of two fold. We did, however, observe a
variability of over 1.5 fold in both FM and FFM regardless of BMI.
Hypothesis 2: In a sample of patients with hip or knee OA, an association between body
composition (FM and FFM), functional [handgrip strength, ADL, WOMAC, and FES-I
questionnaires] and nutritional (protein, fat, energy) variables will be observed. More
specifically:
c) Lower FFM will be significantly associated with:
-
lower values on the handgrip strength test and higher scores on the ADL, WOMAC and
the FES-I questionnaires (indicators of poorer function).
-
lower protein and energy intakes.
d) Higher FM will be significantly associated with:
-
lower values on the handgrip strength test and higher scores on the ADL, WOMAC and
the FES-I questionnaires (indicators of poorer function).
-
higher fat and energy intake.
Hypothesis 2a was partially accepted. In patients with higher FM, those with higher FFM had a
greater prevalence of higher handgrip strength (p=0.023). Lower FFM was not associated with
any measures of ADL; however, there was a significant difference in the proportion of higher
WOMAC score in patients with a higher FM and lower FFM phenotype compared to those with
a lower FM and higher FFM (p=0.038). Additionally, FES-I scores were lower in those with
lower FM and higher FFM compared to patients with a lower FM and lower FFM. In regards to
nutrient intakes, FFM was associated with a higher carbohydrate and caloric intake, but not with
measures of protein or total caloric intake.
45
Hypothesis 2b was also partially accepted. Those with WOMAC scores below the median
displayed significantly lower measurements of fat (specifically, body fat percentage, FMI, and
FM:FFM ratio) compared to those with WOMAC scores above the median. There was a
significant positive association between ADL scores and FMI. The FES-I was associated with
body fat percentage, FMI, and the FM:FFM ratio. Patients with fat consumption above the
AMDR presented with a higher FM.
Hypothesis 3: In a sample of patients with hip or knee OA, abnormal body composition (higher
FM and/or lower FFM) will be associated with greater surgical-related complications. More
specifically:
-
longer length of hospital stay.
-
greater incidence of thromboembolic disease.
-
greater incidence of infections.
-
delayed wound healing.
-
altered laboratory values.
Hypothesis 3 was rejected. In our small sample of patients, no associations were found between
body composition characteristics and surgical-related complications.
5.2 Summary
The major findings of this thesis reveal a wide variability of body composition (FM and
FFM) in patients with hip or knee OA despite BMI. Overall, FM and FFM were more strongly
associated with functional assessments and low back pain compared to simple measures of body
weight. We conclude that the assessment of body composition may be advantageous compared to
simple anthropometric measurements when predicting poor functional status in patients with hip
or knee OA. This remains to be tested in future larger studies.
Patients included in this study appear to present with greater mobility than expected and
the prevalence of impaired ADL was almost null. Likewise, the incidence of significant or severe
surgical outcomes was minimal.
5.3 Discussion of Results
As stated in Chapter One, the purpose of this study was to describe body composition,
functional, and nutritional characteristics of patients with hip or knee OA undergoing THA or
46
TKA surgery (pre-surgery) and to investigate the relationship between abnormal body
composition and surgical-related outcomes.
Anthropometric and Body Composition Characteristics
The original intent of this work was to categorize this cohort of patients as having high or
low FFM and FM using gender and age-specific cut points, such as the ones proposed by Schutz
et al (152) who used BIA to examine the relationship between lean and fat tissues in order to
define sarcopenic obese cut points. We have additionally explored gender, age, and BMIspecific cut points of FMI, FFMI, and FM:FFM ratio, from a sample of NHANES III data
[Chumlea et al (153)] using unpublished cut points reported by Dr. Cristina Gonzalez,
Universidade Catolica de Pelotas (unpublished results, Brazil, 2014). Neither method discerned
differences between groups of patients, that is, the number of patients categorized by differences
in body composition phenotype was not enough to allow for a meaningful analysis. Specifically,
cut points ascertained by Schutz et al (152) classified only 1 female and 2 males as having low
FFMI according to the 50th percentile. These cut points were calculated based on an European
cohort who are expected to have different body composition phenotypes compared to North
Americans. In a comparative study to assess variations in body composition (derived from two
BIA equations) between American and Swiss populations, Kyle et al (154) observed a higher
BMI in American men and women across all age groups. Importantly, all measures of body
composition (FFM, body fat, and body fat percentage) were higher in Americans than in Swiss.
Conversely, the cut points established using a population-representative dataset of the American
population (NHANES III) were expected to identify individuals with different body composition
phenotypes. A possible explanation may relate to the limitations of BIA in assessing body
composition in individuals with a BMI > 34 kg/m2 (47.6% of our cohort) or the limited sample
size of the present study. Of note, we observed an overall higher age and gender-specific values
of FM and FFM among our cohort, which limited our ability to use cut points based on standard
deviations or quantiles from other population studies (152, 155). We speculate that there are
systematic differences in body composition phenotypes in patients with OA which need to be
investigated on a larger scale. Due to the lack of meaningful results (e.g. insufficient number of
patients categorized as having abnormal body composition), this data set has been omitted from
this presentation.
47
In view of the above discussed, we evaluated body composition components as
continuous or categorical variables (separated by the median) and have further explored the
distribution of phenotypes by “quadrants” of FM and FFM; a similar approach as proposed by
Baumgartner (156). The presence of higher FM and lower FFM mass closely mirrors the
sarcopenic obese phenotype. As such, three other phenotypes could be identified: higher FM and
higher FFM, lower FM and lower FFM, and lower FM and higher FFM. Although our sample
size was not sufficient for a gender-specific analysis, this quadrant analysis allowed for the
identification potential patterns of higher functional problems in those with abnormal
phenotypes.
Regardless of the lack of a cut point to classify individuals according to different body
composition phenotypes, a wide variability in FM and FFM was observed regardless of BMI.
These fluctuations in body composition independent of BMI further emphasize the fallacious
nature of simple body weight measurements. This means that although individuals may present
with the same BMI or be classified within the same BMI category, their body composition may
not be identical. A unit of body weight or BMI does not depict the different amounts of fat and
fat-free tissue compartments and may therefore impede the identification of abnormal body
composition phenotypes, which in turn may result in poorer clinical outcomes (102).
Though we did not document a body composition variability of two fold as previously
reported in a cohort of bariatric patients (157), both FM and FFM in this sample demonstrated
over 1.5 fold variability. In independent elderly subjects, Gallagher et al noted an increase in FM
and decline in lean mass (measured by DXA) over an average of a 4.7 years follow up, though
subjects did not manifest weight change (158). Specifically, both men and women presented with
significantly lower indicators of muscle loss and increased FM, but no differences in body
weight was discerned. This finding illustrates that abnormal body composition appears even in
otherwise healthy older (>60) individuals with no apparent alteration in body weight.
Despite the evident need for body composition assessment, the vast majority of studies to
date have largely relied on BMI and body weight to portray this population. However, a clear
link between body composition and the development and severity of OA as well as surgical
outcomes of patients undergoing THA or TKA have been depicted in a limited number of
studies. Sowers et al (159) ascertained the usefulness of assessing body composition in OA
patients and reported that FM and skeletal muscle mass explained both the prevalence and
48
severity of knee OA better than BMI alone. A cross-sectional study of Korean men and women
indicated that sarcopenic obesity was more closely associated with OA than those who were non
sarcopenic obese. Importantly, there were no differences in BMI or body weight between these
two groups, again emphasizing the inadequacy of BMI (22). In a 10-year follow up study, Wang
et al (160) utilized BIA to describe changes in FM and FFM in relation to knee cartilage volume
and defects in health community-dwelling adults. Their findings indicate that both baseline and
current FFM are protective against the degradation of cartilage volume, irrespective of FM.
These abnormal body composition characteristics in patients with lower extremity OA may
persist even after the drastic intervention of total joint arthroplasty. Rasch and colleagues
reported diminished muscle volume and quality (as assessed by CT scans) of the muscles
supporting the hip two years post-operatively in patients who underwent THA (127). These
studies collectively accentuate the clinical significance of fat and lean tissue compared to
measure of body weight alone; however, more studies that explore the interactions between the
metabolic and functional implications of body composition and OA are needed.
In our sample, 69% (n=28) of patients were obese (BMI >30 kg/m2). Interestingly, the
proportion of patients classified as obese included in this study was higher than that reported
among patients undergoing THA or TKA at TOC from January 2009 through September 2011
[N=1132; 33.3% obese (Tallahassee Orthopedic Clinic, unpublished data courtesy of Dr. Robert
Thornberry].
The majority (59.5%) of patients in this study were elderly (i.e. 65 years of age and
older). Older age was associated with lower BMI and lower FFMI, but no associations with
measurements of fat were observed. As we did not observe an association between age and FMI,
it is reasonable to infer that FFMI was driving the waning of BMI. The decline in FFMI with age
may be reflective of the process of sarcopenia, discussed in detailed in Chapter Two. This
decline in FFM with age has been widely acknowledged, though the point at which this decline
begins remains variable. Hull et al (161) used a large and diverse cohort to demonstrate a decline
in FFMI that began in the mid-20s for males and mid-40s for females. Our findings on FM (and
its derivatives) are nonetheless contrary to those reported by Kyle et al (155) who reported a
positive association between BMI and age (higher BMI in individuals >60 years). An increase in
BMI was largely due to a concurrent increase in FM and FM percentage across a sample of
Europeans aged 15 to 98 (155).
49
As approximately 40% of our sample was younger (<65 years), patients were stratified
according to age to investigate different patterns of body composition and its association with
functional outcomes. Upon doing so, the only significant difference observed between groups
was handgrip strength, with older patients presenting with significantly lower grip strength
(p=0.016). It is plausible that a high level of FM could be driving the deleterious effects of
reduced function (described below), but we were unable to capture a loss in FFM that would put
patients at risk. Although it is likely that these patients with OA are at risk, the present cohort
could be right under the threshold clinically detectable for being categorized as having
sarcopenia or sarcopenic obesity.
Comorbidities
Our sample showed a surprisingly low incidence of comorbid conditions. In fact, 47.6%
of patients did not report presence of any comorbidity using our self-reported questionnaire.
Advancing age is often associated with the concurrent appearance of multiple conditions. In the
United States, 35.3% of adults aged 64-79 report having two or more chronic conditions (162).
The presence of OA is also associated with other conditions such as hypertension, cardiovascular
disease, peripheral vascular disease, congestive heart failure, impaired renal function, diabetes,
and respiratory disease (163). In view of the mean age of our patients with OA, a higher than
observed prevalence of comorbidities was expected. Comorbidity is an important predictor of
disability and mortality more so than individual conditions (162). However, the occurrence of
comorbidities observed in our study was much lower than expected which is a finding that may
likely go in line with a better overall health profile in the present cohort, also reflected by the
body composition findings reported above.
Among the reported comorbidities, the only one that was significantly associated with
body composition was patient-reported low back pain. Patients with this condition had a
significantly lower FFMI and higher body fat percentage. Of note, though there was a difference
in body fat percentage, those without low back pain still presented with an average of 38.3%
body fat. Thus, this subsample did not have an overall low body fat percentage per se. The
mediating role of body composition in the development of low back pain has been previously
documented using the FM:FFM ratio (157). Additionally, Urquhart et al also examined the
association between body composition and low back pain. Though BMI was associated with low
back pain, total body and lower limb FM (measured by DXA) were more strongly (and
50
positively) associated with both pain intensity and disability (164). In addition to the exuberant
pressure excess FM exerts on joints, chronic low back pain could be a result of the chronic
inflammatory environment generated by excess adipose tissue (165). In particular, C-reactive
protein may mediate the inflammatory process in both patients with low back pain and OA (166).
Functional Characteristics
As physical function is related not only to overall health but also with quality of life, we
set out to investigate the relationship between measures of function in relation to body
composition and nutritional characteristics. We reported a positive association between handgrip
strength and FFM; in fact this was the only functional parameter that was related to FFM.
Muscle strength is a composite measure of muscle mass, and hence FFM. Both strength and
mass are useful in assessing risk factors for decreased quality of life and the ability to overcome
trauma and illness (167, 168). In our quadratic scatterplot (Figure 4.4), patients with higher FM
and lower FFM presented with a higher prevalence of lower strength (handgrip values below the
gender specific median) in comparison to those with higher FM and higher FFM. No differences
were observed in patients with lower FM, thus indicating that FFM influenced the relationship
between body composition and handgrip strength; this finding is logical in consideration of other
studies. Individuals who are ‘purely’ obese may present with high muscle mass which is a major
determinant of muscle strength, possibly due the burden placed upon muscle to support excess
fat (118). Conversely, obesity concurrent with low FFM (sarcopenic obesity) has been previously
associated with poorer strength (156).
The WOMAC is a widely utilized questionnaire that is designed to assess the progression
of OA and the impact that this condition has on daily life. Most studies only utilize one or more
of the WOMAC subscales when assessing OA patients; regardless, our results of the total score
and subscales (as outlined in Section 4.4) were higher than those reported by others. For
example, McCarthy and others reported scores as high as 10.0 for pain, 4.5 for stiffness, and 30.8
for difficulty (169). Furthermore, Miller et al presented average total scores of 36.6 in a weight
loss intervention for patients with OA (170).
Total WOMAC scores were significantly associated with FMI and FM:FFM ratio, but not
with BMI (which only trended towards significance). As demonstrated in Figure 4.4, patients
with higher FM and lower FFM (as mentioned above, a body composition type that closely
mirrors that of sarcopenic obesity) had a much higher prevalence of WOMAC scores above the
51
median compared with patients with a lower FM and higher FFM phenotype. Other studies have
pinpointed a significant interaction between WOMAC score and BMI (171); however, to our
knowledge, this is the first analysis of body composition and total WOMAC scores and it
emphasizes the heightened sensitivity of body composition measurements in comparison to BMI.
Previous literature has shown that excessive FM increases the odds of having radiographic
evidence of OA while muscle is protective against this condition (22, 172). As the WOMAC
measures disease severity and the effect it has on daily life, it can be postulated that those with
higher indices of FM in our sample have already begun to experience the detrimental effects high
adiposity has on OA pain, stiffness, and function.
The ADL questionnaire was administered in order to measure physical disability in this
population. A total of 4 patients reported no disability at all and numerous others reported low
scores. In regards to the relationship between ADL and body composition measurements, it
appears as though indicators of skeletal muscle mass are imperative in predicting physical
disability (43). Additionally, FM has been cited as having substantial influence on disability in
both cross sectional (76) and longitudinal (173) studies. It is crucial to note that functional
impairment is not synonymous to physical disability. Functional impairment involves limitations
in mobility (activities such as walking) and precedes disability which entails difficulty
performing daily activities. It is plausible that our cohort has begun suffering from functional
limitations (as evidenced by WOMAC scores) but has not reached a threshold at which abnormal
body composition negatively affects daily activities (i.e. disability). Furthermore, a low
incidence of comorbidities in this sample could have influenced the association between body
composition and physical disability.
Not surprisingly, levels of physical activity were negatively associated with BMI, FMI,
and body fat percentage. Upon further evaluation of the relationship among physical activity and
other functional scores, there was a negative correlation between ADL scores and the amount of
physical activity completed each week (r=-0.43, p=0.012). Regular participation in physical
activity has numerous health benefits including the preservation of muscle and the aversion of
excessive fat gain. Additionally, elders who engage in regular exercise have a lesser chance of
developing disability in daily living (174).
Fear of falling as measured by the FES-I scale was positively correlated with BMI, body
fat percentage, FMI, and FM:FFM ratio in our sample. Moreover, those with a lower FM and a
52
lower FFM phenotype presented with a higher prevalence of higher FES-I scores than patients
with a lower FM and higher FFM phenotype. A relationship exists between high body weight
and increased postural sway during standing, which is directly related to risk of falling (175). In
post-menopausal women, postural instability is associated with a BMI above 25 kg/m2 and a
waist to hip ratio above 0.76 suggesting that body weight distribution plays an important role in
the incidence of falling (176). Conversely, Miller et al found no ill effects of a high BMI on the
risk of falling, though the sample size was small (177).
In the present study, an association was established between FES-I scores and the pain
sub score of WOMAC, suggesting that those with increased joint pain have a heightened fear of
falling. This has been previously demonstrated in OA patients in a study conducted by Scott et al
(113) that assessed the progression of radiographic OA, sarcopenia, and falls risk. Over a three
year time span, lower extremity joint pain, stiffness, and dysfunction were associated with
increases in falls risk score, especially in women.
Nutrient Intake
Our population displayed a wide variability of energy intake with an average of
approximately 1700 kcals/day. The overall caloric intake was lower than expected for a cohort of
mostly overweight and obese individuals. It is possible that patients were under-reporting their
intake, or that the daily intake capture by use of the 24-hour recall was not representative of
regular intake.
Though the mean protein intake of our sample (0.81g/kg/day) was slightly above the
RDA, half of the patients did not meet this recommendation. This percentage is much higher
than other reports of prevalence that range from 17-41% of elderly (age 50+) that consume less
protein than the RDA (57). As discussed in Chapter Two, amino acids are essential for muscle
genesis and repair. Therefore, this macronutrient is especially important for geriatric populations
at risk for sarcopenic obesity (52).
Surprisingly, FM was significantly positively correlated to the protein:energy ratio and
FFM was positively associated with carbohydrate intake and total caloric intake, but not with
measurements of protein consumption. This finding was rather startling as protein is known to
have a protective effect on muscle catabolism in the elderly and those who consume more
protein typically have higher amounts of lean mass (54). However, a caloric intake that is
sufficient in carbohydrates and fat (and thus energy) is essential in sparing protein stores from
53
degradation (58). Though the source of protein was not investigated in this sample, the quality of
protein (e.g. animal versus non-animal sources) could possibly reveal differing relationships
between nutrient intake and body composition in analyses with larger sample sizes. Likewise, as
discussed above, it is also imperative to consider the reliability of the 24-hour dietary recall in
any assessment. Though all recalls were conducted in a non-judgmental environment, obese
patients often under-report their dietary intake (178).
When analyzing micronutrients, a positive association between iron consumption and
FFMI and average handgrip strength was ascertained. Iron is a mineral that is essential for the
proper function of numerous enzymes that are critical for oxidative energy production, namely
hemoglobin and myoglobin (179). While iron deficiency can be a result of numerous processes
(e.g. blood loss, increased iron needs, etc.), one potential cause of inadequate iron status is
insufficient intake of this mineral. Elderly (>65 years) who are anemic (iron deficient) have
lower grip strength compared to those with normal iron status (180). In our sample, those with
lower iron consumption could already be experiencing the ill effects of low iron stores without
the diagnosis of anemia.
Surgical Outcomes
The average length of hospital stay was slightly over 3 days for both THA and TKA
patients and was very close the national average of 3.3 days for both these procedures (120). No
associations were found for length of stay and body composition and functional measures. This
finding differs from others who have documented an association between length of stay and grip
strength (181) and body composition (15) in other cohort of patients. The lack of association
found here could be the result of the very small sample size of patients with surgical outcomes
available.
In our cohort, all patients had low levels of hemoglobin and hematocrit post-operatively.
While these low levels are expected due to blood loss during orthopedic surgery, severe
depletion of hemoglobin and hematocrit is not desirable and can lead to anemia, though effects
on short-term function are questionable (182). Half of the patients presented here were
prescribed medication for deep vein thrombosis, a type of venous thromboembolism that is
characterized by a blood clot in a deep vein. Though no differences in body composition were
ascertained between those with deep vein thrombosis and those without the condition, venous
54
thromboembolism has been linked to excess adiposity and decreased strength in post-surgical
patients in previous literature (183).
In measurements of laboratory values, the only observed relationship was a positive
correlation between blood creatinine and FFMI. Creatine is a nitrogenous compound that
participates in energy metabolism and is found predominantly in muscle tissues. Creatinine a
resultant compound of the metabolism of creatine and is excreted in the urine (184) and have
been significantly correlated with body weight and, to a greater extent, lean mass previously
(185).
5.4 Limitations and Future Research
The inability to meaningfully define abnormal body composition phenotypes (particularly
sarcopenic obesity) using previously reported cut points was a limitation of the study. Though
associations were found between body composition and functional and nutritional characteristics,
patients of the higher FM and lower FFM phenotype are not necessarily sarcopenic obese.
Likewise, our small sample size obtained from a convenient sample further aggravated the ability
to explore other body composition cut points established from different populations.
Another limitation of this study is the use of BIA. Though this method is quick, noninvasive, and requires little training, the widespread use of this technique has been questioned.
As previously discussed, regression equations that this technique relies on for body composition
estimates are often specific for the white race (34). Furthermore, results of BIA may be skewed
in those with a BMI > 34 kg/m2. Obese individuals have a great amount of extracellular water as
well as TBW which can subsequently overestimate FFM and underestimate FM. Severe
abdominal obesity may also result in the overestimation of body fat percentage (30). Moreover,
BIA relies on a two-compartment model of body composition and thus does not distinguish
between types of fat, muscle, or bone components and does not elucidate the quality of muscle in
individual subjects.
As this was a pilot study intended to describe body composition, functional, and
nutritional characteristics as well as surgical outcomes in patients with hip or knee OA, data on
few patients are included. Specifically, only 29 patients completed pre-operation assessment in
its entirety and 16 had post-operation notes available. However, this is the first study to our
knowledge to profile all of these factors simultaneously in a population of patients with hip or
55
knee OA. As this is an ongoing study, we anticipate that more significant associations will arise
as the sample size increases.
In light of these limitations, future research should employ more in-depth and accurate
body composition techniques (e.g. DXA or air displacement plethysmography) to better evaluate
the relationship between FM and FFM and comorbidities, physical function (including OA
disease severity), nutrition, and surgical outcomes in patients with hip or knee OA. These
assessments should lead to improved screening methods in those planning to undergo THA and
TKA surgery. Further research should also investigate the effect of pre-operative multi-modal
interventions (such as those with nutrition and exercise) on post-surgical outcomes to ultimately
promote a better quality of life after orthopedic surgery.
56
APPENDIX A
HUMAN SUBJECTS APPROVAL
57
58
APPENDIX B
INFORMED CONSENT FORM
Participant’s Printed Name:
_______________________________________________________
1. I freely and voluntarily and without element of force or coercion, consent to be a participant
in the research project entitled “Prevalence and Clinical Implications of Sarcopenia in Patients
Undergoing Joint Surgery.” This research is being conducted by Dr. Carla Prado (Principle
Investigator), Dr. Michael Ormsbee, Dr. Lynn Panton, Dr. Jeong-Su Kim who are all professors
at the Florida State University (FSU); Dr. Robert Thornberry and Dr. Andrew Wong, both
orthopedic surgeons at Tallahassee Orthopedic Clinic (TOC); Dr. Edgar Vieira from Florida
International University; and Ms. Sarah Purcell and Ashley Artese, both graduate researchers at
FSU.
2. The purpose of the research project is to examine the body composition (specifically, assessing
sarcopenia, or decreased muscle mass) of patients undergoing hip or knee arthroplasty.
Approximately two hundred male and female patients will be enrolled. All volunteers are
scheduled for hip or knee arthroplasty but are otherwise healthy.
3. My participation in this project will involve completing a bioelectrical impedance analysis
(BIA) for body composition (explained on the second part of this consent form) and filling out
additional questionnaires not usually part of the standard care process at the pre-operation visit.
If I opt to complete additional tests, I will be asked to use a hand grip dynamometer to measure
strength, complete a short battery of “fitness tests” (see additional information on the last page),
and a dietary recall to measure nutrition intake. If I opt to complete the follow-up study, the same
measurements will be utilized at the post-operation appointment.
4. There is a possibility of a minimal level of risk involved if I agree to participate in this study.
The risks will be minimized by using trained technicians and by teaching me proper techniques
in testing. I will complete a medical history before I can participate in the study. I will not be
able to participate in this study if I have a condition that could possibly alter body composition
measurements including: oral steroid therapy, inflammatory digestive disease, or recent surgery,
presence of a pacemaker, or HIV+.
Individuals with pacemakers or other internal electrical medical devices cannot
participate in the BIA test. The weak electrical signal associated with BIA measurement may
cause such devices to malfunction.
By signing the form below, I am consenting that I do not have a pacemaker or other
internal electrical medical device. I agree to take part in a bioelectrical impedance analysis test.
59
There are minimal risks or discomforts with answering the enclosed questionnaires. I
may choose not to complete the questionnaires and will still be able to participate in the study.
5. The possible benefits of my participation in this research project include learning about my
body composition. I will also be given a number of tests free of charge and the results will be
given to me and my physician if I wish.
6. The results of this research study may be published but my name or identity will not be
revealed. Information obtained during the course of the study will remain confidential, to the
extent allowed by law. My name will not appear on any of the results. No individual responses
will be reported. Only group findings will be reported in publications. Confidentially will be
maintained to the extent allowed by law by assigning each subject a code number and recording
all data by code number. The only record with the subject’s name and code number will be kept
by the principal investigator, Dr. Carla Prado, in a locked drawer in her office. Data will be kept
for 5 years and then destroyed.
7. I will not be paid for my participation in this research project
8. Any questions I have concerning the research study or my participation in it, before or after
my consent, will be answered by the investigators or they will refer me to a knowledgeable
source. I understand that I may contact Dr. Carla Prado at (850) 645-1522 for answers to
questions about this research project or my rights. Group results will be sent to me upon my
request.
9. In case of injury, or if I have questions about my rights as a subject/participant in this
research, or if I feel I have been placed at risk, I can contact the chair of the Human Subjects
committee, Institutional Review Board, through the Office of the Vice President for Research, at
(850) 644-8633.
10. The nature, demands, benefits and risks of the project have been explained to me. I
knowingly assume any risks involved.
I have read the above informed consent form. I understand that I may withdraw my consent and
discontinue participation at any time without penalty or loss of benefits to which I may otherwise
be entitled. In signing this consent form, I am not waiving my legal claims, rights or remedies. A
copy of this consent will be given to me.
I opt to complete the additional measurements
I opt to complete the follow-up measurements at my post-operation visit
________________________________
Print name
60
________________________________
________________________________
Signature
Date
Additional Information about measurements used in this study:








Bioelectric Impedance Analysis: The BIA machine used in this study is a Tanita Body
Composition Analyzer (TBF-300A). This unit uses a low energy, high frequency
electrical signal (50 kHz, 500 μA) to measure resistance to the flow of electrical current.
When the unit is in operation, current passes through the anterior electrode on the scale
platform, and the voltage drop after passage through the body is measured on the
posterior electrode. Lean tissue has a high water and electrolyte content, and is therefore
less resistant to the flow of electrical current than is fat mass, which accounts for a
relatively low percentage of body water. Percent body fat is calculated using a
proprietary algorithm developed by Tanita. The whole procedure will take a maximum of
five minutes and will an addition to your regularly scheduled pre-operation visit. There
are no risks involved with this measurement and you will not feel the electric current.
The Western Ontario and McMaster Universities Arthritis Index (WOMAC)
calculates the condition of arthritis affected knees and hips. Twenty-four questions.
The Activities of Daily Living Questionnaire (ADL) addresses different areas of
activity: self-care activities, household care, employment and recreation, shopping and
money, travel, and communication. 28 questions.
Aerobics Center Longitudinal Study Physical Activity Questionnaire: this survey
will ask you if you regularly participate in popular physical activities (if any) and the
duration and intensity of each. 15 questions.
Falls Efficacy Scale International (FES-I) is a 16-question assessment that evaluates a
patient’s fear of falling and thus any balance issues.
(Optional): 24-hour dietary recall: You will be asked to recall the types and amounts
of foods consumed in the last 24 hours.
(Optional): Hand Grip Strength: Hand grip strength is a simple measurement that may
be used as an indicator of overall strength and muscle quality. Patients wishing to
participate in this test will be given a small hand held machine called a dynamometer and
will be asked to squeeze with maximum effort for about 5 seconds.
(Optional): Fitness tests: This series of examinations assesses strength, endurance, and
flexibility and will take approximately five minutes to complete. For this research study,
we will use the chair stand test, the two-minute step test, the chair sit and reach test, and
the eight foot up-and-go test.
61
APPENDIX C
PATIENT DATA COLLECTION FORM
Demographics
Name ______________________ Today’s date: ______________
Date of Birth ____/____/_______ Age______
Sex
Male
mm dd yyyy
Race/Ethnicity
White/ Non-hispanic
American Indian
Asian
Other
Female
Black/African American
Please Specify _________________________
Household Income
0 - 30,000
75,000 – 100,000
30,000 – 50,000
50,000 – 75,000
U >100,000
Current Highest Level of Education
Grade School
High School
Se College
Post-graduate
Medical and Surgical History
Acid Refux/Heartburn
Diabetes
Kidney Disease
Anemia
DVT
Liver Disease
Anxiety
Gout
Lung Disease
Asthma
Glaucoma
Osteoporosis
Arthritis
Gallbladder Disorder
Polycystic Ovaries
Cancer(Type__)
Heart Attack
Pulmonary Embolism
Carotid Disease
Heart Disease
Rheumatic/Scarlet Fever
Chest Pain
Heart Murmur
Sleep Apnea
Other:________________________________________________________
62
Past Surgical History:
Surgery:_____________
Surgery:_____________
Surgery:_____________
Surgery:_____________
Surgery:_____________
Surgery:_____________
Year: _________
Year: _________
Year: _________
Year: _________
Year: _________
Year: _________
Psychiatric History
Anxiety
Depression
Bipolar Disorder
Panic Attacks
Alcoholism
Drug Addiction
Schizophrenia
Nervous Breakdown
Anorexia
Binge Eating
Bulimia
Stress
Medications: Including medications, vitamins, supplements, herbals, other “natural”
medications.
What:_____________________________Dosage:_______________________________
What:_____________________________Dosage:_______________________________
What:_____________________________Dosage:_______________________________
What:_____________________________Dosage:_______________________________
What:_____________________________Dosage:_______________________________
What:_____________________________Dosage:_______________________________
What:_____________________________Dosage:_______________________________
What:_____________________________Dosage:_______________________________
What:_____________________________Dosage:_______________________________
Review of Systems: Please check any problems you have had over the last month.
Weakness
Shortness of Breath
Joint Pain
Fatigue
Low Back Pain
Social History
Single
Partner
Tobacco
Drugs
Alcohol
Divorced
Yes
Yes
Yes
No
No
No
Married
Widow/Widower
How often?____________
How often?____________
How often?___________
63
Gynecologic History (For women only)
History of Gestational Diabetes?
No
Yes
Hysterectomy?
No
Yes
Any Hormone Replacement?
No
Yes
Menopausal (menstrual periods stopped)?
No
Yes
64
The WOMAC (Western Ontario and McMaster Universities) Index of Osteoarthritis
Instructions: In Sections A, B, and C, questions will be asked about your hip or knee pain. Please
mark each response with an ‘X’ in the box. If you are unsure about how to answer a question,
please give the best answer you can. Pleas mark only one box for each question.
Please note:
A) that the further right you place your “X”, the more pain you feel
B) that the further to the left you place your “X” the less pain you feel
C) please do not place your “x” outside any of the boxes
Please do not mark in the shaded box to the right; this is for study coordinator use only.
A. Think about the pain you felt in your hip/knee during the last 48 hours
Question: How much pain do
you have?
0
None
1
Mild
2
Moderate
3
Severe
4
Extreme
Score
1. Walking on a flat surface
2. Going up and down stairs
3. In bed at night, pain
disturbs your sleep
4. Sitting or lying
5. Standing upright
B. Think about the stiffness (not pain) you have in your hip/knee during the last 48 hours.
Stiffness is a sensation of decreased ease in moving your joint.
0
None
1
Mild
2
Moderate
3
Severe
4
Extreme
Score
6. How severe is your
stiffness after first waking in
the morning?
7. How severe is your
stiffness after sitting, lying,
or resting in the day?
C. Think about the difficulty you had in doing the following daily physical activities due to your
hip/knee during the last 48 hours. By this we mean your ability to move around and look after
yourself.
Question: What degree of difficulty do you have?
Question: What degree of
0
1
difficulty do you have?
None
Mild
8. Descending stairs
9. Ascending stairs
10. Rising from sitting
11. Standing
65
2
Moderate
3
Severe
4
Extreme
Score
Question: What degree of
difficulty do you have?
0
None
1
Mild
12. Bending to the floor
13. Walking on flat surfaces
14. Getting in and out of a
car, or on and off a bus
15. Going shopping
16. Putting on your socks or
stockings
17. Rising from the bed
18. Taking off your socks or
stockings
19. Lying in bed
20. Getting in or out of the
bath
21. Sitting
22. Getting on or off the
toilet
23. Performing heavy
domestic duties
24. Performing light
domestic duties
66
2
Moderate
3
Severe
4
Extreme
Score
Activities of Daily Living Questionnaire
(ADLQ)
E. Taking pills or medicine
0 = Remembers without help
1 = Remembers if dose is kept in a special
place
2 = Needs spoken or written reminders
3 = Must be given medicine by others
9 = Does not take regular pills or medicine
OR Don’t know
This questionnaire is designed to reveal the
difficulty of everyday activities.
For each activity statements 0-9 refer to a
different level of ability. Thinking of the last
2 weeks, circle the number that represents
your ability.
Only 1 box should be ticked for each
activity.
F. Interest in personal appearance
0 = Same as always
1 = Interested if going out, but not at home
2 = Allows self to be groomed, or does so on
request only
3 = Resists efforts of caretaker to clean and
groom
9 = Don’t know
1. Self-care activities
A. Eating
0 = No problem
1 = Independent, but slow or some spills
2 = Needs help to cut or pour; spills often
3 = Must be fed most foods
9 = Don’t know
2. Household care
B. Dressing
0 = No problem
1 = Independent, but slow or clumsy
2 = Wrong sequence, forgets items
3 = Needs help with dressing
9 = Don’t know
A. Preparing meals, cooking
0 = Plans and prepares meals without
difficulty
1 = Some cooking, but less than usual, or
less variety
2 = Gets food only if it has already been
prepared
3 = Does nothing to prepare meals
9 = Never did this activity OR Don’t know
C. Bathing
0 = No problem
1 = Bathes self, but needs to be reminded
2 = Bathes self with assistance
3 = Must be bathed by others
9 = Don’t know
B. Setting the table
0 = No problem
1 = Independent, but slow or clumsy
2 = Forgets items or puts them in the wrong
place
3 = No longer does this activity
9 = Never did this activity OR Don’t know
D. Elimination
0 = Goes to the bathroom independently
1 = Goes to the bathroom when reminded;
some accidents
2 = Needs assistance for elimination
3 = Has no control over either bowel or
bladder
9 = Don’t know
C. Housekeeping
0 = Keeps house as usual
1 = Does at least half of his/her job
2 = Occasional dusting or small jobs
3 = No longer keeps house
9 = Never did this activity OR Don’t know
67
C. Organizations
0 = Attends meetings, takes responsibilities
as usual
1 = Attends less frequently
2 = Attends occasionally; has no major
responsibilities
3 = No longer attends
9 = Never participated in organizations OR
Don’t know
D. Home maintenance
0 = Does all tasks usual for him/her
1 = Does at least half of usual tasks
2 = Occasionally rakes or some other minor
job
3 = No longer does any maintenance
9 = Never did this activity OR Don’t know
E. Home repairs
0 = Does all the usual repairs
1 = Does at least half of usual repairs
2 = Occasionally does minor repairs
3 = No longer does any repairs
9 = Never did this activity OR Don’t know
D. Travel
0 = Same as usual
1 = Gets out if someone else drives
2 = Gets out in wheelchair
3 = Home- or hospital-bound
9 = Don’t know
F. Laundry
0 = Does laundry as usual (same schedule,
routine)
1 = Does laundry less frequently
2 = Does laundry only if reminded; leaves
out detergent, steps
3 = No longer does laundry
9 = Never did this activity OR Don’t know
4. Shopping and money
A. Food shopping
0 = No problem
1 = Forgets items or buys unnecessary items
2 = Needs to be accompanied while
shopping
3 = No longer does the shopping
9 = Never had responsibility in this activity
OR Don’t know
3. Employment and recreation
A. Employment
0 = Continues to work as usual
1 = Some mild problems with routine
responsibilities
2 = Works at an easier job or part-time;
threatened with loss of job
3 = No longer works
9 = Never worked OR retired before illness
OR Don’t know
B. Handling cash
0 = No problem
1 = Has difficulty paying proper amount,
counting
2 = Loses or misplaces money
3 = No longer handles money
9 = Never had responsibility for this activity
OR Don’t know
B. Recreation
0 = Same as usual
1 = Engages in recreational activities less
frequently
2 = Has lost some skills necessary for
recreational activities
(eg, bridge, golfing); needs coaxing to
participate
3 = No longer pursues recreational activities
9 = Never engaged in recreational activities
OR Don’t know
C. Managing finances
0 = No problem paying bills, banking
1 = Pays bills late; some trouble writing
checks
2 = Forgets to pay bills; has trouble
balancing checkbook; needs
help from others
3 = No longer manages finances
9 = Never had responsibility in this activity
OR Don’t know
68
5. Travel
6. Communication
A. Public transportation
0 = Uses public transportation as usual
1 = Uses public transportation less
frequently
2 = Has gotten lost using public
transportation
3 = No longer uses public transportation
9 = Never used public transportation
regularly OR Don’t know
A. Using the telephone
0 = Same as usual
1 = Calls a few familiar numbers
2 = Will only answer telephone (won’t make
calls)
3 = Does not use the telephone at all
9 = Never had a telephone OR Don’t know
B. Talking
0 = Same as usual
1 = Less talkative; has trouble thinking of
words or names
2 = Makes occasional errors in speech
3 = Speech is almost unintelligible
9 = Don’t know
B. Driving
0 = Drives as usual
1 = Drives more cautiously
2 = Drives less carefully; has gotten lost
while driving
3 = No longer drives
9 = Never drove OR Don’t know
C. Understanding
0 = Understands everything that is said as
usual
1 = Asks for repetition
2 = Has trouble understanding conversations
or specific words
occasionally
3 = Does not understand what people are
saying most of the time
9 = Don’t know
C. Mobility around the neighborhood
0 = Same as usual
1 = Goes out less frequently
2 = Has gotten lost in the immediate
neighborhood
3 = No longer goes out unaccompanied
9 = This activity has been restricted in the
past OR
Don’t know
D. Reading
0 = Same as usual
1 = Reads less frequently
2 = Has trouble understanding or
remembering what he/she has read
3 = Has given up reading
9 = Never read much OR Don’t know
D. Travel outside familiar environment
0 = Same as usual
1 = Occasionally gets disoriented in strange
surroundings
2 = Gets very disoriented but is able to
manage if accompanied
3 = No longer able to travel
9 = Never did this activity OR Don’t know
E. Writing
0 = Same as usual
1 = Writes less often; makes occasional
spelling errors
2 = Signs name but no other writing
3 = Never writes
9 = Never wrote much OR Don’t know
69
70
71
72
APPENDIX D
DISTRIBUTION OF BODY COMPOSITION STRATIFIED BY GENDER
A.
B.
73
C.
Figure A.1 Distribution of Fat Mass (FM) and Fat Free Mass (FFM) and A. HGS, B.
WOMAC Scores, and C. FES-I Scores Stratified by Gender
Symbols represent individual patients. Gray circles indicate worse functional status (below median values
for HGS and above median scores for WOMAC and FES-I) while filled triangles represent more
favorable functional status (above median values for HGS and below median scores for WOMAC and
FES-I). FFM and FM are both in kilograms. For females, median FM is 40.6 kg and median FFM is 47.7
kg. For males, median FM is 28.0 kg and median FFM is 66.2 kg.
A. A total of 11 of 23 females and 6 of 12 males presented with lower HGS.
B. A total of 15 of 25 females and 4 of 13 males presented with higher WOMAC scores.
C. A total of 9 of 13 females and 2 of 9 males presented with higher FES-I scores.
HGS=Handgrip Strength; WOMAC=Western Ontario and McMaster Universities Osteoarthritis Index;
FES-I=Falls Efficacy Scale, International.
74
REFERENCES
1.
Baumgartner RN, Koehler KM, Gallagher D, Romero L, Heymsfield SB, Ross RR, Garry
PJ, et al. Epidemiology of sarcopenia among the elderly in New Mexico. American Journal of
Epidemiology 1998;147:755-763.
2.
Janssen I. Influence of sarcopenia on the development of physical disability: The
Cardiovascular Health Study. Journal of the American Geriatrics Society 2006;54:56-62.
3.
Fantin F, Di Francesco V, Fontana G, Zivelonghi A, Bissoli L, Zoico E, Rossi A, et al.
Longitudinal body composition changes in old men and women: Interrelationships with
worsening disability. Journals of Gerontology Series a-Biological Sciences and Medical Sciences
2007;62:1375-1381.
4.
Visser M, Kritchevsky SB, Goodpaster BH, Newman AB, Nevitt M, Stamm E, Harris
TB. Leg muscle mass and composition in relation to lower extremity performance in men and
women aged 70 to 79: The health, aging and body composition study. Journal of the American
Geriatrics Society 2002;50:897-904.
5.
Landi F, Cruz-Jentoft AJ, Liperoti R, Russo A, Giovannini S, Tosato M, Capoluongo E,
et al. Sarcopenia and mortality risk in frail older persons aged 80 years and older: results from
ilSIRENTE study. Age and Ageing 2013;42:203-209.
6.
Janssen I, Shepard DS, Katzmarzyk PT, Roubenoff R. The healthcare costs of sarcopenia
in the United States. Journal of the American Geriatrics Society 2004;52:80-85.
7.
Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of obesity in the United States,
2009-2010. NCHS data brief 2012:1-8.
8.
National Institutes of Health. Identification, Evaluation, and Treatment of Overweight
and Obesity in Adults--The Evidence Report. Obesity Research 1998:51S-209S.
9.
Roubenoff R. Sarcopenic obesity: does muscle loss cause fat gain? Lessons from
rheumatoid arthritis and osteoarthritis. Ann N Y Acad Sci 2000;904:553-557.
10.
Stenholm S, Harris TB, Rantanen T, Visser M, Kritchevsky SB, Ferrucci L. Sarcopenic
obesity: definition, cause and consequences. Curr Opin Clin Nutr Metab Care 2008;11:693-700.
11.
Chung JY, Kang HT, Lee DC, Lee HR, Lee YJ. Body composition and its association
with cardiometabolic risk factors in the elderly: A focus on sarcopenic obesity. Archives of
Gerontology and Geriatrics 2013;56:270-278.
12.
Baumgartner RN, Wayne SJ, Waters DL, Janssen I, Gallagher D, Morley JE. Sarcopenic
obesity predicts Instrumental Activities of Daily Living disability in the elderly. Obesity
Research 2004;12:1995-2004.
75
13.
Rolland Y, Lauwers-Cances V, Cristini C, van Kan GA, Janssen I, Morley JE, Vellas B.
Difficulties with physical function associated with obesity, sarcopenia, and sarcopenic-obesity in
community-dwelling elderly women: the EPIDOS (EPIDemiologie de l'OSteoporose) Study.
American Journal of Clinical Nutrition 2009;89:1895-1900.
14.
Levine ME, Crimmins EM. The Impact of Insulin Resistance and Inflammation on the
Association Between Sarcopenic Obesity and Physical Functioning. Obesity 2012;20:2101-2106.
15.
Kyle UG, Pirlich M, Lochs H, Schuetz T, Pichard C. Increased length of hospital stay in
underweight and overweight patients at hospital admission: a controlled population study. Clin
Nutr 2005;24:133-142.
16.
Atkins JL, Whincup PH, Morris RW, Wannamethee SG. Sarcopenic obesity and risk of
all-cause and cardiovascular mortality in older men. Journal of Epidemiology and Community
Health 2012;66:A53-A53.
17.
Prado CM, Lieffers JR, McCargar LJ, Reiman T, Sawyer MB, Martin L, Baracos VE.
Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the
respiratory and gastrointestinal tracts: a population-based study. Lancet Oncol 2008;9:629-635.
18.
World Health Organization. Start of the New Millinnium. The burden of musculoskeletal
conditions at the start of the new millennium. World Health Organization technical report series
2003;919:i-cover.
19.
Lawrence RC, Felson DT, Helmick CG, Arnold LM, Choi H, Deyo RA, Gabriel S, et al.
Estimates of the prevalence of arthritis and other rheumatic conditions in the United States.
Arthritis and Rheumatism 2008;58:26-35.
20.
Loeser RF. Age-Related Changes in the Musculoskeletal System and the Development of
Osteoarthritis. Clinics in Geriatric Medicine 2010;26:371.
21.
Park H, Lee SK. Association of obesity with osteoarthritis in elderly Korean women.
Maturitas 2011;70:65-68.
22.
Lee S, Kim TN, Kim SH. Sarcopenic obesity is more closely associated with knee
osteoarthritis than is nonsarcopenic obesity: a cross-sectional study. Arthritis Rheum
2012;64:3947-3954.
23.
Hootman JM, Helmick CG. Projections of US prevalence of arthritis and associated
activity limitations. Arthritis and Rheumatism 2006;54:226-229.
24.
Centers for Disease Control and Prevention. Osteoarthritis. In; 2011.
25.
Zamboni M, Zoico E, Scartezzini T, Mazzali G, Tosoni P, Zivelonghi A, Gallagher D, et
al. Body composition changes in stable-weight elderly subjects: The effect of sex. Aging Clinical
and Experimental Research 2003;15:321-327.
76
26.
Janssen I, Heymsfield SB, Ross R. Low relative skeletal muscle mass (sarcopenia) in
older persons is associated with functional impairment and physical disability. Journal of the
American Geriatrics Society 2002;50:889-896.
27.
Szulc P, Beck TJ, Marchand F, Delmas PD. Low skeletal muscle mass is associated with
poor structural parameters of bone and impaired balance in elderly men--the MINOS study. J
Bone Miner Res 2005;20:721-729.
28.
St-Onge MP. Relationship between body composition changes and changes in physical
function and metabolic risk factors in aging. Current Opinion in Clinical Nutrition and Metabolic
Care 2005;8:523-528.
29.
Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Gomez JM, Heitmann BL,
et al. Bioelectrical impedance analysis - part II: utilization in clinical practice. Clinical Nutrition
2004;23:1430-1453.
30.
Coppini LZ, Waitzberg DL, Campos ACL. Limitations and validation of bioelectrical
impedance analysis in morbidly obese patients. Current Opinion in Clinical Nutrition and
Metabolic Care 2005;8:329-332.
31.
Chumlea C and Sun S. "Bioelectrical Impedance Analysis" In: Human Body
Composition. 2 ed. Champaign, IL: Human Kinetics, 2005.
32.
Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Gomez JM, Heitmann BL,
et al. Bioelectrical impedance analysis principles and methods. Clinical Nutrition 2004;23:12261243.
33.
Scharfetter H, Schlager T, Stollberger R, Felsberger R, Hutten H, Hinghofer-Szalkay H.
Assessing abdominal fatness with local bioimpedance analysis: basics and experimental findings.
International Journal of Obesity 2001;25:502-511.
34.
Bohm A, Heitmann BL. The use of bioelectrical impedance analysis for body
composition in epidemiological studies. European Journal of Clinical Nutrition 2013;67:S79S85.
35.
Demura S, Yamaji S, Goshi F, Kobayashi H, Sato S, Nagasawa Y. The validity and
reliability of relative body fat estimates and the construction of new prediction equations for
young Japanese adult males. Journal of Sports Sciences 2002;20:153-164.
36.
Jebb SA, Cole TJ, Doman D, Murgatroyd PR, Prentice AM. Evaluation of the novel
Tanita body-fat analyser to measure body composition by comparison with a four-compartment
model. British Journal of Nutrition 2000;83:115-122.
37.
Rosenberg I. Summary comments. American Journal of Clinical Nutrition
1989;50:1231S–1233S.
38.
Melton LJ, III, Khosla S, Crowson CS, O'Connor MK, O'Fallon WM, Riggs BL.
Epidemiology of sarcopenia. Journal of the American Geriatrics Society 2000;48:625-630.
77
39.
Gillette-Guyonnet S, Nourhashemi F, Lauque S, Grandjean H, Vellas B. Body
composition and osteoporosis in elderly women. Gerontology 2000;46:189-193.
40.
Gallagher D, Visser M, deMeersman RE, Sepulveda D, Baumgartner RN, Pierson RN,
Harris T, et al. Appendicular skeletal muscle mass: Effects of age, gender, and ethnicity. Journal
of Applied Physiology 1997;83:229-239.
41.
Castillo EM, Goodman-Gruen D, Kritz-Silverstein D, Morton DJ, Wingard DL, BarrettConnor E. Sarcopenia in elderly men and women - The Rancho Bernardo Study. American
Journal of Preventive Medicine 2003;25:226-231.
42.
Pichard C, Kyle UG, Bracco D, Slosman DO, Morabia A, Schutz Y. Reference values of
fat-free and fat masses by bioelectrical impedance analysis in 3393 healthy subjects. Nutrition
2000;16:245-254.
43.
Janssen I, Baumgartner RN, Ross R, Rosenberg IH, Roubenoff R. Skeletal muscle
cutpoints associated with elevated physical disability risk in older men and women. Am J
Epidemiol 2004;159:413-421.
44.
Newman AB, Kupelian V, Visser M, Simonsick E, Goodpaster B, Nevitt M, Kritchevsky
SB, et al. Sarcopenia: Alternative definitions and associations with lower extremity function.
Journal of the American Geriatrics Society 2003;51:1602-1609.
45.
Delmonico MJ, Harris TB, Lee J-S, Visser M, Nevitt M, Kritchevsky SB, Tylavsky FA,
et al. Alternative definitions of sarcopenia, lower extremity performance, and functional
impairment with aging in older men and women. Journal of the American Geriatrics Society
2007;55:769-774.
46.
Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, Martin FC, et
al. Sarcopenia: European consensus on definition and diagnosis: Report of the European
Working Group on Sarcopenia in Older People. Age Ageing 2010;39:412-423.
47.
Iannuzzi-Sucich M, Prestwood KM, Kenny AM. Prevalence of sarcopenia and predictors
of skeletal muscle mass in healthy, older men and women. Journals of Gerontology Series aBiological Sciences and Medical Sciences 2002;57:M772-M777.
48.
Bijlsma AY, Meskers CGM, Ling CHY, Narici M, Kurrle SE, Cameron ID, Westendorp
RGJ, et al. Defining sarcopenia: the impact of different diagnostic criteria on the prevalence of
sarcopenia in a large middle aged cohort. Age 2013;35:871-881.
49.
Hedayati KK, Dittmar M. Prevalence of sarcopenia among older community-dwelling
people with normal health and nutritional state. Ecology of Food and Nutrition 2010;49:110-128.
50.
Kortebein P, Ferrando A, Lombeida J, Wolfe R, Evans WJ. Effect of 10 days of bed rest
on skeletal muscle in healthy older adults. Jama-Journal of the American Medical Association
2007;297:1772-1774.
78
51.
Lee JSW, Auyeung T-W, Kwok T, Lau EMC, Leung P-C, Woo J. Associated factors and
health impact of sarcopenia in older Chinese men and women: A cross-sectional study.
Gerontology 2007;53:404-410.
52.
Mithal A, Bonjour JP, Boonen S, Burckhardt P, Degens H, El Hajj Fuleihan G, Josse R,
et al. Impact of nutrition on muscle mass, strength, and performance in older adults. Osteoporos
Int 2013;24:1555-1566.
53.
Boirie Y. Fighting Sarcopenia in Older Frail Subjects: Protein Fuel for Strength, Exercise
for Mass. Journal of the American Medical Directors Association 2013;14:140-143.
54.
Houston DK, Nicklas BJ, Ding JZ, Harris TB, Tylavsky FA, Newman AB, Lee JS, et al.
Dietary protein intake is associated with lean mass change in older, community-dwelling adults:
the Health, Aging, and Body Composition (Health ABC) Study. American Journal of Clinical
Nutrition 2008;87:150-155.
55.
Campbell WW, Trappe TA, Wolfe RR, Evans WJ. The recommended dietary allowance
for protein may not be adequate for older people to maintain skeletal muscle. Journals of
Gerontology Series a-Biological Sciences and Medical Sciences 2001;56:M373-M380.
56.
Dreyer HC, Volpi E. Role of protein and amino acids in the pathophysiology and
treatment of sarcopenia. Journal of the American College of Nutrition 2005;24:140S-145S.
57.
Kerstetter JE, O'Brien KO, Insogna KL. Low protein intake: The impact on calcium and
bone homeostasis in humans. Journal of Nutrition 2003;133:855S-861S.
58.
WHO/FAO/UNU. Protein and amino acid requirements in human nutrition. Report of a
joint WHO/FAO/UNU Expert Consultation 2007.
59.
Millward DJ. Nutrition and sarcopenia: evidence for an interaction. Proc Nutr Soc
2012;71:566-575.
60.
Ceglia L, Harris SS. Vitamin D and Its Role in Skeletal Muscle. Calcified Tissue
International 2013;92:151-162.
61.
Tieland M, Brouwer-Brolsma EM, Nienaber-Rousseau C, van Loon LJC, De Groot
LCPGM. Low vitamin D status is associated with reduced muscle mass and impaired physical
performance in frail elderly people. European Journal of Clinical Nutrition 2013;67:1050-1055.
62.
Seo MH, Kim MK, Park SE, Rhee EJ, Park CY, Lee WY, Baek KH, et al. The
association between daily calcium intake and sarcopenia in older, non-obese Korean adults: the
fourth Korea National Health and Nutrition Examination Survey (KNHANES IV) 2009. Endocr
J 2013;60:679-686.
79
63.
Fulle S, Protasi F, Di Tano G, Pietrangelo T, Beltramin A, Boncompagni S, Vecchiet L,
et al. The contribution of reactive oxygen species to sarcopenia and muscle ageing. Experimental
Gerontology 2004;39:17-24.
64.
Marzani B, Balage M, Venien A, Astruc T, Papet I, Dardevet D, Mosoni L. Antioxidant
supplementation restores defective leucine stimulation of protein synthesis in skeletal muscle
from old rats. Journal of Nutrition 2008;138:2205-2211.
65.
Howard C, Ferrucci L, Sun K, Fried LP, Walston J, Varadhan R, Guralnik JM, et al.
Oxidative protein damage is associated with poor grip strength among older women living in the
community. Journal of Applied Physiology 2007;103:17-20.
66.
Cesari M, Pahor M, Bartali B, Cherubini A, Penninx B, Williams GR, Atkinson H, et al.
Antioxidants and physical performance in elderly persons: the Invecchiare in Chianti
(InCHIANTI) study. American Journal of Clinical Nutrition 2004;79:289-294.
67.
Lang T, Streeper T, Cawthon P, Baldwin K, Taaffe DR, Harris TB. Sarcopenia: etiology,
clinical consequences, intervention, and assessment. Osteoporosis International 2010;21:543559.
68.
Maggio M, Lauretani F, Ceda GP. Sex hormones and sarcopenia in older persons.
Current Opinion in Clinical Nutrition and Metabolic Care 2013;16:3-13.
69.
Kamel HK, Maas D, Duthie EH. Role of hormones in the pathogenesis and management
of sarcopenia. Drugs & Aging 2002;19:865-877.
70.
Poehlman ET, Toth MJ, Gardner AW. Changes in energy-balance and body-composition
at menopause - a controlled longitudinal-study. Annals of Internal Medicine 1995;123:673-675.
71.
Rolland Y, van Kan GA, Benetos A, Blain H, Bonnefoy M, Chassagne P, Jeandel C, et
al. Frailty, osteoporosis and hip fracture: causes, consequences and therapeutic perspectives.
Journal of Nutrition Health & Aging 2008;12:335-346.
72.
Baumgartner RN, Waters DL, Gallagher D, Morley JE, Garry PJ. Predictors of skeletal
muscle mass in elderly men and women. Mechanisms of Ageing and Development
1999;107:123-136.
73.
Morley JE, Malmstrom TK. Frailty, Sarcopenia, and Hormones. Endocrinology and
Metabolism Clinics of North America 2013;42:391.
74.
Sternfeld B, Ngo L, Satariano WA, Tager IB. Associations of body composition with
physical performance and self-reported functional limitation in elderly men and women. Am J
Epidemiol 2002;156:110-121.
75.
Jankowski CM, Gozansky WS, Van Pelt RE, Schenkman ML, Wolfe P, Schwartz RS,
Kohrt WM. Relative contributions of adiposity and muscularity to physical function in
community-dwelling older adults. Obesity 2008;16:1039-1044.
80
76.
Zamboni M, Turcato E, Santana H, Maggi S, Harris TB, Pietrobelli A, Heymsfield SB, et
al. The relationship between body composition and physical performance in older women.
Journal of the American Geriatrics Society 1999;47:1403-1408.
77.
Osnes EK, Lofthus CM, Meyer HE, Falch JA, Nordsletten L, Cappelen I, Kristiansen IS.
Consequences of hip fracture on activities of daily life and residential needs. Osteoporosis
International 2004;15:567-574.
78.
Boonen S, Autier P, Barette M, Vanderschueren D, Lips P, Haentjens P. Functional
outcome and quality of life following hip fracture in elderly women: a prospective controlled
study. Osteoporosis International 2004;15:87-94.
79.
Panula J, Pihlajamaki H, Mattila VM, Jaatinen P, Vahlberg T, Aarnio P, Kivela SL.
Mortality and cause of death in hip fracture patients aged 65 or older: a population-based study.
BMC Musculoskelet Disord 2011;12:105.
80.
Landi F, Liperoti R, Russo A, Giovannini S, Tosato M, Capoluongo E, Bernabei R, et al.
Sarcopenia as a risk factor for falls in elderly individuals: Results from the ilSIRENTE study.
Clinical Nutrition 2012;31:652-658.
81.
Miller MD, Crotty M, Giles LC, Bannerman E, Whitehead C, Cobiac L, Daniels LA, et
al. Corrected arm muscle area: An independent predictor of long-term mortality in communitydwelling older adults? Journal of the American Geriatrics Society 2002;50:1272-1277.
82.
Enoki H, Kuzuya M, Masuda Y, Hirakawa Y, Iwata M, Hasegawa J, Izawa S, et al.
Anthropometric measurements of mid-upper arm as a mortality predictor for communitydwelling Japanese elderly: The Nagoya Longitudinal Study of Frail Elderly (NLS-FE). Clinical
Nutrition 2007;26:597-604.
83.
Szulc P, Munoz F, Marchand F, Chapurlat R, Delmas PD. Rapid loss of appendicular
skeletal muscle mass is associated with higher all-cause mortality in older men: the prospective
MINOS study. American Journal of Clinical Nutrition 2010;91:1227-1236.
84.
Roubenoff R, Parise H, Payette HA, Abad LW, D'Agostino R, Jacques PF, Wilson PWF,
et al. Cytokines, insulin-like growth factor 1, sarcopenia, and mortality in very old communitydwelling men and women: The Framingham Heart Study. American Journal of Medicine
2003;115:429-435.
85.
Cesari M, Pahor M, Lauretani F, Zamboni V, Bandinelli S, Bernabei R, Guralnik JM, et
al. Skeletal Muscle and Mortality Results From the InCHIANTI Study. Journals of Gerontology
Series a-Biological Sciences and Medical Sciences 2009;64:377-384.
86.
World health Organization. Obesity: Preventing and managing the global epidemic Introduction: Report of a WHO Consultation 2000;894:1-253.
87.
Deurenberg P, Yap M, van Staveren WA. Body mass index and percent body fat: a
metaanalysis among different ethnic groups. International Journal of Obesity 1998;22:11641171.
81
88.
Abernathy RP, Black DR. Healthy body weights: an alternative perspective. Am J Clin
Nutr 1996;63:448s-451s.
89.
Hall KD, Heymsfield SB, Kemnitz JW, Klein S, Schoeller DA, Speakman JR. Energy
balance and its components: implications for body weight regulation. Am J Clin Nutr
2012;95:989-994.
90.
Richards MM, Adams TD, Hunt SC. Functional status and emotional well-being, dietary
intake, and physical activity of severely obese subjects. Journal of the American Dietetic
Association 2000;100:67-75.
91.
Pi-Sunyer FX. The obesity epidemic: Pathophysiology and consequences of obesity.
Obesity Research 2002;10:97S-104S.
92.
Astrup A, Gotzsche PC, van de Werken K, Ranneries C, Toubro S, Raben A, Buemann
B. Meta-analysis of resting metabolic rate in formerly obese subjects. American Journal of
Clinical Nutrition 1999;69:1117-1122.
93.
Neary MT, Batterham RL. Gut hormones: Implications for the treatment of obesity.
Pharmacology & Therapeutics 2009;124:44-56.
94.
Mayes JS, Watson GH. Direct effects of sex steroid hormones on adipose tissues and
obesity. Obesity reviews : an official journal of the International Association for the Study of
Obesity 2004;5:197-216.
95.
Brown LM, Gent L, Davis K, Clegg DJ. Metabolic impact of sex hormones on obesity.
Brain Research 2010;1350:77-85.
96.
Milner JJ, Beck MA. The impact of obesity on the immune response to infection. Proc
Nutr Soc 2012;71:298-306.
97.
Davison KK, Ford ES, Cogswell ME, Dietz WH. Percentage of body fat and body mass
index are associated with mobility limitations in people aged 70 and older from NHANES.
Journal of the American Geriatrics Society 2002;50:1802-1809.
98.
Stenholm S, Sainio P, Rantanen T, Alanen E, Koskinen S. Effect of co-morbidity on the
association of high body mass index with walking limitation among men and women aged 55
years and older. Aging Clin Exp Res 2007;19:277-283.
99.
Kim TN, Park MS, Il Lim K, Yang SJ, Yoo HJ, Kang HJ, Song W, et al. Skeletal muscle
mass to visceral fat area ratio is associated with metabolic syndrome and arterial stiffness: The
Korean Sarcopenic Obesity Study (KSOS). Diabetes Research and Clinical Practice
2011;93:285-291.
100. Schrager MA, Metter EJ, Simonsick E, Ble A, Bandinelli S, Lauretani F, Ferrucci L.
Sarcopenic obesity and inflammation in the InCHIANTI study. Journal of applied physiology
(Bethesda, Md. : 1985) 2007;102:919-925.
82
101. Batsis JA, Barre LK, Mackenzie TA, Pratt SI, Lopez-Jimenez F, Bartels SJ. Variation in
the prevalence of sarcopenia and sarcopenic obesity in older adults associated with different
research definitions: dual-energy X-ray absorptiometry data from the National Health and
Nutrition Examination Survey 1999-2004. J Am Geriatr Soc 2013;61:974-980.
102. Prado CM, Wells JC, Smith SR, Stephan BC, Siervo M. Sarcopenic obesity: A critical
appraisal of the current evidence. Clin Nutr 2012;31:583-601.
103. Oliveira RJ, Bottaro M, Junior JT, Farinatti PTV, Bezerra LA, Lima RM. Identification
of sarcopenic obesity in postmenopausal women: a cutoff proposal. Brazilian Journal of Medical
and Biological Research 2011;44:1171-1176.
104. Bouchard DR, Dionne IJ, Brochu M. Sarcopenic/Obesity and physical capacity in older
men and women: data from the nutrition as a determinant of successful aging (NuAge)-the
Quebec Longitudinal Study. Obesity 2009;17:2082-2088.
105. Villareal DT, Banks M, Siener C, Sinacore DR, Klein S. Physical frailty and body
composition in obese elderly men and women. Obesity Research 2004;12:913-920.
106. Honda H, Qureshi AR, Axelsson J, Heimburger O, Suliman ME, Barany P, Stenvinkel P,
et al. Obese sarcopenia in patients with end-stage renal disease is associated with inflammation
and increased mortality. American Journal of Clinical Nutrition 2007;86:633-638.
107. Taylor WR, Heller MO, Bergmann G, Duda GN. Tibio-femoral loading during human
gait and stair climbing. J Orthop Res 2004;22:625-632.
108. Felson DT, Zhang YQ, Anthony JM, Naimark A, Anderson JJ. Weight-loss reduces the
risk for symptomatic knee osteoarthritis in women - The Framingham Study. Annals of Internal
Medicine 1992;116:535-539.
109. Bennell KL, Hunt MA, Wrigley TV, Lim B-W, Hinman RS. Role of muscle in the
genesis and management of knee osteoarthritis. Rheumatic Disease Clinics of North America
2008;34:731-+.
110. Andersen S, Thygesen LC, Davidsen M, Helweg-Larsen K. Cumulative years in
occupation and the risk of hip or knee osteoarthritis in men and women: a register-based followup study. Occupational and Environmental Medicine 2012;69:325-330.
111. Franklin J, Ingvarsson T, Englund M, Lohmander S. Association between occupation and
knee and hip replacement due to osteoarthritis: a case-control study. Arthritis Research &
Therapy 2010;12.
112. Kim H-S, Yun DH, Yoo SD, Kim DH, Jeong YS, Yun J-S, Hwang DG, et al. Balance
control and knee osteoarthritis severity. Annals of Rehabilitation Medicine 2011;35:701-709.
113. Scott D, Blizzard L, Fell J, Jones G. Prospective study of self-reported pain, radiographic
osteoarthritis, sarcopenia progression, and falls risk in community-dwelling older adults.
Arthritis Care Res (Hoboken) 2012;64:30-37.
83
114. Prieto-Alhambra D, Nogues X, Javaid MK, Wyman A, Arden NK, Azagra R, Cooper C,
et al. An increased rate of falling leads to a rise in fracture risk in postmenopausal women with
self-reported osteoarthritis: a prospective multinational cohort study (GLOW). Annals of the
Rheumatic Diseases 2013;72:911-917.
115. Leveille SG, Bean J, Bandeen-Roche K, Jones R, Hochberg M, Guralnik JM.
Musculoskeletal pain and risk for falls in older disabled women living in the community. Journal
of the American Geriatrics Society 2002;50:671-678.
116. Manini TM, Clark BC. Dynapenia and aging: an update. J Gerontol A Biol Sci Med Sci
2012;67:28-40.
117. Houmard JA. Intramuscular lipid oxidation and obesity. Am J Physiol Regul Integr Comp
Physiol 2008;294:R1111-1116.
118. Koster A, Ding J, Stenholm S, Caserotti P, Houston DK, Nicklas BJ, You T, et al. Does
the amount of fat mass predict age-related loss of lean mass, muscle strength, and muscle quality
in older adults? J Gerontol A Biol Sci Med Sci 2011;66:888-895.
119. Issa RI, Griffin TM. Pathobiology of obesity and osteoarthritis: integrating biomechanics
and inflammation. Pathobiol Aging Age Relat Dis 2012;2.
120. Steiner C AR, Barrett M, Weiss A. Healthcare Cost and Utilization Project,
Mobility/Orthopedic Prodcedres 2011-2012: U.S. Department of Health and Human Services;
2012.
121. Chopra I, Kamal KM, Sankaranarayanan J, Kanyongo G. Factors associated with primary
hip arthroplasty after hip fracture. The American journal of managed care 2013;19:e74-84.
122. Johannson HR, Zywiel MG, Marker DR, Jones LC, McGrath MS, Mont MA.
Osteonecrosis is not a predictor of poor outcomes in primary total hip arthroplasty: a systematic
literature review. International Orthopaedics 2011;35:465-473.
123. Kelly E, Campbell J, Murray P. Total hip replacement: patient satisfaction and early
outcomes. International journal of health care quality assurance 2013;26:262-268.
124. Mehta JS, Kiryluk S, Fordyce MJF. Pain and satisfaction after hip replacement: The
patient's perspective at 5-12 years. Hip International 2005;15:112-118.
125. Vissers MM, Bussmann JB, de Groot IB, Verhaar JAN, Reijman M. Physical functioning
four years after total hip and knee arthroplasty. Gait & Posture 2013;38:310-315.
126. Kramers-de Quervain IA, Kaempfen S, Munzinger U, Mannion AF. Prospective study of
gait function before and 2 years after total knee arthroplasty. Knee 2012;19:622-627.
127. Rasch A, Bystrom AH, Dalen N, Martinez-Carranza N, Berg HE. Persisting muscle
atrophy two years after replacement of the hip. Journal of Bone and Joint Surgery-British
Volume 2009;91B:583-588.
84
128. Huddleston JI, Wang Y, Uquillas C, Herndon JH, Maloney WJ. Age and obesity are risk
factors for adverse events after total hip arthroplasty. Clin Orthop Relat Res 2012;470:490-496.
129. Andrew JG, Palan J, Kurup HV, Gibson P, Murray DW, Beard DJ. Obesity in total hip
replacement. Journal of Bone and Joint Surgery-British Volume 2008;90B:424-429.
130. Davis AM, Wood AM, Keenan AC, Brenkel IJ, Ballantyne JA: Does body mass index
affect clinical outcome post-operatively and at five years after primary unilateral total hip
replacement performed for osteoarthritis? A multivariate analysis of prospective data. In: J Bone
Joint Surg Br. Volume 93. England, 2011; 1178-1182.
131. Vincent HK, Horodyski M, Gearen P, Vlasak R, Seay AN, Conrad BP, Vincent KR:
Obesity and long term functional outcomes following elective total hip replacement. In: J Orthop
Surg Res. Volume 7. England, 2012; 16.
132. Lee JK, Choi C-H. Total knee arthroplasty in rheumatoid arthritis. Knee surgery &
related research 2012;24:1-6.
133. Lutzner J, Hubel U, Kirschner S, Gunther KP, Krummenauer F. Long-term results in total
knee arthroplasty. Chirurg 2011;82:618-624.
134. Mizner RL, Petterson SC, Clements KE, Zeni JA, Jr., Irrgang JJ, Snyder-Mackler L.
Measuring functional improvement after total knee arthroplasty requires both performance-based
and patient-report assessments: a longitudinal analysis of outcomes. Journal of Arthroplasty
2011;26:728-737.
135. Riddle DL, Stratford PW. Body weight changes and corresponding changes in pain and
function in persons with symptomatic knee osteoarthritis: A cohort study. Arthritis Care &
Research 2013;65:15-22.
136. Jarvenpaa J, Kettunen J, Soininvaara T, Miettinen H, Kroger H. Obesity has a negative
impact on clinical outcome after total knee arthroplasty. Scandinavian Journal of Surgery
2012;101:198-203.
137. Collins RA, Walmsley PJ, Amin AK, Brenkel IJ, Clayton RAE. Does obesity influence
clinical outcome at nine years following total knee replacement? Journal of Bone and Joint
Surgery-British Volume 2012;94B:1351-1355.
138. Baker P, Muthumayandi K, Gerrand C, Kleim B, Bettinson K, Deehan D. Influence of
body mass index (BMI) on functional improvements at 3 years following total knee replacement:
A retrospective cohort study. Plos One 2013;8:9.
139. Reuter SE, Massy-Westropp N, Evans AM. Reliability and validity of indices of handgrip strength and endurance. Australian Occupational Therapy Journal 2011;58:82-87.
140. Bohannon RW, Schaubert KL. Test-retest reliability of grip-strength measures obtained
over a 12-week interval from community-dwelling elders. Journal of hand therapy : official
journal of the American Society of Hand Therapists 2005;18:426-428.
85
141. Mathiowetz V, Weber K, Volland G, Kashman N. Reliability and validity of grip and
pinch strength evaluations. Journal of Hand Surgery-American Volume 1984;9A:222-226.
142. Bellamy N, Buchanan WW, Goldsmith CH, Campbell J, Stitt LW. Validation study of
WOMAC: a health status instrument for measuring clinically important patient relevant
outcomes to antirheumatic drug therapy in patients with osteoarthritis of the hip or knee. J
Rheumatol 1988;15:1833-1840.
143. Ethgen O, Bruyere O, Richy F, Dardennes C, Reginster JY. Health-related quality of life
in total hip and total knee arthroplasty - A qualitative and systematic review of the literature.
Journal of Bone and Joint Surgery-American Volume 2004;86A:963-974.
144. Johnson N, Barion A, Rademaker A, Rehkemper G, Weintraub S. The Activities of Daily
Living Questionnaire: a validation study in patients with dementia. Alzheimer Dis Assoc Disord
2004;18:223-230.
145. Oliveria SA, Kohl HW, 3rd, Trichopoulos D, Blair SN. The association between
cardiorespiratory fitness and prostate cancer. Med Sci Sports Exerc 1996;28:97-104.
146. Kohl HW, Blair SN, Paffenbarger RS, Jr., Macera CA, Kronenfeld JJ. A mail survey of
physical activity habits as related to measured physical fitness. Am J Epidemiol 1988;127:12281239.
147. Ainsworth BE HW, Herrmann SD, Meckes N, Bassett Jr DR, Tudor-Locke C, Greer JL,
Vezina J, Whitt-Glover MC, Leon AS. 2011 Compendium of Physical Activities: a second
update of codes and MET values. Medicine and Science in Sports and Exercise 2011;43:15751581.
148. Hauer K, Yardley L, Beyer N, Kempen G, Dias N, Campbell M, Becker C, et al.
Validation of the Falls Efficacy Scale and Falls Efficacy Scale International in geriatric patients
with and without cognitive impairment: results of self-report and interview-based questionnaires.
Gerontology 2010;56:190-199.
149. Moshfegh AJ, Rhodes DG, Baer DJ, Murayi T, Clemens JC, Rumpler WV, Paul DR, et
al. The US Department of Agriculture Automated Multiple-Pass Method reduces bias in the
collection of energy intakes. American Journal of Clinical Nutrition 2008;88:324-332.
150. Oklahoma State University. The 24-Hour Food Recall: An Essential Tool in Nutrition
Education; 2010.
151. National Academy of Sciences. Institute of Medicine. Food and Nutrition Board. Dietary
reference intakes for energy, carbohydrates, fiber, fat, fatty acids, cholesterol, protein, and amino
acids. In; 2004/2005.
152. Schutz Y, Kyle UUG, Pichard C. Fat-free mass index and fat mass index percentiles in
Caucasians aged 18-98y. International Journal of Obesity 2002;26:953-960.
86
153. Chumlea WC, Guo SS, Kuczmarski RJ, Flegal KM, Johnson CL, Heymsfield SB,
Lukaski HC, et al. Body composition estimates from NHANES III bioelectrical impedance data.
International Journal of Obesity 2002;26:1596-1609.
154. Kyle UG, Genton L, Lukaski HC, Dupertuis YM, Slosman DO, Hans D, Pichard C.
Comparison of fat-free mass and body fat in Swiss and American adults. Nutrition 2005;21:161169.
155. Kyle UG, Genton L, Slosman DO, Pichard C. Fat-free and fat mass percentiles in 5225
healthy subjects aged 15 to 98 years. Nutrition 2001;17:534-541.
156. Baumgartner RN. Body composition in healthy aging. In Vivo Body Composition
Studies 2000;904:437-448.
157. Xiao J, Ormsbee M, Contreras R, Prado C. A Descriptive Study of Body Composition
Abnormalities and Health Risks in Patients with Obesity. Tallahassee, Florida: Florida State
University; 2013.
158. Gallagher D, Ruts E, Visser M, Heshka S, Baumgartner RN, Wang J, Pierson RN, et al.
Weight stability masks sarcopenia in elderly men and women. American Journal of PhysiologyEndocrinology and Metabolism 2000;279:E366-E375.
159. Sowers MF, Yosef M, Jamadar D, Jacobson J, Karvonen-Gutierrez C, Jaffe M. BMI vs.
body composition and radiographically defined osteoarthritis of the knee in women: a 4-year
follow-up study. Osteoarthritis and cartilage / OARS, Osteoarthritis Research Society
2008;16:367-372.
160. Wang Y, Wluka AE, English DR, Teichtahl AJ, Giles GG, O'Sullivan R, Cicuttini FM:
Body composition and knee cartilage properties in healthy, community-based adults. In: Ann
Rheum Dis. Volume 66. England, 2007; 1244-1248.
161. Hull HR, Thornton J, Wang J, Pierson RN, Jr., Kaleem Z, Pi-Sunyer X, Heymsfield S, et
al. Fat-free mass index: changes and race/ethnic differences in adulthood. International Journal
of Obesity 2011;35:121-127.
162. Fried LP, Ferrucci L, Darer J, Williamson JD, Anderson G. Untangling the concepts of
disability, frailty, and comorbidity: Implications for improved targeting and care. Journals of
Gerontology Series a-Biological Sciences and Medical Sciences 2004;59:255-263.
163.
I8.
Breedveld FC. Osteoarthritis - the impact of a serious disease. Rheumatology 2004;43:I4-
164. Urquhart DM, Berry P, Wluka AE, Strauss BJ, Wang Y, Proietto J, Jones G, et al. 2011
Young Investigator Award Winner: Increased Fat Mass Is Associated With High Levels of Low
Back Pain Intensity and Disability. Spine 2011;36:1320-1325.
87
165. Kim SK, Jung I, Kim JH. Exercise reduces C-reactive protein and improves physical
function in automotive workers with low back pain. Journal of Occupational Rehabilitation
2008;18:218-222.
166. Stuermer T, Brenner H, Koenig W, Guenther KP. Severity and extent of osteoarthritis
and low grade systemic inflammation as assessed by high sensitivity C reactive protein. Annals
of the Rheumatic Diseases 2004;63:200-205.
167. Kondrop J, Elia M. Basics in Clinical Nutrition. 4 ed. Prague: Galen, 2011.
168. Soeters PB, Reijven PL, van Bokhorst-de van der Schueren MA, Schols JM, Halfens RJ,
Meijers JM, van Gemert WG. A rational approach to nutritional assessment. Clin Nutr
2008;27:706-716.
169. McCarthy CJ, Mills PM, Pullen R, Roberts C, Silman A, Oldham JA. Supplementing a
home exercise programme with a class-based exercise programme is more effective than home
exercise alone in the treatment of knee osteoarthritis. Rheumatology 2004;43:880-886.
170. Miller GD, Nicklas BJ, Davis C, Loeser RF, Lenchik L, Messier SP. Intensive weight
loss program improves physical function in older obese adults with knee osteoarthritis. Obesity
2006;14:1219-1230.
171. Elbaz A, Debbi EM, Segal G, Haim A, Halperin N, Agar G, Mor A, et al. Sex and Body
Mass Index Correlate With Western Ontario and McMaster Universities Osteoarthritis Index and
Quality of Life Scores in Knee Osteoarthritis. Archives of Physical Medicine and Rehabilitation
2011;92:1618-1623.
172. Berry PA, Wluka AE, Davies-Tuck ML, Wang Y, Strauss BJ, Dixon JB, Proietto J, et al.
The relationship between body composition and structural changes at the knee. Rheumatology
(Oxford, England) 2010;49:2362-2369.
173. Cawthon PM, Fox KM, Gandra SR, Delmonico MJ, Chiou C-F, Anthony MS, Caserotti
P, et al. Clustering of Strength, Physical Function, Muscle, and Adiposity Characteristics and
Risk of Disability in Older Adults. Journal of the American Geriatrics Society 2011;59:781-787.
174. Forbes SC, Little JP, Candow DG. Exercise and nutritional interventions for improving
aging muscle health. Endocrine 2012;42:29-38.
175. Melzer I, Kurz I, Oddsson LIE. A retrospective analysis of balance control parameters in
elderly fallers and non-fallers. Clinical Biomechanics 2010;25:984-988.
176. Hita-Contreras F, Martinez-Amat A, Lomas-Vega R, Alvarez P, Mendoza N, RomeroFranco N, Aranega A. Relationship of body mass index and body fat distribution with postural
balance and risk of falls in Spanish postmenopausal women. Menopause-the Journal of the North
American Menopause Society 2013;20:202-208.
177. Miller EM, Matrangola SL, Madigan ML. Effects of obesity on balance recovery from
small postural perturbations. Ergonomics 2011;54:547-554.
88
178. Schebendach JE, Porter KJ, Wolper C, Walsh BT, Mayer LES. Accuracy of self-reported
energy intake in weight-restored patients with anorexia nervosa compared with obese and normal
weight individuals. International Journal of Eating Disorders 2012;45:570-574.
179. Gallagher ML. Krause's Food and Nutrition Therapy. 12 ed. St. Louis, Missouri:
Saunders Elsevier, 2008.
180. Penninx BW, Pahor M, Cesari M, Corsi AM, Woodman RC, Bandinelli S, Guralnik JM,
et al. Anemia is associated with disability and decreased physical performance and muscle
strength in the elderly. J Am Geriatr Soc 2004;52:719-724.
181. Shyam Kumar AJ, Beresford-Cleary N, Kumar P, Barai A, Vasukutty N, Yasin S, Sinha
A. Preoperative grip strength measurement and duration of hospital stay in patients undergoing
total hip and knee arthroplasty. European journal of orthopaedic surgery & traumatology :
orthopedie traumatologie 2013;23:553-556.
182. Cavenaghi F, Cerri C, Panella L. Association of hemoglobin levels, acute hemoglobin
decrease and age with Rehabilitation outcomes after total hip and knee replacement. European
Journal of Physical and Rehabilitation Medicine 2009;45:319-325.
183. Di Nisio M, Di Iorio A, Porreca E, Abate M, Ferrante N, Bandinelli S, Guralnik J, et al.
Obesity, Poor Muscle Strength, and Venous Thromboembolism in Older Persons: The
InCHIANTI Study. Journals of Gerontology Series a-Biological Sciences and Medical Sciences
2011;66:320-325.
184. Heymsfield SB, Arteaga C, McManus C, Smith J, Moffitt S. Measurement of muscle
mass in humans: validity of the 24-hour urinary creatinine method. Am J Clin Nutr 1983;37:478494.
185. Baxmann AC, Ahmed MS, Marques NC, Menon VB, Pereira AB, Kirsztajn GM,
Heilberg IP. Influence of muscle mass and physical activity on serum and urinary creatinine and
serum cystatin C. Clinical Journal of the American Society of Nephrology 2008;3:348-354.
89
BIOGRAPHICAL SKETCH
SARAH PURCELL
Sarah was raised in Tennessee and moved to Tallahassee in 2007. She received her
Bachelors of Science degree in Food and Nutrition with honors and with a minor in psychology
from Florida State University in 2012. Sarah continued onto graduate school directly after the
completion of her undergraduate degree and began working with Dr. Carla Prado.
Sarah has been the recipient of numerous honors, awards, and scholarships during her
undergraduate and graduate careers. During her graduate coursework, Sarah received the Lavina
Laybold scholarship and has participated in the College of Human Sciences Graduate Student
Advisory Council as a department representative for the 2013-2014 academic year. Additionally,
Sarah has worked as a research and teaching assistant while pursuing her degree.
90