Comparative Analysis of Obesity Classification Methods in Aging Adults

COMPARATIVE ANALYSIS OF OBESITY CLASSIFICATION METHODS IN AGING ADULTS

Edward T. Kelley II

A Thesis

Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of

MASTER OF EDUCATION May 2015 Committee: Amy L. Morgan, Advisor Mary-Jon Ludy Lynn Darby ii ABSTRACT

Amy L. Morgan, Advisor

In the United States over 82 million Americans, or approximately 26% of the

population, are aged 50 and older. The National Health and Nutrition Examination Survey

estimate the prevalence of obesity in this group to be 32.6%. However, most healthcare providers

rely upon Body Mass Index (BMI) and Waist Circumference (WC) as indicators of obesity-

related health risks. The purpose of this study was to investigate the accuracy of BMI and WC

as indicators of obesity status and risk for associated health concerns (e.g., cardiovascular

disease, diabetes, hypertension, dyslipidemia). Subjects were 60 healthy males (n=22) and females (n=38) aged 50 and older (59.9±7.9 yrs.). Height and weight measurements were assessed via stadiometer and calibrated electronic scale; BMI was calculated as kg/m2 (M:

28.8±5.3 kg/m2; F: 26.4±6.3 kg/m2). WC was measured using a Gulick tape at two anatomical

points; narrowest waist (WCN)(M: 91.3±6.8cm ; F: 83.6±12.7cm ) and level with the umbilicus

(WCU)(M: 102.0±7.5cm; F: 91.1±13.6cm). Each participant completed body composition analysis via air-displacement plethysmography (ADP)(M: 28.3±6.3%; F: 36.2±8.5%). Percent

fat estimated by ADP (%fat) was used as the criterion measure of body composition in this

investigation. All participants met pretesting requirements (e.g., no food, water, or exercise for 2

hours) to ensure accuracy. Based on NIH-accepted BMI and WC classifications, 26 (6M, 20F)

participants were classified as normal weight, 13 (7M, 6F) as overweight and 21 (9M, 12F) as

obese using BMI. WCN resulted in 40 (16M, 24F) participants classified as having healthy

levels of abdominal fat and 20 (6M, 14F) classified as having unhealthy levels, while WCU iii resulted in 31 (13M, 18F) participants classified as having healthy levels and 29 (9M, 20F) as

having unhealthy levels of abdominal fat. Using %fat via ADP, 29 (7M, 22F) participants were

classified as normal weight, 9 (4M, 5F) as overweight, and 22 (11M, 11F) as obese using sex-

specific cut-points (Gallagher et al., 2000). Sensitivity was calculated comparing BMI and

WCN/WCU to %fat classification. Overall, the sensitivity of BMI in properly identifying men

and women at risk for obesity-related health concerns was 0.90. Sensitivity values for WCN and

WCU were inconsistent between sexes (WCN: 0.40M, 0.91F; WCU: 0.70M, 1.00F). Based on

this investigation, using BMI to estimate obesity-related health risk in men and women over the age of 50 years provided accurate classifications. However, based on WC results, a potential sex difference was found. These findings indicate the need for further research to explore the use of

WC at both anatomical points when identifying obesity-related health risk. In practice, this investigation found that BMI was the best indicator of obesity and obesity-related health risk in adults over the age of 50.

iv ACKNOWLEDGMENTS

I would first like to thank my advisor, Dr. Amy Morgan. Without her guidance and

mentorship, I would not have been able to successfully complete my thesis. From my first visit

to Bowling Green State University throughout my time here, Dr. Morgan has supported my

education and helped me grow as a student and a professional.

In addition to Dr. Morgan, I would like to thank the members of my committee, Dr. Ludy

and Dr. Darby. Through their knowledge and experience, they have helped me become a better

writer, speaker, and student. Without a doubt, my committee has been instrumental in my

success as a graduate student.

To the various faculty, fellow students, and friends that have been there for me, I want to

thank you. Whether I had a question, needed help with data collection, or just needed someone

to help take my mind of this thesis for a while, you have all be a big part of this process.

Lastly, I want to thank my parents, Edward and Terri, for all of the sacrifice and support you have given me to ensure that I succeed in life and my education. From day one, you both have been my driving force to achieve my dreams. Again, I thank you!

TABLE OF CONTENTS

Page

CHAPTER I. INTRODUCTION ...... 1

Purpose of the Study ...... 3

Significance of the Study ...... 3

Hypotheses ...... 4

CHAPTER II. LITERATURE REVIEW ...... 5

Introduction ...... 5

Obesity ...... 6

Aging: Changes in the Body ...... 8

Body Composition ...... 9

Anthropometry and Aging ...... 12

Waist Circumference and Aging ...... 13

Body Mass Index and Aging...... 14

Bioelectrical Impedance and Aging ...... 15

Body Density and Aging ...... 17

Summary ...... 19

CHAPTER III. METHODS ...... 20

Subjects ...... 20

Instrumentation ...... 21

Pre-testing Protocol ...... 21 v

Procedures ...... 22

Statistical Analyses ...... 23

CHAPTER IV. RESULTS ...... 26

Participant Characteristics ...... 26

Correlations ...... 26

Sensitivity and Specificity ...... 27

Positive and Negative Predictive Values ...... 29

CHAPTER V. DISCUSSION ...... 55

Practical Implications...... 60

Limitations/ Further Investigations ...... 63

Conclusion ...... 63

REFERENCES ...... 65

APPENDIX A. INFORMED CONSENT...... 70

APPENDIX B. SCREENING AND DEMOGRAPHIC QUESTIONNAIRE ...... 74

LIST OF TABLES

Table Page

1 Multi-compartment Models at the Five Levels of Body Composition ...... 10

2 Participant Characteristics ...... 30

3 Correlation Matrices for Obesity Indicators in Men ...... 31

4 Correlation Matrices for Obesity Indicators in Women ...... 32

5 Fishers’ Transformation for Significant Differences

between Age Groups and Sexes ...... 33

6 Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive

Value for All Indicators of Obesity, Sexes, and Age Groups ...... 50

vii

LIST OF FIGURES

Figure Page

1 Scatterplot Graphs of Overweight Classifications in Men

via BMI and %fat (ADP) ...... 34

2 Scatterplot Graphs of Overweight Classifications in Women

via BMI and %fat (ADP) ...... 36

3 Scatterplot Graphs of Obese Classifications in Men

via BMI and %fat (ADP) ...... 38

4 Scatterplot Graphs of Obese Classifications in Women

via BMI and %fat (ADP) ...... 40

5 Scatterplot Graphs of Obese Classifications in Men

via WCN and %fat (ADP) ...... 42

6 Scatterplot Graphs of Obese Classifications in Women

via WCN and %fat (ADP) ...... 44

7 Scatterplot Graphs of Obese Classifications in Men

via WCU and %fat (ADP) ...... 46

8 Scatterplot Graphs of Obese Classifications in Women

via WCU and %fat (ADP) ...... 48

9 Sensitivity Results for Body Mass Index using Overweight and Obese

Classifications across Age Groups and Sexes ...... 51

10 Sensitivity Results for Waist Circumference using Narrow and Umbilicus

Measurement Points across Age Groups and Sexes ...... 51

11 Specificity Results for Body Mass Index using Overweight and Obese viii

Classifications across Age Groups and Sexes ...... 52

12 Specificity Results for Waist Circumference using Narrow and Umbilicus

Measurement Points across Age Groups and Sexes ...... 52

13 Positive Predictive Value Results for Body Mass Index using Overweight

and Obese Classifications across Age Groups and Sexes ...... 53

14 Positive Predictive Value Results for Waist Circumference using Narrow

and Umbilicus Measurement Points across Age Groups and Sexes ...... 53

15 Negative Predictive Value Results for Body Mass Index using Overweight

and Obese Classifications across Age Groups and Sexes ...... 54

16 Negative Predictive Value Results for Waist Circumference using Narrow

and Umbilicus Measurement Points across Age Groups and Sexes ...... 54 1

CHAPTER I. INTRODUCTION

Today over 82 million men and women in the United States are 50 years of age and older.

This group of adults has become the fastest growing portion of the population (U.S. Census

Bureau, 2014). Of this vast group, over 26 million are considered obese (CDC, 2014). As a

result of this high prevalence of obesity, healthcare professionals have begun focusing on the

treatment of obesity and associated diseases that reduce the health of older adults in the U.S.

(American Heart Association, 2013). Furthermore, due to the recent classification of obesity as a

disease by the American Heart Association (2013), the accuracy of obesity indicators have come

under scrutiny (DeCaria et al., 2012).

Obesity, as defined by the American Heart Association (2013), is a disease in which a

person is significantly above his or her ideal healthy weight or simply has too much body fat.

This definition exemplifies the complexity of obesity and the difficulties in assessing health risk.

Whereas ideal weight is often assessed using body mass index (BMI), body fat is estimated by

specific body composition tests. While ideal weight may be impacted by muscle mass, excess

fat is detrimental to overall health, leads to diagnosis of associated diseases (e.g., cardiovascular

disease, diabetes, dyslipidemia, hypertension), and increases risk of mortality (Flegal et al.,

2005). With the increasing prevalence and risk of comorbid diseases, the methods by which

healthcare providers assess obesity must be appropriate to accurately determine risk across

discrete populations.

Currently, the most commonly used indicator of obesity is body mass index

(BMI). BMI, an anthropometric index of weight and height (kg/m2), is used as a quick and reliable indicator of obesity in groups of children and adults (Khaodhiar et al., 1999). As obesity 2 is defined as above ideal weight and/or excess body fat, it stands to reason that BMI may not be the best indicator of obesity as it does not assess body fat. Another anthropometric method of obesity assessment is waist circumference (WC). As an anthropometric measure, this method can be used to directly estimate abdominal adiposity through the assessment of fat content around the mid-section. Because there is not a uniform anatomical measurement point for WC and is only measured at one site, this measure may be inadequate in indicating obesity and overall body fat (Mason & Katzmarzyk, 2009).

Other measures using sophisticated laboratory equipment to estimate body fat are available, however, these tests may not be a practical option for medical professionals. These tests include bioelectrical impedance analysis, air-displacement plethysmography, and dual x-ray absorptiometry. At a minimum, each of these techniques provide an estimation of fat mass and fat free mass. Completing body composition measures for everyone at risk of obesity can be costly and time consuming. As such, body composition measures may not be available for practical obesity identification.

When using BMI and other anthropometric measures to indicate obesity, many changes occur with aging that are not taken into account by these measures. During the aging process, the body undergoes changes such as an increase in fat mass, a decrease in muscle mass

(sarcopenia), and a decrease in bone mineral density (Visser & Harris, 2012). BMI is a proportional measure using height and weight, and thus not sensitive to changes in fat, muscle, and/or bone. Additionally, WC is a surrogate measure of fat at only one anatomical point.

Therefore, due to age-related changes in body composition, techniques such as BMI and WC may not be suitable as indicators of obesity, particularly when using current classification criteria. More sophisticated body composition measures may provide insight into the utility of 3 obesity indicators and correct classifications. A comparison of these anthropometric techniques

(i.e., BMI and WC) with more sophisticated measures can provide insight into whether the current classification criteria are appropriate for those undergoing the normal aging process.

Purpose

The purpose of this study was to investigate the accuracy of simple anthropometrics and advanced body composition techniques for predicting obesity in men and women 50 years of age and older. Through the analysis and comparison of various body composition methods, this investigation aimed to determine whether commonly used anthropometric techniques (e.g., BMI,

WC) were appropriate for proper classification of these individuals.

Significance of this Investigation

With the rise in the prevalence of obesity, correct identification may influence the intervention used to improve or maintain health status. Misidentification may lead to the development of further health risks associated with obesity without patient awareness of their risk status. As a result of changes in body composition associated with aging, common obesity indicators (BMI and WC) may not be appropriate in men and women 50 years of age and older.

These adults are at additional risk for obesity and associated health concerns. Therefore, proper identification of obesity is vital in adults as they age.

Hypotheses

It was hypothesized that:

1. Waist circumference, at narrowest (WCN) and umbilicus (WCU), will have a stronger

relationship to percent body fat than body mass index (BMI).

2. Relationships between obesity indicators (i.e., BMI, WCN, and WCU) and percent fat

will decline as age increases.

3. Sensitivity (i.e., proper identification of overweight) will be lower as age increases across

all measurements (BMI, WCN, and WCU).

4. Specificity (i.e., proper identification of normal weight) will be lower as age increases

across all measurements (BMI, WCN, and WCU).

CHAPTER II. LITERATURE REVIEW

Introduction

The population of men and women 50 years of age and older is one of the largest

growing demographics within the United States (U.S.). This increase in the number of aging

adults is a result of the largest generation in U.S. history, the Baby Boomers, born between 1946

and 1964. At the completion of the 2010 Census there were over 82 million adults 50 years or

older, which comprises 26 percent of the U.S. population (U.S. Census Bureau, 2014). With a

current life expectancy of 79 years and a growing population of centenarians (i.e., people aged

100 or older), healthcare professionals in the U.S. are focusing on health concerns within this

population in accordance with the increasing population (World Bank, 2014).

With the growing population of aging Americans, an increase in the prevalence of

preventable disease is of significant concern to healthcare professionals. One health concern is

obesity and the negative impact it has on related diseases (e.g., cardiovascular disease, type 2

diabetes, hypertension, dyslipidemia, stroke, sleep apnea, gallbladder disease, gout, and

osteoarthritis) (Khaodhiar et al., 1999). The American Heart Association (AHA) defines obesity

as a disease in which a person is significantly above his or her ideal healthy weight and/or simply

has too much body fat (AHA, 2013).

Body Mass Index (BMI), an index used to indicate obesity, is calculated by dividing a

person’s weight in kilograms by height squared (kg/m2). The Center for Disease Control and

Prevention (CDC) utilizes two large population-based surveys to estimate the prevalence of obesity, the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health and

Nutrition Examination Survey (NHANES). Using self-reported measurements of height and 6

weight, BRFSS results estimate the prevalence of obesity as 29.4% via BMI; however, most

argue that self-reported measures underestimate BMI and obesity (Dietz, 2015). Estimates from

NHANES, which uses measured height and weight, indicated that the prevalence of obesity

among older adults is 34.6% (CDC, 2014).

Obesity

Obesity has recently been classified as a disease by the American Medical Association

(American Heart Association, 2013). The classification is in response to evidence showing that

obesity increases the risk for cardiovascular disease, type 2 diabetes, and all-cause mortality

(American Heart Association, 2013). Obesity and its associated diseases (e.g., cardiovascular

disease, type 2 diabetes, hypertension, dyslipidemia, stroke, sleep apnea, gallbladder disease,

gout, and osteoarthritis) present a unique challenge to healthcare providers, that is, whether to treat the associated disease or the underlying cause of obesity (American Heart Association,

2013). Currently, healthcare professionals rely primarily upon BMI as an indicator of obesity and associated health risk.

While there is an association between BMI and obesity, controversy exists regarding the

accuracy of BMI as an obesity indicator (Rothman, 2008). While BMI is widely used, numerous

other measures are available to assess obesity, and more importantly body composition. For the

purpose of this discussion, measurements of obesity and body composition will be divided into

three categories: BMI, field measures, and laboratory measures. Each category follows specific

guidelines of how to measure and classify obesity as well as strengths and weaknesses.

BMI, a ratio of height and weight, is commonly used as an indicator of obesity.

Additionally, BMI is favorable as it is easy to calculate and determine classification status. 7

However, BMI is not an assessment of fat mass and as such is not an appropriate indicator of

obesity in certain populations, such as muscular individuals (Rothman, 2008) and older adults

(DeCaria et al., 2012). While BMI is appropriate as an epidemiologic tool to predict obesity,

when identifying risk in a single person, BMI may be inappropriate as it does not take body composition into account.

Field measures are methods of assessing obesity risk and adiposity that can be used in a variety of settings (e.g., WC, skinfold measurement, BMI). Many field measures are anthropometric methods, although some (e.g., WC) may be a more practical assessment of obesity than others (i.e., BMI) (Gallagher et al., 1996). The use of WC to indicate obesity is favorable as the measurement is direct indicator of abdominal obesity rather than BMI, which is an assessment of proportionality. However, WC requires skilled personnel to assess properly.

For these reasons, BMI is used more often than other anthropometric measures.

Laboratory measures are techniques that measure fat mass within the body utilizing advanced technology, such as bioelectrical impedance analysis (BIA) and air-displacement plethysmography (ADP). BIA utilizes an electrical current sent through body tissues to differentiate between fat mass and fat-free mass using the impedance that the current encounters.

Fat mass is then predicted from the impedance that the current encounters as fat is less conductive than fat-free tissue. ADP utilizes volume assessment of air to measure body density.

Fat mass is predicted from body density utilizing various age and race specific equations.

However, these prediction equations are based on limited population data that may not adequately estimate for all populations. While these laboratory techniques are accurate if done properly, they are costly and more time consuming than BMI or WC. 8

The use of other body composition measurement techniques, which estimate fat mass, are more favorable than BMI as indicators of obesity. However, more targeted research into body composition methods is needed to designate an appropriate indicator of obesity in an aging population.

Aging: Changes in the Body

The first 20 years of life are accompanied by dramatic changes in size, including both height and weight (CDC, 2000). At the end of puberty, an individual’s height will stabilize.

Weight stability, however, is affected predominantly by diet and exercise habits. Weight changes occur at a relatively slow pace in adults compared to children and adolescents. From approximately 50 years of age through the rest of the lifespan, rapid changes in body composition may occur.

In terms of body composition, differences between individuals across age groups exist due to changes that occur during the aging process (Lesser et al., 1963). In conjunction with age- related changes, apparent differences in body composition exist between adult males and females with the latter having a higher body fat content overall (Heymsfield et al., 2005). Therefore, it is important to compare weight-related risks on sex and age-dependent spectrums.

Overall, body weight and BMI trend upward as aging occurs, that is to say continually increase until age 70-80 years, when they tend to plateau and eventually decrease (Visser &

Harris, 2012). Changes in body weight may be attributed to a decrease in fat-free mass and an increase in fat mass that can alter an individual’s BMI (Baumgartner et al., 1991; Shaw et al.,

2007; Visser & Harris, 2012; Harris et al., 1999). The increase in BMI affects women more than men; this increase is approximately 0.4% per decade (Meeuwsen et al., 2010). Conversely, a 9 decrease in body fat percentage has been seen in people over the age of 80 years old (Ding et al.,

2007). Additionally, a reduction of height may occur as a result of decreased bone mineral content that accompanies aging. This leads to an apparent change in size and stature (Shaw et al., 2007; Visser & Harris, 2012). Changes in body composition as a result of aging, such as increases in fat mass, decreases in muscle mass, and decreases in bone mineral density, may affect the ability of proper obesity identification using current methods in this population.

Body Composition

Body composition is the field of study tasked with measuring the components that make up the human body. Body composition assessment itself is the breakdown of the human body into its specific parts, known as compartments. Each compartment is comprised of various segments, such as fat or muscle, which are measured differently. For example, there are several methods of measuring body composition. The methods of body composition measurement range from the largest compartments to the smallest elements: whole-body, tissue-organ, cellular, molecular, and atomic (see Table 1) (Heymsfield et al., 2005).

The “whole-body” level is comprised of three compartments that account for the total body weight. The large regions of the whole body are assessed by anthropometric techniques that measure exterior portions of the body. These assessment techniques include circumferences, skinfolds, and length (Heymsfield et al., 2005).

Table 1. Multi-compartment Models at the Five Levels of Body Composition

Level Body Composition Model Number of

Compartments

Atomic BM = H + O + N + C + Na + K + Cl + P + Ca + Mg + S 11

Molecular BM = FM + TBW + TBPro + Mo + Ms + CHO 6

BM = FM + TBW + TBPro + M 4

BM = FM + TBW + nonfat solids 3

BM = FM + Mo + residual 3

BM = FM + FFM 2

Cellular BM = cells + ECF + ECS 3

BM = FM + BCM + ECF + ECS 4

Tissue-organ BW = AT + SM + bone + visceral organs + other tissues 5

Whole-body BW = head + trunk + appendages 3

Note. AT = adipose tissue; BCM = body cell mass; BM = body mass; BW = body weight; CHO = carbohydrates; ECF = extracellular fluid; ECS = extracellular solids; FFM = fat-free mass; FM = fat mass; M= mineral; Mo = bone mineral; Ms = soft tissue mineral; SM = skeletal muscle; TBPro = total body protein; TBW = total body water. Adapted from “Human Body Composition” by S.B. Heymsfield, 2005. Human Kinetics, Champaign, IL.

The anthropometric measures commonly used in body composition analysis include circumferences, subcutaneous adipose tissue skinfold measurement, and measures of segment length. With regards to circumferences, commonly measured points include the waist, abdomen, hips, arm, calf, forearm, thigh, and mid-thigh (Tran & Weltman, 1988; Weltman et al., 1988). 11

Skinfold measurement is also a common method of body composition analysis; this requires a skilled technician to obtain accurate results. Skinfold testing is utilized most often in sports and health clubs or in scientific research. The sites measured can vary based on sex and accuracy desired. The American College of Sports Medicine (ACSM) (2014) outlines the use of 3-site and 7-site testing procedures. These sites include the abdomen, triceps, chest, mid-axillary, subscapular, suprailiac, and thigh (ACSM, 2014). The body length measurements are not commonly used, other than height, except in the case of epidemiologic studies such as Bogin’s investigation into the relationship between leg length and health (Bogin & Varela-Silva, 2010).

Anthropometric measures are a useful assessment tool in large groups of people such as whole populations, large sample research, and health-related populations.

The 2-compartment model of fat mass and fat-free mass (FM vs. FFM) is the most used and recognized model of body composition analysis. Whereas FM is self-explanatory, FFM is composed of any and all material within the body that does not contain adipose tissue. This method assesses adipose tissue content, however, it lacks in distinguishing the components of

FFM. Techniques that use the 2-compartment model include underwater weighing (UWW) and

ADP. Each method uses body volumes in conjunction with the known densities of FM (0.9 g ∙ cm-3) and FFM (1.1 g ∙ cm-3) to derive body density (Siri, 1956). Many body composition predictive equations using body density are available based upon age, sex, and ethnicity. Two- compartment model analysis is relatively accurate when compared to anthropometry; however, the equipment associated with this method makes it unavailable for most individuals.

For the purpose of population-based body composition research, only methods of assessment from the whole body level are used. The use of whole body anthropometry (e.g., circumferences, skinfolds, and lengths) relates to the availability of equipment and accuracy of 12 measurement. Beyond the whole body model, there are four levels of varying complexity that can be utilized. These include tissue-organ, cellular, molecular, and atomic levels of analysis

(see Table 1).

In research using body composition analysis, use of the 2-compartment model is apparent. Encompassing fat and fat-free mass, the 2-compartment model appeals to many as a simplistic yet accurate method of estimating body composition. Many researchers utilize this model as it provides a basis for the assessment of risk associated with a high level of fat mass

(i.e., obesity).

Anthropometry and Aging

For the scope of this investigation, anthropometry includes three measures of the body; height, WC at the narrowest section between the xiphoid and the ilium, and waist circumference at the point level with the umbilicus. The choice of these two waist measurements relates to their use as a surrogate measure for abdominal visceral adipose tissue (Turcato et al., 2000, Harris et al., 1999, Zamboni et al., 1998, Pouliot et al., 1994). The presence of elevated levels of visceral fat is evidence of increased weight-related health risks (Harris et al., 1999).

Apart from their importance in assessing visceral fat, the abdominal measurement site is equally important in measuring subcutaneous adipose tissue. Studies have shown that a redistribution of body fat to a centralized location occurs as a result of aging (Harris et al., 1999;

Turcato et al., 2000; Mukuddem-Petersen et al., 2006). It is well accepted that increased abdominal fat distribution is associated with an increased disease risk, including cardiovascular disease (American Heart Association, 2013). With respect to the relationship between aging and increased risk for disease, an overview of measurement techniques in this population is needed. 13

Waist Circumference and Aging

When measuring WC, the current guidelines of many organizations lack a uniform

protocol. Mason and Katzmarzyk (2009) stated that measurements made at the level of the umbilicus (WCU) and at the narrowest waist (WCN) are commonly used in clinical and research settings. When using either the umbilicus or the narrowest point, Mason and Katzmarzyk have shown that both have high reliability scores without influence of sex or age. The intra-observer reliability of WC measured at the umbilicus and narrowest point are 0.992 and 0.989, respectively (Mason & Katzmarzyk, 2009). Therefore, regardless of the point of measurement reliable results can be obtained. However, the classification of obesity status may be different.

Without regard to specific measurement points, researchers have found that WC is

strongly correlated to body fat (Turcato et al., 2000 (narrowest waist; circumferences), Harris et

al., 1999 (largest circumference; dual-energy x-ray absorptiometry), Pouliot et al., 1994

(umbilicus; hydrodensitometry). Although WC strongly correlates with body fat, discrepancies

may be evident as a result of age-related changes in fat mass location. This is a result of an

increased central storage of adipose tissue without an overall increase in fat mass (Mukuddem-

Petersen et al., 2006).

An interesting finding of Turcato et al. (2000) is that WC, regardless of measurement

point, correlated well with cardiovascular health risks independent of BMI. This finding

illustrates issues with the use of BMI as an obesity indicator. Based on their findings,

Mukuddem-Petersen et al. (2006) suggests that a WCU measurement would be a better indicator

of obesity than BMI. One reason for this is that BMI does not account for age related changes in 14

a weight-stable person who may have lost muscle mass (sarcopenia) and/or gained fat-mass as a result of aging process (Mukuddem-Petersen et al., 2006).

As an epidemiological or public health tool, WC is an adequate measure of weight- related health risk in adult men and women. WC may be the best indicator of obesity as issues with age-related changes in adipose tissue location may influence the classification of a person

and WC may account for this. However, it appears that misclassifications may occur as a result

of inconsistent selection of measurement point. Currently, research suggests that a uniform

measurement procedure is needed (Harris et al., 1999).

Body Mass Index and Aging

The most widely used method to assess weight-related health risk is body mass index

(BMI). This index is a relationship between weight and height (kg/m2). This method is used worldwide by organizations such as the World Health Organization, Centers for Disease Control and Prevention, and the National Institutes of Health. Due to the ease of calculation and simple classification criteria, BMI has become a widely used method of classifying a person as underweight (<18.5), normal weight (18.5-24.9), overweight (25.0-29.9), or obese (30.0 and greater). However, there are potential issues that exist when utilizing BMI as a health risk assessment tool.

Although the International Obesity Task Force (IOTF) has developed sex and age specific percentiles for classifying children and adolescents via BMI, other organizations classify young adults on the same scale as an elderly adult (IOTF, 2014). This example illustrates that in young individuals, age-related changes in growth and development are accounted for using BMI percentiles. However, no such system exists for older adults undergoing changes in fat, muscle, 15

and bone as part of the aging process. This suggests the need for age and sex-specific criteria for

BMI across the lifespan.

Due to age-related changes, BMI may not reflect important changes in body composition

as we age. The discrepancies in BMI with regards to age can be addressed by factoring in age

and sex to properly classify this population, similar to what Ode et al. (2007) did in college

students and athletes. In their investigation, Ode et al. used sensitivity and specificity to determine proper classification criteria in athletes that were inaccurately classified as obese due to high volume of muscle mass. The methods may be repeated in a population at risk of changes in body composition that can affect BMI.

Bioelectrical Impedance and Aging

Bioelectrical impedance analysis (BIA) is a common method of body composition due to relative ease of use and availability. From simple handheld units, bathroom scales with built-in sensors, to complex research-grade systems, BIA measurement has become primary equipment used when measuring body composition. Simple systems with 2 contact points and a single frequency, such as a handheld unit or a scale-based system, are relatively inexpensive and easy to use, however they may provide less accurate measurements due to lower grade technology.

More costly, multi-frequency BIA systems that utilize multiple contact points can be extremely

accurate and reliable if conditions are met (e.g., no recent food or drink). If pre-test conditions

are not met, fluid balance may be affected and the test cannot be considered valid as BIA

depends on a normal fluid balance within the body.

The technology utilized by these systems is simplistic in nature based on the concept of

water conducting electricity. Any BIA system sends a small current through the body at one 16

point and receives it at another. This current moves quickly through lean mass and more slowly

through fat mass due to the conductive properties of each. The system measures the impedance

of the current as a result of fat mass. These systems rely on proprietary prediction equations

(from the manufacturer) to estimate fat mass and muscle mass (Visser & Harris, 2012). Issues

with BIA technology may occur if protocols are not followed, however Jackson et al. (1988)

validated BIA to the result of 94% and 96% in women and men, respectively. Researchers also

reported reliability between trials and measurers of 0.957 or greater for both men and women

(Jackson et al., 1988).

As with all measures of body composition with relation to an older adult population,

there are certain physiological changes that occur that affect the measurements. With respect to

BIA, issues may arise from prediction equations based on younger adults as well as with hydration levels affecting the measurement accuracy (Visser & Harris, 2012). In individuals with low hydration levels (i.e., dehydrated), BIA would overestimate body fat as the dehydrated tissue would slow the current and lead to inaccurate results. Conversely, in an overhydrated individual, the current may travel faster leading to a lower body fat estimation. These changes in hydration and the effect on BIA measurements may lead to inaccurate findings in an older population as total body water decreases with aging.

Through use of this technology, changes in body composition among an aging population can be examined. However, there can be issues with underestimating in non-obese persons and overestimating in obese persons when compared to the criterion measure of dual energy x-ray absorptiometry (DEXA) (Boneva-Asiova & Boyanov, 2011). Although limitations do exist, many researchers and public health officials recommend the use of BIA technology in the measurement of fat mass and associated health risk assessment. 17

Body Density and Aging

Densitometry, in relation to body composition, is the field of study relating to measuring

the density of the various compartments of the human body. For all intents and purposes, the

most common type is a two-compartment model in which the body is divided into a fat-mass compartment and a fat-free mass compartment. The fat-mass contains both visceral (essential) and subcutaneous (non-essential) adipose tissue. The fat-free mass contains lean body tissue, bone, total body water, and other non-adipose tissue components. Both fat and fat-free masses have different densities due to the properties of each compartment. These differences in density

and related properties have led to several techniques to assess body density.

In the field of body composition, hydrostatic weighing is considered the most accurate

assessment of body density and as such is the “real” gold standard. Based on the Archimedes

principle of displacement, by way of density differences, fat-free mass sinks and fat-mass floats.

By weighing a person completely underwater, and accounting for residual lung volume,

researchers can determine a body density using measured body mass and volume of water

displaced. This density is then used in an equation to estimate body fat mass content. Due to the

scientific principles at work, most body composition measurement methods are based on and

tested upon hydrostatic weighing values.

One such method is a technique called air-displacement plethysmography (ADP). This

method utilizes air volume displacement versus water displacement to estimate body volume

with an accuracy within 1% (Benton et al., 2011). This method, most commonly employed by

COSMED’s BODPOD GS Body Composition Tracking System, is being used more often as it is

easier to use and does not cause undue stress to the participant. Due to participant comfort and 18

accuracy of measurement, the BODPOD system has become a practical “gold standard” measure

in the area of body composition.

The aging process has been associated with an increase in fat mass and a decrease in

muscle mass independent of weight change (Bertoli et al., 2008). These changes relate directly

to the 2-compartment model of body composition analysis, thus rendering ADP an appropriate

measurement tool for body composition in people 50 and older. However, the aging process

does not only affect the fat mass compartment of the model.

Age has been shown to influence bone mineral content and hydration status in older

populations that may lead to inaccurate assumptions as to the density of compartments (Aleman-

Mateo, et al., 2007, Baumgartner et al., 1991; Bertoli et al., 2008; Wells & Fuller, 2001). These changes in hydration and bone may also be a result of sex related differences. When using the

ADP technology to assess body composition in older adults, these issues should be taken into account. In order to account for these changes, Aleman-Mateo et al. (2007) suggest the advent of a correction factor for total body water to account for changes in hydration status (Aleman-

Mateo et al., 2007). However, other researchers conclude that by comparison to 3-compartment and 4-compartment models, which are inherently more accurate, that the BODPOD is a valid and reliable method of body composition analysis (Aleman-Mateo, et al., 2007, Wells & Fuller,

2001). Although the ADP method may have errors associated with changes in the body as a result of aging, it appears to be an accurate and reliable technique when assessing body composition in older adults.

Summary

In conclusion, there is a large amount of research that supports the use of obesity indicators among adults, including BMI, WC, BIA, and ADP. However, there is also research that emphasizes the value of each measure over the others as appropriate tools for indicating obesity risk. It is not clear whether BMI, WCU, and WCN can be used to properly classify individuals who have undergone age-related changes in body composition. Further research is needed to assess each method as an indicator of obesity, particularly in regard to age-related changes in body composition.

CHAPTER III. METHODS

Subjects

Sixty men (n=22) and women (n=38) 50 years of age and older (59.9±7.9 years) were recruited for this investigation. Potential participants were screened prior to inclusion in the study to ensure that they met study parameters such as not claustrophobic (due to nature of testing equipment), weight less than 500 pounds (manufacturer’s limit of scale), over the age of

50 years, and willing to have body composition measured. Advertisement for potential participants were distributed and posted in common areas in the Bowling Green area such as libraries, senior centers, community centers, restaurants, retail establishments, campus postings, and online via Campus Update services. Additionally, participants were recruited via personal and professional communication with the research team members.

Upon initial contact, an explanation and screening questionnaire were given to each participant prior to inclusion. Interested participants were asked to report to the Exercise

Physiology Laboratory at Bowling Green State University for their testing visit. Following arrival and introduction, participants completed an informed consent (see Appendix A) and a screening and demographic questionnaire (see Appendix B). The Human Subjects Review

Board at Bowling Green State University approved this investigation and each participant signed an informed consent document prior to participation.

Instrumentation

A multi-frequency bioelectrical impedance analysis system, the InBody 230 (Biospace

Inc., Cerritos, CA) was used. This tetrapolar system utilizes contact points on each foot and each

hand to measure resistance of small currents throughout the body (20 kHz and 100 kHz).

Resistance was used to calculate fat mass, fat-free mass (including lean body mass and skeletal muscle mass), total body water, and percent body fat.

The criterion measure, or “gold standard”, of body composition used in this investigation

was the BODPOD® GS Body Composition Tracking System (COSMED USA Inc., Chicago,

IL). This system utilizes air-displacement plethysmography (ADP) to assess body density in a

manner similar to that of hydrodensitometry. The BODPOD® system is equipped with a dual-

chamber configuration to measure body volume and an electronic scale to measure body mass.

The body volume and mass were used to calculate body density. Using body density, fat mass,

fat-free mass, and body fat percentages were calculated using a two-compartment estimation

equation. Estimation equation usage is dependent upon ethnicity and sex. General population

participants (i.e., non-African American) utilized the Siri (1956) equation. When testing African

American women (n=2), the Ortiz equation was used (Ortiz et al., 1992).

Pre-testing Protocol

Prior to testing, participants were instructed to follow certain requirements to ensure

accuracy of measurements. The pretesting requirements included no ingestion of food or drink

for 4 hours leading up to testing time and no vigorous exercise 4 hours prior to testing. The

researchers verbally verified these requirements at the beginning of the testing visit. If any 22 participants did not meet pre-testing criteria, the testing date was rescheduled and participants were instructed on the importance of meeting requirements.

Upon arrival participants were instructed to remove any clothing, such as long sleeves, which would interfere with blood pressure measurement. Participants were asked to remain seated during the informed consent and questionnaire process to allow heart rate and blood pressure to reach near-resting levels. The researchers measured blood pressure (BP) via auscultation. Resting heart rate (HR) was measured via 15-second count of the radial pulse.

Resting values were assessed as a precaution against adverse reactions that may affect testing measures (e.g., nervousness, hyperventilation, anxiety attack). Once HR and BP measures were recorded, the participant was instructed to change into proper attire for testing. Proper attire included compressive, Lycra®-based shorts, sports bra (for female participants), non-padded swimsuit, and Lycra® swim cap. Compressive, synthetic clothing was required, as it does not trap air against the body which can cause inaccurate measurements.

Procedures

Testing began with a height measurement via a stadiometer with subjects’ head in the

Frankfort plane. Body weight was measured using the electronic scale as part of the BODPOD® system. Height and weight were recorded to the closest 0.1-centimeter and 0.001 kilogram, respectively. These values were used to calculate body mass index (BMI); the equation used was weight/stature squared (kg/m2). This index was used to classify the participants’ weight-related health risk according to accepted BMI categories (World Health Organization, 2014; Centers for

Disease Control and Prevention, 2014). 23

Participants were asked to stand to have their waist circumference (WC) measured. WC was measured in two separate locations, at the narrowest point and at the level of the umbilicus.

The narrow WC (WCN) measurement was taken at the narrowest point of the abdomen between

the lower ribs and the iliac crest with the tape measure parallel to the floor (Lohman et al., 1992).

The umbilicus WC (WCU) measurement was taken over the umbilicus with the tape parallel to

the floor (Rexrode et al., 1998; Grinker et al., 2000). All measurements were recorded to the

nearest 0.1-centimeter using a spring-loaded Gulick tape as recommended by the ACSM (2014).

Next, participants underwent bioelectrical impedance analysis (BIA). The participant

was instructed to step onto the surface of the machine with bare feet and to grasp handles firmly.

Analysis measured three body composition compartments (i.e., fat mass, lean mass, total body

water).

The final measure was air-displacement plethysmography (ADP) using the BODPOD®

GS Body Composition Tracking System. The equipment was calibrated in accordance with

manufacturer’s specifications. The participant was instructed to enter and sit quietly in the

chamber for 2-3 trials of ~45 seconds each. They were asked to breathe normally while limiting

movement. Following the ADP body composition analysis, testing was completed. Participants

changed into their street clothes and were thanked for their time.

Statistical Analyses

Participants were dichotomized by sex and placed in subcategories according to age (e.g.,

50-59, 60-69, 70+ years) for analyses. Results were compiled and analyzed using IBM SPSS v.21 (IBM Corp., Armonk, NY). Analyses included Pearson correlations, Fisher’s r-to-z 24 transformation, and calculations of sensitivity, specificity, positive, and negative predictive values.

Correlations were calculated to observe relationships between all measures of weight related health risk and body composition. Fisher’s transformations were used to determine if there were significant differences in assessment technique correlation between sexes and age groups. The use of Fisher’s determined which (if any) techniques provided a significantly better predictor of obesity according to sex and age groups.

To dichotomize variables for sensitivity and specificity analyses, health-risk cut-points were defined as BMI ≥25 kg/m2 (National Institutes of Health (NIH) standard cut-point for overweight) and BMI ≥30 kg/m2 (NIH standard cut-point for obese). Cut-points of >88 cm for females and >102 cm for males were used for waist circumference at the narrow point (WCN) and waist circumference at the umbilicus (WCU) (NIH standard cut-point for high waist). Age appropriate (>50 years) body fat cut-points were used for females and males. Cut-points of ≥

36.5% for overweight and ≥ 42% for obese classification were utilized for females. For males, ≥

24% was used for overweight and ≥ 30% was used for the obese classification (as based on multiple regression by Gallagher et al., 2000).

Sensitivity and specificity analyses were conducted in a manner similar to Ode et al.

(2007). Participants were classified into the following categories: 1) true positive (TP; overweight/high waist and overfat), 2) false positive (FP; overweight/high waist and normal fat),

3) false negative (FN; normal weight/normal waist and overfat), and 4) true negative (TN; normal weight/normal waist and normal fat). Sensitivity was calculated as the ability of the anthropometric index (i.e., BMI or WC) to correctly classify overweight/high waist participants 25 as overfat based on body composition measurement with ADP (i.e., TP/ (TP+FN)). Specificity was calculated as the ability of the anthropometric index to correctly classify normal weight/waist participants as normal fat based on ADP (i.e., TN/ (TN+FP)). Positive predictive value (PPV) was calculated as the ability of the anthropometric index to correctly classify participants as overfat who were actually overfat based on ADP (i.e., TP/(TP+FP)). Negative predictive value (NPV) was calculated as the ability of the anthropometric index to correctly classify participants as normal weight/waist who were actually normal fat based on ADP (i.e.,

TN/ (TN+FN)). These values are presented as decimals between 0.0 and 1.0.

CHAPTER IV. RESULTS

Participant Characteristics

Participant characteristic data (mean ± SD) of age, height, weight, BMI, WCN, WCU,

%fat via BIA, and %fat via ADP are reported in Table 2. As shown in this table, the male

participant group was significantly taller, heavier, and had larger waist circumferences than the

female group. Females had significantly higher levels of body fat as measured by BIA and ADP.

Within male and female groups, no significant differences between age groups were observed.

Correlations

Table 3 shows the correlations between obesity indicators (BMI, WCN, WCU, BIA, and

ADP) for all male participants, males 50-59 years old, males 60-69 years old, and males 70+

years old. In the aforementioned table, significant correlations were found between all obesity

indicators in all males and males age 50-59; however, in males 60-69 and 70+ years old correlations ranged from 0.304-0.997 with very few significant relationships. Shown in Table 4 are the correlations between obesity indicators (BMI, WCN, WCU, BIA, and ADP) for all female participants, females 50-59 years old, females 60-69 years old, and females 70+ years old. As can be seen in this table, significant correlations were found between all obesity indicators in all females and females age 50-59; however, in females 60-69 and 70+ years old

correlations ranged from 0.742-0.995 with several significant relationships.

Significant differences between obesity indicators and ADP among sex and age groups

are shown in Table 5. It is shown that males age 50-59 had a significantly stronger relationship

between BMI and ADP than males age 60-69. Also shown is that females age 70+ had 27

significantly stronger relationships between WCN and ADP than all other female age groups.

No significant differences were found between sexes.

Sensitivity and Specificity

Figures 1 through 8 illustrate the participant results when comparing each indicator of

obesity to the criterion measure of body fat percentage via ADP. Results are shown for all

males, all females, and each age group by sex for each indicator (BMI-Overweight, BMI-Obese,

WCN, and WCU). Each participant is designated as either true positive (TP), false positive (FP), true negative (TN), or false negative (FN) based on test criteria and sex-specific body fat percentage criteria. Using these results, the values for sensitivity, specificity, positive, and negative prediction can be calculated. As shown in Table 6, all male participants have specificity values for BMI-Overweight, BMI-Obese, WCN, and WCU of 0.93, 0.50, 0.40, and

0.70, respectively. Accordingly, all females have specificity values for BMI-Overweight, BMI-

Obese, WCN, and WCU of 0.88, 0.91, 0.91, and 1.0, respectively.

Table 6 shows the sensitivity, specificity, positive, and negative predictive values for both sexes, all age groups, and for each measure (BMI, WCN, and WCU). Values for BMI are reported using both overweight and obese classification criteria. The ability to indicate obesity of each measure was found using each of the four statistical tests (sensitivity, specificity, positive, and negative predictive values). The majority of males have specificity values for BMI-

Overweight, BMI-Obese, WCN, and WCU of 0.93, 0.50, 0.40, and 0.70, respectively.

Accordingly, the majority of females have specificity values for BMI-Overweight, BMI-Obese,

WCN, and WCU of 0.88, 0.91, 0.91, and 1.0, respectively. 28

For all male participants, BMI-Overweight and WCU show strong values (≥0.80) for all four tests (sensitivity, specificity, positive, and negative predictive values); however, using BMI-

Obese and WCN measures the results were inconsistent. Within each age group for male participants, the resulting values ranged from 0.00-1.00. For all female participants, all measures

(BMI, WCN, and WCU) provided strong values (≥0.80) in all four statistical tests (sensitivity, specificity, positive, and negative predictive values). Among the female age groups, results ranged from 0.46-1.00 for each measure using the four tests. Overall, results for female participants using sensitivity, specificity, positive, and negative predictive values are visibly stronger when compared to male participants. This finding is more apparent among older age groups.

Figures 9 and 10 display the sensitivity values for BMI and WC across age groups and sexes. In Figure 9, it is demonstrated that there is strong sensitivity (≥0.80) in all age groups and sexes with the exception of all age groups of males using the obese classification. With a range from 0.25 to 0.75, BMI is not a strong indicator of obesity in men. Figure 10 illustrates the findings for sensitivity using WC measurements. In males, WCU and WCN are poor indicators of obesity (<0.80) across the age groups; however, WCU is a strong indicator in men over 70 years of age. Both WCU and WCN are very strong indicators (1.0) of overweight and obesity in women with the exception of females over 70 years using the WCN point (0.50).

Figures 11 and 12 represent specificity values for BMI and WC across age groups and sexes. In Figure 11, BMI is not a consistent indicator of overweight and obesity across male age groups with specificity values ranging from 0.5-1.0. In females, BMI had a strong specificity across all age groups (0.75-1.0) showing a strong ability to indicate normal weight status.

Specificity for WC is displayed in Figure 12 with strong specificity (>.80) for all males 29 regardless of measurement point. Females had strong specificity values across age groups (0.75-

1.0) using WCN; however, values decreased among females 50-59 and 60-69 years old using

WCU (0.50-0.65).

Positive and Negative Predictive Values

Figures 13 and 14 illustrate positive predictive values (PPV) for each obesity indicator across age groups and between sexes. Across BMI, the PPV’s were all above 60% with an exception in the male 60-69 age group with a value of 33% for obese classification. In WC, the PPV results were variable; however, in females WCN provided strong values (0.67-1.00) across age groups.

Similar findings were seen in males with the use of WCU (0.67-1.00).

Figures 15 and 16 illustrate negative predictive values (NPV) for each obesity indicator across age groups and between sexes. When identifying overweight status using BMI, the NPV provided strong results (above 60%) in both males and females across age groups. However, distinct sex differences were shown when using BMI to correctly identify non-obesity. Results were very high (near 100%) in women across age groups; however, in men the NPV values ranged from 50-75% indicating a sex difference. When using WC, all NPV values were above

60% between men and women across age groups with exception of WCN in men 70 and older.

Table 2. Participant Characteristics (N=60) Male Female 50-59 60-69 70+ All 50-59 60-69 70+ All n 9 10 3 22 26 7 5 38 Age 62.1±7.9 58.7±7.6 55.2±3.7 64.0±3.4 76.7±3.5 54.6±3.3 62.3±2.3 74.6±4.2 (yrs) (50-80) (50-81) Height 177.2±7.2** 164.3±6.8 176.4±8.8 176.4±5.6 182.6±6.8 165.1±5.8 167.3±8.1 156.1±3.8 (cm) (162-190) (153-179) Weight 86.5±13.2** 71.6±17.9 92.7±16.7 82.6±8.2 80.8±10.5 71.8±18.0 78.3±19.0 61.7±14.1 (kg) (68.2-115.8) (45.0-129.8) BMI 28.8±5.3 26.4±6.3 30.7±6.6 27.5±3.7 27.6±5.4 26.1±5.6 28.9±8.5 24.6±6.9 (kg/m2) (21.2-44.1) (18.9-45.8) WCN 95.9±9.3** 83.6±12.7 98.7±12.1 94.7±6.7 91.3±6.8 83.6±13.1 87.4±13.0 78.3±10.3 (cm) (80.9-113.7) (64.3-108.9) WCU 100.4±10.8** 91.1±13.6 103.3±14.7 97.3±6.9 102.0±7.5 90.3±13.3 93.6±14.8 91.4±16.4 (cm) (84.4-127.7) (67.8-117.9) 24.6±7.3 33.8±9.4** %Fat BIA 27.3±8.0 23.0±5.7 22.1±10.3 32.8±8.5 38.3±10.0 32.9±13.5 (10.6-42.1) (14.5-52.2) 28.3±6.3 36.2±8.5** %Fat ADP 29.8±5.3 26.1±6.9 31.0±7.0 34.8±8.8 40.3±7.7 37.9±8.5 (14.2-36.8) (17.8-51.4) Note: * p≤0.05; ** p≤0.01; Significant differences between sexes. No differences between age groups were noted for any variable. BMI: body mass index; WCN: waist circumference narrow; WCU: waist circumference umbilicus; BIA: bioelectrical impedance analysis; ADP: air-displacement plethysmography.

Table 3. Correlation Matrices for Obesity Indicators in Men (n=22) a. All %FAT %FAT BMI WCN WCU BIA ADP BMI .876** .907** .845** .625**

WCN .937** .754** .616** WCU .776** .701** %FAT BIA .788** %FAT ADP b. 50-59 yrs. (n=9) %FAT %FAT BMI WCN WCU BIA ADP BMI .870** .924** .920** .903** WCN .972** .801** .875** WCU .850** .889** %FAT BIA .860** %FAT ADP c. 60-69 yrs. (n=10) %FAT %FAT BMI WCN WCU BIA ADP BMI .864** .885** .655* .304 WCN .969* .595 .456 WCU .679* .532 %FAT BIA .789** %FAT ADP d. 70+ yrs. (n=3) %FAT %FAT BMI WCN WCU BIA ADP BMI .988 .997 .866 .928 WCN .997* .934 .975 WCU .903 .955 %FAT BIA .990 %FAT ADP Note: * p≤0.05; ** p≤0.01

BMI: body mass index; WCN: waist circumference narrow; WCU: waist circumference umbilicus; BIA: bioelectrical impedance analysis; ADP: air-displacement plethysmography. 32

Table 4. Correlation Matrices for Obesity Indicators in Women (n=38) a. All %FAT %FAT BMI WCN WCU BIA ADP BMI .885** .874** .790** .718** WCN .940** .778** .711** WCU .794** .741** %FAT BIA .879** %FAT ADP

b. 50-59 yrs. (n=9)

%FAT %FAT BMI WCN WCU BIA ADP BMI .937** .922** .749** .673** WCN .956** .784** .707** WCU .780** .715** %FAT BIA .944** %FAT ADP

c. 60-69 yrs. (n=10)

%FAT %FAT BMI WCN WCU BIA ADP BMI .796* .781* .853* .893** WCN .986** .875** .742 WCU .828* .766* %FAT BIA .797* %FAT ADP d. 70+ yrs. (n=3)

%FAT %FAT BMI WCN WCU BIA ADP BMI .915* .940* .862 .913* WCN .983 .803 .995** WCU .838 .977** %FAT BIA .847 %FAT ADP Note: * p≤0.05; ** p≤0.01

BMI: body mass index; WCN: waist circumference narrow; WCU: waist circumference umbilicus; BIA: bioelectrical impedance analysis; ADP: air-displacement plethysmography. 33

Table 5. Fishers’ Transformation for Significant Differences between Age Groups and Sexes

BMI WCN WCU BIA Male 50-59 ≠ 60-69 .03* .12 .13 .68 50-59 ≠ 70+ .88 .44 .66 .21 60-69 ≠ 70+ .21 .11 .22 .13 Female 50-59 ≠ 60-69 .25 .88 .83 .26 50-59 ≠ 70+ .32 <.01** .07 .47 60-69 ≠ 70+ .89 .01* .16 .85 Male ≠ Female 50-59 ≠ 50-59 .14 .30 .25 .29 60-69≠ 60-69 .07 .45 .50 .97 70+ ≠ 70+ .93 .50 .77 .25 All Male ≠ All .54 .54 .77 .28 Female Note: * p≤0.05; ** p≤0.01

BMI: body mass index; WCN: waist circumference narrow; WCU: waist circumference umbilicus; BIA: bioelectrical impedance analysis.

Figure 1. Scatterplot Graphs of Overweight Classifications in Men via BMI and %fat (ADP) a. All (N=22)

FP TP

TN FN

b. Age 50-59 years (n=9)

FP TP

TN FN

c. Age 60-69 years (n=10)

FP TP

TN FN

d. Age 70+ years (n=3)

FP TP

TN FN

Note: Quadrants are labeled to illustrate correct classifications (TP and TN) and misclassifications (FP and FN). The X-axis reference line represents the age and sex specific cut-point for overfat based on body fat percentage. The Y-axis reference line represents NIH cut- points for overweight/obesity based on anthropometric measure. BMI: body mass index, TP: true positive, TN: true negative, FP: false positive, FN: false negative. 36

Figure 2. Scatterplot Graphs of Overweight Classifications in Women via BMI and %fat (ADP) a. All (N=38)

FP TP

TN FN

b. Age 50-59 years (n=26)

FP TP

TN FN

c. Age 60-69 years (n=7)

FP TP

TN FN

d. Age 70+ years (n=5)

FP TP

TN FN

Figure 3. Scatterplot Graphs of Obese Classifications in Men via BMI and %fat (ADP) a. All (N=22)

FP TP

TN FN

b. Age 50-59 years (n=9)

FP TP

TN FN

c. Age 60-69 years (n=10)

FP TP

TN FN

d. Age 70+ years (n=3)

FP TP

TN FN

Figure 4. Scatterplot Graphs of Obese Classifications in Women via BMI and %fat (ADP) a. All (N=38)

FP TP

TN FN

b. Age 50-59 years (n=26)

FP TP

TN FN

c. Age 60-69 years (n=7)

FP TP

TN FN

d. Age 70+ years (n=5)

FP TP

TN FN

Figure 5. Scatterplot Graphs of Obese Classifications in Men via WCN and %fat (ADP) a. All (N=22)

FP TP

TN FN

b. Ages 50-59 years (n=9)

FP TP

TN FN

c. Ages 60-69 years (n=10)

FP TP

TN FN

d. Ages 70+ years (n=3)

FP TP

TN FN

Figure 6. Scatterplot Graphs of Obese Classifications in Women via WCN and %fat (ADP) a. All (N=38)

FP TP

TN FN

b. Ages 50-59 years (n=26)

FP TP

TN FN

c. Ages 60-69 years (n=7)

FP TP

TN FN

d. Ages 70+ years (n=5)

FP TP

TN FN

Figure 7. Scatterplot Graphs of Obese Classifications in Men via WCU and %fat (ADP) a. All (N=22)

FP TP

TN FN

b. Ages 50-59 years (n=9)

FP TP

TN FN

c. Ages 60-69 years (n=10)

FP TP

TN FN

d. Ages 70+ years (n=3)

FP TP

TN FN

Figure 8. Scatterplot Graphs of Obese Classifications in Women via WCU and %fat (ADP) a. All (N=38)

FP TP

TN FN

b. Ages 50-59 years (n=26)

FP TP

TN FN

c. Ages 60-69 years (n=7)

FP TP

TN FN

d. Ages 70+ years (n=5)

FP TP

TN FN

Table 6. Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value for All Indicators of Obesity, Sexes, and Age Groups

Sex Age Test Sensitivity Specificity PPV NPV BMI_OV 0.93 0.71 0.88 0.83 BMI_OB 0.50 0.67 0.56 0.62 ALL WCN 0.40 0.83 0.67 0.63 WCU 0.70 0.83 0.78 0.77 BMI_OV 1.00 1.00 1.00 1.00 BMI_OB 0.75 0.60 0.60 0.75 50-59 WCN 0.75 0.80 0.75 0.80 WCU 0.75 0.80 0.75 0.80 Male BMI_OV 0.83 0.50 0.71 0.67 BMI_OB 0.25 0.67 0.33 0.57 60-69 WCN 0.25 0.83 0.50 0.63 WCU 0.50 0.83 0.67 0.71 BMI_OV 1.00 1.00 1.00 1.00 BMI_OB 0.50 1.00 1.00 0.50 70+ WCN 0.00 1.00 0.00 0.33 WCU 1.00 1.00 1.00 1.00 BMI_OV 0.88 0.82 0.78 0.90 BMI_OB 0.91 0.96 0.91 0.96 ALL WCN 0.91 0.85 0.71 0.96 WCU 1.00 0.67 0.55 1.00 BMI_OV 0.80 0.75 0.67 0.86 BMI_OB 0.83 0.95 0.83 0.95 50-59 WCN 1.00 0.85 0.67 1.00 WCU 1.00 0.65 0.46 1.00 Female BMI_OV 1.00 1.00 1.00 1.00 BMI_OB 1.00 1.00 1.00 1.00 60-69 WCN 1.00 0.75 0.75 1.00 WCU 1.00 0.50 0.60 1.00 BMI_OV 1.00 1.00 1.00 1.00 BMI_OB 1.00 1.00 1.00 1.00 70+ WCN 0.50 1.00 1.00 0.75 WCU 1.00 1.00 1.00 1.00 Note: Results displayed as percentage of proper classification. BMI_OV: BMI Overweight Classification; BMI_OB: BMI Obese Classification; WCN: Waist Circumference Narrow; WCU: Waist Circumference Umbilicus. Sensitivity: the ability of an obesity indicator to properly identify obesity. Specificity: the ability of an obesity indicator to properly identify normal weight. Positive Predictive Value (PPV): the proportion of correct identification of obesity to all obesity identifications. Negative Predictive Value (NPV): the proportion of correct identification of normal weight to all normal weight identifications. 51

Figure 9. Sensitivity Results for Body Mass Index using Overweight and Obese Classifications across Age Groups and Sexes

BMI Sensitivity Across Age Groups and Sexes 1.00

0.80

0.60

0.40

0.20

0.00 Overweight Overweight Obese Obese Male Female Male Female

50-59 60-69 70+

Note: BMI Overweight refers to ≥25 kg/m2; BMI Obese refers to ≥30 kg/m2

Figure 10. Sensitivity Results for Waist Circumference using Narrow and Umbilicus Measurement Points across Age Groups and Sexes

WC Sensitivity Across Age Groups and Sexes 1.00

0.80

0.60

0.40

0.20

0.00 WCN WCN WCU WCU Male Female Male Female

50-59 60-69 70+

Note: WCN: Waist circumference measured at the narrowest anatomical point; WCU: Waist circumference measured at the umbilicus point 52

Figure 11. Specificity Results for Body Mass Index using Overweight and Obese Classifications across Age Groups and Sexes

BMI Specificity Across Age Groups and Sexes 1.00

0.80

0.60

0.40

0.20

0.00 Overweight Overweight Obese Obese Male Female Male Female

50-59 60-69 70+

Note: BMI Overweight refers to ≥25 kg/m2; BMI Obese refers to ≥30 kg/m2

Figure 12. Specificity Results for Waist Circumference using Narrow and Umbilicus Measurement Points across Age Groups and Sexes

WC Specificity Across Age Groups and Sexes 1.00

0.80

0.60

0.40

0.20

0.00 WCN WCN WCU WCU Male Female Male Female

50-59 60-69 70+

Note: WCN: Waist circumference measured at the narrowest anatomical point; WCU: Waist circumference measured at the umbilicus point 53

Figure 13. Positive Predictive Value Results for Body Mass Index using Overweight and Obese Classifications across Age Groups and Sexes

BMI Positive Predictive Values 1

0.8

0.6

0.4

0.2

0 Overweight Overweight Obese Obese Male Female Male Female 50-59 60-69 70+

Note: BMI Overweight refers to ≥25 kg/m2; BMI Obese refers to ≥30 kg/m2

Figure 14. Positive Predictive Value Results for Waist Circumference using Narrow and Umbilicus Measurement Points across Age Groups and Sexes

WC Positive Predictive Values 1

0.8

0.6

0.4

0.2

0 WCN WCN WCU WCU Male Female Male Female 50-59 60-69 70+

Note: WCN: Waist circumference measured at the narrowest anatomical point; WCU: Waist circumference measured at the umbilicus point 54

Figure 15. Negative Predictive Value Results for Body Mass Index using Overweight and Obese Classifications across Age Groups and Sexes

BMI Negative Predictive Values 1

0.8

0.6

0.4

0.2

0 Overweight Overweight Obese Obese Male Female Male Female 50-59 60-69 70+

Note: BMI Overweight refers to ≥25 kg/m2; BMI Obese refers to ≥30 kg/m2

Figure 16. Negative Predictive Value Results for Waist Circumference using Narrow and Umbilicus Measurement Points across Age Groups and Sexes

WC Negative Predictive Values 1

0.8

0.6

0.4

0.2

0 WCN WCN WCU WCU Male Female Male Female 50-59 60-69 70+

Note: WCN: Waist circumference measured at the narrowest anatomical point; WCU: Waist circumference measured at the umbilicus point 55

CHAPTER V. DISCUSSION

In this investigation, the majority of men and women over the age of 50 were correctly classified as normal weight when using ≤24.9 kg/m2 BMI criteria. With 71% of men and 82% of women over 50 years old having an ideal body weight based on BMI, these findings illustrate that BMI is an appropriate tool to estimate obesity in the majority of our participants in this age group. According to the American Heart Association definition of obesity, these individuals have a body weight at ideal standards and as such are not at risk for obesity and associated health concerns such as cardiometabolic diseases (American Heart Association, 2013). However, according to DeCaria et al. (2012) and Rothman (2008), as part of the normal aging process, increases in fat mass and decreases in muscle mass, without a change in total body mass, may negatively influence the use of BMI when identifying individuals as at risk for health-related concerns. In this investigation, BMI proved to be a strong identifier of overweight/obesity and changes associated with normal aging did not appear to impact these values.

Specificity values (i.e., proper identification of normal weight) for BMI across age groups provided various results (0.50-1.00) without a clear picture in men (Figure 11). In women, however, proper identification of normal weight across age groups resulted in strong findings (0.75-1.00) using accepted classifications (Figure 11). Although proper identification of normal weight was strong in women as they age, the current findings suggest that BMI may not be adequate in identifying normal weight in men. These findings demonstrate a potential sex difference in the use of BMI to identify normal weight (i.e., ideal body weight). However, as sample sizes were limited in some groups, particularly men and women 70 years and older, this finding requires further investigation. 56

Current findings illustrate that both WCN and WCU correctly identified healthy levels of abdominal fat in men and women between 67% and 85% of the time. Compared to BMI, WC provided similar results when used to identify individuals with healthy levels of abdominal fat.

These findings suggest that WC was an appropriate tool to estimate abdominal fat following the

American Heart Association’s criteria of excess body fat.

The proper identification of healthy levels of abdominal fat in men across age groups using WCN is consistently high (see Figure 12). Similar to findings in men across age groups, the ability to identify healthy abdominal fat in women using WCN yielded variable, but consistently high, results (i.e., 75% to 100%) (Figure 12). Additionally, the findings when using

WCU to properly identify healthy levels of abdominal fat in men demonstrated similar results ranging from 80-100% across age groups. Conversely, the results were less consistent when utilizing WCU to identify healthy levels of abdominal fat in women across age groups (0.50-

1.00) (Figure 12). These findings demonstrate a strong ability to identify healthy levels of abdominal fat in men across age groups when using WCU, and a moderate ability in women.

These results suggest the possible presence of a sex difference in the utility of waist circumference measures as a tool to identify healthy levels of abdominal fat in individuals over the age of 50 years.

Utilizing sensitivity (i.e., whether an individual is properly identified as overweight) comparisons of BMI compared to %fat, this investigation found that men and women who were overweight were properly classified 93% and 88% of the time, respectively. Similar to identification of normal weight, BMI was an appropriate estimate of overweight or above ideal body weight in both men and women. When calculating the rate of proper classification, it was 57

found that using BMI, sensitivity values were all above 80% for both men and women across all age groups (Figure 9). Therefore, BMI was shown to be a strong indicator of overweight status, which may be used to identify weight-related health concerns. These findings are in agreement with the recommendations from the American Heart Association (2013) on the use of BMI to identify health risk.

According to this investigation, BMI was determined to be an appropriate indicator of overweight status in men and women across age groups. These findings are in agreement with

Heymsfield et al. (2000) and Visser & Harris (2012) who demonstrated that using BMI to identify risk in individuals as they age remains stable. However, these findings contradict the findings by Rothman (2008) and Meeuwsen at al. (2010) that demonstrated the influence of sex in the use of BMI to identify overweight status in a population of aging adults. Specifically, both

Rothman and Meeuwsen demonstrated that BMI was more appropriate in women. The current study was similar to the investigations of Heymsfield et al. (2000) and Visser & Harris (2012) in terms of sample characteristics (e.g., race (Visser) and age (Heymsfield, Visser)) and comparison between anthropometry and body composition (Heymsfield, Visser). Further, differences may be the result of limited power (i.e., 60 vs. 14,107 participants (Meeuwsen)) and/or criterion method used (i.e., ADP vs. BIA (Meeuwsen)/DEXA(Rothman)).

Using BMI to identify obesity, current findings indicate that men were properly classified

50% of the time and women 91% of the time. Therefore, this investigation suggested that use of

BMI may lead to improper classifications in men when screening for obesity. Across age groups, it was found that using BMI, sensitivity values ranged from 25-75% (Figure 9).

Conversely, using BMI to identify obesity in women resulted in strong sensitivity (83-100%) 58

across all age groups (Figure 9). The findings of potential sex differences using BMI and obesity criteria may be unique to this investigation. The sex differences observed may be a result of unequal sample size (22 men vs. 38 women).

This investigation illustrated similar mean values of BMI between the sexes (28.8 M,

26.4 F), however, the body fat percentages were significantly different (28.3% M, 36.2% F).

This finding suggests that BMI is only appropriate when used to identify weight-related risk and not risk associated with excess fat. According to criteria suggested by Gallagher et al. (2000),

28.3% is above the cut-point (24%) for health risk in men and 36.2% is below the cut-point

(36.5%) for health risk in women. These values demonstrate the difficulty of relying on BMI as an indicator of excess fat.

Similar to BMI, distinct differences were illustrated when utilizing waist circumference

(WCU and WCN) measures to properly classify unhealthy levels of abdominal fat in men and women. In men, using waist circumference to identify unhealthy levels of abdominal fat resulted in a sensitivity of 40% (WCN) and 70% (WCU) (see Figure 10). Conversely, in women, classification of unhealthy levels of abdominal fat at the narrowest and umbilicus resulted in

91% and 100%, respectively (Figure 10). Regardless of WC measurement point, differences between men and women were shown in the identification of unhealthy levels of abdominal fat.

In addition to sex differences, there also appear to be differences in proper identification of healthy vs. unhealthy abdominal fat across age groups, particularly in men. Sensitivity ranged from 50-100% (Figure 10). As suggested by Baumgartner (2000), these findings may be related to the centralization of abdominal fat that occurs as part of the aging process. That would decrease the validity of WC at the narrowest point. These results contradict the findings of 59

Willis et al. (2007) and Mason & Katzmarzyk (2009) who found WCU to be an adequate

indicator of abdominal fat in both men and women. Willis et al. (2007) found a difference in

proper classification rate of only 14% between sexes, while the current investigation found a

difference of 30%. Mason & Katzmarzyk (2009) also showed a smaller sex difference (3%) when using WCU to identify unhealthy levels of abdominal fat. With inconsistent findings between sexes, this investigation illustrated some limitations to using WC to identify unhealthy

levels of abdominal fat in men over the age of 50.

Among women, proper identification of unhealthy levels of abdominal fat using WCN

provided strong results (Figure 10) with the exception of the 70+ age group. However, when

using WCU, a strong ability to identify unhealthy levels of abdominal fat in women resulted in

100% identification across all age groups. Using the results from this investigation, researchers

suggest that WCU is an adequate indicator of healthy levels of abdominal fat in women. These

data are in agreement with previous research by Willis et al. (2007), who found WCU to be the

most appropriate indicator of health risk. Further, these findings may be a result of the

relationship between unhealthy levels of abdominal fat and obesity-related health risk. This

relationship indicates that unhealthy levels of abdominal fat directly relate to increased risk of

comorbidities of obesity and has been identified by several investigators including Pouliot et al.

(1994), Harris et al. (1999), and Turcato et al. (2000).

The findings of potential sex differences in the use of obesity indicators may be a result of hormonal differences associated with the aging process in men and women as discussed by

Vermeulen (2002). Specifically, reductions in testosterone and growth hormone have been shown to influence declines in muscle mass, decreases in bone mineral density, and have a 60

negative correlation with abdominal fat mass. The rate of reduction in testosterone and growth

hormone is mediated by sex-specific biological processes (i.e., menopause and andropause). Sex

differences in body composition have been well established (Baumgartner, 2000; DeCaria et al.,

2012; Visser & Harris, 2012). Therefore, it stands to reason that potential sex differences would

occur when using BMI and WC to identify obesity in aging men and women.

Practical Implications

Methods to estimate obesity, such as BMI (≥ 30 kg/m2), WCU (>102 cm: M; >88 cm: F), and WCN (>102 cm: M; >88 cm: F), are commonly used in healthcare settings. However, if an individual is improperly classified utilizing these criteria, dangerous health implications such as unidentified risk for disease and comorbidities may occur. While misclassifications are present, the current findings illustrate proper classification using all indicators of obesity (i.e., BMI,

WCN, and WCU) across the entire sample 40-93% of the time.

Age-related changes in muscle mass and bone mineral density are well established and widely accepted (Baumgartner, 2000; DeCaria et al., 2012; Visser & Harris, 2012). These changes include a decrease in muscle mass (sarcopenia) over time that accelerates as part of the normal aging process. In addition to a loss of muscle mass, a loss in bone mineral density is evident in the aging process. As individuals age, changes in muscle mass and bone mineral density directly affect body density and percent fat. However, current estimates that rely on anthropometric measures may not account for these changes and may result in improper classification of these individuals. 61

When using BMI, investigators properly classified 85% of individuals as obese, while

15% were misclassified. Of those misclassifications, 10% (2/3) were false positives - i.e., not classified as overfat by body fat but obese when using current BMI classification. False positives in this population may raise awareness of the importance of healthy diet and exercise habits. However, weight loss is highly related to morbidity as a person ages. If classified improperly and an individual chooses to reduce weight, they may put themselves at risk, as weight loss in an older individual is associated with functional limitations and increased mortality (Baumgartner, 2000). Conversely, false negatives account for 5% of our sample. This is a concern as these individuals are overweight or obese, however, they are not identified as at risk based on BMI criteria. This could potentially lead to the continuance of unhealthy behaviors that may increase the risk for associated diseases such as cardiovascular disease, diabetes, hypertension, high cholesterol, and other conditions.

When using WCN, individuals were properly identified 78.3% of the time with 21.6% being misclassified. Similar to BMI findings, misclassifications can either be false positive (less concerning) or false negative (more concerning). However, a false positive test demonstrates increased levels of abdominal fat that may indicate an increased risk for various metabolic diseases such as type 2 diabetes, dyslipidemia, and metabolic syndrome (American Heart

Association, 2013). When using WCN to classify obesity, 10% (6/60 participants) were false positives and 11.6% (7/60 participants) were false negatives. With 21.7% of the sample misclassified based on WCN results, it is not recommend that waist circumference measured at the narrowest waist be used to indicate obesity in this population. These findings are in agreement with previous investigations including Mason and Katzmarzyk (2009) and Willis et 62

al. (2007) that found waist circumference at the narrowest point to be a poor measure of abdominal fat and obesity.

When using WCU, investigators properly identified 76.6% of the sample with 23.3% being misclassified. Of the misclassifications, 18.3% of participants were false positives (11/60) and 5% (3/60) were false negatives. With a total misclassification rate of 23.3%, it is recommended to use caution when indicating obesity risk via waist circumference at the umbilicus. However, with only 5% of misclassifications being false negative, an argument may be made for the use of WCU. These findings agree with previous research by Willis et al. (2007) and Pouliot et al. (1994) that found waist circumference measured at the umbilicus to be a good indicator of obesity.

With results that vary among men and women and across age groups, it is recommended that healthcare providers use a method that best fits their practices with the knowledge that improper classifications can occur regardless of the method used. Common practices of healthcare providers include the measurement of height, weight, and waist circumference. Using these three values, healthcare providers are able to utilize both BMI and WC to estimate obesity with minimal interruption to patient comfort. Healthcare providers must use the most accurate method available to them when classifying an individual for obesity risk. As such, healthcare providers must be aware of factors that may influence the validity of BMI, WCN, and WCU across populations.

Limitations/ Further Investigations

This investigation provides results to determine validity of various common estimates of obesity in Caucasians. While these findings are applicable to Caucasians, further work with more diverse populations is important, as racial differences may exist (Deurenberg et al., 1998).

With regards to participants, this sample included self-selected individuals, who are likely to be healthier than the general population. An assumption that this investigation made was the reliance on prediction equations that may or may not be appropriate for the sample measured.

This investigation relied on the ability of ADP to provide accurate assessments of body composition. However, ADP does not account for losses in bone mineral density as a result of aging. Due to changes that occur with aging, including increased fat mass, decreased muscle mass, and decreased bone mineral density, the use of dual energy x-ray absorptiometry (DEXA) is recommended in future investigations. As DEXA analysis can distinguish between muscle, bone, and fat, these results may provide insight into the impact of changes in muscle and bone on obesity indicators as individual’s age.

Conclusion

The current findings suggest that BMI is adequate as an indicator of obesity in both men and women over 50 years of age. Using a single measure, BMI may provide the best indication of overweight/obesity in this sample of adults. However, BMI is not an exact tool and may lead to improper classification of certain individuals. Based on our findings, this investigation suggests the use of WC at the level of the umbilicus as an additional measurement to ensure proper classification when using BMI as the primary indicator of obesity. WCU provided the best ability to properly identify normal weight across all age groups and between sexes. 64

Therefore, based on the results of the current investigation, it is recommended that the use of

WCU in conjunction with BMI will provide a stronger estimate of obesity-related health risk in adults over the age of 50 years.

REFERENCES

Aleman-Mateo, H., Huerta, R., Esparza-Romero, J., Mendez, R., Urquidez, R., & Valencia, M. (2007). Body Composition by the Four-Compartment Model: Validity of the BOD POD for Assessing Body Fat in Mexican Elderly. European Journal of Clinical Nutrition, 830- 836.

American College of Sports Medicine. (2014). Guidelines for Exercise Testing and Prescription. Baltimore: Wolters Kluwer Health Lippincott Williams & Wilkins.

American Heart Association. (2013). Guideline for the Management of Overweight and Obesity in Adults. Circulation, 1-69.

Baumgartner, R. (2000). Body Composition in Healthy Aging. Annals of New York Academy of Sciences, 437-448.

Baumgartner, R., Heymsfield, S., Lichtman, S., Wang, J., & Pierson, R. (1991). Body Composition in Elderly People: Effect of Criterion Estimates on Predictive Equations. American Journal of Clinical Nutrition, 1345-1353.

Benton, M., Swan, P., Schlairet, M., & Sanderson, S. (2011). Comparison of Body Composition Measurements with Whole Body Multi-frequency Bioelectrical Impedance and Air Displacement Plethysmography in Healthy Middle-aged Women. Health Care for Women International, 1068-1078.

Bertoli, S., Battezzati, A., Testolin, G., & Bedogni, G. (2008). Evaluation of Air-Displacement Plethysmography and Bioelectrical Impedance Analysis vs. Dual-Energy X-ray Absorptiometry for the Assessment of Fat-free Mass in Elderly Subjects. European Journal of Clinical Nutrition, 1282-1286.

Bogin, B., & Varela-Silva, M. (2010). Leg Length, Body Proportion, and Health: A Review with A Note on Beauty. International Journal of Environmental Research and Public Health, 1047-1075.

Boneva-Asiova, Z., & Boyanov, M. (2011). Age-related Changes of Body Composition and Abdominal Adipose Tissue Assessed by Bioelectrical Impedance Analysis and Computed Tomography. Endocrinology and Nutrition, 472-477.

CDC. (2000). Clinical Growth Charts. Centers for Disease Control and Prevention. http://www.cdc.gov/growthcharts/clinical_charts.htm

CDC. (2014). Healthy Weight- it’s not a diet, it’s a lifestyle! Centers for Disease Control and Prevention. http://www.cdc.gov/healthyweight/assessing/bmi/index.html 66

DeCaria, J., Sharp, C., & Petrella,R. (2012). Scoping Review: Obesity in Older Adults. International Journal of Obesity, 1141-1150.

Deurenberg, P., Yap, M., & van Staveren, WA. (1998). Body Mass Index and Percent Bdoy Fat: A Meta-Analysis among Different Ethnic Groups. International Journal of Obesity, 1164-1171.

Dietz, WH. (2015). The Responses of the US Centers for Disease Control and Prevention to the Obesity Epidemic. Annual Review of Public Health, 575-596.

Ding, J., Kritchevsky, S., Newman, A., & Taaffe, D. (2007). Effects of Birth Cohort and Age on Body Composition in a Sample of Community-based Elderly. American Journal of Clinical Nutrition, 405-410.

Flegal, K., Graubard, B., Williamson, D., & Gail, M. (2005). Excess Deaths Associated with Underweight, Overweight, and Obesity. Journal of American Medical Association, 1861- 1867.

Gallagher, D., Heymsfield, S., Heo, M., Jebb, S., Murgatroyd, P., & Sakamoto, Y. (2000). Healthy Percentage Body Fat Ranges: An Approach for Developing Guidelines based on Body Mass Index. The American Journal of Clinical Nutrition, 694-701.

Gallagher, D., Visser, M., Sepulveda, D., Pierson, R., Harris, T., & Heymsfield, S. (1996). How Useful Is Body Mass Index for Comparison of Body Fatness across Age, Sex, and Ethnic Groups. American Journal of Epidemiology, 228-239.

Grinker, J., Tucker, K., Vokonas, P., & Rush, D. (2000). Changes in Patterns of Fatness in Adult Men in Relation to Serum Indices of Cardiovascular Risk: the Normative Aging Study. International Journal of Obesity, 1369-1378.

Harris, T., Visser, M., Everhart, J., Cauley, J., Tylavsky, F., Fuerst, T., Zamboni, M., Taaffe, D., Resnick, H., Scherzinger, A., & Nevitt, M. (1999). Waist Circumference and Sagittal Diameter Reflect Total Body Fat Better Than Visceral Fat in Older Men and Women: The Health, Aging and Body Composition Study. Annals New York Academy of Sciences, 462-473.

Heymsfield, S., Lohman, T., Wang, Z., & Going, S. (2005). Human Body Composition. Champaign: Human Kinetics.

Heymsfield, S., Nunez, C., Testolin, C., & Gallagher, D. (2000). Anthropometry and Methods of Body Composition Measurement for Research and Field Application in the Elderly. European Journal of Clinical Nutrition, S26-S32. 67

IOTF. (2014). International Obesity Task Force. http://www.iuns.org/affiliated_bodies/international-obesity-task-force-iotf

Jackson, A., Pollock, M., Graves, J., & Mahar, M. (1988). Reliability and Validity of Bioelectrical Impedance in Determining Body Composition. Journal of Applied Physiology, 529-534.

Khaodhiar, L., McCowen, K., & Blackburn, G. (1999). Obesity and Its Comorbid Conditions. Clinical Cornerstone, 17-31.

Lesser, G., Kumar, I., & Steele, J. (1963). Changes in Body Composition with Age. Annals of New York Academy of Sciences, 578-588.

Lohman, T., Roache, A., & Martorell, R. (1988). Anthropometric Standardized Reference Manual. Human Kinetics, Champaign, IL.

Mason, C., & Katzmarzyk, P. (2009). Variability in Waist Circumference Measurements According to Anatomic Measurement Site. Obesity, 1789-1795.

Meeuwsen, S., Horgan, G., & Elia, M. (2010). The Relationship between BMI and Percent Body Fat, Measured by Bioelectrical Impedance, in a Large Adult Sample is Curvilinear and Influenced by Age and Sex. Clinical Nutrition, 560-566.

Mukuddem-Petersen, J., Snijder, M., van Dam, R., Dekker, J., Bouter, L., Stehouwer, C., Heine, R., Nijpels, G., & Seidell, J. (2006). Sagittal Abdominal Diameter: No Advantage Compared With Other Anthropometric Measures as a Correlate of Components of the Metabolic Syndrome in Elderly from the Hoorn Study. American Journal of Clinical Nutrition, 995-1002.

Ode, J., Pivarnik, J., Reeves, M., & Knous, J. (2007). Body Mass Index as a Predictor of Percent Fat in College Athletes and Nonathletes. Medicine & Sciences in Sports & Exercise, 403- 409.

Ortiz, O., Russel, M., Daley, T., Baumgartner, R., Waki, M., Lichtman, S., Heymsfield, S. (1992). Differences in Skeletal Muscle and Bone Mineral Mass between Black and White Females and their Relevance to Estimates of Body Composition. American Journal of Clinical Nutrition, 8-13.

Pouliot, M., Despres, J., Lemieux, S., Moorjani, S., Bouchard, C., Tremblay, A., Nadeau, A., & Lupien, P. (1994). Waist Circumference and Abdominal Sagittal Diameter: Best Simple Anthropometric Indexes of Abdominal Visceral Adipose Tissue Accumulation and Related Cardiovascular Risk in Men and Women. The American Journal of Cardiology, 460-468. 68

Rexrode, K., Carey, V., Hennekens, C., Walters, E., Colditz, G., Stampfer, M., Manson, J. (1998). Abdominal Adiposity and Coronary Heart Disease in Women. Journal of American Medical Association, 1843-1848.

Rothman, K. (2008). BMI-related Errors in the Measurement of Obesity. International Journal of Obesity, S56-S59.

Shaw, K., Srikanth, V., Fryer, J., Blizzard, L., Dwyer, T., & Venn, A. (2007). Dual Energy X- Ray Absorptiometry Body Composition and Aging in a Population-based Older Cohort. International Journal of Obesity, 279-284.

Siri, W. (1956). The Gross Composition of the Body. Advanced Biological and Medical Physics, 239-280.

Tran, Z. & Weltman, A. (1988). Predicting Body Composition of Men from Girth Measurements. Human Biology, 167-175.

Turcato, E., Bosello, O., Di Francesco, V., Harris, T., Zoico, E., Bissoli, L., Fracassi, E., & Zamboni, M. (2000). Waist Circumference and Abdominal Sagittal Diameter as Surrogates of Body Fat Distribution in the Elderly: Their Relation with Cardiovascular Risk Factors. International Journal of Obesity, 1005-1010.

U.S. Census Bureau. (2014). U.S. and World Population Clock. http://www.census.gov/popclock/

Vermeulen, A. (2002). Ageing, Hormones, Body Composition, Metabolic Effects. World Journal of Urology, 7-23.

Visser, M., & Harris, T. (2012). Body Composition and Aging. The Epidemiology of Aging, 275- 292.

Wells, J. & Fuller, N. (2001). Precision of Measurement and Body Size in Whole-Body Air- Displacement Plethysmography. International Journal of Obesity, 1161-1167.

Weltman, A., Levine, S., Seip, R., & Tran, Z. (1988). Accurate Assessment of Body Composition in Obese Females. The American Journal of Clinical Nutrition, 1179-1183.

Willis, L., Slentz, C., Houmard, J., Johnson, J., Duscha, B., Aiken, L., & Kraus, W. (2007). Minimal versus Umbilical Waist Circumference Measures as Indicators of Cardiovascular Disease Risk. Obesity, 753-759.

World Bank. (2014). World Development Indicators. http://data.worldbank.org/country/united- states 69

Zamboni, M., Turcato, E., Armellini, F., Kahn, H., Zivelonghi, A., Santana, H., Bosello, O. (1998). Sagittal Abdominal Diameter as a Practical Predictor of Visceral Fat. International Journal of Obesity, 655-660.

APPENDIX A: INFORMED CONSENT

Informed Consent for Body Composition Study

Introduction: You are being invited to participate in a study examining body composition measures (how much fat and muscle someone has) in individuals 50 and older. This project is a collaboration between Dr. Amy Morgan, a faculty member in Exercise Science, Dr. Mary-Jon Ludy, a faculty member in Clinical Nutrition, and several students.

Purpose: The purpose of this study is to compare several measures of body composition in adults. In general, the study will help to determine if we are classifying individuals correctly. Proper classification helps exercise professionals provide the most accurate health information.

Benefits of being a participant include:

- A comprehensive body composition analysis.

- Access to results and expert feedback after data is collected. This includes your testing results, which would cost approximately $200 at a health club.

Testing Procedures:

1. Arrive at laboratory

We will ask you to come to the laboratory at least 4 hours after any exercise and at least two hours after eating or drinking.

a. Read and sign informed consent document. a. You will read this entire informed consent document. b. You will ask any questions about participating in this study. c. After all your questions have been answered, you will have the option of:

Signing the informed consent (meaning that you agree to participate in this study), or

Deciding not to participate.

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b. Screening and demographic questionnaire.

You will complete a questionnaire asking about your sex, age, ethnic/racial background, height, weight, phone number, and email.

Upon completion of informed consent and pre-testing we will begin testing.

2. Testing (45 minutes)

- You will sit while completing informed consent, demographic, and physical activity questionnaire.

- You will dress in a swimsuit or tights shorts with sports bra (if applicable) for your body composition measurements.

- You will have your waist circumference measured by having a measuring tape placed around your waist.

- You will have your height measured while standing against the wall.

- You will have your abdomen measured while lying on your back on a table. The distance from the top of your stomach at your navel to the surface of the table will be measured.

- You will have your body composition measured using 2 methods.

o Method 1 (bioelectrical impedance): You will stand on an electronic scale and place your hands around handgrips. You should not participate in this measurement if you have a pacemaker or other artificial electrical medical device/electrical system.

o Method 2 (BOD POD): You will place a swim cap on your head and sit in an airtight chamber for 2-3 brief measurements (about 45 seconds each). You should not participate in this measurement if you are claustrophobic.

- You will dress in your own clothes.

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After Data Collection

You will have access to your results within one month after testing. Research team members can answer any questions regarding your results.

Voluntary nature: Your participation is completely voluntary. You are free to withdraw at any time. You may decide to skip questions (or not do a particular task) or discontinue participation at any time without penalty. Deciding to participate or not will not affect your relationship with Bowling Green State University.

Confidentiality: Your participation in this study will remain confidential. Hard copies of all data will be stored in a locked filing room. The principal investigator, co- investigators, and student assistants will be the only people with access to the data. The hard- copies will be retained for 3 years after the project ends, after which they will be destroyed by shredding. Electronic files will be stored on a portable flash drive in password-protected documents and will not be destroyed. The study will not be anonymous. Your name will be used when signing consent forms, at the screening visit, and when entering data into computer hardware for body composition testing. You will receive a “subject ID” number, which will be used on all paper documents after screening.

Risks: Minimal risk may be encountered during body composition assessments:

- BOD POD: There is a risk that you will experience anxiety and/or uneasiness when placed in the confined windowed chamber. This procedure, involving 2-3 measurements of approximately 45 seconds, will be monitored by laboratory staff and can be discontinued at any point as necessary. The BOD POD also has a “panic button” that you may press at any point during the assessment to stop the test. To minimize this risk, if you are claustrophobic, you will be excluded from the study.

- Bioelectrical impedance analysis: There is a risk that the small electrical signal transmitted through bioelectrical impedance analysis (to measure resistance of body tissues to the electrical flow, and thus estimate body fat and muscle mass) will interfere with implanted electrical devices. To avoid this risk, if you report having a pacemaker or other artificial electrical medical device/electrical system, you will be excluded from the study.

Contact information: If you have any questions about this research or your participation in this research, please contact the study investigators.

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Principal Investigator: Dr. Amy Morgan, Associate Professor, Exercise Science [email protected] 419-372-0596

Co-Investigator: Edward Kelley, Graduate Student, School of HMSLS [email protected] 419-372-0212

You may also contact the Chair, Human Subjects Review Board at 419 372-7716 or [email protected], if you have any questions about your rights as a participant in this research.

I have been informed of the purposes, procedures, risks and benefits of this study. I have had the opportunity to have all my questions answered and I have been informed that my participation is completely voluntary. I agree to participate in this research.

Participant Signature Date

Participant Name - Printed

BGSU HSRB - APPROVED FOR USE IRBNet ID # _552670 EFFECTIVE 01/10/2015 EXPIRES 01/09/2016 74

APPENDIX B: SCREENING AND DEMOGRAPHIC QUESTIONNAIRE

Name: ______

Subject ID: ______

Visit Date: ______

SCREENING AND DEMOGRAPHIC QUESTIONNAIRE

Please circle TRUE or FALSE for the following questions.

TRUE FALSE 1. I am not claustrophobic.

TRUE FALSE 2. I do not have a pacemaker or artificial electrical medical device(s)/electrical system(s).

TRUE FALSE 3. I am willing to attend 1 test visit lasting about 45 minutes each.

TRUE FALSE 4. I am willing to answer questions about my physical activity.

TRUE FALSE 5. I am willing to have my blood pressure measured.

TRUE FALSE 6. I am willing to have my weight measured.

TRUE FALSE 7. I am willing to have my height measured.

TRUE FALSE 8. I am willing to have my waist size measured.

TRUE FALSE 9. I am willing to have my abdomen measured.

TRUE FALSE 10. I am willing to wear a swimsuit or tight shorts with a sports bra (if applicable) to have my muscle and body fat measured. 75

Please fill-in or circle your answers to the following questions.

11. Sex: ______male; ______female

12. Age: ______years

13. Birthday (month/day/year): ______

14. Ethnic/Racial Background

1. White/Caucasian (non-Hispanic)

2. Asian/Pacific Islander

3. Hispanic

4. Black/African American

5. American Indian/Alaskan

6. Other (name): ______

7. Prefer not to answer

15. Height: ______inches

16. Weight: ______pounds

17. Phone Number: ______

18. Email: ______

Thanks for completing the screening and demographic questionnaire!