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Electronic Theses, Treatises and Dissertations The Graduate School

2017 Identifying Osteosarcopenic Obesity in a Group of Older Women Julia E. (Julia Ellen) Inglis

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COLLEGE OF HUMAN SCIENCES

IDENTIFYING OSTEOSARCOPENIC OBESITY IN A GROUP OF OLDER WOMEN

By

JULIA E. INGLIS

A Dissertation presented to the Department of Nutrition, Food and Exercise Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy

2017

Julia E. Inglis defended this dissertation on February 28, 2017. The members of the supervisory committee were:

Jasminka Ilich-Ernst Professor Directing Dissertation

Dan McGee University Representative

Bahram H. Arjmandi Committee Member

Lynn Panton Committee Member

The Graduate School has verified and approved the above-named committee members, and certifies that the dissertation has been approved in accordance with university requirements.

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To my grandfather, Harry B. Eisberg, Jr, MD, who also studied bone health

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ACKNOWLEDGMENTS

I would like to offer my utmost appreciation for Dr. Jasminka Ilich-Ernst, who was more than an advisor to me. She played a key role in guiding me with unlimited patience and energy and gave me all the support I could ask for. Her experience and knowledge has enlightened my education and my life, providing me with a vision for my person and my career. I would also like to thank my committee members Dr. Bahram Arjmandi, Dr. Lynn B. Panton, and Dr. Dan McGee for their valuable ideas, encouragement and compassion. I am also grateful to Pamela Black, ultrasound technician and Joni Jones, for allowing me to utilize the ultrasound equipment in the Health Center at the Florida State University for this research. I am grateful to all of my previous professors for my master’s degree and bachelor’s degree including Dr. Maria T. Spicer and all those at the University of North Florida. I am grateful to Pegah Jafarinasabian for her tireless commitment to this project and support as a laboratory partner. I am grateful to my local church for all of their prayers that supported me. I also wish to thank all my friends who have added richness to my life. I am endlessly grateful to my parents, Dr. Craig M. Inglis and Ingrid E. Inglis for their support every step of the way.

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TABLE OF CONTENTS

List of Figures ...... vi

List of Tables ...... vii

List of Abbreviations ...... viii

Abstract ...... ix

1. BACKGROUND AND SIGNIFICANCE ...... 1

2. LITERATURE REVIEW ...... 8

3. METHODS ...... 22

4. RESULTS ...... 29

5. DISCUSSION ...... 43

6. CONCLUSION ...... 52

APPENDICES ...... 54

A. IRB APPROVAL ...... 54 B. CONSENT FORM ...... 58 C. INITIAL SCREENING ...... 62 D. DEMOGRAPHIC SURVEY ...... 64 E. ALLIED DUNBAR SURVEY OF HABITUAL PHYSICAL ACTIVITY ...... 66 F. EXERCISE ...... 68 G. SF-36 FORM ...... 70 H. MINI-MENTAL STATE EXAMINATION (MMSE) ...... 75 I. ANTHROPOMETRICS ...... 78 J. 3-DAY DIETARY RECORD INSTRUCTIONS ...... 79 K. CDC FALL RISK QUESTIONNAIRE ...... 81 L. SMOKING PAST AND PRESENT ...... 82 M. PHYSICAL PERFORMANCE TESTS DATA SHEET ...... 84

References ...... 85

Biographical Sketch ...... 101

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LIST OF FIGURES

Fig. 1 Classification of participants ...... 31

Fig. 2 Ultrasound image of Rectus femoris muscle ...... 36

Fig. 3 Results (mean ± SEM) for sit-to-stand among the four distinct groups of women...... 38

Fig. 4 Results (mean ± SEM) for knee extension among the four distinct groups of women ...... 38

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LIST OF TABLES

Table 1. Clinical and anthropometric characteristics of OSO, OO, obese-only and osteopenic/sarcopenic non-obese groups: mean (SD), min-max (N=59) ...... 33

Table 2. Muscle quality (knee extension), echo intensity and phase angle, of OSO, OO, obese- only and osteopenic/sarcopenic non-obese groups: mean (SD), min-max ...... 35

Table 3. Physical performance of OSO, OO, obese-only and osteopenic/ sarcopenic non-obese, mean (SD), min-max ...... 37

Table 4. Assessment and scoring of functional performance and corresponding cut-off values ..39

Table 5. Percentage of participants with functional performance below the cutoff for each test indicating functional decline ...... 40

Table 6. Average performance score and percentage of participants OSO, OO, obese-only and osteopenic/sarcopenic non-obese with functional performance below the cutoff, n (%) ...... 41

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LIST OF ABBREVIATIONS

ALM Appendicular Lean Mass AMPK Adenosine monophosphate-activated protein kinase ANOVA Analysis of Variance BIA Bioelectrical Impedance Analysis BMD Bone Mineral Density BMI Body Mass Index BMSi Bone material strength index CDC Centers for Disease Control CRP C-reactive protein CT Computed Tomography DXA Dual-Energy X-Ray Absorptiometry EI Echo Intensity IL-6 Interleukin-6 IL-8 Interleukin-8 IL-12 Interleukin-12 L1-L4 Lumbar Spinal Discs L1, L2, L3, L4 MAPK Mitogen activated protein kinase MAT Marrow adipose tissue min minute MRI Magnetic Resonance Imaging mRNA Messenger Ribonucleic Acid NIH National Institute of Health OO Osteopenic obesity OPG Osteoprotegerin OSO Osteosarcopenic Obesity PI Pixel Intensity PPAR Peroxisome proliferator-activated receptors RANK Receptor activated nuclear factor-k RANKL RANK ligand SAS Statistical analysis software SF-36 Short Form Health Survey – 36 questions SMI Skeletal muscle mass index SO Sarcopenic obesity sTnT skeletal muscle specific troponin TNF-α Tumor necrosis factor alpha

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ABSTRACT

A cross-sectional study in older women was designed and conducted to identify prevalence, specific characteristics and diagnostic criteria for the newly identified condition named osteosarcopenic obesity syndrome. Osteosarcopenic obesity (OSO) ), a syndrome recently identified by Dr. Ilich-Ernst, is a condition where an older adult experiences bone loss, sarcopenia and increased fat mass, the latter either as a clinically diagnosed overweight/obesity or infiltrated fat into bone and muscle. The study lasted 24 months and a total of N=59 Caucasian ambulatory women aged 76.0±7.3 years (mean ± SD) and BMI of 27.0±5.5 kg/m2 from the local area were assessed for body composition measurements (bone, muscle, fat tissue) using dual energy x-ray absorptiometry scans (DXA). Osteoporosis/osteopenia was identified based on femoral neck and lumbar spine (L1-L4) T-scores (≤-1). A linear regression model was created to identify sarcopenic obesity in the sample, based on the appendicular lean mass, controlling for height (m) and fat mass (kg). Obesity status was based on percent body fat with a cut-off at ≥32%. Muscle quality was measured via echo intensity obtained via ultrasound scans as well as knee extension scores divided by lower appendicular lean mass. Bioelectrical impedance analyses were used to calculate phase angle to assess for frailty in the population. The participants were also tested on physical performance measures including handgrip strength, one- leg stance, 4-m timed normal and brisk walking test, 2-minute walking test, sit-to-stand, knee extension and arm flexion. In the final analysis, the women were divided into four groups based on body composition: OSO (n=10), osteopenic obese (OO; n=35), obese-only (obese; n=10) and osteopenic/sarcopenic non-obese (non-obese; n=4). There was also one participant who had sarcopenic obesity (SO), however due to the small sample size (n=1), was not included in the calculations. All data analyses were performed in SAS 9.4.with p<0.05 deemed as statistically significant. As expected, the OSO women presented with lower bone mineral density (BMD) at most skeletal sites in comparison to other groups (although not always significantly different): right femoral neck BMD (OSO: 0.778±0.080; OO: 0.792±0.082; obese: 1.013±0.102; non-obese: 0.773±0.048 g/cm2, p<0.05), left femoral neck BMD (OSO: 0.759±0.066; OO: 0.801±0.074; obese: 0.957±0.048; non-obese: 0.747±0.018 g/cm2, p<0.05) and lumbar spine BMD (OSO: 1.133±0.139; OO: 1.182±0.184; obese: 1.394±0.212; non-obese: 1.064±0.136 g/cm2, p<0.05). The OSO women had significantly lower appendicular lean mass (OSO: 15.9±1.7; OO: 17±2.4;

ix obese: 18.5±2.7; non-obese: 14±1.3 kg, p<0.05) and higher percent body fat (OSO: 45±4.9; OO 42.2±5.7; obese: 43.7±3.8; non-obese: 27.1±3.8%, p<0.05), on average than women in other groups. The OSO and OO women had the poorest scores on physical performance tests, with the OSO women scoring significantly lower on sit-to-stand (OSO: 10.3±1.6; OO: 11.9±4; obese: 10.9±3.3; non-obese: 14.3±2.6 times/30 sec, p<0.05) and the knee extension (OSO: 43±11; OO: 56±14.1; obese: 65.5±15.2; non-obese: 41.5±19.1 kg, p<0.05) than other groups. The OSO women also had lower muscle quality based on echo intensity (right quadriceps) from ultrasound measurements (OSO: 65.7±9.9; OO: 65.5±12; obese: 71±13.9; non-obese: 76.6±6.7 pixel intensity) and from the knee extension (OSO: 3.8±0.8; OO: 4.4±1.0; obese: 4.9±0.7; non-obese: 3.8±1.8 kg) calculation than other groups. Results from this study can be used to better recognize OSO syndrome in older women using body composition measures via DXA, physical performance tests and muscle quality measurements. These results also indicate that in situations when DXA is not available for body composition measurements for the OSO diagnosis, simple physical performance measures could be used for preliminary assessment and patient referrals for further evaluations.

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CHAPTER 1

BACKGROUND AND SIGNIFICANCE

Significance

Conditions related to aging, such as osteopenia/osteoporosis (bone loss), sarcopenia or loss of muscle mass and strength and increased adiposity (body fat) with age need to be evaluated in tandem. While some attempts are made to investigate bone and muscle tissues concomitantly, fat tissue is still kept out of the picture in most cases, and not evaluated within its interaction with the former two. Increasing numbers of Americans are aging in the midst of an obesity epidemic, straining medical resources and potentially leading to increased comorbidities (Lakdawalla et al., 2005; Ilich et al., 2014a). With age, many older adults suffer from increased body fat, as well as from increased bone loss and fat infiltration into bone, leading to a condition termed osteopenic obesity (OO) and/or increased muscle loss/loss of strength combined with fat infiltration into muscle, leading to sarcopenic obesity (SO), as defined by Rubenoff (Roubenoff et al. 2000). When both OO and SO are present, a combined condition was recently identified by Dr. Ilich-Ernst termed osteosarcopenic obesity (OSO) syndrome, the ultimate impairment of bone, muscle and adipose tissues (Ilich et al. 2014a). Each of the underlying conditions, OO and SO, lack consistent diagnostic criteria in the medical community (Ilich et al., 2014a; Cruz-Jentoft et al., 2014). In addition, these individuals are often overlooked in the medical community because of the belief that obesity is protective against bone fractures and sarcopenia (Crepaldi et al., 2007; Scott et al. 2016; Domiciano et al., 2012). Bone loss, such as that which is seen in osteopenia/osteoporosis, is considered a major predictor for fractures in older adults by the World Health Organization (WHO; Divittorio et al., 2006), and when combined with obesity, may decrease mobility and increase fall risk more than osteoporosis alone (Ilich et al., 2014a). Older women with SO appear to have less physical strength and functional capacity and are at greater risk of falls and disability than those who are just obese or sarcopenic (Domiciano et al.2013; Baumgartner 2000). Therefore, the new triad of bone, muscle and adipose tissues impairment, the OSO syndrome, is particularly important in older women (Ilich et al., 2014a).

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A recent analysis from our laboratory indicates that women with this newly identified OSO syndrome, trend towards greater frailty and immobility than women with obesity alone or even OO or SO alone (Ilich et al., 2015). Out of 258 postmenopausal obese Caucasian women, those with OSO (12.4%) had the lowest handgrip-strength scores, the slowest normal and brisk walking speed, and the shortest time for one-leg standing. Although the difference was found to be only significant between OSO women and obese-only women, the OSO women still had poorer physical performance than the women with OO or SO alone. Additionally, more than half of the women in this study had osteopenia/osteoporosis, even while being classified as obese confirming that body fat is not necessarily protective of bone, as previously believed (Crepaldi et al., 2007; Scott et al., 2016). In general, based on this new definition of OSO syndrome, an older woman with OSO has lost significant bone mineral density (BMD), muscle mass, and strength even while experiencing increasing levels of adiposity, either as an overt overweight or infiltration of fat into bone and muscle tissue. It needs to be noted that even the individuals who are not overweight or obese, may suffer from OSO due to infiltration of adipose tissue into bone and muscle and its redistribution around visceral organs, both typically seen with aging. Overall, women with OSO may be at even higher risk of falls and fractures, and possibly long-term immobility and/or premature death as they age (Ilich et al., 2015; Ilich et al. 2014a; Cruz-Jentoft et al., 2010). A more rigorous and in-depth study is needed to evaluate the condition of OSO, to define diagnostic cutoff criteria and to characterize the impact of this combined condition on health status of aging women. This study is the first original research of this kind and the measurements obtained could serve as the basis for the creation of diagnostic criteria and/or cutoffs to identify women with OSO.

Objectives and Hypotheses

The overall purpose of this study is to quantify the deterioration of bone, muscle and fat tissues in older women living in both the community and long-term care facilities, and to determine the prevalence of OSO, along with establishing the diagnostic cutoffs from the measurements of bone, body composition and functionality status. The specific aims are as follows.

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Specific Aim 1: To identify the prevalence of OSO in a sample of older women (>65 years). We will recruit at least 50 older women from independent living facilities and the community. We will measure their bone density and body composition (fat and lean mass) with DXA. These measures will provide means to identify: a) OO from BMD T-scores (from DXA). b) SO from ALM index and negative residuals in linear regression falling below the expected values for lean mass based on their height and fat mass from DXA. c) Overall obesity from DXA. This will provide a means to identify each condition and those who should be classified as OSO. Hypothesis 1a: We hypothesize that N ≥ 50 will give us enough power to identify OSO among obese older women and that ≥ 10% of participants will have OSO (based on our previous work). Hypothesis 1b. Based on the DXA measurements, we hypothesize that the BMD and muscle mass will be the lowest and body fat the highest in the OSO group. Specific Aim 2: To identify characteristic values for the condition of OSO by utilizing bioelectrical impedance analysis (BIA) and ultrasound measurements of quadriceps measures. a) BIA will be used as a technique to measure frailty by taking the reactance and resistance

values to calculate phase angle as a measure of tissue health using the following formula: phase angle = arc-tangent reactance/resistance x 180°/π (Barbosa-Silva et al., 2005). b) Ultrasound measurements of quadriceps muscle and subsequent analysis of echo intensity (reflecting possible fat infiltration) will be used to measure muscle quality. Hypothesis 2: We hypothesize that women identified with OSO will have greater frailty, lower phase angles and poorer muscle quality compared to OO and obese-only women. Specific Aim 3: To assess functional performance to better characterize the conditions of OSO, OO, SO and obese-only women. We will assess functional performance by measuring upper and lower body strength, locomotion, balance and endurance (Reuben et al, 2013; Ilich et al., 2016): a) Grip strength of both hands for upper body strength b) One-leg stance to assess balance in each leg c) 4-m timed normal and brisk walking tests for locomotion 3

d) 2-min walk test for endurance. e) Timed chair sit-to-stand to measure lower body strength and function. f) Leg extension to measure lower body strength and assess muscle quality. Hypothesis 3: Women identified with OSO will have significantly poorer outcomes in functional performance tests, compared to OO, SO and obese only women. Introduction With age, there are changes in body composition such as loss of bone, loss of muscle mass and strength, and increased body fat (Ilich 2014a). These age-related changes place older adults at increased risk of developing conditions such as osteoporosis, sarcopenia, obesity, or even a combination of several of these conditions (Ilich 2014a, Ilich et al., 2015; Morley et al., 2014a). Some combined conditions such as OO and SO, remain under-recognized. OO, a little- recognized condition of increased adiposity, either as an overt overweight or redistribution and infiltration of fat into bone, combined with bone loss, is often overlooked in clinical settings due to assumptions that obesity is protective of bone (Ilich et al., 2014a). Increasing research points to a negative relationship between adiposity and bone as people age, where increased inflammation from fat tissue may damage and even infiltrate bone, weakening it (Bredella et al., 2014; Scheller & Rosen 2014; Ilich et al., 2014a). Damage from age-related increase in adiposity may also impact individuals who are not clinically overweight as defined by body mass indices (BMI). Recent findings in women indicate a threshold as low as 33% and 38% of total body fat at which femoral neck and lumbar spine BMD, respectively, start to decline (Liu et al., 2014). Recent studies show a negative correlation between marrow adipose tissue (MAT), the fat tissue stored in bones, also known as yellow marrow, and BMD (Paccou et al., 2015; Bredella et al., 2014; Liu et al., 2010; Tang et al., 2010). This yellow marrow appears to increase after menopause and may play a role in osteoporotic fractures (Bredella et al., 2014; Tang et al., 2010). At present, there is still a lack of definitive research in the scientific community about the condition of OO and the full impact of obesity, inflammation and MAT on bone. Another poorly identified condition termed SO occurs when older adults suffer from increased adiposity (body fat amount, redistribution and/or infiltration into muscle) combined with sarcopenia (Baumgartner et al., 1998; Domiciano et al., 2013). To date, the medical and scientific communities have trouble identifying or treating SO. Although larger, obese adults 4 may have more lean mass and therefore not appear sarcopenic by traditional methods, however this level of musculature may not be adequate for their overall size and optimal physical function (Domiciano et al. 2013). Additionally, overweight patients with SO may be overlooked in the medical field, based on the assumption that they have adequate muscle mass and strength. These individuals may be at greater risk for falls and immobility than matched populations of adults with obesity or sarcopenia alone (Domiciano et al., 2013; Woo et al., 2015; Mijnarends et al., 2015). Some researchers now consider sarcopenia to be central in the development and treatment of frailty; this may be of particular concern to the obese since they are underdiagnosed (Morley et al., 2014b; Bischoff-Ferrari et al., 2015; Ilich et al., 2015). Even more, increasing evidence points to a growing number of older women who develop a combined condition of OO and SO, recently termed OSO (Ilich et al. 2014a). In this combined condition, increased body fat may promote damage to both bone and muscle tissues simultaneously, weakening the bone structure and accelerating the aging process and subsequent disabilities. A loss of strength appears particularly dangerous in someone with bone loss, as this increases the risk for falls, subsequent fractures, and long-term immobility (Binkley et al., 2013; Ilich et al., 2014a, Ilich et al., 2015). Rationale for the Chosen Demographics For this study, and based on previous research from our laboratory, our target population are postmenopausal, Caucasian women. The decision to focus on Caucasian women is that they are at higher risk for bone loss and fractures than other racial groups as well as on the need to control for confounding variables such as age, sex, and race that impact the endocrine system, bone, muscle mass, and body fat differently in different races/ethnicities (IOF racial differences in fracture risk, http://nof.org/articles/2). Postmenopausal women experience a marked decrease in hormones such as estrogen and even testosterone, which affect bone, muscle, and adipose tissues differently than young women or older men. Caucasian women are ~ four times more likely to develop osteoporosis than African American women and twice as likely as Latina women (International Osteoporosis Foundation: racial differences in fracture risk http://nof.org/articles/235#caucasian). Moreover, >90% of the residents in the retirement facilities where we have obtained support for recruitment/research are Caucasian. In view of all the above, choosing Caucasian women for this study was scientifically justified, as well as practically possible. 5

Assessing the OSO Syndrome in Older Women The standard method to assess osteopenia/osteoporosis is by obtaining T-scores of skeletal sites via DXA measurements of BMD (NIH Consens Statement, 2000). However, a combination condition, such as OO, characterized by bone loss and obesity, is still not well defined and lacks clear diagnostic criteria (Ilich et al. 2014a). Obesity rates are increasing, and evidence suggests that obesity is not as protective of bone as once thought. At the same time, more research needs to be completed on how increased body fat and osteoporosis interact in different individuals. The DXA would at least provide a means to measure the bone mass, lean mass, regional fat mass, and percent body fat in this population. There is still no universal diagnostic criteria for diagnosing sarcopenia and SO (Ilich et al., 2014a; Bonewald et al., 2013). Baumgartner proposed a method of identifying sarcopenia by dividing ALM (obtained via iDXA scans) by height in meters squared, establishing a cutoff of 5.45 and 7.26 kg/m2 for females and males respectively (Baumgartner et al., 1998). The European Working Group recently suggested handgrip strength and gait speed as criteria for sarcopenia (Cruz-Jentoft et al., 2010), however, their methods appear to require further validation as these criteria seem to overestimate sarcopenia in the population in comparison to other methods, so that those who did not have significant loss of lean mass or strength were also classified as sarcopenic (Lourenco et al., 2015). Those with SO may fall outside or above the range normally used to identify sarcopenia and might not be diagnosed in medical examinations (Ilich et al., 2015; Domiciano et al., 2013). Many older overweight/obese adults have increased muscle mass, putting them above the usual cutoffs, however, their actual muscle mass and strength may not be adequate for their overall body size (Domiciano et al., 2013; Ilich et al., 2015). For our previous analysis, we used a linear regression model proposed by Newman et al. (2003) to identify SO to find a more relevant measure of muscle loss in the overweight/obese population. High frequency ultrasound equipment is another method to measure aging muscle, muscle atrophy and even quality (Mourtzakis et al., 2014; Qin et al., 2014, Watanabe et al., 2013). An ultrasound probe can evaluate the quality of skeletal muscle, such as the quadriceps, by finding the echo intensity using a computer-aided gray scale analysis, such as Adobe Photoshop (Watanabe et al. 2013). Echo intensity is negatively associated with strength and age (Watanabe et al. 2013). 6

Bioelectrical impedance analysis (BIA) is another method that can measure body fat and lean mass (Lee et al., 2015; Zwierzchowska et al., 2014; Kasvis et al., 2014; Cruz-Jentoft 2010). The BIA is noninvasive, portable, quick and simple to use (Lee et al., 2015). In addition to calculating fat mass, percent body fat, and lean mass, the phase angle calculations can be made using the reactance and resistance measurements from the BIA. Phase angle is a measurement obtained from reactance and resistance and can indicate overall health of the tissue (Barbosa- Silva 2005). Because of its ability to predict body cell mass or changes and losses in body tissue with aging, phase angle has also been used as a nutritional indicator for those at risk of unintentional weight loss and frailty with age (Nagano et al. 2000). In addition, physical performance tests are very important in measuring frailty and assessing possible future risk of morbidity, as discussed in more detail in the literature review (Lindsey et al., 2007). In our previous analysis we used physical performance tests such as handgrip strength, timed chair sit-to-stand, normal/brisk walking, timed up-and-go test, and one-leg-stance to evaluate their relationship with bone/body composition parameters in postmenopausal women (Ilich et al., 2015; Shin et al., 2014; Lindsey et al., 2005). Again, the obese individual may have more bone mass or muscle, but this may not be adequate to support the overall increase in body size, nor protective of immobility and future fracture risk (Domiciano et al., 2013; Ilich et al., 2014a). As described in more detail in the Review of Literature, fat mass appears to enact long-term damage to bone and muscle, weakening these tissues. Therefore, physical performance measures should provide a means to confirm loss of function in older adults with OO, who are obese-only and those with OSO.

Summary Established research shows that there is risk associated with each described condition of osteopenia/osteoporosis, sarcopenia, and obesity. However, the combination seen in OSO syndrome, where older adults experience all three conditions simultaneously, may promote greater frailty, (Ilich et al., 2014a, Ilich et al., 2015). The lack of diagnostic criteria and demographic data for OSO, or even OO and SO, prompted us to design and conduct this study.

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CHAPTER 2

LITERATURE REVIEW

The Aging Body: Changes in Body Composition with Age

The aging process uniquely influences physiology and body composition (Ilich et al. 2014a; Ilich 2015). Some of the bone loss, decreased muscle mass/strength and obesity can be attributed to sedentariness or reduced physical exercise. However, there are also countless physiological changes attributable solely to the aging process: reduced estrogen and testosterone, increases in visceral fat and pro-inflammatory markers, and altered metabolism which have a long-term impact on body composition (Ilich et al. 2014a; Ilich et al. 2014b). Recent findings even indicate that alterations in the gut bacteria of older adults have a profound connection with body composition (Inglis & Ilich 2015). What is the impact of aging and increased adiposity on bone? Sir Astley Cooper recognized the effects of aging on the skeleton in bone fractures as early as the 18th century (Singal et al., 2010). The term ‘osteoporosis’ was first used in 1833 by the French pathologist Jean M. Lobstein to identify a condition where older patients with porous bones developed fractures (Brand, 2011). By the 1940’s, Fuller Albright established that osteopenia/osteoporosis is a condition in postmenopausal women and is related to endocrinal and lifestyle factors (Reifenstein & Albright 2011). In recent years, researchers have evaluated the impact of obesity on bone, with differing conclusions. On the one hand, obesity appears to have a protective role, providing mechanical loading to bone growth and helping to maintain mass. Established research using DXA, computed tomography (CT) scans, ultrasound and blood markers of bone turnover point to a positive relationship between body weight and BMD (Mpalaris et al., 2015; Gonnelli et al., 2014). Increased fat mass leads to higher estrogen levels, a hormone that is protective against bone loss and fracture (Reifenstein & Albright 2011). In fact, adipose tissue becomes the sole source of estrogen in aging women (Reid, 2008; Ilich et al., 2014a; Belanger et al., 2002). Indeed, the fat-bone relationship appears stronger in women than in men, in the postmenopausal, and more strongly in sedentary populations than in those who exercise (Reid, 2008).

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However, there are other concerns regarding bone health and obesity. Beyond mechanical loading, adipose tissue also plays the role of an endocrine organ, releasing hormones (beyond estrogen) and inflammatory cytokines, which may play a role in bone health and/or bone loss (Ilich et al., 2014a). Older people often gain visceral fat, a type of adipose tissue lying deep within the abdominal cavity that promotes chronic low-grade inflammation and long-term morbidity (John Hopkins Medicine May 2012; Ilich et al., 2014b). Visceral fat has been shown to be a unique pathogenic fat depot that has a negative impact on bone health and strength (Gilsanz et al., 2009; Zhang et al., 2015). With increased visceral fat accumulation, circulating levels of inflammatory cytokines such as TNF-α, IL-1 IL-6, and C-reactive protein are higher, promoting a state of chronic inflammation (Sanz & Moya-Pérez, 2014; Pradhan et al., 2001; Liu et al., 2012). Mechanisms of chronic low-grade inflammation may at times cause and/or perpetuate both obesity and osteoporosis simultaneously, which may partially explain bone loss in the obese (Ilich et al., 2014b). Gilsanz et al. (2009) found that subcutaneous fat may be protective of bone in some individuals, while visceral fat was not and was actually detrimental to bone strength and structure (Gilsanz et al., 2009). Zhang et al. (2015) recently determined that increased BMI was negatively associated with bone mass and that visceral fat has a significant impact on bone loss (Zhang et al., 2015). Furthermore, findings indicate that increased overall body fat appears to promote visceral fat accumulation in aging adults. Looking at women, Bosch et al., identified a cutoff of 38.3% body fat as an inflection point where the slope of the relation between visceral fat and percent body fat increases significantly (Bosch et al., 2015). Weight gain in older adults leads to greater visceral fat accumulation as well, and possibly long-term bone loss as a consequence. Another aspect to adiposity and bone is the relationship of MAT to osteoporosis. Some suggest that MAT should be considered a biomarker for osteoporosis risk in older adults (Scheller & Rosen, 2014). MAT appears to reduce osteoblast and osteoclast activity, slowing bone turnover and decreasing the rate of bone accumulation or loss (Scheller & Rosen, 2014). Recent studies show a negative correlation between MAT and BMD (Paccou et al., 2015; Bredella et al., 2014; Liu et al., 2010; Tang et al., 2010). Still, there is lack of evidence as to whether this relationship with MAT and osteoporosis is correlative or causative (Scheller & Rosen, 2014).

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Obesity and sarcopenia, like osteoporosis, are also often thought to be mutually exclusive conditions. Increased body mass provides mechanical loading to stimulate muscle accrual (Kim & Choi, 2014). However, again, in the aging adult, obesity and the impact of visceral fat accumulation appears to be detrimental to muscle mass (Zhang et al., 2015). Looking at a prospective study by Kim et al. (2014), postmenopausal women with visceral fat lost significantly more lean mass over a 27-month period than those women with low visceral fat (Kim et al., 2014). This decrease in muscle did not result in a parallel change in BMI, as fat appeared to replace the lost lean mass, possibly infiltrating it (Zhang et al., 2015; Ilich et al., 2014a). A change from muscle mass or bone to fat may have a causal relationship. As stated earlier, in the obese and those with visceral fat accumulation, circulating levels of pro- inflammatory markers TNF-α, IL-1, IL-6, and C-reactive protein are higher, signaling chronic inflammation (Ilich et al., 2014b, Sanz & Moya 2014; Pradhan et al., 2001; Liu et al., 2012; Saito et al., 2012). Multiple studies support a causal role for pro-inflammatory cytokines such as TNF-alpha and IL-6 in inflammatory muscle wasting (Subramaniam et al., 2015; Schaap et al., 2009). The inflammatory markers of TNF-alpha, IL-6, and C-reactive protein are elevated with sarcopenia and SO (Schaap et al., 2009; di Renzo et al., 2013; Mavros et al., 2014). What is more, muscle mass or lean mass is the main determinant of resting energy expenditure, and so logically, that loss of muscle would in turn also promote weight gain and fat accumulation (Hector et al., 2015). Hence, muscle loss and visceral fat accumulation appear to be part of a sort of vicious cycle where increased inflammation from visceral fat favors sarcopenia, and loss of lean mass promotes obesity and perhaps greater visceral fat accumulation as well (Ilich et al., 2014b, Hector et al., 2015; Bosch et al., 2015). Another aspect of aging recently identified in older adults is myosteatosis, or fat infiltration into muscle. As skeletal muscle ages, muscle fibers are less able to process triglycerides, resulting in increased storage of lipid droplets along the muscle cell membrane (Lang et al., 2010). Myosteatosis is seen in older women, even if they do not appear clinically obese. This myosteatosis, combined with muscle loss, contributes to age-related loss of muscle strength and function (Lang et al., 2010; Visser et al., 2003; Domiciano et al., 2013). Loss of lower extremity performance puts the older adult at increased risk of loss of mobility, falls, and even bone fractures. Furthermore, myosteatosis may lead to developing disorders such as 10 diabetes that also increase the risk of falls secondary to vision loss and/or limb pain (Lang et al., 2010). This may help explain the increased risk of frailty in older adults with SO (Domiciano et al., 2013). Besides estrogen, a number of other key hormones/cytokines affect body composition and the OSO syndrome. The classic adipocyte hormone is leptin (Reid, 2008; Abenavoli & Peta 2014). This member of the cytokine family has structural similarities to IL-6, stimulating the pro-inflammatory action of IL-6, IL-12, and TNF-alpha (Pires et al., 2014; Abenavoli & Peta 2014). Leptin is higher in women and higher in obesity (Pires et al., 2014; Abenavoli & Peta 2014). Obesity can lead to leptin resistance, hyperleptinemia and eventual lower bone mass; leptin appears to activate pro-inflammatory pathways in osteoblasts (Upadhyay et al., 2015). Conversely, decreased serum leptin is regarded as a marker for sarcopenia (Hubbard et al., 2008). However, the hyperleptinemia in OSO may mask this, leading to patients with OSO to be overlooked in clinical settings (Hubbard et al., 2008, Ilich et al., 2014a). Fat cells also release adiponectin, which is an adipokine or cytokine also produced by bone-forming cells, muscle cells and is even expressed in the brain (Lee & Shao 2012). Adiponectin levels increase with aging, are lower in overweight/obese people but higher in weight loss (Ambroszkiewicz et al., 2015; Ilich et al., 2014a; Lewerin et al., 2014). Low adiponection levels or hypoadiponectinemia (<4 μg/mL), are associated with osteoporosis (Kishida et al. 2014), however, adiponectin’s overall impact on bone is controversial. Some studies show that adiponectin negatively regulates bone mass by decreasing osteoblast proliferation possibly via mediation of the RANK/RANKL/OPG axis [receptor activated nuclear factor-k (RANK), RANK ligand (RANKL) and osteoprotegerin (OPG) (Lewerin et al., 2014; Luo et al., 2006). Other studies show that through the same RANK/RANKL/OPG axis, adiponectin may inhibit osteoclastogenesis and increase osteoblastic activity and mRNA expression (Oshima et al., 2005; Williams et al., 2009). Adiponectin is involved in metabolism and may inhibit obesity by increasing fatty acid oxidation in adipose tissue by activation of AMPK phosphorylation and in muscle by AMPK, p38 MAPK (mitogen activated protein kinase) and PPAR (Ghoshal & Bhattacharyya 2015, Lecke et al., 2011; Yoon et al., 2006). Furthermore, adiponectin has anti-inflammatory effects inhibiting the action of TNF-alpha and IL-8 (Ghoshal & Bhattacharyya, 2015). To further connect adiposity, inflammation and adiponection, hypoadiponectinemia (<4 μg/mL) is 11 positively associated with visceral fat and obesity-related diseases (Kishida et al. 2014). These combined effects of adiponectin appear to favor lean mass and possibly promote decreased body fat accumulation. Predictably, adiponectin levels would be lower in OSO women, due to increased fat mass and decreased lean mass. However, adiponectin also increases with age, which may mask its decline in OSO women. Recent research indicates that skeletal muscle specific troponin (sTnT) is an important biomarker in identifying sarcopenia. Serum sTnT drops proportionally with improvements in handgrip strength and overall physical fitness in older adults (Abreu et al., 2014; Kalinkovitch & Livshits, 2015). Troponins do not leak into the blood under normal conditions, being normally undetectable, but are elevated during sarcopenia and cachexia (Kalinkovitch & Livshits, 2015). Therefore, sTnT is now considered an emerging biomarker for sarcopenia (Kalinkovitch & Livshits, 2015). Vitamin D, a hormone, has a relationship to bone and obesity in older adults, even to lean mass. Although, scientists are not completely certain of the mechanism, there is a strong relationship between vitamin D deficiency and increasing levels of obesity (Pereira-Santos et al., 2015). Even when controlling for sunlight exposure, obese individuals are significantly more likely to be vitamin D deficient (Cheng et al., 2010; Pereira-Santos et al., 2015). Vitamin D is essential for bone growth, maintenance and metabolism of the minerals calcium, phosphorous, and magnesium (Pereira-Santos et al., 2015; Marquardt et al., 2015). A decrease in this vitamin could disturb calcium homeostasis and bone health. Ironically, vitamin D deficiency has also recently been associated with sarcopenia and decreased grip strength in older adults (Tieland et al., 2013, Lee et al., 2013; Visser et al., 2003). Considering that deficiency is seen in osteoporosis, sarcopenia and obesity, vitamin D status might be a key factor in assessing OSO syndrome. In addition to low vitamin D status, recent studies show that vitamin D status and parathyroid hormone (PTH) levels together are associated with muscle mass and strength as well as with physical function (Tieland et al., 2013; Lee et al., 2013). A study in the Netherlands investigating older women and men showed that low vitamin D and high PTH increased the risk of sarcopenia, as reflected in lower muscle mass and grip strength (Visser et al., 2003). Tying this to bone, another study has found that patients with insufficient vitamin D levels and low BMD are also more likely to develop sarcopenia (Lee et al., 2013). 12

Ever more research is connecting bone, muscle and fat tissues. Chung et al. (2016) found that older adults with SO had greater risk of osteoporosis, as the physical decline from SO appeared to promote greater loss of bone (Chung et al., 2016). Other studies have also found low muscle mass to have a negative impact on BMD, especially in the femoral neck (Orsatti et al., 2011, He et al., 2015). As stated earlier, adipose tissue perpetuates inflammation and can infiltrate into both bone and muscle compromising their integrity and function. Overall the physical decline/lack of physical function from any of these three conditions, OO, SO and/or obesity could potentially aggravate the other two conditions. Someone with impaired mobility from SO, for example, may go on to suffer greater bone loss and fractures (Drey et al., 2015; Chung et al., 2016; Ilich et al., 2015). Taken together, overall body composition, physical performance and corresponding biomarkers should be evaluated when assessing the OSO syndrome in older adults (Ilich 2014a; Binkley 2013).

Possible Methods to Identify the OSO Syndrome Bone The most accurate or ‘gold standard’ method for measuring BMD as a proxy of potential fracture risk in postmenopausal women is DXA (NIH Consens Statement, 2000; Oh et al., 2013). The current method of diagnosing osteopenia/osteoporosis is DXA scans to determine bone mass and a T-score (two standard deviations below a young healthy population of the same race and gender). Additionally, bone fragility may be assessed using a portable and newly developed instrument called the OsteoProbe. This device is utilized for analysis of bone strength and quality. Technology for the OsteoProbe is based on reference point micro-indentation measurements to determine the bone material’s ability to resist separation of mineralized collagen fibrils. The output is a bone material strength index (BMSi) – a ratio of the indentation distance in bone vs. reference material (Randall et al., 2013; Farr et al., 2014). This measurement of micro-indentation enables further identification of bone fragility independent of BMD (Appelman-dijkstra, 2014). These two methods, the DXA and the OsteoProbe, provide a way to determine osteopenia/osteoporosis from BMD T-scores and BMSi. However, recent research indicates that DXA measurements may be less accurate in overweight/obese individuals and during weight changes (Yu et al., 2012). In addition, women with OO may be overlooked based on the assumption that obesity is protective of decreased 13 bone mass, as discussed earlier (Ilich et al., 2014a; Ilich et al., 2015). There is a stubborn belief in the medical field that overweight women do not have significant risk of fractures. Still, there are a number of researchers who are starting to challenge the assumption that bone loss alone explains the exponential increase in fractures that come with age (Ilich et al., 2015, Binkley et al., 2013). There is evidence that overweight women are impacted by loss of bone strength and quality, and do experience fractures as they age. As discussed earlier, MAT may be a biomarker for osteoporosis (Bredella et al., 2014, Scheller & Rosen, 2014). Currently, magnetic resonance imaging (MRI) is the best technique to assess MAT, although costly (Hanrahan & Shah, 2011; Liu et al., 2010). At this time, MRI is the only clinical imaging technique that allows direct visualization of bone marrow with high spatial resolution (Hanrahan & Shah 2011; Liu et al., 2010). Muscle Sarcopenia and SO are both only weakly defined and do not have universal diagnostic guidelines the way that osteoporosis does (Ilich et al., 2014a; Bonewald et al., 2013). SO can be identified by measuring lean mass and fat mass through DXA and then running a linear regression model, via a statistical program such as JMP or SAS, to compare a participant’s lean mass to the participant’s height and fat mass. Those with lean mass that falls below the expected value for their height and level of body fat could be identified as having SO (Newman et al., 2003; Domiciano et al., 2013). The concept with the obese being, that although they have more lean mass, it may not be sufficient to protect them from frailty, falls, and even mortality (Domiciano et al., 2013; Ilich et al., 2014a). The DXA can be problematic for assessing sarcopenia. The DXA measures lean mass, but this encompasses more than just muscle mass alone, and therefore, DXA may underestimate sarcopenia. Researchers often only use the appendicular mass measurements, but even this may not always be completely accurate (Newman et al., 2003). Often a second instrument, such as the BIA is useful to give further measures of lean mass (Lee et al., 2015; Zwierzchowska et al. 2014; Kasvis et al., 2014). Some studies show that the BIA may overestimate body fat percentage in lean participants, and underestimate body fat percentage in those who are overweight (Kasvis et al., 2014). MRI is considered more accurate in measuring muscle mass. However, MRI is very costly (Pahor et al., 2009). This machine accurately measures myofiber cross-sectional areas and is able to detect small differences in muscle hypertrophy or atrophy 14

(Bellamy et al., 2014; Segal et al., 2014). The MRI gives a more comprehensive assessment of muscle mass than DXA, emits no radiation and can even detect muscle steatosis or fat infiltration into muscle (Long et al., 2010). High frequency ultrasound equipment precisely measures muscle mass, skeletal muscle length and density, and can even track muscle atrophy (Mourtzakis et al., 2014; Qin et al., 2014). In addition, ultrasound uses no ionizing radiation and is less costly than clinically comparable methods such as the MRI. Finding the echo intensity from ultrasound scans is also useful in identifying myosteatosis and the aging muscle. Echo intensity is negatively correlated with muscle strength, muscle thickness and the participant’s age (Watanabe et al., 2013). An ultrasound probe can evaluate the quality of skeletal muscle, such as the quadriceps, by finding the echo intensity using a computer-aided gray scale analysis, such as Adobe Photoshop (Watanabe et al., 2013). Sarcopenia can also be assessed based on a formula proposed by Baumgartner et al. (1998) where a patient’s ALM from DXA is divided by his or her height in meters squared. Baumgartner’s method establishes a cutoff for ALM/ht2 that is two standard deviations below the reference mean of a young healthy population to determine cut-off points for sarcopenia, 5.45 kg/m2 for women and 7.26 kg/m2 for men (Baumgartner et al., 1998). The European Working Group, on the other hand, suggests using reduced ALM, and declining handgrip strength at a cutoff value of ≤20 kg for women and ≤30 kg for men on the dominant hand, and slowed gait speed with a cutoff of 0.8 m/s or less (Cruz-Jentoft et al., 2010; Lourenco et al., 2015). The methodology proposed by the European Working Group has recently been brought into question, as these methods appear to overestimate sarcopenia in older adults (Lourenço et al., 2015). However, these assessment tools may underestimate sarcopenia in overweight/obese individuals (Domiciano et al., 2013). Absolute lean mass (Newman et al., 2003) and bone mass are often higher in the obese. The overweight/obese may have greater grip strength simply due to increased mass. Despite this, obese individuals may not have adequate lean mass and/or bone mass to support their weight during illness or just to maintain activities of daily living and may suffer limited functional ability (Newman et al., 2003). This loss of function could increase risk for falls and/or bone fractures. Again, like OO, SO may be overlooked in the clinical setting. Newman et al. (2003) proposed using a linear regression model to assess sarcopenia in 15 overweight individuals. In this model, Newman compares the patients’ ALM (kg) to both their height (m) and their fat mass (kg). SO could then be determined by those whose lean mass falls below the ‘line’ or expected value for their height and fat mass. This model has been used to assess SO by other researchers as well (Domiciano et al., 2013; Oliveira et al., 2011). Obesity

Obesity assessments in the clinical setting are often based on BMI however, this may not accurately reflect the impact of fat mass on muscle, bone, and physical function. Additionally, inadequacy of BMI for the classification of individuals into normal-weight, overweight, and/or obese category, has been addressed previously (Ehrampoush et al., 2016). Some individuals who are particularly muscular have been categorized as obese, implying that they erroneously have extra body fat, while among older women the impact of visceral fat and fat infiltration into bone and muscle may be overlooked. There are concerns that body fat increases independently of BMI with age, especially in women (BMI: Considerations for Practitioners. https://www.cdc.gov/obesity/downloads/BMIforPactitioners.pdf). BMI appears to be inaccurate in detecting age-related accumulation of fat in older adults, and inaccurate in quantifying visceral fat in the abdominal area (Ehrampoush et al., 2016; BMI: Considerations for Practitioners. https://www.cdc.gov/obesity/downloads/BMIforPactitioners.pdf). Older individuals with a normal body weight may develop health problems related to increased body fat or adiposity, especially if they have visceral fat accumulation, bone loss and/or loss of muscle. As stated earlier, greater MAT is associated with impaired skeletal health and may be a biomarker for osteoporosis, increasing with estrogen deficiency and reduced mechanical unloading (Kim &Schafer 2016; Hamrick et al. 2016; Bredella et al., 2014, Scheller & Rosen, 2014). Myosteatosis, or fatty infiltration of skeletal muscle increases with age, disuse, sex steroid deficiency, similarly to bone MAT accumulation, and is associated with loss of strength, increased bone fractures and mortality in the elderly (Hamrick et al., 2016). In older women especially, it would be more appropriate to assess percent body fat using the DXA (Shea et al., 2012). BIA also measures body fat percentage and is considered comparable to the DXA by some studies, but less accurate in measuring body fat in others (Zwierzchowska et al., 2014; Kasvis et al., 2014). At the same time, there is still no consensus as to what level of body fat defines obesity in women. In recent estimates from the Obesity Medicine Association, some

16 researchers and fitness professionals put the level of body fat as low as 32% as the cutoff for obesity in women (Ace Fitness, Fit Life 2009; OMA, Obesity Algorithm 2015-2016; Wanner et al., 2016).

Functional Performance

Decreased physical function, as has just recently been detected in OSO (Ilich et al. 2015), can indicate an increased risk for morbidity, disability, and a need for long-term care (Ilich et al. 2015; Morley et al., 2014a). Therefore, physical abilities such as balance, endurance, and strength should be determined to gauge one’s risk (Woo et al., 2015; Cruz-Jentoft et al., 2010). The NIH Toolbox physical performance tests are simple and not over-burdensome to older women (Reuben et al., 2013; Ilich et al., 2015). These include: grip strength of both hands for upper body strength; knee isometric strength for both legs and sit-to-stand for lower body strength; 4-m timed normal and brisk walk tests for locomotion; and a 2-minute walk test for endurance. In addition to the Toolbox, a test such as the one-leg stance is a clear measure of balance (Fong et al., 2016).

Handgrip strength is a powerful predictor of functional abilities in older adults and its decline is associated with increased risk of falls and disability (Savino et al., 2013; Cruz-Jentoft et al., 2010), as well as lower BMD (Lindsey et al., 2005; Shin et al., 2014; Kim et al., 2012). A cutoff for handgrip for women and sarcopenia has been suggested at ≤20 kg (Lourenco et al., 2015). Additionally, a strong relationship was identified between handgrip strength and bone at all forearm BMD sites (Shin et al., 2014; Lindsey et al., 2005). The sit-to-stand test measures the patient’s dynamic balance and range of motion in the lower extremities (Nevitt et al., 2003; Lord et al., 2002). The Centers for Disease Control and Prevention (CDC) established cut-off points for the 30-second sit-to-stand to determine risk for falls based on age and gender (CDC 2016). Women ages 75-79 years who performed less than ten sit-to-stands in 30 seconds are classified as ‘below average’ (CDC 2016) for lower body strength. Walking speed is currently considered a ‘vital sign’ in older adults. A decrease in walking speed is viewed as a compelling predictor of functional decline and risk for the development of frailty (Lusardi et al., 2012; Theou et al., 2011; Viana et al., 2013). The European Working Group suggests a cutoff of ≤0.8 m/second regarding gait speed and sarcopenia (Lourenco et al., 2015). Higher body fat in the lower extremities appears to predict slower walking speeds for both normal and brisk walking, 17 whereas higher lean mass predicts faster walking speeds (Shin et al., 2014). Furthermore, BMD, particularly of the femoral neck, is positively correlated with normal and brisk gait speeds (Lindsey et al., 2005). The one-leg stance is a reliable assessment of physical and cognitive function and helps to identify older adults with possible balance deficits, lower BMD (Lindsey et al., 2005), and higher body fat (Shin et al., 2014), complementing other clinical tests (Carlsson & Rasmussen-Barr, 2013; da Silva et al., 2013). A recent study showed that an increase of 1% fat in the total body and gynoid regions decreased the chances of being able to stand with each leg for 30 seconds by 21.4% and 21.7%, respectively (Shin et al., 2014). These tests not only assess fall risk parameters but; are indicative of changes or deficits in body composition, and may signal the OSO syndrome. These findings indicate that factors impacting bone health are also tied to overall muscle strength and mass in older individuals and should all be evaluated when detecting OSO. Another possible indicator for poor health outcomes in older adults is phase angle, obtained from the reactance and resistance from the BIA measurements. Phase angle can be calculated from the arc of the tangent of the reactance and resistance ratio converted to degrees (Barbosa-Silva et al., 2005; Wilhelm-Leen et al., 2013). Reactance measures the capacitance properties of the cell membrane whereas the resistance relates to the opposition offered from the body to the flow of alternating electrical current from the BIA and is inversely related to the water and electrolyte content of tissue. Phase angle may be used to assess overall health of the tissue and be interpreted as an indicator of membrane integrity and water distribution between the intra and extracellular spaces (Barbosa-Silva et al. 2005). Additionally, because of its ability to predict the depletion of body cell mass (muscle tissue, organ tissue, intracellular and extracellular water, and bone tissue), phase angle may also indicate nutrition status in aging, sarcopenia and chronic disease such as HIV/AIDS (Nagano et al. 2000). Although there is currently no gold standard in estimating nutritional status with phase angle, one study found a strong correlation where phase angle values increased with body weight, arm muscle circumference and serum albumin in children and went down in critical illness in children (Nagano et al., 2000). Because larger individuals have more muscle and/or fat cells, they have larger phase angle values so that the latter is also positively associated with BMI (Barbosa-Silva et al, 2005). Logically, older adults, those with greater frailty and women have lower phase angle values than young people or men; thus, phase angle is negatively associated with age and 18 illnesses (Barbosa-Silva et al. 2005; Wilhelm-Leen et al., 2014). Wilhelm-Leen et al. (2014) recently developed some prognostic criteria and cutoffs for phase angle values in both women and men and individuals with certain morbidities (Wilhelm et al., 2014). Possible Interventions for Preventing and Treating Osteosarcopenic Obesity Established interventions for treating osteopenia, muscle loss and obesity in older women, point to a high-protein diet (1.0-1.2 grams protein/kg), and possible supplementation with a variety of micronutrients including calcium, vitamin D, as well as water-soluble vitamins (Kelly et al., 2016a; Mithal et al., 2013; Ilich & Kerstetter 2000; Walsh et al., 2006). Resistance exercise and aerobic exercise also aid in building bone and muscle mass and promote both lower levels of body fat and weight maintenance (Panton et al., 2009; Campanha-Verisiani et al., 2017). Both bone and muscle tissues are maintained or increased by mechanical loading (Hamrick et al., 2016). Nutrition and exercise are the most important and vital components in promoting healthy bone, muscle and adipose tissue health, in general, but particularly in women as they age. There are several nutrients that have been investigated. A high-protein diet addresses muscle wasting in older people and aids in appetite control and weight loss (Arciero et al., 2006). In addition, older adults with higher protein intake appear to have lower osteoporotic fracture risk (Langsetmo et al., 2016). According to some research, soy protein may be particularly protective of bone in postmenopausal women, especially for those who are not on hormone replacement therapy (Arjmandi et al., 2003). Many older women (particularly in the US) take calcium and vitamin D supplements. There is a positive relationship between calcium intake, BMD and bone quality and calcium supplementation is considered an effective strategy in preventing/treating osteoporosis (Compston et al., 2013; Kim et al., 2015). Calcium is important for both bone and muscle and for controlling fat mass accrual, as well as in weight management/control (Skowronska-Jozwiak et al., 2016; Kelly et al., 2016a). Vitamin D has a well-known role in bone metabolism and low vitamin D status is associated with osteoporosis and fractures (Kim et al., 2015). Vitamin D is positively correlated with osteocalcin, a bone formation marker; it up-regulates osteocalcin and increases its expression and production (Holick, 1999). Activation of the vitamin D receptor in skeletal muscle appears to stimulate muscle synthesis and prevent muscle fiber atropy so that low vitamin D status is also associated with sarcopenia and frailty, in addition to osteoporosis (Tieland et al., 2013; Lee et al., 2013). A 19 study in the Netherlands investigating older adults, showed that low vitamin D and high PTH increased the risk of sarcopenia, reflected in lower muscle mass and grip strength (Visser et al., 2003). Relating this to bone, some studies have found that patients with insufficient vitamin D levels and low BMD are also more likely to develop sarcopenia (Lee et al., 2013; Tanaka et al., 2014; Gunton et al., 2015). Consequently, vitamin D deficiency is higher in obese individuals, possibly putting them at risk of other health complications related to vitamin D deficiency with aging (Cheng et al., 2010; Pereira-Santos et al., 2015). Vitamin D deficiency might contribute to OSO in older women, as recently analyzed by Kelly et al. (Kelly et al., 2016a). Other micronutrients are also tied to bone, muscle and fat mass. Decreased levels of vitamins B6, folate and B12 are associated with bone loss, as recently analyzed by examining NHINES data (Kelly et al. 2016a). Vitamins B6 and B12 work together to lower homocysteine levels, and higher homocysteine is associated with increased fracture risk (Kelly et al., 2016a; McLean et al., 2008). Adequate vitamin C levels are associated with decreased fracture risk and higher bone mass (Kelly et al., 2016a; Zhu et al., 2012). Magnesium is necessary for calcium metabolism and plays a key role in bone health. Magnesium deficiencies are associated with increased inflammation (Kelly et al., 2016a). Many older adults in the Western World are deficient in vitamin B6, D and magnesium (Kelly et al., 2016a; Chawla et al., 2014). The elderly population often have reduced intake or loss of appetite due to side effects from medications or suffer from conditions such as dementia and Alzheimer’s where they may even forget to eat (Johansson et al., 2015; Roque et al., 2013; Inglis & Ilich 2015). They are at an increased risk of low protein, vitamin, mineral and fiber intake (Kelly et al., 2016a; Kelly et al. 2016b). Many replace more nutrient-dense foods with processed carbohydrates that promote obesity (Kelly et al., 2016b). Furthermore, many older women may not be comfortable with exercise, have a history of not exercising and feel particularly uncomfortable with resistance exercise (weight lifting) (Smith et al., 2015; Cox et al., 2013). Therefore, the challenges of addressing adequate nutrition and exercise habits in this population may prove harder than initially thought. Therefore, older adults would possibly benefit from nutrition counseling and physical training that involves resistance exercise (Kim et al., 2015). In conclusion, preserving bone mass, muscle mass and reducing fat accumulation in older adults involves a multifaceted approach involving sound nutritional guidelines as well as lifestyle approaches involving exercise. 20

Concluding Thoughts Based on the discussions above, OSO syndrome is a complex condition, with concomitant changes in bone, muscle and adipose tissue with aging or some other chronic conditions. Therefore, it should be considered as such and given up most attention for its diagnosis, prevention and management. Increased adiposity increases inflammation, which in turn influences muscle and bone health, perpetuating obesity (Ilich et al., 2014b). Damage to or decline of one tissue could signal changes in the other. Therefore, the easiest way is for clinicians first to assess functional decline through physical performance tests, then assess bone markers and inflammatory markers, and finally assess bone and muscle mass. Many questions still remain unanswered and need more investigation: Do women with OSO present with higher levels of chronic inflammation than women with obesity or sarcopenia alone? Is osteopenia/osteoporosis or sarcopenia a sign of fat infiltration into bone and muscle, respectively? Does chronic inflammation exacerbate bone loss, worsen sarcopenia, or lead to lower functionality? Could reduction in obesity improve bone health and muscle mass and strength in older people? Our previous investigations have found that women with OSO may be at greater functional decline and at higher risk for falls and fractures beyond the normal risk level of their peers with osteopenia/osteoporosis or sarcopenia (Ilich et al., 2015; Cruz-Jentoft et al., 2010). Therefore, research into OSO syndrome should evaluate not only the level of frailty in older adults but also the overall interconnecting links between bone loss, muscle loss and obesity (Ilich et al., 2014a). In view of all the research and new concepts presented above, the conduction and subsequent findings of this study are seminal and absolutely crucial in moving this field forward and in changing the established paradigms about bone, muscle and fat tissues.

21

CHAPTER 3

METHODS

Research Strategy

The overall objective of this study was three-fold: 1) to establish diagnostic cutoffs for women with OSO from the collected data; 2) to assess functional performance, in women with OSO; and 3) to identify the prevalence of OSO in the recruited population based on the collected data. Study Design The study design was cross-sectional in nature. Bone density, body composition (lean and fat mass), tissue health, muscle quality, and physical performance (function and strength) were measured in a population of postmenopausal (≥ 65 years) Caucasian women. The study commenced after approval by the Florida State University (FSU) Institutional Review Board (IRB; Appendix A). All women signed the informed consent (Appendix B). All measurements were performed at the initial visit lasting ~2-3 hours, with the exception of the ultrasound scan. Some participants came back for a second visit to complete the ultrasound scan. Participants and Recruitment After the study was approved by the FSU IRB, 60 older Caucasian women (≥ 65 years) from retirement communities and long-term care facilities, as well as those living independently in the Leon County area were recruited over a two-year period. The participating facilities were Cherry Laurel Independent Senior Living Community and Westminster Oaks, both from Tallahassee, FL, as well as the Tallahassee Senior Center. For the recruitment process, we prepared a brief PowerPoint presentation for some of these facilities, distributed flyers and brochures, and collected screening information from potential participants. The women recruited were all ambulatory, able to visit the FSU laboratory facilities and complete the entire study. Exclusion criteria: Participants were required to be free from chronic diseases such as uncontrolled hypertension, uncontrolled diabetes, active cancer, autoimmune disease, and cognitive impairment. These conditions could put the participant at particular risk while taking part in the study and/or impact body composition. Participants were also excluded if on

22 hormone replacement therapy or any other medication known to affect bone metabolism, body composition, or the endocrine system three months prior to enrolling in the study. Calcium, vitamin D, and/or multivitamin intake were permitted but assessed during the study. Other criteria that would exclude a potential participant included smoking more than one pack of cigarettes/day and alcohol consumption of more than one drink/day with one drink defined as 12 oz beer, 5 oz wine, and/or 1.5 oz 80-proof distilled spirits/hard liquor (DGA. Educational Toolkit on Beverage Alcohol Consumption: 2010; Ilich et al., 2002). Measurements and Questionnaires All participants came to the laboratory in the early morning, after at least an eight-hour fast, and from refraining from physical activity for 24 hours for the DXA and BIA measurements. For the DXA scan, participants were advised not to wear anything that had metal or metal jewelry and to wear comfortable clothing and walking shoes for the physical performance tests. They were encouraged to drink plenty of water and bring a snack to break their fast after the DXA scan and BIA measurements. Questionnaires (Appendices C-K): The demographic survey, the mini-mental state examination to screen for mental deficits, and other forms on health and wellness were completed in the laboratory shortly after participants signed the informed consent. Anthropometric measures: Weight was measured with participants in their bare feet, using a Seca scale (Seca, Germany). Height was measured with participants standing against the wall, heels together and barefoot, using a Seca wall stadiometer (Seca, Germany). These measures provided data for the BMI (kg/m2) calculation. Waist, hip and abdominal circumferences were measured using a Gulick tape measure (Fabrication Enterprises, Elmsford, NY) by the trained researcher. With the participant standing, arms at sides, feet together, and abdomen relaxed, a horizontal waist measurement was taken at the narrowest part of the torso (above the umbilicus and below the xiphoid process; 1-2 cm below the last rib) on an exhale. The abdomen was measured next at the height of the iliac crest and the hip measurement was measured at the maximal circumference of the buttocks with feet together (Williams & Wilkins, 2014). If the two measurements were not within 5 mm a third measurement was taken (Williams & Wilkins, 2014). Waist-to-hip ratio was also calculated. Blood pressure was measured on the participant’s non-dominant arm in a sitting position with an automated blood pressure machine (HEM-907XL, Omron Healthcare, Inc., Bannockburn, 23

IL). Blood pressure was taken three times within the same morning that the participant was in the laboratory, and the average of the three measures calculated. The purpose of measuring blood pressure was to confirm that blood pressure was controlled in all participants. The participant was excluded from the study if her blood pressure was greater than >160/100 mmHg. Bone mineral density (BMD) of total body, lumbar spine, right and left femurs and left forearm (non-dominant) was measured using total body and specialized regional software on the LUNAR (GE Medical Systems, Madison WI) densitometer (iDXA). The iDXA provided an analysis of body composition, including the T-score for the regions described above indicating osteopenia/osteoporosis and BMD. The participants reclined in a supine position on the DXA machine for all scans. Total and ALM, fat mass and percentages were measured using iDXA. The ALM was assessed by combining lean mass (kg) from the arms and legs. The iDXA also evaluated total percent body fat, percent body fat per region, total fat mass (kg) and analyses of body fat and lean tissue in android and gynoid regions. Bioelectrical Impedance Analysis (BIA; Biodynamics Model 310e, Seattle, Washington) was used to estimate phase angle. This device generates a current, which passes through the participant’s body. The dorsal side of the right hand and the dorsal side of the right foot were wiped gently with an alcohol wipe. Lying in a supine position with right arm abducted 15º from body, and legs comfortably separated, a black current-injection electrode was placed on the dorsal surface of the right hand proximal to the first and second metacarpal phalangeal (approximately ½ inch above the knuckle line toward the middle of the hand of pointer finger) joints (Biodynamics, 2015; CDC 2000). The corresponding red voltage-detector electrode was placed on the mid-line between the prominent ends of the right radius and ulna of the wrist (closest to the heart). The sock was removed from the participant’s foot and a black current- injection electrode was placed on the dorsal surface of the right foot proximal to the first and second metatarsal phalangeal joints. The corresponding red voltage-detector electrode was placed on the mid-line between the prominent ends of the medial and lateral malleoli of the right ankle (closest to the heart) (Biodynamics 2015; CDC 2000). The phase angle was calculated using data from the BIA, by finding the arc tangent of the reactance to resistance ratio using the following formula: arc-tangent reactance/resistance x 180°/π. This measure is referred to as another measure of overall tissue health (Barbosa-Silva 2005). 24

Ultrasound measurements: Muscle quality was examined by finding the echo intensity of the quadriceps muscle with ultrasound and analyzed with computer-aided grayscale via Adobe Photoshop (Adobe Systems, San Jose, CA, USA). The ultrasound machine used for this study was located in the diagnostic laboratory, at the FSU Health Center. A Toshiba model Aplio 300 model TUS-A300 and a PLT-805AT model probe (Toshiba, Tokyo, Japan) was used for the measurement of muscle quality. With the participant standing, completely relaxed in shorts or hospital gown, the midpoint of the thigh was identified as being between the anterior superior iliac spine and lateral epicondyle of the femur. This midpoint was identified laterally on the anterior thigh, and marked with an ink pen. Ultrasound transmission gel was applied to the scanning head of the probe before placement of the participant’s body. The probe was then placed perpendicular to the longitudinal axis of the rectus femoris on the midpoint to take the scan or image. This image, a cross section of the mid-point of the quadriceps was saved to a CD. From the CD a part of the lean mass of the upper thigh including rectus femoris and part of the vastus intermedius, was analyzed to find the echo intensity or mean pixel intensity, as part of a grayscale analysis using the histogram function in the Adobe Photoshop. Echo intensity was compared at the end of the study to evaluate correlations with physical performance, and other measures of muscle quality (knee extension 1RM divided by lean leg mass) and body composition. Physical Performance Tests All women participated in physical performance tests, on the same day but after anthropometric, bone and body composition measurements were taken. All physical performance tests were performed at the end, after participants had broken their fast. The tests included: a. The 4-m timed normal and brisk walking tests were completed to measure locomotion. The walking tests were performed first to allow the muscles to warm up. The participants performed all tests with comfortable clothing and walking shoes. The starting place was marked with duct tape, then four meters was measured out and this place was also marked with duct tape to indicate the end. The participants performed the normal walking test first, walking the marked four meters at a normal walking pace. Next they walked this distance again at a ‘brisk’ pace as ‘if they were about to miss the bus’ for the brisk walking test. They repeated these tests twice, and the best score was used for analysis. 25 b. The 2-minute (50 ft) walking test was completed to measure endurance. A fifty-foot course was measured out in the Sandels Building. The beginning of the course was marked with duct tape, fifty feet was measured and marked at the end with an orange cone. The participants were asked to walk the course, going to the orange cone and back, making laps, until the end of two minutes. The researcher stopped the timer at exactly two minutes, recording the exact number of feet and inches the participants covered in the two-minute time interval, each completed lap being one hundred feet. c. The arm flexion and grip strength of both hands were performed to assess upper body strength. For arm flexion, the participants performed forearm curls while sitting in an armless chair, lifting a five-pound dumbbell by flexing their arm at the elbow. The number of repetitions the participant performed in a 30-second period was recorded. For the handgrip test, the participants extended their arm at 45°, holding the hand dynamometer (Lafayette 78010 Dynamometer, Lafayette, IN), and on exhaling squeezed the hand dynamometer with maximum force. The researcher recorded the value in kg. The handgrip test was repeated twice on each hand and recorded. In accordance with recent findings by the European Working Group the maximal measured grip strength was used as the participant’s grip strength in the final analyses (Bahat et al., 2016). d. The one-leg stance (30 seconds maximum) was used for balance assessment. For one-leg stance, participants were asked to stand on one leg for up to 30 seconds while lifting their contralateral limb (opposite foot not touching the ground). The test was performed on both the right and left legs. The test stopped when the participant touched any surface, lowered the contralateral limb to the ground, or at the end of the 30 seconds. This test was repeated twice and the highest scores were used for analyses. e. The timed chair sit-to-rise was performed to measure lower body strength. The participants were asked to cross their arms over their chest with feet flat on the floor and sit down and rise repeatedly from an armless chair. The number of consecutive chair sit-to- stands were recorded in a 30-second period, with the last time the participant sat down in the chair being the final count. f. The knee extension was performed to measure lower body strength and to assess muscle quality. Knee extension was determined for both legs using measurements from a resistance machine near the end of the study after the participants had warmed up from the walking 26

test (MedX Inc., Ocala, FL, USA). For the knee extension, participants extended both legs while seated in the leg extension machine. They pressed their back firmly against the back of the chair and maintained legs firmly in position. After warming up, they progressed towards the maximum weight that they could lift one time through a full range of motion to achieve a one-repetition maximum (1RM) as outlined by the American College of Sports Medicine (ACSM 2016). The participants were asked to rest for one to three minutes before increasing the weight and completing another repetition. This 1RM from the knee extension was later divided by leg lean mass (Woods et al. 2011; Brooks et al., 2006) as a further measure of muscle quality. Data Analysis/Statistical Methods Sarcopenia was identified from the skeletal muscle mass index (SMI) of ≤ 5.45 kg/m2 for women (Baumgartner et al. 1998). This value was utilized to identify SO based on the Newman et al. (2003) equation, derived from the negative residuals of the linear regression model in which appendicular muscle mass is adjusted for fat mass and height (Domiciano et al. 2013; Newman 2003). Linear regression, for negative residuals, to determine SO was later analyzed via JMP (SAS). Those with negative residuals below the 20th percentile for the group were classified as SO. All participants with 32% body fat or higher were placed into a separate table. Using the linear regression model, those participants below the 20th percentile for ALM based on height and fat mass were placed into a separate table for SO. Among these two tables, participants with femoral neck T-scores or lumbar spine T-scores ≤ -1, were classified as either OO, or if they also had SO, as OSO. Data analyses were performed via standard statistical and graphical methods using the Statistical Analysis Software (version 9.4; 2013, SAS Institute Inc., Cary, North Carolina), with the consultation from the Statistical Department. Descriptive statistics were calculated for all variables and comparisons among the four groups of participants were calculated in SAS 9.4 (Cary, NC 2013) using the ANCOVA (analysis of covariance) model with an LSMEANS and Tukey test. This model allowed the comparison among the four groups of women, as well as the comparisons among different variables at the same time. Some of the data did not follow a normal distribution, due to the small sample size, so each variable was also tested with the Wilcoxon t-test, a non-parametric test, also in SAS 9.4. Linear regression models were run in JMP (SAS) to further examine the data. For all analyses, p <0.05 was considered statistically significant. 27

Timeline for the study Month 1 Month 2-19 Month 6-24 Months 20-24 - Study approvals - Protocols finalized -Recruitment - Statistical Analyses - Recruitment starts -1st Laboratory visit: - Abstracts/Publications Anthropometrics, DXA, ultrasound, BIA, physical performance

28

CHAPTER 4

RESULTS

Allocation and Classification of Participants Based on Body Composition From the local community, 72 women were screened, 70 qualified for the study and 60 were able to participate. Two were eliminated due to either not being in the required age-range or of a different race/ethnicity than the study called for. Ten were not able to make the appointments in the laboratory due to scheduling conflicts and time constraints. The ultrasound machine in the College of Human Sciences was not available for this study. Thus initially 22 participants missed the ultrasound scans, before another instrument at the Florida State University Health Center was secured for the remainder to participate in the ultrasound scans of the quadriceps. A small percentage could not participate in all of the physical performance exercises such as knee extension due to knee arthritis or weakness, however each participant attempted the physical performance exercises. The flowchart of allocation and classification of participants is shown in Figure 1. At the end of the study 60 participants were classified based on body composition into one of five four groups: OSO (n=10), OO (n=35), SO (n=1), obese- only (n=10) or osteopenic/sarcopenic non-obese (n=4). The purpose of these allocations was to allow for the final Objectives and Hypotheses of this study to be met through Specific Aim 1: To identify the prevalence of OSO in the recruited sample of older women (>65 years); Specific Aim 2: To identify characteristic values for the condition of OSO by utilizing bioelectrical impedance analysis (BIA) to assess tissue heath and ultrasound measurements of the quadriceps to assess muscle quality; and Specific Aim 3: To assess functional performance to better characterize the conditions of OSO, OO, SO and obese- only women. Identifying Obesity: From the 60 women who finished the study, 56 were identified as overweight/obese based on percent body fat ≥ 32%. The four women that did not meet the criteria for obesity based on percent body fat were still included in the analysis. These four women were eventually allocated to a new group based on their BMD, T-scores and lean mass called the osteopenic/sarcopenic non-obese group. Sarcopenic Obese: As stated earlier, Baumgartner’s criterion for sarcopenia < 5.45 (ALM/height in m2) is not the most appropriate way of assessing sarcopenia in older, obese 29 women (Domiciano et al. 2013; Newman et al., 2003). Eight participants from the original 60 had sarcopenia based on Baumgartner’s criteria, and three of these eight were less than 32% body fat, and therefore not in the overweight/obese group. Using the linear regression model to identify SO, based on the expected level of ALM for the individual’s height and level of fat mass (Newman et al., 2003; Domiciano et al., 2013), proved more effective in this population of older women. Therefore, the 56 overweight/obese participants were first assessed for sarcopenia based on Baumgartner’s criterion (ALM/height in m2) and then a linear regression model was created in the JMP program to compare ALM/height2 by total body fat (kg) and height (meters) based on the model developed by Newman et al. (2003). Of the original 56 participants, 30 fell below the line, or expected value for ALM based on their height and total body fat. A cutoff was designated at the 20th percentile based on the model from previous research by Newman et al. (2003) for SO, so that eleven women (~18%) were allocated to the SO group. OSO: From the eleven SO participants, ten had significant bone loss, a T-score

< -1 in the left or right femoral neck and/or lumbar spine (L1-L4), and therefore, they were classified as OSO (17%). There was only one participant in the study who was only SO, without significant bone loss. She was not included in the analysis, because the statistical tests do not run with a group of n=1. Thus this group will not be discussed further. OO: As stated earlier, 56 participants were identified as overweight/obese (> 32% body fat). After running the linear regression model in JMP, and identifying the participant at the 20th percentile, 45 participants were found to be above the 20th percentile and therefore not SO, having substantial levels of ALM. Of these 45 participants, 35 had significant bone loss, (T- score < -1 in the left or right femoral neck and/or lumbar spine (L1-L4)), and were classified as OO (64%). Obese-Only: There were ten remaining participants who had no significant bone loss or sarcopenia, but were overweight/obese (> 32% body fat) so that they were classified into the obese-only group (17%). Osteopenic/Sarcopenic Non-Obese: There were four participants who surprisingly, had lower levels of body fat (<32%) and therefore were considered non-obese. These women all had significant bone loss (T-score < -1 in the left or right femoral neck and/or lumbar spine (L1-L4)), and three of the four had significant loss of lean mass based on Baumgartner’s criteria for sarcopenia in women ((<5.45 ALM (kg)/height (m2)). They were not analyzed with the other 30 women in the linear regression model used to identify SO, however, but participated in all other tests. Overall, the sample had a very high prevalence of bone loss, with 82% (45/55) of the women presenting with osteopenia and 11% (6/55) having frank osteoporosis (T-score ≤ -2.5). This analysis fulfilled Specific Aim 1, To identify the prevalence of OSO in the analyzed sample of older women (>65 years) and Hypothesis 1 that N ≥ 50 will give us enough power to identify OSO among obese older women and that ≥ 10% of participants will have OSO. From the sample, 17% of participants had OSO. [see Figure 1].

Identification

Healthy Caucasian postmenopausal women (N=60)

Obese women % body fat % (n=56) Low BMD, non-obese (n=4) • % body fat <32% • T-score of L1-L4 and/or Allocation femoral neck > -1

Sarcopenia (n=11) 18% No sarcopenia (n=45) 82% • Sarcopenic obese • Appendicular lean mass (ALM) Appendicular lean mass (ALM) • residual value of > -1.45 residual value of ≤ -1.45 • Above 20th percentile • Below 20th percentile

Low BMD (n=10) Normal BMD (n=1) Low BMD (n=35) Normal BMD (n=10) • T-score of L1-L4 • T-score of L1-L4 • T-score of L1-L4 • T-score of L1-L4 and/or femoral and/or femoral and/or femoral and/or femoral neck < -1 neck > -1 neck < -1 neck > -1

Classification Osteopenic/ Osteosarcopenic Sarcopenic Osteopenic Osteoporotic Obese-Only Obese (OSO) Obese (SO) non-obese Obese (OO) (n=10) 17% (n=10) 17% (1) (4) 7% (n=35) 58%

Figure 1. Classification of participants

Comparing Clinical and Anthropometric Characteristics Among OSO, OO, Obese-Only and Osteopenic/Sarcopenic Non-Obese Groups

Comparisons among the four groups of participants: OSO (n=10, 17%), OO (n=35, 58%), obese-only (n=10, 17%) and osteopenic/sarcopenic non-obese (n=4, 7%) were calculated using SAS 9.4 (Cary, NC 2002-2012). However, much of the data did not follow a normal

31 distribution based on the Kolmogorov-Smirnov test, due to the small sample size and had numerous outliers. Therefore, each variable was tested with the Wilcoxon t-test, a nonparametric test, also in SAS 9.4. Results from this analysis confirmed a preliminary power calculation based on previous research on OSO syndrome and physical performance using handgrip strength (Ilich et al., 2015). In this calculation using an alpha of 0.05, and power of 84%, the calculated sample size was n=50 participants needed to detect differences in handgrip strength between the groups of women. This study provided enough participants for meaningful calculations and our hypothesis 1a was confirmed . In keeping with Specific Aim 1, all participants were analyzed and compared based on body composition from DXA including bone mass and T-scores, lean mass, fat mass and percent body fat. Although the OSO and osteopenic/sarcopenic non-obese women were older than the other groups of women, age was not statistically significant among groups in the dataset. The OSO women also were on average taller than women in all other groups. The OSO women weighed less and had a lower BMI than the OO and obese-only women, but the osteopenic/sarcopenic non-obese women had a significantly lower weight and BMI compared to all other women. Systolic blood pressure was significantly higher in the osteopenic/sarcopenic non-obese than the obese-only and diastolic blood pressure was significantly lower in the osteopenic/sarcopenic non-obese than the OSO group. Waist circumference was significantly lower in the osteopenic/sarcopenic non-obese than the OO or obese-only groups. The obese- only had significantly higher hip circumference values than the OSO or osteopenic/sarcopenic non-obese. Whereas abdominal circumference was significantly lower in the osteopenic/sarcopenic non-obese than the OSO or obese-only groups, and significantly lower in the OSO group than the obese-only group. There was no significant difference in waist-to-hip ratio between groups of women. Total body fat and percent body fat were also significantly lower in the osteopenic/sarcopenic non-obese women. The OSO and osteopenic/sarcopenic non- obese women had significantly lower lean mass and ALM than the women from all other groups in the study. The obese-only group had significantly higher ALM than all other groups. The obese-only group had significantly higher total BMD, total T-scores, right femoral neck BMD, right femoral T-scores, right femoral BMD, and right femoral neck T-scores than all other groups. The obese-only group had significantly higher left femur BMD than all other groups and the osteopenic/sarcopenic non-obese had significantly lower left femur BMD than 32 the OO group or obese-only group. The obese-only group had significantly higher left femur T- scores and the osteopenic/sarcopenic non-obese group had significantly lower left femur T- scores than all other groups. The obese-only had significantly higher left femoral neck BMD and left femoral neck T-scores than all other groups and the OSO and osteopenic/sarcopenic non-obese groups had significantly lower left femoral neck T-scores than the other groups. The obese-only group had significantly higher L1-L4 spine BMD and T-scores than all other groups. All results from these data analyses are presented in Table 1. Results from this analysis support Hypothesis 1b: that women identified with OSO will have lower BMD, lower muscle mass, and poorer overall body composition and anthropometric measures, compared to OO and obese-only women. Although at most sites the OSO group had lower BMD than the OO or obese-only women, repeatedly, this was only significantly lower than the obese-only group. The exception was that the left femoral T-score was significantly lower in the OSO group than the OO or obese-only group, but similar to the osteopenic/sarcopenic non-obese group. The OSO group had overall higher BMD than the osteopenic/sarcopenic non-obese group. Lean mass was significantly lower in the OSO group than the other obese groups: OO and obese-only, but not lower than the osteopenic/sarcopenic non-obese group. Levels of body fat and weight were also not significantly different between obese groups, but significantly lower in the non-obese. Overwhelmingly, in keeping with Specific Aim 1, the DXA data provided a means to identify OSO in the population.

Table 1. Clinical and anthropometric characteristics of OSO, OO, obese-only and osteopenic/sarcopenic non-obese groups: mean (SD), min-max (N=59). Osteosarcopenic Obese Osteopenic Obese Obese-Only Osteopenic/ Sarcopenic Variables (n=10) (n=35) (n=10) Non-Obese (n=4) Mean (SD) Min-max Mean (SD) Min-max Mean (SD) Min-max Mean (SD) Min-max Age 77.1 (7.7) 63.9-87.2 75.8 (7.5) 65.2-92.9 74.3 (7.3) 63.6-85.6 77.7(6.8) 72.2-87.5 Height (cm) 163.7 (5) 154.9-170.2 162.6 (8.9) 147.3-172.7 162.9 (8.6) 149.9-175.8 159.7 (8.8) 52.4-170.2

Weight (kg) 69.9 (15.4)a 58.2-110.9 71.8 (13.1)a 51.4-109.1 78.4 (13.3)a 59.1-96.4 49.9 (4.4)b 45.5-55.9 BMI (kg/m2) 26.2 (6.3)a 21-43 27.4 (4.6)a 19.6-41.2 29.6 (5.2)b 23.8-38 19.8 (3.6)c 15.7-24

Systolic Blood Pressure 139.4 (21.1) 109-165.5 143.4 (18.3) 99.5-183 128.9 (17.9)a 108-163.5 158.6 (31.0)b 114-180.5

Diastolic Blood Pressure 82.6 (10.1)a 65-100 77.5 (12.7) 50-112 75.1 (11.0) 66-92.5 74.0 (14.3)b 54-87 Waist Circumference 80.4 (13.9) 60-114 85.1 (11.5)a 64.5-103 86.0 (12.3)a 63-101 69.0 (10.0)b 60-80 (cm) Hip Circumference (cm) 102.8 (16.5)a 68-137 104.2 (11.4) 81-131 110.9 (10.7)b 94-126 91.8 (6.2)c 85.5-100.3 Abdominal 98.2 (19.4)a 66-145 97.6 (10.6) 79-124.5 104.1 (12.6)a 86-123 87.1 (9.8)b 75-95.3 Circumference (cm) 33

Table 1 continued.

Osteosarcopenic Obese Osteopenic Obese Obese-Only Osteopenic/ Sarcopenic Variables (n=10) (n=35) (n=10) Non-Obese (n=4) Mean (SD) Min-max Mean (SD) Min-max Mean (SD) Min-max Mean (SD) Min-max Waist to Hip Ratio 0.7 (0.1) 0.7-0.9 0.8 (0.1) 0.6-1.0 0.7 (0.1) 0.6-0.9 0.7 (0.1) 0.7-0.8 Total body fat (kg) 31.0 (10.6)a 22.1-58.5 29.7 (9.0)a 15.1-53.2 32.9 (7.8)a 21.4-43.4 13 (2.5)b 9.4-15 Total body fat (%) 45.0 (4.9)a 38.3-54.9 42.2 (5.7)a 32.4-53.6 43.7 (3.8)a 37.5-49.3 27.1 (3.8)b 21.6-30.5 Total Lean mass (kg) 36.6 (4.3)a 32.7-48.1 39.3 (4.5)b 32.4-49.6 41.6 (4.8)b 34.5-49.3 34.7 (2.9)a 31.9-38.8 ALM (kg) 15.1 (1.7)a 13.2-19 17 (2.4)b 12.6-23 18.5 (2.7)c 15.3-23 14 (1.3)a 12.4-15.4 1.018 (0.105)a 0.815- 1.040 (0.099)a 0.797- 1.178 (0.099)b 1.010- 0.999 (.097)a 0.931- Total BMD (g/cm2) 1.206 1.259 1.341 1.142 Total T-score -0.6 (1.0)a -2.16-1.2 -0.4 (1.0)a -2.8-1.8 1.0 (1.0)b -0.7-2.6 -0.8 (1.0)a -1.5- 0.6 Right femur BMD 0.806 (0.111)a 0.664- 0.849 (0.093)a 0.691- 1.013 (0.102)b 0.922- 0.798 (0.056)a 0.718- (g/cm2) 0.968 1.017 1.229 0.843 Right femur T-score -1.4 (0.8)a -2.6- -0.3 -1.3 (0.7)a -2.5-0.1 -0.2 (0.7)b -0.8-1.4 -1.7 (0.5)a -2.3- -1.3 Right femoral neck 0.778 (0.080)a 0.657- 0.792 (0.082)a 0.617- 1.013 (0.102)b 0.922- 0.773 (0.048)a 0.705- BMD (g/cm2) 0.881 0.971 1.229 0.819 Right femoral neck T- -1.9 (0.6)a -2.7- -1.1 -1.8 (0.6)a -3.0- -0.5 -0.2 (0.7)b -0.8-1.4 -1.9 (0.4)a -2.4- -1.6 score 0.809 (0.105)a 0.611- 0.867 (0.095)a 0.676- 1.033 (0.091)b 0.906- 0.782 (0.043)c 0.725- Left femur BMD (g/cm2) 0.916 1.016 1.149 0.827 Left femur T-score -1.6 (0.9)a -3.2- -0.7 -1.1 (0.7)a -2.6-0.1 0.2 (0.7)b -1.1- -0.8 -1.8 (0.3)ac -2.2- -1.4 Left femoral neck BMD 0.759 (0.066)a 0.624- 0.801 (0.074)a 0.607- 0.957 (0.048)b 0.909- 0.747 (0.018)a 0.723- (g/cm2) 0.839 0.947 1.033 0.766 Left femoral neck T- -2.0 (0.5)a -3.0- -1.4 -1.7 (0.5)b -3.1- -0.7 -0.6 (0.3)c -0.9-0 -2.1 (0.1)a -2.3- -2.0 score

a a b a L1–L4 spine T-score -0.4 (1.2) -2.2-1.0 0.02 (1.5) -2.1-4.0 1.8 (1.8) -0.8-3.9 -0.9 (1.1) -2.2-0.5

Values within a row with different superscript letters indicate that they are significantly different from each other (p<0.05).

Comparing Muscle Quality (Knee Extension), Echo Intensity and Phase Angle Among OSO, OO, Obese-Only and Osteopenic/Sarcopenic Non-Obese Groups In keeping with Specific Aim 2, results from the BIA measurements to determine phase angle and measurements of muscle quality from both ultrasound scans and calculations from the knee extension were next analyzed among the four groups of women. Based on calculations from data gathered using the BIA, the phase angle values did not differ significantly among groups, although the OSO group had the highest value. Using ultrasound scans of the right and left quadriceps, as depicted below [Figure 2], echo intensity was assessed by measuring pixel intensity of the image in the Adobe Photoshop program as a measure of muscle quality. From this analysis, echo intensity in the right quadriceps was found to be significantly higher in the osteopenic/sarcopenic non-obese than in the OO group and both the obese-only group and osteopenic/sarcopenic non-obese group had significantly higher echo intensity in the left 34 quadriceps than the OO group. Muscle quality measured by dividing knee extension strength (kg) by lower ALM (kg), was lower in the OSO women than other obese women, and significantly lower in the OSO women than in the obese-only group of women. The results of this analysis are presented below in Table 2. Based on the findings from this analysis, overall, I must reject Hypothesis 2: that women identified with OSO would have lower muscle quality compared with other groups of women. Based on findings from the Phase Angle calculation derived from the BIA, there were no significant differences among groups. The ultrasound echo intensity (EI) measure showed that the OO group had the lowest scores but this was not significant from the OSO group. The osteopenic/sarcopenic non-obese had the highest EI values from ultrasound scans. At the same time, the OSO group did have lower values for muscle quality and significantly lower than the obese-only group. Based on these results, Hypothesis 2, should be partially rejected. There was no difference in phase angle or frailty values among groups. Nor were phase angle values low enough to qualify any participants as being frail. The OSO group did not present with significantly different EI values from ultrasound, however, the OSO group did have significantly poorer muscle quality based on the knee extension calculation.

Table 2. Muscle quality (knee extension), echo intensity and phase angle, of OSO, OO, obese-only and osteopenic/sarcopenic non-obese groups: mean (SD), min-max. Osteopenic/ Sarcopenic Variables Osteosarcopenic Obese Osteopenic Obese Obese-Only Non-Obese (n=10) (n=35) (n=10) (n=4) Mean (SD) Min-max Mean (SD) Min-max Mean (SD) Min-max Mean (SD) Min-max Muscle Quality 3.8 (0.8)a 2.7-4.8 4.4 (1.0) 2.0-5.8 4.9 (0.7)b 4.0-5.7 3.8 (1.8) 1.8-5.2 (knee extension)

Echo Intensity right a b quadriceps (PI*) 65.7 (9.9) 48.7-76.8 65.5 (12) 33-88.4 71.3 (13.9) 53.6-93.4 76.6 (6.7) 70.4-85.3

Echo Intensity left 67 (11.4) 47.4-80.9 65.3 (9.7)a 34.8-82 74.3 (16)b 51.1-92.7 76.2 (2)b 69.6-83.9 quadriceps (PI) Phase Angle º 7.3 (1.6 ) 5.7-8.2 6.9 (0.7 ) 5.7-9.0 6.9 (0.7 ) 5.7-7.7 6.4 (0.2 ) 6.2-6.6 Values within a row with different superscript letters indicate that they are significantly different from each other (p<0.05). *PI or pixel intensity, a measure of gray scale analysis via Adobe Photoshop.

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Subcutaneous adipose tissue

Lean mass: Rectus femoris

Lean mass: cross- section at mid-point of quadricep

Lean mass: Vastus intermedius

Femur (bone)

Figure 2. Ultrasound image of Rectus femoris muscle. Lean mass: Rectus femoris and Vastus intermedius composed area analyzed to obtain echo intensity or pixel intensity via the Adobe Photoshop.

Comparing Physical Performance of OSO, OO, Obese-Only and Osteopenic/ Sarcopenic Non-Obese Groups Physical performance test results were analyzed among groups of women based on Specific Aim 3: To assess functional performance to better characterize the conditions of OSO, OO, and obese-only women. All participants scored 24-30 points on the mini-mental state examination implying no cognitive impairment and having mental competence to actively participate in the study. Results showed that handgrip strength for the right, left and dominant hands was significantly higher in the obese-only group than the OO or osteopenic/sarcopenic non-obese groups. The sit-to-stand scores were lowest in the OSO group and significantly lower in the OSO group than the OO or osteopenic/sarcopenic non-obese groups as depicted in Figure 3. The osteopenic/sarcopenic non-obese had significantly higher sit-to-stand scores than the OSO 36 or OO groups. For one-leg stance on the right side, the OO group had significantly lower scores than the osteopenic/sarcopenic non-obese. For one-leg stance on the left side, the osteopenic/ sarcopenic non-obese had significantly lower scores than all other groups. The osteopenic/ sarcopenic non-obese group had significantly slower 4-m walking speed than the OSO group and significantly slower 4-m brisk walking speed than all other groups. There were no significant differences among groups for the 2-minute walk or for the arm flexion test. The OSO group had significantly lower knee extension than the OO or obese-only groups as depicted in Figure 4. Results for these data analyses are presented in Table 3 below.

Table 3. Physical performance of OSO, OO, obese-only and osteopenic/ sarcopenic non- obese, mean (SD), min-max. Osteopenic/ Osteosarcopenic Obese Osteopenic Obese Obese-Only Sarcopenic Non-Obese (n=10) (n=35) (n=10) Variables (n=4) Mean (SD) Min-max Mean (SD) Min-max Mean (SD) Min-max Mean (SD) Min-max Sit-to-Stand (times/30 10.3 (1.6) a 8-13 11.9 (4)b 1-15 10.9 (3.3) 5-19 14.3 (2.6)c 12-18 sec) One leg stance right 14.4 (12.1) 0-30 12.7 (11)b 1.5-30 17.9 (11.6) 3.3-30 28.2 (3.4)a 23.3-30 (sec) One leg stance left (sec) 15.2 (3.9) a 2-30 12.7 (13.1)a 2-30 18.2 (10.5)a 6-30 30 (0)b 30-30 Normal 4-meter walk 0.85 (0.2) a 0.5-1.1 1.0 (0.2) 0.3-1.3 0.9 (0.3) 0.4-1.3 0.2 (0.1)b 0.9-1.3 (m/sec) Brisk 4-meter walk 1.2 (0.3) a 0.7-1.6 1.3 (0.2)a 0.6-1.8 1.2 (0.3)a 0.7-1.7 0.8 (0.1)b 0.7-0.8 (m/sec) 2-minute walk (m) 143.8 (24.8) 121.9-205.3 134.8 (34.7) 15.2- 137.7 (42.1) 61.9- 144 (30.8) 122-188 193.7 203.3 Arm flexion (times/30 16.3 (6.9) 10-31 15.2 (4.8) 5-25 16.6 (5.9) 9-26 18.5 (9.2) 12-25 sec) Knee extension (kg) 43 (11.0) a 30-57.3 56 (14.1)b 22.7-77.3 65.5 (15.2)b 54.5-95.5 41.5 (19.1) 22.7-60.9 Values within a row with different superscript letters indicate that they are significantly different from each other (p <0.05).

Based on the results of this analysis, we partially reject Hypothesis 3: Women identified with OSO will have significantly poorer outcomes in functional performance tests, compared to OO, and obese-only women. Although the OSO group had significantly lower scores for the sit- to-stand test and the knee extension test, in other tests such as the handgrip strength, one-leg stance and two-minute walking test, they had similar or better scores than the OO and osteopenic/sarcopenic non-obese groups. Again, as in the previous analyses for Hypothesis 2, there was not enough evidence to fully confirm Hypothesis 3.

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18 c 14.3 Osteosarcopenic Obese 16 b a 14 a 11.9 Osteopenic Obese 10.9 10.3 12 Obese-Only 10

8 Osteopenic/Sarcopenic Non-Obese 6

4 times/30sec

2

0 Sit-to-Stand

Figure 3. Results (mean ± SEM) for sit-to-stand among the four distinct groups of women. Bars represent mean±SEM. OSO had significantly lower sit-to-stand strength than the OO group or osteopenic/sarcopenic non-obese. The osteopenic/sarcopenic non-obese group had significantly higher sit-to-stand score than the OSO or obese-only group at p<0.05.

b 80 65.5 Osteosarcopenic Obese 70 b 56 Osteopenic Obese 60 a a 43 41.5 50 Obese-Only

40 kg Osteopenic/Sarcopenic Non-Obese 30

20

10

0 Knee Extension

Figure 4. Results (mean ± SEM) for knee extension among the four distinct groups of women. Bars represent mean±SEM. The OSO group and osteopenic/sarcopenic non-obese Groups had significantly lower knee extension strength than the OO or obese-only groups at p <0.05.

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Assessment of OSO, OO, Obese-Only and Osteopenic/Sarcopenic Non-Obese Groups Using Functional Performance Scores As discussed earlier, physical performance can be used to measure functional decline and is independently considered by some researchers to be a way to assess sarcopenia (Delmonico et al., 2009; Dong et al., 2016). In a recent review, we proposed an assessment of OSO based on functional performance derived from previous studies (Ilich et al., 2016; Ilich et al., 2015; Cruz-Jentoft et al., 2010; Reuben et al., 2013). Previously established cutoffs in research can be used as theoretical diagnostic criteria for conditions such as OSO in addition to body composition (Ilich et al., 2016, Ilich et al., 2015). The cutoff for handgrip strength in women is set at ≤20 kg, the cutoff for one-leg stance ≤16 seconds, normal gait speed ≤0.8 meter/seconds, and sit-to-stand ≤20 times (Ilich et al. 2016; Cruz-Jentoft et al., 2010; Lauretani et al., 2003). From these cut-off values, a score can be obtained for each participant; the poorer the participant’s functional performance, the lower the score (see Table 4)

Table 4. Assessment and scoring of functional performance and corresponding cut-off values (Table and scoring reproduced from Table 2, Ilich et al., 2016). Handgrip One-leg- Sit-to-stand Gait speed Total Functional status strength stance chair test (≤0.8 m/sec) score (≤20 kg) (≤16 sec) (≤20 times) Major functional decline 0 0 0 0 0 Major functional decline* 0 1 0 0 1 Moderate functional decline** 0 0 1 1 2 Minor functional decline*** 0 1 1 1 3 No functional decline 1 1 1 1 4 The score of “0” is assigned to each test performed barely at or below the given cut-off and the score of “1” to each test performed above the cut-off value. * Any one performance could be scored as “1”, if it is above the cut-off for a given functionality ** Any two performances could be scored as “1”, if they are above the cut-off for given functionality *** Any three performances could be scored as “1”, if they are above the cut-off for given functionality The total score of 0 or 1 would indicate major functional decline, The total score of 2 indicates moderate functional decline, The total score of 3 indicates minor functional decline, The total score of 4 indicates no functional decline.

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In keeping with Specific Aim 3: To assess functional performance to better characterize the conditions of OSO, OO, and obese-only women, the data were further assessed based on the criteria for functional decline from Table 4 (Ilich et al., 2016). These finding showed that the OSO women and OO women had the highest percentage of women with overall major functional decline. Among the participants, 70% of the OSO women, 83% of the OO women, 40% of obese-only women and 100% of the osteopenic/sarcopenic non-obese scored below the cut-off value for handgrip in both the right and left hands. For one-leg stance, 60% of OSO women, 63% of OO women, 40% of the obese-only group and none of the osteopenic/ sarcopenic non-obese performed below the cut-off value. For normal gait speed, 30% of OSO women and 26% of OO women, 30% of obese-only women and none of the osteopenic/ sarcopenic non-obese women had slower gait speed than the cut-off value. For sit-to-stand, all OSO women scored below the cut-off value, 97% of OO women, 80% of obese-only participants and 75% of the osteopenic/ sarcopenic non-obese scored below the cut-off (see Table 5).

Table 5. Percentage of participants with functional performance below the cutoff for each test indicating functional decline. Osteopenic/ Functional Performance Test Osteosarcopenic Osteopenic Obese Only Sarcopenic Obese (n=10) Obese (n=35) (n=10) Non-Obese (n=4) 70% 83% 40% 100% Handgrip strength (≤20 kg) 60% 63% 40% 0% One-leg stance (≤16 sec) 30% 26% 30% 0% Normal gait speed (≤0.8 m/sec) 100% 97% 80% 75% Sit-to-stand chair test (≤20 times)

For average scores among groups, the OSO and OO groups tied at 1.3, the obese-only group scored 1.7, while the osteopenic/sarcopenic non-obese had the highest average score of 2.3 indicating greatest functional ability (see Table 6). This may reinforce the concept that older women with loss of bone and muscle are also more prone to loss of overall function than those who have normal bone and/or muscle mass and that obesity is correlated with loss of physical

40 function. Loss of functional performance is associated with sarcopenia, loss of balance with age, and an overall decline in ability to ambulate (Ilich et al., 2015, Dong et al., 2016; Cawthorn et al., 2009).

Table 6. Average performance score and percentage of participants OSO, OO, obese-only and osteopenic/sarcopenic non-obese with functional performance below the cutoff, n (%). Osteopenic/ Functional Performance Osteosarcopenic Osteopenic Obese Obese Only Sarcopenic Test Obese (n=10) (n=35) (n=10) Non-Obese (n=4) Average total score: 1.3 1.3 1.7 2.3

Major functional decline 5 (50%) 22 (63%) 4 (40%) 0% 3 (30%) 11 (31%) 2 (20%) (3) 75% Moderate functional decline 2 (20%) 4 (40%) Minor functional decline 2 (6%) (1) 25% 0 0 0 0 No functional decline

Based on this analysis, I would reject Hypothesis 3: Women identified with OSO will have significantly poorer outcomes in functional performance tests, compared to OO, and obese only women. The OSO group of women had lower levels of functional ability than the obese- only group or osteopenic/sarcopenic non-obese group. However, the level of functional decline seen in the OSO women in this sample was comparable to the OO group of women, indicating similar levels of physical performance in all tests. There was a greater percentage of OO women with decreased handgrip strength and one-leg stance values, but a greater number of OSO women with lower normal gait speed and sit-to-stand scores. Overall, although the OSO group suffered from high levels of major functional decline, their functional performance was similar to the level in the OO group and so therefore, could not confirm Hypothesis 3. Summary The results allowed for the partial fulfillment of Specific Aims 1-3. Based on data obtained from DXA scans, Specific Aim 1 was met. The prevalence for OSO was identified for osteopenia/osteoporosis based on femoral neck and/or lumbar spine (L1-L4) T-scores, SO from the linear regression model with a cutoff of at the 20th percentile, and obesity status based on

41 percent body fat. The sample size provided enough power to identify the prevalence of OSO among 60 participants, using the data from initial DXA body composition analyses so that ten women (17%) were identified as having OSO Syndrome. In partially meeting Specific Aim 2, the BIA and ultrasound scans did not find specific differences in phase angle values or muscle quality for the OSO women, however OSO women had significantly lower muscle quality based on knee extension scores. Specific Aim 3 was also only partially confirmed through the physical performance tests. Although the OSO women scored significantly lower in a few tests, there was no difference in the tests overall. The functional performance tables were more promising where the OSO group overall presented with major functional decline, however, the OO women had similar poor scores. In summary, the OSO group weighed less and had lower BMD at most sites, lower lean mass and ALM than the OO or obese-only groups, but higher percent body fat than the OO or obese-only. Only the osteopenic/sarcopenic non-obese weighed less than the OSO women having lower BMD at most sites, lower lean mass and lower percent body fat (which was to be expected). The OSO group had the lowest muscle quality scores but the highest phase angle values. The OSO group of women had the lowest sit-to-stand scores and lower knee extension scores than the OO or obese-only women. For other physical performance tests, such as handgrip strength and one-leg-stance, the OO group performed most poorly. The functional decline table showed that the OSO and OO groups had the highest percentage of participants with major functional decline while the osteopenic/sarcopenic non-obese had no participants with major functional decline. Overall, in this study, women who have obesity combined with bone loss and/or sarcopenia appear to have poorer physical abilities and greater functional decline than those who are only obese or with bone loss alone and no obesity.

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CHAPTER 5

DISCUSSION

This research compared anthropometric characteristics, physical performance tests and other measures of muscle quality and frailty among older OSO women, OO women, obese-only women and osteopenic/sarcopenic non-obese women (≥ 65 years). For this purpose, 60 women were recruited and tested in the laboratory over a ten-month period. For the final analysis, 59 of the participants were included [see Figure 1]. Therefore, we were able to recruit enough participants for this pilot study to investigate the diagnostic criteria for the OSO syndrome and compare physical performance measures among different groups. BMD, Muscle Mass and Quality, Overall Body Composition and Anthropometric Measures Based on Specific Aim 1, body composition measures were taken via DXA. Participants were identified as being obese or non-obese based on percent body fat from the iDXA. The body mass index (BMI) classified far fewer into the overweight/obese category. Using the BMI, 35 (64%) participants were classified as overweight (BMI ≥ 25 kg/m2) and only 14 (25%) were classified as obese (BMI ≥ 30 kg/m2). This measure precluded older adults who may have had a lower BMI, but may have had visceral fat and fat infiltration into muscle tissue and bone, promoting inflammation and further weakening these tissues (Ilich et al., 2014a, 2014b). In addition, as noted in this study, body composition varied greatly among older women. Some older adults have more adipose tissue and less lean mass and/or bone mass for their height and weight than other older adults with the same BMI. The disadvantages of using BMI for classifying participants into overweight/obese categories has been already addressed (Ehrampoush et al., 2016; Body Mass Index: Considerations for Practitioners. https://www.cdc.gov/obesity/downloads/BMIforPactitioners.pdf) in the literature review (p. 17). According to the World Health Organization, obesity in women can be identified with waist-to- hip ratios >0.85, however, on average none of the groups scored above 0.85, although many women in the OO, OSO and obese-only groups individually have waist-to-hip scores above this value (Geneva: World Health Organization. Diagnosis and Classification of Diabetes Mellitus. 1999). There also was not a significant difference in waist-to-hip ratio scores among groups. Therefore, participants for the present study were identified as obese based on percent body fat 43 of ≥ 32% obtained from the iDXA (Ace Fitness, Fit Life 2009; OMA, Obesity Algorithm 2015- 2016; Wanner et al., 2016). It needs to be noted that there is no final consensus for the amount/percentage of body fat to diagnose obesity. Many researchers state that 35% body fat defines obesity in women (WHO 1995), and others define obesity as 40% (Dufour et al., 2013). Recent research findings suggest a cutoff value as low as 32% body fat for obesity in older women (Ace Fitness, Fit Life 2009; OMA, Obesity Algorithm 2015-2016; Wanner et al., 2016). These findings do not necessarily make clear whether or not this applies the same way in identifying obesity in older, postmenopausal women as for younger women. However, for this research we decided on this lower value (32%), because of the impact of visceral fat and damage resulting from fat infiltration into bone and muscle (myosteatosis) in many older adults, even those who may not appear obese or overweight. Our lower cutoff for obesity was also based on the previous study in overweight/obese postmenopausal women, where percent body fat between 33-38% showed negative relationships with various skeletal sites (Liu 2014). At the same time, the majority of individuals in this study had high levels of body fat, the average value exceeding 40% across all groups, except the osteopenic/sarcopenic non-obese group of women, providing for the robust analysis of the OSO syndrome and comparison among different groups. Older women may have high levels of body fat without appearing overweight either visually or based on BMI. As discussed earlier in the literature review, this increase in fat can weaken bone and muscle, aggravating osteopenia/osteoporosis and sarcopenia (Ilich et al., 2014a; Domiciano et al., 2013). The majority of women in this study had body fat percentages > 35%, and osteopenia/osteoporosis mostly in the hips, but also in the lumbar spine. This strengthens the argument that body fat is not necessarily protective of bone in older adults (Ilich et al., 2014a; Liu et al., 2014). Additionally, of the 11 women with SO, all but one also had bone loss, so that SO and OO appeared to go together, confirming and strengthening our OSO syndrome definition. This relates to Hypothesis 1 from Specific Aim 1 that OSO prevalence could be identified in the sample. Lang et al. (2010) found that fat infiltration, or myosteatosis of thigh muscles, led to an increase in hip fracture in overweight older adults, and hip fractures accelerate the development of sarcopenia in older adults (Lang et al., 2010; Morley et al., 2014). Therefore, when assessing body fat in older adults, total body composition as well as fat infiltration into other tissues should be taken into account. 44

Of the 56 obese participants, eleven (18%) had SO based on the linear regression model. This is similar to findings by Domiciano et al., (2013) who also using the linear regression model (ALM adjusting for fat mass), found 20% of older women out of a sample of 611 to have SO (Domiciano et al., 2013). Using the Baumgartner model (ALM/height2), only eight participants or 14%, had sarcopenia. These methods for sarcopenia assessment might be underestimating the prevalence, especially in view of the physical performance measures. Some reports, such as the one from the European Working Group, place more emphasis on physical performance measures such as handgrip strength and walking speed to identify sarcopenia in older adults (Cruz-Jentoft et al., 2014; Dong et al., 2016). Using this criteria and the functional decline model from Ilich et al. (2016), 70% of OSO women and 83% of OO women would have sarcopenia based on reduced handgrip strength (< 20kg) and 30% of OSO women and 26% of OO women would have sarcopenia based on reduced gait speed [see Table 5]. These findings relate to Hypothesis 3 from Specific Aim 3: that women identified with OSO will have significantly poorer outcomes in functional performance tests, compared to OO, and obese-only women; although the OO women had similarly poor functional performance scores in this study. Physical performance tests may also over-diagnose sarcopenia. Although some researchers cite that a loss of strength and function precedes loss of lean mass in older women (Giampoli et al., 1999; Delmonico et al., 2009; Dong et al., 2016), physical mass and form follow physical function, or loss of function. Some researchers state that women with SO have greater physical decline than women with obesity or sarcopenia alone, due to the apparent synergistic effect of increased fat mass and muscle loss (Domiciano et al., 2013; Baumgartner et al., 2000). This may be due to myosteatosis, or fat infiltration into muscle, which damages muscle. It may also simply indicate that someone who develops SO has more physical decline and was less healthy throughout their life. In this study, in the OSO category also had SO and had overall poorer physical performance in many areas than women who were only-obese or OO. The OSO women, who were also the SO women, had the lowest sit-to-stand scores, slowest normal walking speed, and the poorest knee extension scores. Regarding sit-to-stand and knee extension, the OSO group was significantly lower than the obese-only group, confirming our Hypothesis 3: that OSO women would have significantly poorer outcomes in physical performance tests. Analyzing functional decline, the OSO group scored more poorly than the obese-only group, but the OO 45 group had similar levels of functional decline, based on the model by Ilich et al. (Ilich et al., 2016), [see Table 6]. This may indicate a relationship not just between SO and physical decline, but also a relationship between physical decline and osteopenia/osteoporosis. Physical exercise is believed to build and help maintain both bone and muscle (JafariNasabian et al., 2016). Individuals with bone loss are thought to have lower levels of physical activity throughout their lives or at least in recent years and therefore, have greater functional decline with age (JafariNasabian et al., 2016). In our previous analysis, OSO women had significantly lower physical performance than those who were obese-only (Ilich et al. 2015). In this present study, physical decline in OSO and OO women was similar, both being lower than the obese-only group in almost every area. This is probably partly due to a history of poorer physical fitness as well as a loss of strength, balance and other areas of functionality due to SO and/or osteopenia/osteoporosis. This decline, especially in the OSO group, may translate into health risks in other areas, such as increased risk for immobility, falls, fractures and further health decline (Ilich et al., 2014a; Binkley et al., 2013). As stated earlier, the prevalence of osteopenia/osteoporosis was remarkably high in this cohort. According to the International Osteoporosis Foundation, 20% of women age 70 years and 40% of women age 80 years have osteoporosis (NOF 2016), but in this study over 80% of the women had osteopenia and 10% had osteoporosis based on femoral neck and lumbar spine

(L1–L4) T-scores. The classification of the participants into the groups was affected by this large number of osteopenic/osteoporotic participants, so that none could be classified as SO. This is different from our previous analysis, where 28 out of 258 participants, or ~10% had SO alone, without significant bone loss. This difference may be due to age (Ilich et al., 2015). The average age of the participants in the previous study was ~62 years, whereas for the present study, the inclusion to participate was an age ≥ 65 years and many women in the present study were in their 70’s and 80’s. From the eleven women identified with SO, ten were placed in the OSO classification due to significant bone loss. This also presented the problem of limited data for SO in calculations and comparisons among groups. Without an adequate sample of SO women, it was not possible to ascertain whether poorer outcomes in physical performance and other measures for OSO women could be attributed to SO alone or to the combined impact of OSO. This may be the largest drawback to this study. On the other hand, this also presents a picture of sarcopenia and osteopenia as comorbidities: a probable relationship even existing between the 46 two. In the future, it might be recommended for researches to evaluate older women for both sarcopenia and osteopenia at the same time, as the presence of one may signal the other, and to include data on both in research findings (Ilich et al., 2014a, Ilich et al., 2015). BIA/Phase Angle Measurements Phase angle, derived from the BIA, is calculated as the arc-tangent or the angle that has a tangent equal to a given number of the ratio between resistance (frequency dependent opposition to an alternating current) and reactance (inversely proportional to water and electrolyte concentration across sectional areas of the measured body segment) (Wilhelm-Leen et al., 2013; Souza et al., 2016). Increased phase angle values indicate better body cell function and higher BMIs (Souza et al., 2016; Barbosa-Silva et al., 2005). Phase angle decreases as people age, become less fit, become frailer and/or increase levels of fat mass. In this study there was no significant difference among the phase angle values of the different groups. The OSO women had slightly higher values, which may reflect the fact that they had significantly higher percent body fat. The majority of the participants had phase angle values > 6 º, which is above levels that other researchers associate with frailty. Wilhelm-Leen et al., (2013), found that women, regardless of morbidity status had higher frailty and mortality with phase angles between 2-6 º (Wilhelm et al., 2013). Although most older women participating in this study were not frail, their phase angle values may also have been elevated due to obesity since higher BMI values are also associated with higher phase angle values (Barbosa-Silva et al., 2005). Therefore, Hypothesis 2 from Specific Aim 2, to develop cutoff values for OSO from BIA phase angle values was not possible from this study. Perhaps looking at larger sample sizes and older populations in the future with greater frailty might yield more significant results. Ultrasound and Echo Intensity Echo intensity (EI), measured from an ultrasound image and then analyzed by measuring pixel intensity in the Adobe Photoshop program, is also believed to reflect muscle quality (Watanabe et al., 2013; Palmer & Thompson 2016). Echo intensity from the rectus femoris muscle is significantly negatively associated with muscle quality and knee extension strength (Rech et al., 2014; Watanabe et al., 2013). There was not a clear relationship between physical performance and EI in this study. However, EI is also negatively associated with muscle mass (Watanabe et al., 2013) which may explain why the osteopenic/sarcopenic non- obese had the highest EI, but lowest muscle mass. In this study, EI values were significantly 47 lower in the OO group of women than the obese-only and osteosarcopenic/sarcopenic non- obese. Therefore, referring to Hypothesis 2 from Specific Aim 2, the OSO group did not have significantly lower EI ultrasound values than the obese-only or osteopenic/sarcopenic non- obese, and had similar EI values to the OO group, so that a clear cutoff could not be established for OSO. Muscle Quality Derived from Knee Extension The knee extension test measures the strength of the quadriceps muscles and was used in this study to calculate muscle quality by dividing knee extension (kg) strength by lower extremity lean mass (kg) measured by the IDXA. Muscle quality can be defined as ‘maximal force production per unit of muscle mass’ (Brooks et al., 2007). In this study, a one-repetition- max from the knee extension was divided by leg lean mass (kg) (Wood et al., 2011) as a measurement of muscle quality. Some researchers view muscle quality as a better measure of functional status than strength alone (Brooks et al., 2007). Muscle quality decreases with age, myosteatosis and chronic disease, but is increased with strength training and is higher in younger and fitter populations (Taaffe et al., 2009, Brooks et al., 2007; Watanabe et al., 2013). The OSO women had significantly lower knee extension scores than the obese-only and OO women and the OSO women and the osteopenic non-obese had lower overall knee extension and muscle quality scores than any other group. This may be indicative of greater functional decline. These findings further relate to Hypothesis 2 from Specific Aim 2 that muscle quality would be lower in OSO women and also Hypothesis 3 from Specific Aim 3 that physical performance would be lower in the OSO group of women. Functional Performance Tests As stated earlier, handgrip strength is used by some researchers to help identify sarcopenia (Cruz-Jentoft et al., 2010). The cut-off for grip strength indicating sarcopenia is ≤ 20 kg for women and ≤ 30 kg for men (Cruz-Jentoft et al., 2010; Ilich et al., 2016). The handgrip strength was actually lowest in both hands and the ‘dominant hand’ test in the OO and the osteopenic non-obese group. Only six out of 35 (17%) OO women and three (27%) out of ten OSO women had handgrip scores for either handgrip above 20 kg. Six (60%) out of ten obese- only women had handgrip greater than 20 kg for either hand. This corroborates with several other studies finding that loss of handgrip strength is associated with lower BMD and osteopenia/osteoporosis (Shin et al., 2014; Lindsey et al., 2005). Lower handgrip strength is also 48 associated with malnutrition, which further impacts aging and loss of bone and lean mass (Lu et al., 2016; Bin et al., 2010). Importantly, in this study participants were tested for right hand strength, left hand strength, and then separately comparing the hand with the highest score, or the dominant hand. The hand with the highest score, assumed to be the ‘dominant’ hand, is classified by some as the participant’s official ‘grip strength’ (Bahat et al. 2016). Using the dominant handgrip strength, 70% of OSO women, 83% of OO women, 40% of obese-only women and 100% of the osteopenic non-obese had handgrip strength below the cutoff of ≤ 20 kg (see Table 7). Overall, the different handgrip tests did not change the overall result that the obese-only group had the strongest grip strength. This corroborates with our previous analysis where OSO women and OO women had lower handgrip strength than the obese-only group (Ilich et al., 2015). This correlates somewhat to Hypothesis 3 from Specific Aim 3 that OSO women would have poorer physical performance, although regarding handgrip, the differences were not significant. The sit-to-stand chair test is considered a measure of fitness and lean mass in older adults so that a ‘fit’ adult should be able to complete ≥ 20 sit-to-stands in 30 seconds (Jones et al., 1999; Ilich et al., 2016). None of the participants in this study were able to complete 20 or more of these exercises in 30 seconds. Recent research shows that lower sit-to-stand test scores is associated with poorer walking speed outcomes and or should be used along with other tests such as gait speed (Bernabeu-Mora et al., 2016; Shin et al., 2014). The OSO women had significantly lower scores than the OO group or osteopenic non-obese women, which may further reinforce the association between the combined impact of bone loss, sarcopenia and obesity with overall loss of fitness. This partially supports Hypothesis 3 from Specific Aim 3 that OSO women would have poorer physical performance scores. The one-leg stance is a measure of balance and scores for this test decrease with age (Najafi et al., 2016; Ilich et al., 2016). Balance impairment is a major cause of long-term disability and is associated with reduced gait speed (Peirone et al., 2014). An average score or cut-off for healthy older adults is 16 seconds, but younger participants will average 30 seconds or longer (Najafi et al., 2016; Ilich et al., 2016). There was no significant difference in one-leg stance among groups in this study. The OSO and the OO groups had the lowest average one-leg stance scores for both the right and left sides. This does not support Hypothesis 3 from Specific

49

Aim 3 that OSO women would have poorer physical performance, although the difference in one-leg stance was not significant. There was no significant difference in walking speed among groups, however, normal walking speed was slowest in the OSO and osteopenic/sarcopenic non-obese groups, brisk walking speed was slowest in the osteopenic/sarcopenic non-obese group and the OSO and osteopenic/sarcopenic non-obese groups had the slowest scores for the two-minute walk. These findings are supported by other studies showing that slower walking speed is also associated with sarcopenia, which was present more in the OSO and osteopenic/sarcopenic non-obese groups. In addition, loss of strength often precedes loss of lean mass in older women (Giampoli et al., 1999; Delmonico et al., 2009; Dong et al., 2016). The loss of functionality in measures like walking speed may signal that these women are sarcopenic or in the process of developing sarcopenia. Normal walking speed, or gait speed can be used as a measure for sarcopenia, the cut-off value being ≤ 0.8 m/sec (Ilich et al., 2016). However, in this study, 30% of the OSO women had scores less than 0.8 m/s, 26% of the OO women, 30% obese-only women and 0% of the osteopenic/sarcopenic non-obese had a below normal walking speed (≤ 0.8 m/sec), so that walking speed is not clearly indicating sarcopenia in this sample. Walking speed in this study does not support Hypothesis 3 from Specific Aim 3 that OSO women would have poorer physical performance. Arm flexion, or elbow flexion measured in this study, is a test to measure strength (Toosizadeh et al., 2016, Takagi et al, 2016). Comparably to the handgrip strength test, the OO women had the lowest scores, the OSO had the second lowest, and the osteopenic non-obese women had the highest arm flexion scores. This could possibly be indicative of a greater loss of strength overall. This partially supports Hypothesis 3 from Specific Aim 3 that OSO women would have poorer physical performance, although here OO women scored poorly as well. As stated earlier, the OSO women had significantly lower knee extension scores than the obese-only and OO women and the OSO women and the osteopenic non-obese lower overall knee extension scores than any other group. This may be indicative of greater functional decline. Both the knee extension machine and the sit-to-stand exercise use the quadriceps muscles, important for walking and balance. Perhaps this significant decline in the OSO women regarding sit-to-stand and knee extension scores indicates greater loss of strength in the

50 quadriceps muscles. Although the difference was not significant, the OSO group had lower scores for normal walking speed, which may be related to loss of quadriceps strength (Cruz- Jentoft et al., 2010). The EI test, which measured muscle quality in the quadriceps, was also 51 lower in the OSO women. Overall, loss of quadriceps strength and/or muscle quality may be an indicator for OSO, although the results from this study did not present enough significant results to confirm this with any certainty and therefore, cannot be used to confirm Hypothesis 2 from Specific Aim 2. The results regarding physical performance measures partially support Hypothesis 3 from Specific Aim 3, indicating that OSO women have poorer physical performance. Limitations Limitations to this study include: a smaller sample size, the lack of an adequate sarcopenic or SO group to compare with the other participants and the lower statistical power of nonparametric tests such as the Wilcoxon t-test. The ultrasound machine in the laboratory at the College of Human Sciences was not available to our participants. Due to that difficulty, the study was delayed for 14 months before another ultrasound machine was located and secured at The Florida State University Health Center at the generosity of Joni Jones, Radiology Manager / QI and Accrediation Coordinator. This delay resulted in missing of ultrasound data for initial participants. Strengths The strengths of this study include: a homogenous sample of women all the same race, age group (postmenopausal; elderly) and gender and accurate testing equipment such as the iDXA machine for measuring body composition and ultrasound scans for muscle quality measures. Future Research Based on the findings from this pilot study, more research needs to be completed examining the relationship between bone loss, sarcopenia, muscle quality and increased body fat in older adults and how this relates to their overall health and physical function. Larger and more diverse study populations are needed, including men, women and different racial groups to better investigate the OSO syndrome. In addition, other tests from MRI or CT scans, could also be utilized to further compare body composition measures in future studies to validate the diagnostic criteria obtained by DXA, BIA, ultrasound and functionality measures. 51

CHAPTER 6

CONCLUSION

In conclusion, we were able to identify 17% of OSO women in our population, confirming our Hypothesis 1a. The physical decline in OSO and OO women was similar, both being greater than the obese-only group in almost every area and often greater than the osteopenic/sarcopenic non-obese women. In light of Hypothesis 1b from Specific Aim 1, the OSO group had lower BMD, lower lean mass and ALM than the OO or obese-only groups (as hypothesized), but higher percent body fat than the OO or obese-only groups. Only the osteopenic/sarcopenic non-obese had comparably lower BMD at most sites and lower lean mass. Similarly, the OSO group had the lowest muscle quality scores but the highest phase angle values, again, confirming Hypothesis 2. The higher phase angle scores in the OSO women may be reflective of their higher percent of body fat. The osteopenic/sarcopenic non-obese group had the highest EI values which may reflect their lower levels of muscle mass. In keeping with Hypothesis 3 from Specific Aim 3, the OSO group of women had significantly lower sit- to-stand scores than the OO group or osteopenic non-obese group and significantly lower knee extension scores than the OO or obese-only women. For other physical performance tests such as handgrip strength and one-leg-stance, the OO group performed most poorly, which may reflect a relationship between bone loss, obesity and physical decline. Overall, the obese-only group performed the best on physical performance/functionality tests. The functional decline table showed that the OSO and OO groups had the highest percentage of participants with major functional decline while the osteopenic/sarcopenic non-obese had no participants with major functional decline. Overall, in this study women who had obesity combined with bone loss and/or sarcopenia, appear to have poorer physical abilities and greater functional decline than those who were obese-only or only osteopenic/sarcopenic but non-obese. Based on this study, greater attention should be given to older women with obesity, sarcopenia and bone loss regarding functional decline as well as considering further study on decreased muscle quality in quadriceps either through physical performance tests such as knee extension, EI or other tests, to detect the risk of OSO early on. Early recognition of this syndrome, which appears to impact at least 10% of older Caucasian women based on this study, might guide older adults to seek

52 treatment through nutrition, exercise or even pharmaceutical interventions. Body composition parameters from DXA, the functional decline table as well as muscle quality of the quadriceps, could be an initial diagnostic criteria to consider when examining an older adult for OSO syndrome.

53

APPENDIX A

IRB APPROVAL

The Florida State University Office of the Vice President For Research Human Subjects Committee Tallahassee, Florida 32306-2742 (850) 644-8673 · FAX (850) 644-4392

RE-APPROVAL MEMORANDUM

Date: 10/13/2016

To: Julia Inglis

Address: Department of Nutrition, Food and Exercise Sciences, The Florida State University, 120 Convocation Way, 418 Sandels Building, Tallahassee, FL 32306 Dept.: NUTRITION FOOD AND EXERCISE SCIENCES

From: Thomas L. Jacobson, Chair

Re: Re-approval of Use of Human subjects in Research Identifying Osteosarcopenic Obesity in Older Women

Your request to continue the research project listed above involving human subjects has been approved by the Human Subjects Committee. If your project has not been completed by 10/11/2017, you must request a renewal of approval for continuation of the project. As a courtesy, a renewal notice will be sent to you prior to your expiration date; however, it is your responsibility as the Principal Investigator to timely request renewal of your approval from the committee.

If you submitted a proposed consent form with your renewal request, the approved stamped consent form is attached to this re-approval notice. Only the stamped version of the consent form may be used in recruiting of research subjects. You are reminded that any change in protocol for this project must be reviewed and approved by the Committee prior to implementation of the proposed change in the protocol. A protocol change/amendment form is required to be submitted for approval by the Committee. In addition, federal regulations require that the Principal Investigator promptly report in writing, any unanticipated problems or adverse events involving risks to research subjects or others.

By copy of this memorandum, the Chair of your department and/or your major professor are reminded of their responsibility for being informed concerning research projects

54 involving human subjects in their department. They are advised to review the protocols as often as necessary to insure that the project is being conducted in compliance with our institution and with DHHS regulations.

Cc: Jasminka Ilich-Ernst, Advisor HSC No. 2016.19220

The formal PDF approval letter: http://humansubjects.research.fsu.edu/pdf/printapprovalletter.aspx?app_id=19220 The Florida State University Office of the Vice President For Research Human Subjects Committee Tallahassee, Florida 32306-2742 (850) 644-8673 · FAX (850) 644-4392

APPROVAL MEMORANDUM

Date: 11/19/2015

To: Julia Inglis

Address: Department of Nutrition, Food and Exercise Sciences, The Florida State University, 120 Convocation Way, 418 Sandels Building, Tallahassee, FL 32306 Dept.: NUTRITION FOOD AND EXERCISE SCIENCES

From: Thomas L. Jacobson, Chair

Re: Use of Human Subjects in Research Identifying Osteosarcopenic Obesity in Older Women

The application that you submitted to this office in regard to the use of human subjects in the research proposal referenced above has been reviewed by the Human Subjects Committee at its meeting on 10/14/2015. Your project was approved by the Committee.

The Human Subjects Committee has not evaluated your proposal for scientific merit, except to weigh the risk to the human participants and the aspects of the proposal related to potential risk and benefit. This approval does not replace any departmental or other approvals, which may be required.

If you submitted a proposed consent form with your application, the approved stamped consent form is attached to this approval notice. Only the stamped version of the consent form may be used in recruiting research subjects.

If the project has not been completed by 10/12/2016 you must request a renewal of approval for continuation of the project. As a courtesy, a renewal notice will be sent to you prior to your expiration date; however, it is your responsibility as the Principal Investigator to timely request 55 renewal of your approval from the Committee.

You are advised that any change in protocol for this project must be reviewed and approved by the Committee prior to implementation of the proposed change in the protocol. A protocol change/amendment form is required to be submitted for approval by the Committee. In addition, federal regulations require that the Principal Investigator promptly report, in writing any unanticipated problems or adverse events involving risks to research subjects or others.

By copy of this memorandum, the Chair of your department and/or your major professor is reminded that he/she is responsible for being informed concerning research projects involving human subjects in the department, and should review protocols as often as needed to insure that the project is being conducted in compliance with our institution and with DHHS regulations. This institution has an Assurance on file with the Office for Human Research Protection. The Assurance Number is FWA00000168/IRB number IRB00000446.

Cc: Jasminka Ilich-Ernst, Advisor HSC No. 2015.16546

APPROVAL MEMORANDUM

Date: 12/16/2014

To: Jasminka Ilich-Ernst

Address: 1493 Dept.: NUTRITION FOOD AND EXERCISE SCIENCES

From: Thomas L. Jacobson, Chair

Re: Use of Human Subjects in Research The triad of bone, muscle and adipose tissue deterioration in older women: Detecting the osteosarcopenic obesity syndrome

The application that you submitted to this office in regard to the use of human subjects in the research proposal referenced above has been reviewed by the Human Subjects Committee at its meeting on 12/10/2014. Your project was approved by the Committee.

The Human Subjects Committee has not evaluated your proposal for scientific merit, except to weigh the risk to the human participants and the aspects of the proposal related to potential risk and benefit. This approval does not replace any departmental or other approvals, which may be required.

If you submitted a proposed consent form with your application, the approved stamped consent form is attached to this approval notice. Only the stamped version of the consent form may be used in recruiting research subjects. 56

If the project has not been completed by 12/9/2015 you must request a renewal of approval for continuation of the project. As a courtesy, a renewal notice will be sent to you prior to your expiration date; however, it is your responsibility as the Principal Investigator to timely request renewal of your approval from the Committee.

You are advised that any change in protocol for this project must be reviewed and approved by the Committee prior to implementation of the proposed change in the protocol. A protocol change/amendment form is required to be submitted for approval by the Committee. In addition, federal regulations require that the Principal Investigator promptly report, in writing any unanticipated problems or adverse events involving risks to research subjects or others.

By copy of this memorandum, the Chair of your department and/or your major professor is reminded that he/she is responsible for being informed concerning research projects involving human subjects in the department, and should review protocols as often as needed to insure that the project is being conducted in compliance with our institution and with DHHS regulations.

This institution has an Assurance on file with the Office for Human Research Protection. The Assurance Number is FWA00000168/IRB number IRB00000446.

Cc: Michael Delp, Dean HSC No. 2014.14283 The formal PDF approval letter: http://humansubjects.magnet.fsu.edu/pdf/printapprovalletter.aspx?app_id=14283

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APPENDIX B

CONSENT FORM

CONSENT FORM FOR PARTICIPATION IN A RESEARCH PROJECT AT FLORIDA STATE UNIVERSITY THE FRAILTY STUDY

PI Name: Julia E. Inglis, MS, RD Project Title: Identifying Osteosarcopenic Obesity in Older Women

Invitation to Participate You are invited to participate in a research study that will evaluate the impact of bone and muscle loss, and obesity on physical performance and frailty. A total of ~70 participants will be recruited from the North Florida area. The study will last two years, but each participant’s enrollment will be completed within 1-2 visits to the university campus.

Description of the Study Recently, we identified a new syndrome osteosarcopenic obesity. This condition is where an older adult presents with obesity, sarcopenia (loss of muscle mass and strength) and osteopenia/osteoporosis (loss of bone mass) at the same time. Based on our preliminary studies, the women with osteosarcopenic obesity have higher risk of falls and fractures and possible long-term disabilities, compared to age matched obese-only women. This project is about identifying osteosarcopenic obesity in older (>65 y) women who might not be clinically obese (e.g. determined by body mass index), but yet have increased body fat and perhaps fat infiltration both in the muscle and bone. We will identify these conditions by using bone densitometer (DXA) and ultrasound, – both non- invasive techniques. Subsequently, we will use short physical performance measures (e.g. handgrip strength, balance, walking abilities, etc.), along with the selected biomarkers from blood, to compare the physical fitness level of osteosarcopenic obese women with that of only obese, only sarcopenic, and normal women. These tests, with the specific criteria, will enable us to develop the cut-off points for the diagnosis of osteosarcopenic obesity. These diagnostic points could be used for future identification of osteosarcopenic obesity without having to use expensive equipment like MRI or DXA.

58

Timing and procedures: Initial introduction of the study and distribution of screening forms and consent at retirement homes and in the lab First visit -Height and weight measurements, food intake, physical activity questionnaires; surveys for demographic information and smoking and alcohol consumption. Participants will perform short physical performance tests. DXA and ultrasound measurements to assess bone mineral density, muscle/lean mass, and percent body fat. A blood draw will also be taken at this time to measure biomarkers related to osteosarcopenic obesity and inflammation. Second visit – Participants will need to come back to the lab a second time to complete the ultrasound and other tests if they do not have times for everything on the first visit as well as receive a vitamin D (400 IUs/day) / Calcium (630 mg/day) supplement to take for three months. Third Visit – Participants will come back six months after the second visit to be measured again with DXA, BIA, anthropometric measurements, and physical performance tests to assess the impact of the vitamin D/calcium supplement to see if there was a change/improvement in bone mineral density, muscle mass, weight, and physical mobility and strength.

Measurements and Surveys that will be taken: 1. Height, weight, waist, abdomen and hip circumferences, blood pressure, and heart rate (with portable instruments if measured at nursing facility or residence) 2. Questionnaires: a Demographic data b Self-reported SF-36 form and habitual, daily physical activity c Smoking and alcohol intake/history. d Food Intake by a three-day recall with addition of a few general questions on eating habits. 3. Bone mineral density and body composition 4. Muscle mass 5. Physical Performance Tests 6. Fasting Blood Draw ~10 cc: to measure biomarkers indicating inflammation, sarcopenia, and bone status

Risks and Inconveniences ▪ There might be a discomfort caused by taking anthropometric measurements and collecting demographic, health, dietary and life-style information. The completion of the questionnaires may take around 45-60 minutes. To minimize this discomfort, a professional, caring, non-judgmental staff will be employed and confidentiality will be in place at all times. ▪ The measurements of bone and body composition by DXA will expose a participant to a small amount of radiation. However, the amount of radiation 59

received during the study is comparable to the amount which is received from the cosmic radiation while flying in an airplane (for example from New York to San Francisco). The radiation received during the measurements will be minimal and <0.1% of the maximal allowed environmental dose for one year. ▪ Other measurements, like ultrasound, are not invasive, however, they might cause discomfort by participants having to lie down or sit while being measured. ▪ There is a small risk with taking blood samples and there may be some bruising at the site where the blood is drawn. There is also the possibility of risk of infection due to breakage of the skin. To minimize this risk and discomfort, a trained individual will draw blood following strictly the universal precaution’s measures and sanitary rules. ▪ Functionality testing (e.g. walking 6 m distance, chair sit-to-stand, or grip strength), may be difficult for some to perform. All precautions will be taken not to push any measurements or activity on a participant who shows difficulty or might be in distress, or otherwise disturbed.

Benefits ▪ Individualized bone mineral density and body composition assessment ▪ Results from all measurements will be shared with each participant, enabling with better insight and awareness of their health. ▪ Dietary and vitamin D status evaluation ▪ Participants will be provided a vitamin D (400 IUs/day) / Calcium (630 mg/day) supplement for six months

Economic Considerations ▪ There will be a small monetary award during and upon completion of the study (provided that the study is funded by NIH).

Confidentiality All information about you and your participation in this study will be confidential and your file will be kept private in a locked file cabinets for the next 10 years, after which time the files will be destroyed. We will use a code number to refer to participants and the names will not appear in any publication. The FSU Institutional Review Board (IRB) and the Office of Research Compliance may inspect study records.

In Case of Injury The FSU does not provide insurance coverage to compensate you if you are injured during the research. However, you may still be eligible for compensation.

60

Voluntary Participation The participation in this study is voluntary and there is no penalty for nonparticipation. You do not have to be in this study if you do not want to. If you agree to be in the study, but later change your mind, you may drop out at any time.

Questions Take as long as you need before you make a decision about participation. We will be happy to answer any questions you have about this study. If you have further questions about this project or if you have a research-related problem, you may contact the principal investigator, Jasminka Ilich-Ernst, -, or coordinator of the study Julia E. Inglis, . You may also contact the FSU IRB Human Subjects Office at (850) 644-.

Authorization:

I have read this form and decided that ______will (name of participant) participate in the project described above. Its general purposes, the particulars of involvement and possible hazards and inconveniences have been explained to my satisfaction. My signature also indicates that I have received a copy of this consent form.

Signature: ______

Date: ______

______Signature of Primary Investigator OR Signature of Person Obtaining Consent

61

APPENDIX C

INITIAL SCREENING

FSU Frailty Study Initial screening (to be filled out by researchers)

Date: ______

Name: ______

Date of Birth: ______Must be ≥70 (born in or before 1945)

Address: ______

Telephone: ______E-mail ______

Self reported: Weight: lbs Height: feet inch BMI: _ _ _ (Lbs*703/inch2)

Yes No

1) Can you move around on your own? If NO, what kind of aid do you need for moving around? Walker, cane OK; wheelchair is disqualifier 2) Do you have Parkinson’s disease or multiple sclerosis

3) Do you have systemic lupus or thyroid disease 3) Do you have severe rheumatoid arthritis or osteoarthritis

4) Do you have chronic fatigue or vitiligo

5) Have you ever had cancer or presently have cancer? 62

If YES, what kind and how long ago? OK, if cancer-free for 3 years

6) Do you have high blood pressure? _ _

If YES, are you taking medications, what kind and for how long? 7) Do you have severe osteoporosis? _ _

If YES, are you taking medications, what kind and for how long?

8) Do you have diabetes? _ _

If YES, are you taking medications, what kind and for how long?

9) Have you taken corticosteroids in the last 3 months? _ _

10) Are you taking hormone replacement therapy or other postmenopausal drugs

OK, if free for 3 mos. And not planning to resume _ _

11) Are you presently taking diuretics? _ _

12) Are you taking any supplements? _ _

Please list any supplements you are taking and bring them with you at the appointment ______

13) Are you taking any medications ______

Please list any medications you are taking and bring them with you at the appointment

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APPENDIX D

DEMOGRAPHIC SURVEY

FSU Frailty Study

Demographic Survey

ID: ______Date: ______

Date of Birth: ____/____/______

Address: ______City: ______State: _____ Zip ______

Phone (_____) ______E-mail: ______

If in Nursing Facility, which one: ______

Occupation (past and/or present) ______

Working: Part or Full Time or Retired (circle one)?

If retired, since when? ______

Education: ______

Marital Status: □Married □Separated □Divorced □Widowed □Single □Cohabitating Live with: □Spouse □Partner □Relatives □Children □Friends □Parents □Alone Live independently: ____ Live in Nursing Home:____ Live in Assisted Living ____

Income level range/year in thousands$ (circle): 20-30; 30-40; 40-50; 50-60; 60-70; 70-80; 80-90; 90-100; above 100

Health HistorGenetic Disease______Bleeding easily______Anemia_____ Allergies/Hayfever______Asthma______Eczema______Cancer/Tumor______Diabetes______High blood pressure______64

Heart Disease______Stroke______Arthritis/Rheumatism______Kidney disease______Thyroid trouble______Diabetes______Stom/Duod ulcer______Tuberculosis______Glaucoma______Mental illness______Epilepsy______Dementia______Alcohol addiction______Drug addiction______Other______

Recent Hospitalizations and/or Surgeries: (include reason/diagnosis and dates) 1) ______2)______3) ______

X-rays and Special studies: (include reasons and dates) X-rays______CAT scan______MRI Electrocardiogram______Electroencephalogram______Other______

Current Medications: (include name, dosage, and frequency of use) Prescription drugs 1) ______5) ______2) ______6) ______3) ______7) ______4) ______8) ______

Over-the-counter drugs: including supplements

1) ______5) ______2) ______6) ______3) ______7) ______4) ______8) ______Age of menopause: ______

Social Assessment: Has any of the following happened in the last year? (describe if yes)

Death of spouse ______

Death of other close family member or friend______

Change in health of family member______

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APPENDIX E

ALLIED DUNBAR SURVEY OF HABITUAL PHYSICAL ACTIVITY

ID Date:

How many minutes on average during a day, week or month do you spend doing the following:

Housework Type of Housework Minutes/day Minutes/week Minutes/month

Vacuuming

Scrubbing floors by hand

Walking with heavy loads of groceries Spring cleaning (moving furniture, washing windows/walls) Wet Mopping

Type of Gardening Minutes/day Minutes/week Minutes/month

Mowing the lawn Digging, moving earth Trimming hedges Raking leaves Weeding, hoeing, pruning, picking Planting flowers, seeds

66

Chopping wood Shoveling snow Gardening Do it Your Self Activities Type of DIY activity Minutes/day Minutes/week Minutes/month

Car cleaning Painting Wallpapering Furniture repair

WALKING WHAT IS YOUR USUAL WALKING PACE? (SLOW, AVERAGE, BRISK OR FAST)

Type of Walking Number of times How long do you walk for? per week or

month Fast Pace

Brisk

Steady Average

Slow

Stair climbing About how many flights of stairs do you climb at home on average per day (NOT counting going down) (DO NOT count steps to enter house or garage)? ______How many steps are in your stairs? ______

About how many flights of stairs do you climb elsewhere during the week?

______

67

APPENDIX F

EXERCISE

Type of Exercise Number Number Number Number Do you of times/ of times/ of times / of get out of day week month minutes breath or each time sweaty? Aerobics (high impact) Aerobics (low impact) Aqua aerobics Bowling Canoeing Cycling Dancing for Fitness Elliptical Exercises (leg lifts, situps, crunches) Golf Hand wts (10lb) Hand wts (2lb) Hand wts (5lb) Hiking Rollerblading Jogging Running Sailing Skiing Social Dancing Step machine Stretching Swimming Table Tennis Tai Chi Tennis Therabands

68

Tread mill (mph) Volleyball Yoga

69

APPENDIX G

SF-36 FORM

FSU Frailty Study SF-36 Form

1. In general, would you say your health is:

Excellent 1 Very good 2 Good 3 Fair 4 Poor 5 2. Compared to one year ago, how would your rate your health in general now?

Much better now than one year ago 1 Somewhat better now than one year ago 2 About the same 3 Somewhat worse now than one year ago 4 Much worse now than one year ago 5 The following items are about activities you might do during a typical day. Does your health now limit you in these activities? If so, how much? (Circle One Number on Each Line) Yes, Yes, Limited Limited No, Not a Lot a Little limited at All

3. Vigorous activities, such as running, [1] [2] [3] lifting heavy objects, participating in strenuous sports

4. Moderate activities, such as moving [1] [2] [3] a table, pushing a vacuum cleaner, bowling, or playing golf

5. Lifting or carrying groceries [1] [2] [3]

6. Climbing several flights of stairs [1] [2] [3] 70

7. Climbing one flight of stairs [1] [2] [3]

8. Bending, kneeling, or stooping [1] [2] [3]

9. Walking more than a mile [1] [2] [3]

10. Walking several blocks [1] [2] [3]

11. Walking one block [1] [2] [3]

12. Bathing or dressing yourself [1] [2] [3]

During the past 4 weeks, have you had any of the following problems with your work or other regular daily activities as a result of your physical health? (Circle One Number on Each Line) Yes No

13. Cut down the amount of time you spent on work or other 1 2 activities

14. Accomplished less than you would like 1 2

15. Were limited in the kind of work or other activities 1 2

16. Had difficulty performing the work or other activities (for 1 2 example, it took extra effort)

During the past 4 weeks, have you had any of the following problems with your work or other regular daily activities as a result of any emotional problems (such as feeling depressed or anxious)? (Circle One Number on Each Line) Yes No

17. Cut down the amount of time you spent on work or other 1 2 activities

18. Accomplished less than you would like 1 2

19. Didn't do work or other activities as carefully as usual 1 2

20. During the past 4 weeks, to what extent has your physical health or emotional problems interfered with your normal social activities with family, friends, neighbors, or groups? (Circle One Number)

Not at all 1

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Slightly 2

Moderately 3

Quite a bit 4

Extremely 5

21. How much bodily pain have you had during the past 4 weeks? (Circle One Number)

None 1

Very mild 2

Mild 3

Moderate 4

Severe 5

Very severe 6

22. During the past 4 weeks, how much did pain interfere with your normal work (including both work outside the home and housework)? (Circle One Number)

Not at all 1

A little bit 2

Moderately 3

Quite a bit 4

Extremely 5

These questions are about how you feel and how things have been with you during the past 4 weeks. For each question, please give the one answer that comes closest to the way you have been feeling. How much of the time during the past 4 weeks . . . (Circle One Number on Each Line)

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None All of Most A Good Some A Little of the of the Bit of of the of the the Time Time the Time Time Time Time

23. Did you feel 1 2 3 4 5 6 full of pep?

24. Have you 1 2 3 4 5 6 been a very nervous person?

25. Have you 1 2 3 4 5 6 felt so down in the dumps that nothing could cheer you up?

26. Have you 1 2 3 4 5 6 felt calm and peaceful?

27. Did you 1 2 3 4 5 6 have a lot of energy?

28. Have you 1 2 3 4 5 6 felt downhearted and blue?

29. Did you feel 1 2 3 4 5 6 worn out?

30. Have you 1 2 3 4 5 6 been a happy person?

31. Did you feel 1 2 3 4 5 6 tired?

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32. During the past 4 weeks, how much of the time has your physical health or emotional problems interfered with your social activities (like visiting with friends, relatives, etc.)? (Circle One Number)

All of the time 1

Most of the time 2

Some of the time 3

A little of the time 4

None of the time

How TRUE or FALSE is each of the following statements for you.

(Circle One Number on Each Line) Definitely Mostly Don't Mostly Definitely True True Know False False

33. I seem to get sick 1 2 3 4 5 a little easier than other people

34. I am as healthy as 1 2 3 4 5 anybody I know

35. I expect my health 1 2 3 4 5 to get worse

36. My health is 1 2 3 4 5 excellent

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APPENDIX H

MINI-MENTAL STATE EXAMINATION (MMSE)

STANDARDIZED MINI-MENTAL STATE EXAMINATION (SMMSE)

QUESTION TIME SCORE ALLOWED 1 a. What year is this? 10 seconds /1 b. Which season is this? 10 seconds /1 c. What month is this? 10 seconds /1 d. What is today’s date? 10 seconds /1 e. What day of the week is this? 10 seconds /1 2 a. What country are we in? 10 seconds /1 b. What state are we in? 10 seconds /1 c. What city/town are we in? 10 seconds /1 d. IN HOME – What is the street address of this 10 seconds /1 house? IN FACILITY – What is the name of this building? e. IN HOME – What room are we in? IN FACILITY – 10 seconds /1 What floor are we on? 3 SAY: I am going to name three objects. When I am 20 seconds /3 finished, I want you to repeat them. Remember what they are because I am going to ask you to name them again in a few minutes. Say the following words slowly at 1‐second intervals ‐ ball/ car/ man 4 Spell the word WORLD. Now spell it backwards. 30 seconds /5 5 Now what were the three objects I asked you to 10 seconds /3 remember? 6 SHOW wristwatch. ASK: What is this called? 10 seconds /1 7 SHOW pencil. ASK: What is this called? 10 seconds /1 8 SAY: I would like you to repeat this phrase after me: 10 seconds /1 No ifs, ands or buts. 9 SAY: Read the words on the page and then do what it 10 seconds /1 says. Then hand the person the sheet with CLOSE YOUR EYES on it. If the subject reads and does not

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close their eyes, repeat up to three times. Score only if subject closes eyes

10 HAND the person a pencil and paper. SAY: Write any 30 seconds /1 complete sentence on that piece of paper. (Note: The sentence must make sense. Ignore spelling errors) 11 PLACE design, eraser and pencil in front of the person. 1 minute /1 SAY: Copy this design please.

Allow multiple tries. Wait until person is finished and hands it back. Score only for correctly copied diagram with a ‐sided figure between two ‐sided figures. 12 ASK the person if he is right or left‐handed. Take a 30 seconds piece of paper and hold it up in front of the person. SAY: Take this paper in your right/left hand (whichever

is nondominant), fold the paper in half once with both hands and put the paper down on the floor . Score 1 point for each instruction executed correctly. /1 Takes paper correctly in hand /1 Folds it in half Puts it on the floor /1

TOTAL TEST SCORE /30 Note: This tool is provided for use in British Columbia with permission by Dr. William Molloy. This questionnaire should not be further modified or reproduced without the written consent of Dr. D. William Molloy. Provided by the Alzheimer’s Drug Therapy Initiative for physician use.

GLOBAL DETERIORATION SCALE (GDS) Stage Deficits in cognition and function Usual care setting 1 Subjectively and objectively normal Independent

2 • Subjective complaints of mild memory loss. Independent • Objectively normal on testing. • No functional deficit

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3 Mild Cognitive Impairment (MCI) Independent • Earliest clear-cut deficits. • Functionally normal but co-workers may be aware of declining work performance. • Objective deficits on testing. • Denial may appear. 4 Early dementia Might live • Clear-cut deficits on careful clinical interview. independently – Difficulty performing complex tasks, e.g. perhaps with handling finances, travelling. assistance from • Denial is common. Withdrawal from family or caregivers. challenging situations. 5 Moderate dementia At home with live-in • Can no longer survive without some family member. assistance. In seniors’ • Unable to recall major relevant aspects of their residence with current lives, home support. e.g. an address or telephone number of many Possibly in facility years, names of grandchildren, etc. Some care, especially if disorientation to date, day of week, season, or behavioural to place. They require no assistance with problems or toileting, eating, or dressing but may need help comorbid physical choosing appropriate clothing. disabilities. 6 Moderately severe dementia Most often in • May occasionally forget name of spouse. Complex Care • Largely unaware of recent experiences and facility. events in their lives. • Will require assistance with basic ADLs. May be incontinent of urine. • Behavioural and psychological symptoms of dementia (BPSD) are common, e.g., delusions, repetitive behaviours, agitation. 7 Severe dementia Complex Care • Verbal abilities will be lost over the course of this stage. • Incontinent. Needs assistance with feeding. • Loses ability to walk.

Adapted by Dr. Doug Drummond from Reisberg B, Ferris SH, Leon MJ, et al. The global deterioration scale for assessment of primary degenerative dementia. American Journal of Psychiatry 1982;139:1136-1139. Provided by the Alzheimer’s Drug Therapy Initiative for physician use.

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APPENDIX I

ANTHROPOMETRICS

ID______Date______Anthropometrics

Weight: ______kg Height: ______cm BMI: ______kg/m2 Waist Circumference: ______cm Hip Circumference: ______cm Abdominal Circumference: ______cm Waist-to-Hip Ratio: ______Blood Pressure: ______mmHg ______mmHg AVERAGE ______mmHg Heart Rate: ______bpm ______bpm AVEAGE ______bpm BIA (body fat percentage):______% BIA (lean mass): ______% Resitance: ______OHMS Reactance: ______OHMS

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APPENDIX J

3-DAY DIETARY RECORD INSTRUCTIONS

3-day dietary Record Instructions

Choose three typical days (2 week and 1 weekend day) – NOT necessarily consecutive. Try to eat the way you usually do while you are keeping the food record. Please follow the instructions below as carefully as you can. If you have questions, please call:

INSTRUCTIONS FOR COMPLETING THE FOOD RECORD: 1. Record all the food you eat or drink for each day you select. Most people find it helpful to do this as soon after the meal or snack as they can. 2. Write only 1 food item on a line. Describe the type of food eaten as clearly as you can. Use the samples provided on page 3 as a guide. List ingredients to help describe any unusual casserole or salad. Indicate whether the food is canned fresh, frozen or diet. List the brand manes of foods if you know them. 3. Describe the amounts of food you eat and drink as clearly as you can. Use the following examples as a guide. Liquid - List as *cup, parts of cups, or fluid ounces – 1cup = 8 oz Meat, fish, cheese, egg - List in ounces, by number, or size. Specify if the amount given is in cooked or raw weight. Example: Chicken drumstick Baked, no skin 1 medium Lean ground beef patty Broiled ¼ lb, raw Cheddar cheese Kraft slice XX/ Fruits - List as *cup, parts of cups, or by number -- 1cup = 8 oz Include the size (diameter and/or length) of fresh fruits. Example: Banana 1 small (6 in. long)

Vegetables - List as *cup, parts of cups, or by number. 1cup = 8 oz

Example: Green Beans DelMonte ½ cup

Bread, Rolls, Crackers -- List by number or size.

Example: Whole wheat bread WonderBread 1 slice Triscuits Nabisco 4

Cereal, Rice, Noodles, Potato -List by cups, parts of cups, or number 1cup=8oz Example: Spaghetti Cooked 1 cup Pancakes, Waffles- List by number or size.

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Example: Betty Crocker Buttermilk Mix 2 (5” diameter)

Fats Margarine, butter -- List by teaspoons, tablespoons, or pats.

Salad dressing, cream, cooking oil, gravy – List by teaspoons, tablespoons, or pats.

Bacon, sausage -- list by number of slices or links.

Sweets: Jam, jelly, honey, sugar, syrup -- list by teaspoons, tablespoons. Candy -- list number and size of bar or pieces. Desserts: Jell-O, puddings, ice cream -- List as cups or parts of cups. Cookies -- list by number and size. Pie, cake -- list by number and size (length and width at longest end). Example: Cookie, choc. Chip Mrs. Fields ½ diam. Strawberry ice cream cone Hagen Daze 1 scoop, sugar cone Chocolate cake with chocolate icing Homemade / of layer cake

4. Describe how the food was prepared. For example: baked, broiled, fired, raw, scrambled or other. Include butter, margarine, oil, sauces, dressings, gravies, dessert topping added in cooking or at the table.

5. Eating out: Give the name of the restaurant so that we may call for more information if necessary. Describe the food items eaten as carefully as you can. le: Pizza Hut Pizza, Sausage & cheese, 1 slice of medium 4”X6” McDonald’s Quarterpounder with cheese. 6. Remember to list everything you eat or drink including gum, cough drops, pickles, catsup, and tartar sauce. 7. If you take a vitamin-mineral supplement please write down how much you take and the brand name (or bring the package label with you to your appointment),

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APPENDIX K

CDC FALL RISK QUESTIONNAIRE

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APPENDIX L

SMOKING PRESENT AND PAST

1. Do you smoke? YES______NO ______If you have never smoked, please proceed to the Alcohol and Caffeine Questionnaire on the back of this form. 2. If YES, How old were you when you first started smoking cigarettes regularly (at least 1 cigarette every day for 30 days) years 3. During the past 30 days, on the days you smoked, how many cigarettes did you smoke? Less than 1 cigarette per day 1 cigarette per day ____ cigarettes per day 4. If you DO NOT smoke presently, did you ever smoke? YES NO 5. How long have you smoked regularly? (at least 1 cigarette every day for 30 days) years 6. During the time when you smoked, how many cigarettes did you smoke per day? Less than 1 cigarette per day 1 cigarette per day cigarettes per day Alcohol and Caffeine Questionnaire 1. How many cups of caffeinated coffee do you usually drink a day? cups (Include iced coffee, cappuccino, and espresso) (1 cup= 8 oz) I do not drink 2. Is the caffeinated coffee you drink usually brewed or instant (circle)?

3. How many cups of caffeine tea do you usually drink a day? cups I do not drink

4. How many cups of caffeinated soda do you usually drink a week? cups (Pepsi, coke, Sunkist orange, mountain dew, Dr. Pepper)

I do not drink

5. Do you take any caffeine containing over-the-counter medications? ______

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6. How often do you usually have an alcoholic drink of any kind?

Every day Almost every day 3-4 times a week 1-2 times a week About once every 2 weeks About once a month Less than once a month I do not drink

7. How many drinks do you have on average when you drink? ______

Glasses of wine ______beer ______hard liquor ______

1 drink = 5 oz wine 12 oz beer 1.5 oz spirits 8. Have you ever drunk heavily on a regular basis? 9. How long ago did you stop drinking heavily on a regular basis?

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APPENDIX M

PHYSICAL PERFORMANCE TESTS DATA SHEET

ID: ______Date: ______Physical Performance Tests Data Sheet

Handgrip Strength:

Right hand: ______(kg)

Left hand: ______(kg)

5- lb Arm Curl ______(repetitions/ 30 seconds)

Knee Extension:

Right knee ______(deg/sec)

Left knee ______(deg/sec)

Timed Sit-to-Rise (30 sec): ______(sec)

One Leg Stance (1 minute each): R ______(sec) L______(sec)

Four-Meter Timed Normal Walk Test: ______(sec)

Four-Meter Timed Brisk Walk Test: ______(sec)

Two-Minute Walk Test (50 ft): ______(ft, in)

*kilogram, kg; degrees/second, deg/sec; second, sec; feet, ft; inches, in

Research Staff (Print Name): ------

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BIOGRAPHICAL SKETCH

EDUCATION B.S. 2010 University of North Florida, summa cum laude, Tallahassee, Florida (Major, Health Science)

M.S. 2012 Florida State University, Tallahassee, Florida in Nutrition Science / Didactic Program in Dietetics (08/10-05/11)

Ph.D. 2017 Florida State University, Tallahassee, Florida in Nutrition Science Thesis Advisor: Dr. Jasminka Ilich-Ernst

PROFESSIONAL CREDENTIAL (S) 09/15-present Registered Dietitian/ Registration ID# 86079145, Dietetic Internship, Florida State University - completed 08/15. 07/13-10/16 Licensed limited x-ray /basic machine operator BMO84805. 05/06-present Licensed Massage Therapist #MA0047659.

HONORS AND AWARDS 12/16 COGS Travel Award, Florida State University, 2016. Award $200. 12/16 Travel Awardee, American Society for Nutrition, Annual Conference, Orlando, FL. Award $350.

10/15 Dissertation Research Grant Top Tier Awardee, The Graduate School, Florida State University. Funded by Florida State University. Total award $1,000. 02/15 Third Place at Research and Creativity Day 2015 poster presentation, ‘Assessing Nutritional and Vitamin D Status of Postmenopausal Obese and Osteosarcopenic Obese Women’, College of Human Sciences, Florida State University. Award $50. 02/14 Outstanding Teaching Assistant Award Nominee 2014, Florida State University. 10/13 Poster presentation ASBMR Conference & Muscle Symposium 2013, Baltimore, MD. ‘Defining Osteosarcopenic Obesity and Identifying its Prevalence in Women Across a Wide Age-Range’. 10/13 Presidential Award Nominee 2013, ASBMR Conference, Baltimore, MD 2013. 10/13 COGS Travel Award, Florida State University 2013. Award $200. 02/13 First Place Research and Creativity Day 2013 for oral presentation, ‘Identifying Osteosarcopenic Obesity in Caucasian Women,’ College of Human Sciences, Florida State University. Award $150. 05/10 Graduated summa cum laude, B.S., University of North Florida.

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RESEARCH EXPERIENCE/ TECHNICAL SKILLS 11/15 Bioelectrical impedance machine: body composition and phase angle

03/15 Ultrasound machine: muscle detection/quality (echo intensity via Adobe Photoshop)

10/13 Dual energy x-ray absorptiometry (DXA) machine: bone mineral density and body composition

05/13-8/13 Laboratory technician, Biology Department, Florida State University: basic laboratory skills

05/12-2/13 Research assistant: The Effects of Symbiotic on Lactose-Intolerance Individuals NFES Department, Florida State University.

01/11-5/11 Research assistant: Unigen Study, NFES Department, Florida State University.

SCIENTIFIC PRESENTATIONS 12/16 Inglis JE, JafariNasabian P, Ave MP, Hebrock H, Goosby K, Beyer E., et al. Older Women with Osteosarcopenic Obesity Have Lower Handgrip Strength and Knee Extension Strength Compared to Osteopenic or Obese-only Women. Poster & oral presentation. 2016. Presented at the American Society for Nutrition. Annual Conference 2016, Orlando, FL. 02/15 Inglis JE, Kelly OJ, Ilich JZ. Assessing Nutritional and Vitamin D Status of Postmenopausal Obese and Osteosarcopenic Obese Women/ Poster presentation. Research and Creativity Day 2015, College of Human Sciences, Florida State University, FL. 09/14 Ilich JZ, Inglis JE, Kelly OJ. Osteosarcopenic Obesity Is Associated with Reduced Handgrip Strength and Walking Abilities in Postmenopausal Women/Poster presentation. American Society for Bone and Mineral Research Annual Conference 2014, Houston, TX. 02/13 Inglis JE, Panton LB, Ormsbee MJ, Kelly OJ. Ilich JZ. Identifying Osteosarcopenic Obesity in Caucasian Women College of Human Sciences/ Oral presentation. Research and Creativity Day 2013, Florida State University, FL.

TEACHING EXPERIENCE 01/16-05/16 Instructor/Teaching Assistant, DIE4424L, Medical Nutrition Therapy II Lab, The Florida State University.

08/12-12/15 Instructor/Teaching Assistant, HUN1201, Science of Nutrition, The Florida State University (seven semesters).

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NEW COURSE DEVELOPMENT 1/16-4/16 HUN1201, Science of Nutrition—Online. FSU COLLEGE SERVICE 01/13-12/15 Authored and updated ‘Teaching Assistant/Instructor Manual for Science of Nutrition’ for instructors/TAs. HUN1201. NFES Department, The Florida State University. 02/14 Mediator, College of Human Sciences: Oral Presentations, FSU CHS Research & Creativity Day.

PEER-REVIEWED JOURNAL PUBLICATIONS 1. JafariNasabian P, Inglis JE, Kelly OJ, Ilich JZ. Osteosarcopenic obesity in women: Impact, prevalence, and management challenges. Int J Womens Health. December 2016. In press. 2. JafariNasabian P, Inglis JE, Reilly W, Kelly OJ, Ilich JZ. The Aging Body: Changes in Body Composition on Tissue and Organism Levels and Nutritional Status. J Endocrinol. November 2016. In press. 3. Ilich JZ, Kelly OJ, Inglis JE. Osteosarcopenic Obesity Syndrome: What is it and how can it be identified and diagnosed? Curr Gerontol Geriatr Res. 2016;2016:7325973. doi: 10.1155/2016/7325973. 4. Inglis JE, Ilich JZ. The Microbiome and Osteosarcopenic Obesity in Older Individuals in Long-Term Care Facilities. Curr Osteoporos Rep. 2015 Oct;13(5):358-62. doi: 10.1007/s11914-015-0287-7. 5. Ilich JZ, Inglis JE, Owen KJ, McGee DL. Osteosarcopenic obesity is associated with reduced handgrip strength, walking abilities, and balance in postmenopausal women. Osteoporos Int. 2015. doi:10. 1007/s00198-015-3186-y. 6. Ilich JZ, Kelly OJ, Inglis JE, Panton LB, Duque G, Ormsbee MJ. Interrelationship among muscle, fat, and bone: connecting the dots on cellular, hormonal, and whole body levels. Ageing Res Rev. 2014; 15:51–60.

ABSTRACTS 1. Inglis JE, JafariNasabian P, Ave MP, Hebrock H, Goosby K, Beyer E, et al. Older Women with Osteosarcopenic Obesity Have Lower Handgrip Strength and Knee Extension Strength Compared to Osteopenic or Obese-only Women. 2016. Presented at the American Society for Nutrition Annual Conference, Orlando, FL. 2. JafariNasabian P, Inglis JE, Ave MP, Hebrock H, Hall KJ, Nieto SE, et al. The Relationship of Adiponectin with Body Adiposity and Bone Mineral Density in Older Women. 2016. Presented at the American Society for Nutrition Annual Conference, Orlando, FL.

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3. Inglis JE, JafariNasabian P, Gilman JC, Kelly OJ, Ilich JZ. Possible nutritional etiology of osteosarcopenic obesity syndrome. FASEB J. 2016;30:1156.8. http://www.fasebj.org/content/30/1_Supplement/1156.8. 4. Inglis JE, Panton LB, Ormsbee MJ, Kelly OJ, Ilich JZ. Defining osteosarcopenic obesity and identifying its prevalence in women across the age span. J Bone Miner Res. 2013. http://www.asbmr.org/asbmr-2013-abstractdetail?aid=da7d06cc-1551-4abf-8445- 51dc6e53bcf7.

COMMUNITY/ FIELD EXPERIENCE 05/16-present Clinical Dietitian/Nutrition Care Manager. Glenn Mor Nursing Home/Grady General Hospital/Archbold Medical Center, Thomasville, & Cairo, GA. 03/16 Radio interview: Tom Flanagan, Nutrition/North FL Vegfest 2016. WFSU (NPR). 10/14-present Planning Committee Member: North FL VegFest 2015-2016. Accounts payable, public relations service. 03/15 Radio Interview: Tom Flanigan. Nutrition/North FL Vegfest 2015. WFSU (NPR). 03/15 Television Interview: Julz Graham. Nutrition/North FL Vegfest 2015. WFSU TV. 11/13 Taught nutrition class on ‘Sorting Through Different Diets to Find Meaning and Purpose’ Unitarian Universalist Church, young adults group, Tallahassee, Florida. 03/09 Nine-day community nutrition field trip (conducted health fair, taught/ practiced community nutrition) in nation of Belize, University of North Florida. 02/04-04/05 Administrative Assistant/Assistant Chef, Southcare Senior Services, Meals-on- Wheels, Alachua, Florida.

MEMBERSHIP IN PROFESSIONAL SOCIETIES 09/16-present Association for Women in Science (AWIS) 09/08-present Academy of Nutrition and Dietetics Member 09/13-09-15 American Society for Bone Mineral Research 08/11-8/13 Golden Key International Honor Society membership 05/10-1/12 Phi Kappa Phi Honor Society membership

LANGUAGE SKILLS: English: fluent; Spanish: fluent in conversation/ basic reading level.

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