Honors thesis - Final Draft

EFFECTS OF DIETARY PROTEIN AND AEROBIC EXERCISE ON FUNCTIONAL CONNECTIVITY IN BRAIN REWARD CENTERS: A RESTING-‐STATE fMRI STUDY

By

Lexie Buchs

A Thesis Submitted in Partial Fulfillment Of the Requirements for an Undergraduate Degree with Honors

(Dietetics)

The College of Health and Human Sciences

Purdue University

May 2015

West Lafayette, Indiana

Approved by:

Reader: Richard Mattes, Ph.D.

Reader: Tara Henagan, Ph.D.

___________________________________________________________________

Honors Research Mentor: Wayne Campbell, PhD

2

ABSTRACT

The Salience Network (SN) interprets internal and external stimuli for emotion,

homeostatic regulation, and reward. The Default Mode Network (DMN) reflects resting

state brain activity. Previous data have demonstrated a disruption of these networks in

obesity. The purpose of this study was to examine the effects of dietary protein and aerobic

exercise on resting state activity in the SN and DMN using functional Magnetic Resonance

Imaging (fMRI) in 8 women ages 18-‐45 years old with a BMI of 30 to 40 kg/m2. On testing

days, breakfast and lunch were identical while dinner meals varied in protein (Normal

Protein: 15% vs. High Protein: 30% of energy as protein). Total energy intake on testing

days was prescribed at approximately 80% of the participants’ estimated daily energy

requirements to stimulate one day of moderate energy restriction. Participants completed

a pre-‐dinner scan five hours after lunch. After the pre dinner scan, subjects either rested or

exercised for 30 minutes at 60% of their estimated VO2max. Dinner was consumed

immediately after exercise or rest. The postprandial fMRI scan was completed one hour

after dinner. The independent component analysis did not reveal a SN but did reveal a

DMN. However, DMN activity was not influenced by meal consumption, acute aerobic

exercise, or the amount of protein at dinner. Resting state brain activity may not be

influenced by acute interventions and therefore long term inventions may be necessary for

normalizing resting-‐state neural activity in obese women.

3

ACKNOWLEDGEMENTS

I would like to thank Dr. Campbell for his guidance and support throughout this

project and for giving me many opportunities to learn about research. Thank you to Drew

Sayer for patiently mentoring me and for all of his help with this project. Without your

guidance and direction, I would not have been able to complete the honors degree. Thank

you to Greg Tamer for completing the data analysis and for providing his expertise

throughout the study. I would also like to thank the study participants for their dedication

and compliance to this study. This study was funded by the Indiana CTSI.

4

TABLE OF CONTENTS

Abstract …………………………………………………………………………………………………………..…………….2

Acknowledgements ………………………………………………………………………………………..………………3

List of Tables and Figures …………………………………………………………………………….………………...5

Introduction ………………..…………………………………………………………………………….………………......6

Subjects and Methods …………………………………………………………………………..…………………..........9

Results ……………………………………………………………………………..............................................................12

Discussion ……………………………………………………………………………......................................................13

References ……………………………………………………………………………......................................................17

Appendix …………………………………………………………………………….........................................................26

5

LIST OF TABLES AND FIGURES

Table 1: Subject Characteristics.…………………………………………………………………………………….19

Table 2: Dinner (High Protein or Normal Protein) …………………………………………………………20

Figure 1: Study Design …………………………………………………………………………………….……………22

Figure 2: Default Mode Network …………………………………………………………………...………………23

Figure 3. Pre-‐Meal Default Mode Network Activity………………………………………...……………….24

Figure 4. 1-‐Hour Post-‐Meal Default Mode Network Activity……………………………………..……..25

6

INTRODUCTION

Increased activity in a brain region results in a locally increased blood response in

that area and also an increased ratio of oxygenated to deoxygenated blood. Functional

Magnetic Resonance Imaging (fMRI) scan detects the difference of magnetization in

oxygen-‐rich versus oxygen-‐poor blood [1]. The resultant blood flow response is detected

by the fMRI scan as an increase in the blood-‐oxygen-‐level-‐dependent (BOLD) contrast, and

this is used as a marker of brain activity.

The human brain is organized into networks and the intrinsic activity of these

networks can be measured in the resting state using fMRI. These networks are important,

because it is becoming increasingly evident that they are organizational features of the

brain [2]. The Salience Network (SN) and the Default Mode Network (DMN) are two

networks that have been shown to be associated with feeding behavior. The DMN consists

of the posterior cingulate cortex, cuneus/precuneus, medial prefrontal cortex, medial

temporal lobe, and inferior parietal cortices. The SN consists of the anterior cingulate

cortex and insula. The DMN reflects baseline brain function in the resting state. The SN

reflects feeding behavior and reward and involves assessing internal and external stimuli.

Previous studies have found activation of the DMN and SN to be increased in overweight

and obese individuals in comparison to lean individuals [2]. Results from previous studies

have led to the idea that abnormal or increased activation in these networks may

contribute to overeating, and there is also a correlation between obesity and activation of

these networks [3]. Understanding these networks in overweight and obese individuals

and how acute and long-‐term changes in network activity are associated with food intake

7

behavior would be helpful when strategizing how to normalize network activity to reduce

overeating.

Moderate increases in dietary protein [4] and exercise [13] are common strategies

for weight control and therefore may represent potential interventions for normalizing

resting activity in obese individuals. For example, a 6-‐month exercise intervention

decreased resting state activity in the DMN although the intervention did not change

resting state activity in the SN [2]. However, the effects of acute exercise on the resting

state activity of the SN and DMN, or whether dietary protein modulates resting state

activity of these networks, have not been investigated. The purpose of this study is to

investigate the acute effects of aerobic exercise and dietary protein on the resting state

activity in the SN and DMN.

The broad aim of the study is to determine the acute effects of dietary protein intake

and aerobic exercise on resting state activity in the SN and DMN of obese women. Our

decision to include only obese women was guided by previous research demonstrating

greater neural responses to visual food cues in obese compared to healthy-‐weight

individuals [5-‐10] and also in women compared to men [11]. We hypothesize that network

resting state activity will be decreased 1-‐hour after consuming dinner compared to the pre

dinner assessment. We further hypothesized that a high protein dinner will elicit a greater

reduction in resting state activity compared to a normal protein dinner. Acute aerobic

exercise will result in a relatively greater resting state activity compared to rest.

8

The rank order of resting state SN and DMN activity under all conditions and time points is

hypothesized to be:

NPEx > NPR > HPEx > HPR

NPR: Normal Protein/Rest

HPR: High Protein/Rest

NPEx: Normal Protein/Exercise

HPEx: High Protein/Exercise

9

SUBJECTS AND METHODS

Subjects

Potential participants were recruited from public advertisements (flyers). Study

inclusion was based on the following criteria: 1) Women ages 18-‐45 years; 2) body mass

index between (BMI) 30-‐40 km/m2; 3) non-‐smoking; 4) not diabetic; 5) not pregnant or

lactating; 6) weight stable (± 3kg) for 3 months; 7) not severely claustrophobic; 8) and

willing to eat study food. Due to the use of the MRI scanner, participants with implanted

pacemakers and/or automated defibrillators or any ferromagnetic metal implanted in their

body were excluded from the study.

There were 41 total contacts, of which 11 women were screened for inclusion

criteria. Of these, 9 women were approved and began the study. Eight women completed all

study procedures.

The Purdue Biomedical Institutional Review Board approved all study procedures.

All subjects provided written informed consent regarding purpose, procedures, and

potential risks of the study. Each subject received monetary compensation for

participation.

Baseline Assessments

BMI (kg/m2) was determined by measuring the participants weight and height.

These measurements were completed at the Clinical Research Center at Purdue University.

The YMCA cycle sub-‐maximal exercise test was used to estimate each participant’s

maximal oxygen consumption.15

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Experimental Design and Procedures

The study consisted of five testing days for each participant. On the first testing day

the sub-‐maximal exercise test was completed. The remaining four testing days were

completed in random order and each testing day was separated by at least seven days. The

following four experimental conditions were evaluated: normal dietary protein with rest

(NPR), high dietary protein with rest (HPR), normal dietary protein with exercise (NPEx),

and high dietary protein with exercise (HPEx). On testing days, breakfast and lunch were

consumed in the metabolic research kitchen and dinner consumed at the Purdue MRI

Facility. Breakfast, lunch, and dinner provided approximately 20%, 30%, and 30% of the

participants estimated energy requirement, respectively. Total meals provided to the

participants included approximately 80% of the estimated daily energy requirement to

simulate one day of moderate energy restriction. Breakfast and lunch were identical on all

testing days but dinner meals varied in macronutrient distribution. The macronutrient

distribution of breakfast and lunch were 15% protein, 60% carbohydrate, and 25% fat. The

normal protein (NP) dinners were 15% protein, 60% carbohydrate, and 25% fat, while the

high protein (HP) dinner provided 30% of energy as protein, 45% carbohydrate, and 25%

fat., (Table 2) . Subjects were blinded to the protein level of the dinner meals. Dietary fat

intake was held constant and carbohydrate intakes adjusted to offset differences in protein

intake for the HP and NP dinners. On two of the four testing days participants pedaled on a

cycle ergometer for 30 minutes at 60% of their VO2max. On the other two testing days

participants rested for 30 minutes in a waiting room at the MRI facility. Participants

arrived at the Purdue MRI Facility on each of the four testing days at 5 pm. The study

design is found in Figure 1.

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Appetite Questionnaire: On testing days, participants rated their appetite (hunger and

fullness) every hour from 8am until 5pm as well as immediately before and after 1)

consumption of meals, 2) the exercise/sedentary activity, and 3) fMRI scans. Appetite was

rated using a 100-‐mm quasilogarithmic visual analog scale, with descriptors ranging from

“barely detectable” to “strongest sensation imaginable of any kind” [12].

Brain Scan using fMRI: Participants lay in a supine position and closed their eyes with no

external interaction but were instructed to stay awake. Participants were scanned in a 3

Tesla MRI scanner (GE Signa HDx). The entire head was scanned, and the areas of interest

were the SN and DMN.

Statistical Analysis: Independent Component Analysis (ICA) was utilized to identify

resting state networks (SN and DMN). This analysis was completed using the AFNI

software (available from: http://afni.nimh.nih.gov/). Repeated measure ANOVA (Mixed

Procedure) was used to examine main effects of exercise (exercise vs. rest), protein (high

vs. normal), time (before vs. 60 minutes after dinner), and all interactions on resting state

networks. These analyses were completed using SAS (Version 9.2). All data are presented

as mean ± SEM. Statistical significance was assigned when P < 0.05 and Tukey-‐Kramer

adjustment was used for post-‐hoc analyses as needed.

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RESULTS

Subject Characteristics

According to our inclusion criteria, the 8 women who completed the study

procedures were 29 ± 3 years old and had an average BMI of 35 ± 1.1 kg/m2 (Table 1).

Salience Network

After analyzing the resting state scans, the ICA did not reveal a SN.

Default Mode Network

The DMN was revealed and is shown in Figure 2. There was no change in DMN

activity among interventions indicating that the high protein dinner versus normal protein

dinner, aerobic exercise versus rest did not have independent or interactive effects on

network activities (Figure 3 and Figure 4). The ANOVA model demonstrated trend

(unadjusted p=0.0454, adjusted p=0.1134) for an increase in DMN activity 1-‐hour after

eating when subjects rested before dinner. However, this was not statistically confirmed

after correcting for multiple comparisons.

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DISCUSSION

The present study evaluated the effects of protein consumption and aerobic exercise

on the acute activity of two resting-‐state reward networks, the SN and DMN. Acute changes

in these two networks have never been studied. We hypothesized there would be a general

meal-‐induced reduction in SN and DMN activity 60 minutes after dinner. We further

hypothesized that a high protein dinner would result in a greater reduction in resting state

activity compared to a normal protein dinner. Acute aerobic exercise would result in a

relatively greater resting state activity compared to rest.

These hypotheses were based on previous research showing dietary protein [4] and

aerobic exercise [5] influencing subjective appetite sensations. Previous research has

shown that acute higher protein diets increase satiety in comparison to lower protein diets

and this results in a decreased energy intake [4]. A long-‐term high protein diet has been

shown to result in weight loss [16]. The relationship between dietary induced

thermogenesis and satiety [4], specifically because the thermic effect of protein is greater

then fat and carbohydrate, may be the reasoning behind dietary protein’s satiating effects.

Previous research has also demonstrated that aerobic exercise influences subjective

appetite and energy balance, though the results are sometimes conflicting [13]. Further, it

has been suggested that exercise effects on appetite may differ in men versus women;

specifically exercise has a tendency to increase hunger in women relative to men [13].

Sensations of appetite may be influenced by activity in DMN and SN-‐related brain

structures [2,14]. Also, exercise training has previously been shown to decrease DMN

activity [2]. This did not occur in this study, but instead there were no significant changes

in DMN activity after meal consumption and among interventions. These results suggest

14

that acute interventions may not influence resting state brain activity and therefore long-‐

term inventions may be necessary for normalizing resting-‐state neural activity in obese

women. Another possibility is that greater intensity, duration, and caloric expenditure of

exercise may be necessary to elicit acute changes in brain activity.

Looking at Figures 3 and 4, it seems that primarily the high protein with rest

condition drove the trend for an increase of DMN activity on resting days. These results are

contrary to our hypothesis of a greater reduction of DMN activity with a high protein meal.

However, the increase in DMN activity was not statistically confirmed after correcting for

multiple comparisons. The independent component analysis did not reveal a SN, and

therefore intervention effects on SN activity could not be evaluated.

A previous study assessed the effects of a 6-‐month exercise training intervention on

the DMN and SN in overweight and obese males and females. DMN activity was decreased

following the 6-‐month exercise-‐training program relative to baseline. However, greater fat

mass loss was associated with greater reductions in DMN activity [2]. This correlation

between fat loss and DMN activity cannot be used to infer causality. It is possible that

exercise training and improvements in fitness reduced DMN activity. Conversely, exercise

training may decrease fat mass, which may also decrease DMN activity. Our results show

that acute aerobic exercise, which did not influence overall fitness level or fat mass, did not

influence DMN activity. These results suggest that modulation of resting state brain activity

may be driven by adaptations to chronic exercise training rather than acute exercise.

The resting state SN and DMN are important because they process homeostatic

information. The DMN is specifically associated with self-‐monitoring behavior [3] and is

more active during interoceptive processing, which is related to processing of internal

15

stimuli. The SN is associated with the reward system and shows greater activation when an

individual is anticipating food consumption [3]. We expected to observe a SN because

previous studies have revealed this network using the same standard techniques [2, 3, 11].

However our analysis did not reveal this network.

Strengths and Limitations

The strengths of this study include extensive dietary controls and supervised

exercise sessions to ensure adherence to our diet and exercise interventions. All subjects

were blinded to the protein content of the meals, so any cognitive biases were avoided.

Our small homogenous group of subjects, obese young women, is a limitation. A

larger subject group may provide greater statistical power to detect a SN and changes in

DMN activity among interventions. Including a more heterogeneous group of men and

various age groups would increase the generalizability of these findings. Inclusion of a

normal weight group would enable comparisons of resting state brain activity in normal

weight versus obese women. Also this would allow an investigation of whether weight

status influences acute effects of exercise and meal consumption on resting state brain

activity. In this study, scanning was completed in the evenings, beginning at 5pm; whereas

most existing research completed resting state scanning in the morning. This may have

influenced our results, however further research is needed to confirm time of day effects.

Further Research

Since this pilot study was the first to test and analyze the effect of protein

consumption and aerobic exercise on acute activity in these reward networks, further

research should be done to confirm that there is no change in activity from these

interventions. Further research should especially be done with a larger subject group,

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along with both men and women of varying BMI’s. Previous research showed decreased

reward network activity in a 6-‐month exercise training intervention [2], therefore further

research should be done to determine at what time point exercise training begins to

decrease network activity.

Conclusion

In conclusion, neither high protein meals nor aerobic exercise had acute effects on

DMN activity in obese women ages 18-‐45 years old. Conclusions cannot be made regarding

the effects of dietary protein or exercise on SN activity. Acute dietary protein and aerobic

exercise may not be modulators of resting-‐state neural activity in obese women and

therefore may not be effective strategies for decreasing resting-‐state neural activity in

obese women.

17

REFERENCES

1. Huettel, S.A., A.W. Song, and G. McCarthy, Functional magnetic resonance imaging. 2nd ed. 2008, Sunderland, Mass.: Sinauer Associates. xvi, 542 p.

2. McFadden, K.L., et al., Effects of exercise on resting-‐state default mode and salience network activity in overweight/obese adults. Neuroreport, 2013. 24(15): p. 866-‐71.

3. Garcia-‐Garcia, I., et al., Alterations of the salience network in obesity: A resting-‐state fMRI study. Hum Brain Mapp, 2012.

4. Halton, T.L. and F.B. Hu, The effects of high protein diets on thermogenesis, satiety and weight loss: a critical review. J Am Coll Nutr, 2004. 23(5): p. 373-‐85.

5. Martin, L.E., et al., Neural mechanisms associated with food motivation in obese and healthy weight adults. Obesity (Silver Spring), 2010. 18(2): p. 254-‐60.

6. Karhunen, L.J., et al., Regional cerebral blood flow during food exposure in obese and normal-‐ weight women. Brain, 1997. 120 ( Pt 9): p. 1675-‐84.

7. Rothemund, Y., et al., Differential activation of the dorsal striatum by high-‐calorie visual food stimuli in obese individuals. Neuroimage, 2007. 37(2): p. 410-‐21.

8. Stice, E., et al., Relation of reward from food intake and anticipated food intake to obesity: a functional magnetic resonance imaging study. J Abnorm Psychol, 2008. 117(4): p. 924-‐35.

9. Horstmann, A., et al., Obesity-‐Related Differences between Women and Men in Brain Structure and Goal-‐Directed Behavior. Front Hum Neurosci, 2011. 5: p. 58.

10. Goldstone, A.P., et al., Fasting biases brain reward systems towards high-‐calorie foods. Eur J Neurosci, 2009. 30(8): p. 1625-‐35.

11. Cornier, M.A., et al., Sex-‐based differences in the behavioral and neuronal responses to food. Physiol Behav, 2010. 99(4): p. 538-‐43.

12. Stubbs, R.J., et al., The use of visual analogue scales to assess motivation to eat in human subjects: a review of their reliability and validity with an evaluation of new hand-‐held computerized systems for temporal tracking of appetite ratings. Br J Nutr, 2000. 84(4): p. 405-‐15.

13. Stensel, D., Exercise, appetite and appetite-‐regulating hormones: implications for food intake and weight control. Ann Nutr Metab, 2010. 57 Suppl 2: p. 36-‐42.

14. Tregellas, J.R., et al., Altered default network activity in obesity. Obesity (Silver Spring), 2011. 19(12): p. 2316-‐21.

15. Thompson, W.R., N.F. Gordon, and L.S. Pescatello, eds. ACSM's Guidelines for Exercise Testing and Prescription. 8th Edition ed. 2010, Lippincott Williams & Wilkins: Philadelphia, PA.

16. Wycherley, T.P., L.J. Moran, P.M. Clifton, M. Noakes, and G.D. Brinkworth, Effects of

18

energy-‐restricted high-‐protein, low-‐fat compared with standard-‐protein, low-‐fat diets: a meta-‐analysis of randomized controlled trials. Am J Clin Nutr, 2012. 96(6): p. 1281-‐ 98.

19

TABLES AND FIGURES

Table 1. Subject Characteristics

20

Table 2. Dinner (High Protein or Normal Protein) Macronutrient Composition1 Property High Protein Meal Normal Protein Meal Total Energy (kcals) 811.9 811.9 Protein (g, % Energy) 60.9, 30% 30.4, 15% Carbohydrate (g, % Energy)

91.3, 45% 121.8, 60%

Fat (g, % Energy) 22.6 25% 22.6, 25% 1All values are mean ± SEM.

21

Figure1: Study Design. Schematic of testing day procedures.

Figure 2: Default Mode Network. AFNI was used to create statistical parametric maps to

depict resting state Default Mode Network activity with all sessions combined (n=64 total

sessions).

Figure 3: Pre-‐Meal Default Mode Network Activity. All values are mean ± SEM. Repeated

measures ANOVA (MIXED Procedures, SAS, version 9.2) was used to test for differences in

Default Mode Network Activity on the 4 testing days. Default Mode Activity was not

different on these testing days.

Abbreviations: NPR, Normal Protein/Rest; HPR, High Protein/Rest; NPEx, Normal

Protein/Exercise; HPEx, High Protein/Exercise

Figure 4: Post-‐Meal Default Mode Network Activity. All values are mean ± SEM. Repeated

measures ANOVA (MIXED Procedures, SAS, version 9.2) was used to test for differences in

Default Mode Network Activity on the 4 testing days. Default Mode Activity was not

different on these testing days.

Abbreviations: NPR, Normal Protein/Rest; HPR, High Protein/Rest; NPEx, Normal

Protein/Exercise; HPEx, High Protein/Exercise

22

Figure 1. Study Design

23

Figure 2. Default Mode Network

24

Figure 3. Pre-‐Meal Default Mode Network Activity

0

1

2

3

4

5

6 D

MN

Act

ivity

(z-s

core

)

NPR HPR NPEx HPEx

Pre-‐Meal Default Mode Network Activity

25

Figure 4. 1-‐Hour Post-‐Meal Default Mode Network Activity

0

1

2

3

4

5

6 DMN Activity (z-‐score)

NPR HPR NPEx HPEx

1-‐Hour Post-‐Meal Default Mode Network Activity

26

APPENDICES

27

APPENDIX 1

Recruitment Flyer

28

Women Ages 18 to 45

Needed for a Research Study

Prof. Wayne Campbell

Department of Nutrition Science, Purdue University

We are looking for overweight women who would like to volunteer for a research study evaluating whether exercise performed before dinner

affects brain activity in response to viewing pictures of food. Participants will be compensated $200 for completing this study.

INTERESTED VOLUNTEERS SHOULD BE:

ü Female ü Age: 18 to 45 ü Overweight ü Not Smoking ü Not Pregnant

Measurements taken during the study will include brain activity using

functional magnetic resonance imaging, questionnaires about appetite, and a blood draw.

FOR MORE INFORMATION, contact

Drew @ (765) 494-8313 or Email: [email protected] Department of Nutrition Science, Purdue University; West Lafayette,

IN 47907

Drew

[email protected]

765-‐494-‐8313

John

ap

olza

n@pu

rdue

.edu

76

5-49

6-64

80

Drew

[email protected]

765-‐494-‐8313

Drew

[email protected]

765-‐494-‐8313

John

ap

olza

n@pu

rdue

.edu

76

5-49

6-64

80

Drew

[email protected]

765-‐494-‐8313

John

ap

olza

n@pu

rdue

.edu

76

5-49

6-64

80

Drew

[email protected]

765-‐494-‐8313

John

ap

olza

n@pu

rdue

.edu

76

5-49

6-64

80

Drew

[email protected]

765-‐494-‐8313

Drew

[email protected]

765-‐494-‐8313

John

ap

olza

n@pu

rdue

.edu

76

5-49

6-64

80

Drew

[email protected]

765-‐494-‐8313

John

ap

olza

n@pu

rdue

.edu

76

5-49

6-64

80

Drew

[email protected]

765-‐494-‐8313

John

ap

olza

n@pu

rdue

.edu

76

5-49

6-64

80

29

APPENDIX 2

Consent Form

31

APPENDIX 3

Appetite Questionnaire

32

APPETITE LOG Study code:___________

Please place one mark on each scale that best reflects your answer to each of the following questions at this time.

1. How strong is your feeling of hunger? 1. ____

Not at all Extremely

2. How strong is your feeling of fullness? 2. ____


3. How strong is your desire to eat? 3. ____


4. How strong is your “urge to eat”? 4. ____


5. How strong is your preoccupation with thoughts of food? 5. ____


6. How strong is your feeling of thirst? 6. ____


33

7. How strong is your desire to eat something salty? 7. ____


8. How strong is your desire to eat something fatty? 8. ____


9. How strong is your desire to eat something sweet? 9. ____


10. The shakiness of your hand is… 10. ____


11. How strong is your grip? 11. ____


12. How itchy is your scalp? 12. ____


34

APPENDIX 4

YMCA Submaximal Protocol

35

YMCA Submaximal Protocol

1st Stage 150 kgm (0.5 kp)

HR: <80 HR: _____

750 kgm (2.5 kp)

HR: _____

900 kgm (3.0 kp)

HR: _____

1050 kgm (3.5 kp)

HR: _____

HR: 80-‐89 HR: _____

600 kgm (2.0 kp)

HR: _____

750 kgm (2.5 kp)

HR: _____

900 kgm (3.0 kp)

HR: _____

HR: 90-‐100 HR: _____

450 kgm (1.5 kp)

HR: _____

600 kgm (2.0 kp)

HR: _____

750 kgm (2.5 kp)

HR: _____

HR: >100 HR: _____

300 kgm (1.0 kp)

HR: _____

450 kgm (1.5 kp)

HR: _____

600 kgm (2.0 kp)

HR: _____

36

APPENDIX 5

Resume

37

Lexie Buchs Education Purdue University – West Lafayette, IN 10/2011 – Present Bachelor of Science degree with Honors, Major: Dietetics, GPA: 3.40

Study Abroad - Dublin Institute of Technology – Dublin, Ireland 1/2014 – 5/2014

Adult and Child First Aid/CPR/AED Certified Blood Born Pathogen Certified

Work Experience Wiley Dining Court – Purdue University 10/2014 – Present Food Preparation, Food Service, Meal Preparation, Cleaning and Sanitation Campbell Nutrition Science Lab – Purdue University 5/2014 – Present MRI Secondary Operator, Data Entry into Microsoft Excel, Miscellaneous Lab Work Metabolic Kitchen – Purdue University 5/2014 - 8/2014 Data Entry, Food Preparation for scientific research studies Buffalo Wild Wings – Auburn, IN 5/2012 - 8/2012

Waitress, Cashier, Greeter, Cleaning and Sanitation Auburn Community Pool – Auburn, IN 5/10 - 8/10 & 5/11 - 8/11 Lifeguard, Swim Lesson Instructor Brown House Restaurant – Auburn, IN 3/2010 - 7/2010 Cook, Cashier, Food Service, Cleaning and Sanitation

Volunteer Experience Data Collection for a Pantry Study in a Purdue University Nutrition Lab Fall 2014 Completed 24-hour recalls with study participants and entered data into Microsoft Excel Lafayette Soup Kitchen Fall 2014 Serve and Prepare Food Mentor at the Ireland Pre-Departure Meeting 11/20/2014 Assisting students in finding housing, and preparing students to leave for Ireland Mentor at the Study Abroad Fair 9/10/2014 Advocating to interested students about the perks of studying abroad and answering questions Ecuador Medical Mission Trip 12/15/2012 - 12/23/2012 Assisted doctors and nurses in hospitals and visited children in orphanages Delta Zeta Painted Turtle 5K – Benefitting the Starkey Hearing Foundation 4/27/2013 Organized and participated in the race event Delta Zeta Turtle Tug - Benefitting the Painted Turtle Camp 10/2011 & 10/2012 Organized the competition, Team Leader Delta Zeta ‘Bowlarama’ - Benefitting the Starkey Hearing Foundation 4/2011 Organized the tournament, facilitated the event

Organizations & Societies Accomplishments & Awards Academy of Nutrition & Dietetics Honors Society.org Purdue University Nutrition Society Phi Sigma Theta Honors Society Purdue University Caduceus Club National Society of Collegiate Scholars Saint Michael’s Church Parishioner Intel International Science Fair 1st Place Air force Award 2011, Competitor 2010 & 2011

4546 Cr 16 Waterloo, IN 46793 � 260 908 1652 � [email protected]

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Honors thesis - Final Draft