GRADUATE PROGRAMS IN HUMAN NUTRITION: OREGON HEALTH & SCIENCE UNIVERSITY

GRADUATE PROGRAMS IN HUMAN NUTRITION: OREGON HEALTH & SCIENCE UNIVERSITY Self-reported fruit and vegetable intake does not significantly correlate to body composition measures: body mass index, waist circumference, and percent body fat Amanda Sullivan, Jessie Nindel-Edwards, & Molly Jennings 6/6/2011

Introduction Obesity, defined as a body mass index (BMI) greater than 30 kg/m 2, continues to be a growing problem with a prevalence of over 30% among adults in the United States (U.S.) (1). The American Dietetic Association (ADA) Dietetic Practice Group (DPG) for Weight Management states that obesity is a chronic disease that can further complicate other diseases such as diabetes, coronary artery disease, and cancer. The United States Department of Agriculture (USDA) has evaluated extensive research to create the 2010 Dietary Guidelines for Americans (2). With the increasing prevalence of obesity and chronic diseases it is important to discover what factors have an effect on these two public health concerns. Dietary intake has been shown to significantly impact body weight and overall health status. One study reported that an energy-dense, low-fiber, high-fat diet is associated with higher fat mass and greater odds of excess adiposity (3). One way to reduce dietary energy density is to decrease consumption of high fat foods and refined carbohydrates and to increase consumption of fruits and vegetables (4). Another study found that intake of fruits, vegetables and low-fat dairy are inversely related to BMI and waist circumference (WC) in women (5). Further, following a vegetarian diet is associated with having a lower BMI (6). More will be understood about how diet affects health by gaining a greater understanding of how fruit and vegetable (FV) intake specifically impacts weight distribution and body composition measurements. The strategies used to measure body composition vary in acceptance and application. The most common method used is BMI which only requires the weight and height of an individual to calculate. A limitation to measuring BMI is that it does not distinguish body weight from body composition such as fat and muscle mass. Recently, measuring WC to determine central weight 2

distribution has been thought to be an equal or better indicator of disease risk (7). This strategy depends greatly on the skill and consistency of the individual measuring and is based on the idea that central adiposity is more harmful than total body composition. Measuring percent body fat (%BF) seeks to determine health while accounting for total body fat. This measure is commonly assessed by using bioelectrical impedance analysis which measures the amount of time it takes an electrical current to travel through the body. The results of the test are dependent upon the hydration status of the participant (8). There are not any currently recognized standards for %BF taking into account age as well as gender, but recommendations based on BMI ranges have been proposed (9). While each measure of body composition, BMI, WC, and %BF, have some limitations, each are useful to help assess the health status of a population. This study explores how fruit and vegetable consumption affects measures of body composition. The primary aim of this study was to determine statistical correlations between self-reported FV intake in an adult population of males and females aged 20 to 85 years and each measure of body composition (BMI, WC, %BF). Our secondary aim was to visually compare the relationship between fruit and vegetable intake and measures of body composition. We hypothesized that self-reported fruit and vegetable intake would be correlated with healthy or recommended categories of BMI, WC, and %BF in male and female adults aged 20 to 85 years. To address this research query, we analyzed data collected at the Let s Get Healthy! Fair conducted by the Health Discoveries Program of Oregon Health and Science University. The results of this study will provide insight into the role a diet rich in FV plays in promoting healthy weight and body composition in adults. 3

Methods This project was designed as a cross-sectional, observational study to determine the correlation between fruit and vegetable intake and measures of body composition by analyzing data collected from the Let s Get Healthy! Fair. A total of 2,442 adults, aged 20-85 years, volunteered to participate in the Let s Get Healthy! Fair. The fair has been held in various locations around Oregon such as Oregon Museum of Science & Industry (OMSI), Hermiston, Madras and around the greater Portland metropolitan area. Participants were recruited to attend the fair using fliers, online videos, and a website. Prior to enrollment, participants were given information sheets about the study and, if they agreed to participate, a wristband imprinted with a random 8-digit barcode number so that data gathered at each station could be linked anonymously to the Let s Get Healthy! Health Discoveries Program (HDP) Database. Participation at each station was voluntary allowing for a randomized sample collection. Upon entry into the fair, demographic information collected from attendees including age, gender, race, and ethnicity was encoded to their wristband. Dietary intake was assessed using the Block Fruit and Vegetable questionnaire, which consisted of 28 questions (10). This questionnaire was designed to be completed in less than five minutes on a touch-screen computer. Of the 28 questions, seven were related to FV intake. Participants were provided with instant feedback regarding daily average dietary intake based on their answers to frequency and portion size of foods (10). At the body measurement station trained volunteers and medical experts used measuring tapes to gather height, weight, and WC and a Tanita bioimpedence scale were used to gather %BF 4

measures. BMI was calculated based on measured height and weight. All measures of body composition were entered by hand into the HDP Database. The original data gathered from participants was saved in its entirety on OCTRI s bioinformatics secure server. A subset of data was transferred to a Microsoft Excel spreadsheet for this study. We created a new document for filtering data of participants who did not meet the inclusion criteria. We were able to clean the data set by filtering out participants with incomplete data. Participants were excluded from our dataset if they were less than 20 years of age, greater than 85 years of age, had an incomplete dietary assessment, and/or had incomplete body composition measurements. Of the 2,442 adults who participated at the fairs, only 616 fit the inclusion criteria for this study, see Figure 1. The cleaned data was then used to quantify the incidence of overweight and obesity, determine self-reported fruit and vegetable intake, and analyze the relationship between fruit and vegetable intake and each measure of body composition (BMI, WC, and %BF) using Microsoft Excel s countif formulas. Body Composition Measures We compiled reference tables for each body composition measure, see Tables 1-3 for each gender and 3 different age groups (9,11). We categorized participants using reference tables for each measure of body composition into each body composition category. BMI categories were underweight, healthy weight, overweight, and obese. Categories for BF% were recommended, overweight, and obese. WC categories were recommended and at risk. Due to lack of standardized guidelines for gender, age, and categories of %BF (recommended, overweight and obese) we constructed %BF guidelines to be tailored to our project. In the literature we found 5

guidelines that only pertained to sports nutrition, see Table 4, and guidelines created by our research mentor that summarized data gathered in a recent study, see Table 5 (9,12). We compared and combined these tables to create %BF guidelines that took into account age, gender, and categories of %BF (recommended, overweight, and obese). Fruit and Vegetable Intake Our project followed USDA MyPyramid recommendations for males and females, see Table 6, with one cup of fruits or vegetables equal to one serving (13). Using these recommendations we were able to divide Fruit/Vegetable Scores (FV score) in terms of below recommended (FV score less than 11) and recommended (FV score greater than 15) FV intake for males and females. Participants labeled as consuming below the recommended amount of FV were consuming less than five servings of FV per day, while participants labeled as consuming the recommended amount of FV were consuming five servings or more of FV. The FV score for each participant was calculated using the Block Fruit-Vegetable-Fiber Screener, see Figure 2 for complete FV score calculations. We calculated servings of fruit/vegetable by using the fruit and vegetable score, which allowed us to divide the study population into those consuming below and recommended amounts of FV. Statistical Analysis Power was calculated using Simple Interactive Statistical Analysis; see Table 7 (14). An alpha value of 0.05 was adopted as a significant value. This alpha value with our population size of 616 resulted in an 89% power to detect a correlation between fruit and vegetable intake and measures of body composition. Our study population is described using mean, standard 6

deviation, and range. In addition, Pearson correlations were performed between fruit and vegetable intake and measures of body composition. EZ-Analyze (version 3.0 for windows, 2007) using Microsoft Excel (version 12.0 for Windows, 2006, Microsoft Corp, United States) was used for all the statistical analyses. P-values <0.05 were considered significant. Bar graphs illustrating the relationship between FV intake and measures of body composition were visually compared and analyzed. Results Population demographics are described in Table 8. Participant ages ranged from 20-85 years, BMI ranged from 13.5-55 kg/m 2, %BF ranged from 1.9-71%, and WC ranged from 21-58 inches. The study population was 63% female and 37% male. Comparison of Tables 9-12 for body composition measures and FV intake are described below. Measures of Body Composition: BMI, WC, & %BF For the overall population, 4% were underweight, 46% were within the healthy BMI range and 50% were considered overweight and obese. Segregating the data by gender, 49% of females and 41% of males were in the healthy BMI range. Separating the data further by age groups and gender, males aged 41 to 60 years had the highest percent in the overweight and obese categories (64%) and females aged 20 to 40 years had the lowest percent in the overweight and obese categories (41%). Compared to the total population of males, those aged 20 to 40 had the highest percent in the healthy BMI range (8%). Compared to the total population of females, those aged 20 to 40 also had the highest percent in the healthy BMI range (24%). Complete results for BMI are presented in Table 9 and Figure 3. 7

For the overall population, 73% fell into the recommended WC category and 27% fell into the at risk category. Segregating the data by gender, 17% of males were in the at risk category while 34% of females fell into the at risk category. Analyzing the data further by age groups within genders, males aged 20-40 years had the highest percent of recommended WC measures (87%) while females aged 61-85 years had the lowest percent of recommended WC measures (62%). Compared to the total population, females aged 20-40 had the highest percent in the recommended WC range (20%). Complete results for WC are presented in Table 10 and Figure 4. Of the total population, 61% fell within the recommended %BF range and 39% fell within the overweight and obese categories. Separating data by gender 53% of the males and 30% of the females were considered overweight or obese. Analyzing the data further by age groups within genders, females aged 41-60 years had the highest percentage in the recommended %BF category (69%), while males aged 20-60 had the lowest percent in the recommended category (45%). Complete results for &BF are presented in Table 11 and Figure 5. Fruit and Vegetable Intake The results showed that 47% of the population ate below the recommended servings of FV while 53% were eating the recommended amount. Segregating the data by gender, 18% of males and 35% of females reported consuming the recommended amounts of FV. Separating the data further by gender, males ages 20-40 had the highest percentage of participants in the recommended category for FV consumption, while females ages 41-60 had the highest 8

percentage of participants in the recommend category for FV consumption. Males ages 20-60 had the highest percentage for those consuming bellowing recommended amounts of FV while females ages 20-40 had the highest percentage for consuming below the recommended amounts of FV for that gender. Complete results for FV intake are presented in Table 12. Data for this project was also described in scatter plot graphs in Figures 6-8. When FV score was plotted against measures of body composition for the whole population, there were not any significant correlations found (p<0.05 indicating significance). Correlations for each graph are found in Table 13. Discussion The 2010 USDA Dietary Guidelines for Americans describe a diet plan appropriate for men and women to lead healthy lifestyles (2). These guidelines state that overconsumption of calories leads to overweight and obesity resulting in the development of chronic diseases. The USDA recommends that adults eat 1.5-2 cups of fruit and 2-3 cups of vegetables per day (see see Table 6 (13). These guidelines also report that in the U.S., on average, only 42% of adults consume the recommended amounts of fruits and only 59% consume the recommended amounts of vegetables. In previous studies, consumption of FV has not been directly compared to obesity prevalence or multiple measures of body composition. Our data showed similar results, when compared with USDA data, with only 52.6% of our population consuming the recommended amounts of FV. We predicted that individuals consuming below the recommended amount of FV would have body composition measures in 9

the overweight, obese, and at risk categories for BMI, %BF, and WC. However, many of the participants that consumed below the recommended amounts of FV were in the healthy and recommended categories for body composition measures. Most notably, of the participants consuming below the recommended amounts of FV, 73% fell within the recommended category for WC. About half of our population were considered overweight or obese by BMI, but only 27.3% were considered at risk based on WC. Central adiposity is deemed to be more indicative of chronic disease with a WC greater than 35 inches in women and greater than 40 inches in men associated with an increased risk of chronic diseases (15). A recent study found fruit, vegetable, and low-fat dairy intake was inversely correlated with BMI and WC in women (5). Within that study, WC, height, and weight were measured using standardized protocol, and BMI was calculated (kg/m 2 ). Measured WC and BMI were used, as measures of body composition, but %BF was not measured or compared. Overall females had larger WC measures than males. This is congruent with current research that has found the WC of women to be mildly larger than men and growing at the same or faster rate each year (16). Because of the wide range of results, the %BF measurements from the data set may not be accurate. The measurements for %BF ranged from 1.9-70%. Bioelectrical impedance, used to measure %BF, has about a 3% error margin, but results will vary depending on hydration status. Therefore, we concluded that %BF was not as good of an indicator as BMI and WC. 10

In our study we did not find a significant correlation between FV consumption and measures of body composition. When FV intake was plotted against body composition measures no statistically significant correlations were found (p<0.05 indicating significance). Therefore, we conclude that self-reported FV consumption does not correlate with BMI, WC, or %BF in our sample population. These findings could be attributed to individuals attending the fair, being more health conscious overall, consuming an overall lower calories diet in addition to low FV intake, and participating in regular physical activity. A major limitation to this study is the number of participants who did not completely fill out the dietary assessment form or submit to all the measurements collected at the body composition station. Since attendees of the fair did not have to participate at all of the stations, many of them were missing a component of data need for out study. Because of this we had to exclude over half of the participants from our data set, starting with 2442 participants. However, this still left a substantial data set of 616 participants (63% female, 37% male). While this study did not find any significant correlations between self-reported FV intake and body composition measures (BMI, WC, %BF), the role of the RD should still be to encourage FV intake. The new USDA guidelines recommend that Americans increase their intake of fruits and vegetables. Another USDA recommendation is to make half of each meal plate made up with vegetables. Registered Dietitians should continue to encourage clients to consume five servings or more of fruits and vegetables per day along with a balanced diet and regular physical activity to promote healthy weights and body composition measures. 11

Table 1 Body mass index (BMI) categories (11) Ranges (kg/m 2 ) Classifications Below 18.5 Underweight 18.5 to 24.9 Healthy weight 25.0 to 29.9 Overweight 30 or higher Obese Appendix Table 2 Waist circumference (WC) categories (11) Gender Recommended (inches) At Risk (inches) Males <40 >40 Females <35 >35 Table 3 Percent body fat (%BF) categories (9,12) Males Females Age Recommended Overweight Obese Recommended Overweight Obese 20-40 <19% 20-25% >25% <33% 33-38% >38% 41-60 <22% 22%-25% >25% <35% 35-38% >38% 42-85 <25% 25% >25% <36% 36-37% >38% Table 4 Suggested percent body fat for standards for adults (12) Classification Males Females Lean <8% <13% Optimal 8-15% 13-23% Slightly overfat 16-20% 24-27% Fat 21-24% 28-32% Obese (overfat) >25% >33% Table 5 Recommended percent body fat (%BF) based on body mass index (BMI) measures (9) Age Males Females 20-40 <19% <33% 41-60 <22% <35% 61-79 <25% <36% Table 6 Fruit and vegetable (FV) intake recommendations (13) Gender Age Fruit Vegetables Total Servings Females 19-51+ 1.5-2 cups 2-2.5 cups 3.5-4.5 cups Males 19-51+ 2 cups 2.5-3 cups 4.5-5 cups Table 7 Purpose of power for statistical analysis Correlation (r1) Power 0.05.89 0.01.60 Table 8 Population demographics Age (years) BMI (kg/m 2 ) Body Fat WC (inches) Ht (inches) Wt (pounds) (%) Mean+SD 43.9+15.4 25.9+5.6 26.5+11.0 34.4+5.7 66.1+4.1 162.0+39.8 Range 20-85 13.5-55 1.9-71 21-58 50-80 48-348 12

Table 9 Number of participants (n=616) in each body mass index (BMI) category Male Female Age Underweight Healthy Overweight Obese Underweight Healthy Overweight Obese (yrs) weight weight 20-40 1 49 40 21 7 92 47 21 41-60 3 27 32 22 4 76 49 33 61-85 0 17 10 6 8 23 16 12 Overall 4 93 82 49 19 191 112 66 Table 10 Number of participants (n=616) in waist circumference (WC) category Male Female Age Recommended At risk Recommended At risk (yrs) 20-40 97 14 120 47 41-60 67 17 101 61 61-85 26 7 37 22 Overall 190 38 258 130 Table 11 Number of participants (n=616) in each percent body fat (%BF) category Male Female Age (yrs) Recommended Overweight Obese Recommended Overweight Obese 20-40 50 31 30 118 23 26 41-60 38 9 37 112 11 39 61-85 19 0 14 40 3 16 Overall 107 40 81 270 37 81 Table 12 Number of participants (n=616) in each fruit and vegetable (FV) intake category Male Female Age (yrs) Below Recommended Below Recommended Recommended Recommended 20-40 53 58 81 86 41-60 29 35 68 94 61-85 17 16 24 35 Overall 119 109 173 215 Table 13 Pearson correlations for body composition measures vs. fruit & vegetable (FV) score based on Figures 6-8 Pearson P-value* Correlation BMI -0.052 p=0.199 WC -0.008 p=0.842 %BF 0.010 p=0.799 *p <0.05 indicates significance 13

Figure 1 Participant Inclusion & Exclusion Criteria 14

Figure 2 Calculation of Fruit & Vegetable (FV) Score Fruit and vegetable data came from the answers captured using the Block Fruit-Vegetable-Fiber Screener. The first seven questions on this screener pertained to fruit and vegetable intake. The Fruit/Vegetable (FV) score is the sum of items scores for the first 7 items on the Block Fruit-Vegetable-Fiber Screener. Each question regarding intake had the following coding: 0 = less than once a week 1 = once a week 2 = 2 to 3 times a week 3 = 4 to 6 times per week 4 = once a day 5 = 2 or more times a day. Fruit/vegetable servings (MyPyramid definition of servings per day) = -0.23 + [0.37*(FV score)] [0.55*Sex] Coding: Sex: Male = 0, Female = 1 Fruit & Vegetable Score Categories Score Result Summary <11 You are not eating enough fruit and vegetables 11-12 Your diet is like most Americans; low in fruits and vegetables 13-15 You are doing better than most people, but you are still not eating 5 servings of fruits and vegetables >15 You re doing very well in fruits and vegetables Fruit & Vegetable Servings Males Females Fruit/Vegetable Score Daily servings of fruits and vegetables Fruit/Vegetable Score Daily servings of fruits and vegetables <11 3.47 <11 2.92 11-12 3.84-4.21 11-12 3.29-3.66 13-15 4.58-5.32 13-15 4.03-4.77 >15 5.69 >15 5.14 Fruit & Vegetable Intake Categories Males Females USDA Recommended servings FV Score 4.5-5 cups # of servings USDA Recommended servings FV Score 3.5-4.5 cups # of servings Below Recommended <13 3.47-4.21 Below Recommended <12 2.92-3.29 Recommended >13 5.58-5.69 Recommended >12 3.66-5.14 15

Figures 3, 4, 5 Distribution of participants consuming below recommended amounts of fruits and vegetables in each body composition categories (BMI, WC, %BF) 16

Figures 6, 7, 8 Graphs correlating fruit and vegetable (FV) score with measures of body composition (BMI, WC, %BF) 17

References 1. Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and Trends in Obesity among US Adults, 1999-2008. JAMA: The Journal of the American Medical Association. 2010;303:235-41. 2. Dietary Guidelines for Americans, 2010. Washington, DC: US Dept of Agriculture and US Dept of Health and Human Services; 2010. USDA and DHHS publication 7th edition. 3. Mendoza JA, Drewnowski A, Christakis DA. Dietary Energy Density Is Associated With Obesity and the Metabolic Syndrome in U.S. Adults. Diabetes Care. April 2007;30:974-9. 4. Bes-Rastrollo M, van Dam RM, Martinez-Gonzalez MA, et al. Prospective study of dietary energy density and weight gain in women. The American Journal of Clinical Nutrition. 2008;88:769-77. 5. McNaughton SA, Mishra GD, Stephen AM, Wadsworth ME. Dietary patterns throughout adult life are associated with body mass index, waist circumference, blood pressure, and red cell folate. J Nutr. 2007;137:99-105. 6. Craig WJ, Mangels AR, American Dietetic Association. Position of the American Dietetic Association: vegetarian diets. J Am Diet Assoc. 2009;109:1266-82. 7. Huxley R, Mendis S, Zheleznyakov E, Reddy S, Chan J. Body mass index, waist circumference and waist hip ratio as predictors of cardiovascular risk--a review of the literature. Eur J Clin Nutr. 2010;64:16-22. 8. Segal KR, Van Loan M, Fitzgerald PI, Hodgdon JA, Van Itallie TB. Lean body mass estimation by bioelectrical impedance analysis: a four-site cross-validation study. Am J Clin Nutr. 1988;47:7-14. 9. Gallagher D, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y. Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr. 2000;72:694-701. 10. Block G, Gillespie C, Rosenbaum EH, et al. A rapid food screener to assess fat and fruit and vegetable intake. Am J Prev Med. 2000;18:284-8. 11. Weight-control Information Network. National Institute of Diabetes and Digestive and Kidney Disease (NIDDK) web site. http://www.win.niddk.nih.gov/publications/tools.htm#circumf. Accessed January 16, 2011. 12. Nieman DC. Fitness and sports medicine: A health-related approach. 3 rd ed. Palo Alto, CA: Bull Publishing Co; 1995. 13. Inside the Pyramid. USDA web site. http://www.choosemyplate.gov/foodgroups/index.html. Accessed June 5, 2011. 14. Uitenbroek, D. G. (1997). SISA Binomial web site. http://www.quantitativeskills.com/sisa/distributions/binomial.htm. Accessed February 7, 2011. 15. Janssen I, Katzmarzyk PT, Ross R. Waist circumference and not body mass index explains 18

obesity-related health risk. The American Journal of Clinical Nutrition. 2004;79:379-84. 16. Howel D. Trends in the Prevalence of Abdominal Obesity and Overweight in English Adults. Obesity. 2011. 19