The Relationship between Diet Quality, as Assessed by the Healthy Eating Index 2005, and Disease Risk Factors in Overwei...

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The Relationship between Diet Quality, as Assessed by the Healthy Eating Index 2005, and Disease Risk Factors in Overweight Children
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Woods, Alexis Letes'e
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Master's ( M.S.)
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University of Florida
Degree Disciplines:
Food Science and Human Nutrition
Committee Chair:
Mathews, Anne
Committee Members:
Janicke, David M
Percival, Susan S

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block -- children -- diet -- disease -- eating -- factors -- florida -- food -- frequency -- healthy -- index -- kids -- obesity -- overweight -- quality -- risk -- rural
Food Science and Human Nutrition -- Dissertations, Academic -- UF
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Food Science and Human Nutrition thesis, M.S.
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Abstract:
Objective: To evaluate the relationship between diet quality (2005 Healthy Eating Index (HEI-2005)) and disease risk factors in overweight children living in rural areas of north central Florida. Background: Estimates show that approximately 32% of children and adolescents, ages 6-11, are overweight, 17% are obese and 11% are extremely obese. Reducing childhood obesity is important because it predicts chronic diseases which are seen as early as childhood, as well as in adulthood. Even though there is existing literature assessing the diet quality of overweight children, limitations exist (i.e. there is little to no research on disease risk factors and its associations with diet quality, in overweight children). Methods: This cross-sectional study presents baseline data from the Extension Family Lifestyle Intervention Program. One hundred seventy eight overweight 7-12 year olds completed the Block Kids Food Frequency Questionnaire 2004 (FFQ) and underwent various physical measurements of height, weight, waist circumference, blood pressure (BP), and finger prick blood collection to determine hemoglobin A1c (HbA1c), triglycerides (TG), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C) and total cholesterol (TC) levels. Data from the FFQs were used to calculate the HEI-2005 score for each participant. Results: The majority of participants were female (56%), Caucasian (65%), and >97th percentile weight for height (82%). The mean total HEI-2005 score was 61.2 + or - 10.6. Only 2% of the participants met the government recommendations (80/100) for total HEI-2005 score. Participants scored poorly for Whole Grains, Sodium and calories from Solid Fat and Added Sugar component scores in addition to several that needed improvement. Results revealed several associations for children in the 85th to 95th percentile weight for height group: Total Cholesterol (TC) (p<0.01,r=-0.76) and LDL-Cholesterol (p<0.01,r=-0.74) were inversely associated with Total HEI-2005 score; TC was inversely associated with Total Fruit (p=0.05,r=-0.57), Total Vegetables (p=0.05,r=-0.57), Saturated Fat (p<0.01,r=-0.74) and Sodium (p<0.01,r=-0.77) component scores. Conclusions: Results of this study suggest that future weight management interventions should be designed to focus on improving dietary quality components in addition to decreasing caloric intake, as this also reduces chronic disease risks in overweight children.
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by Alexis Letes'e Woods.
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Thesis (M.S.)--University of Florida, 2012.
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Adviser: Mathews, Anne.
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1 THE RELATIONSHIP BETWEEN DIET QUALITY, AS ASSESSED BY THE HEALTHY EATING INDEX 2005, AND DISEASE RISK FACTORS IN OVERWEIGHT CHILDREN By A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2012

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3 ACKNOWLEDGMENTS It is a pleasure to thank the many people who made this thesis possible. It is her enthusiasm, her inspiration, and her great efforts to explain things clearly and simply she helped to make nutrition fun for me. I would like to thank the many people who have taught me nutrition/food science: my undergraduate teachers at University of Florida (especially Dr. Angeleah Browdy and Janis Mena), and my graduate teachers (espec ially Dr. Neil Shay). For their kind assistance with writing letters, giving wise advice, helping with various applications, and so on, I wish to thank Dr. Lynn Bailey, Dr. Bobbi Henken, and Dr. Harry Sitren. To Dr. Delores C.S. James, I thank her for prov iding encouragement, sound advice, good teaching, good company, and lots of great ideas. I would have been lost without her. I wish to thank my best friends, Shanita Bartlett Cummings and Emily (Nikki) Hines and my best friends as a graduate student, Just in Forde, Cheryl Rock, and Jonathan Wiggins, for helping me get through the difficult times, and for all the emotional support, camaraderie, entertainment, and caring they provided. I am grateful to the staff in the Department of F ood Science and Human Nu trition for helping the department to run smoothly and for assisting me in many different ways. Shelia Parker Hall, Marianne Mangone, Carmen Graham, Rhonda Herring, Julie Barber and Bridget Stokes deserve special mention. I wish to thank my entire extend ed family for providing a loving environment for me. My sister Rodrikca, and brother, Julius were particularly supportive. I would like to thank my favorite uncle /father figure David Harp, for all of his many prayers and every ad, that has helped to mold and encourage me. And to the

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4 man, with whom, I will spend the rest of my life, Keith A. Barr, thanks for always believing in me and loving me, no matter what. Lastly, and most importantly, I wish to thank my parents, Betty and J ulius Carroll. They bore me, raised me, supported me, taught me, and loved me. M y father taught me that the best kind of knowledge to have is that which is learned for its own sake. M y mother taught me that even the largest task can be accomplished if it i s done one step at a time. To them, I dedicate this thesis. I love you all

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5 TABLE OF CONTENTS p age ACKNOWLEDGMENTS ................................ ................................ ................................ .. 3 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ .......... 9 LIST OF ABBREVIATIONS ................................ ................................ ........................... 10 ABSTRACT ................................ ................................ ................................ ................... 12 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 14 2 LITERATURE REVIEW ................................ ................................ .......................... 16 Childhood Obesity ................................ ................................ ................................ .. 16 Body Weight Status ................................ ................................ ................................ 18 Contributing Factors of Obesity ................................ ................................ .............. 19 Genetic Factors ................................ ................................ ................................ 19 Environmental Factors ................................ ................................ ...................... 19 Behavioral Factors ................................ ................................ ........................... 21 Consequences of Obesity ................................ ................................ ....................... 22 Physical Health Consequences ................................ ................................ ........ 22 Social and Psychological Consequences ................................ ......................... 29 Healthy Eating Index ................................ ................................ ............................... 30 Background ................................ ................................ ................................ ...... 30 Original HEI Scoring ................................ ................................ ......................... 31 HEI 2005 Scoring ................................ ................................ ............................. 34 Current Use and Implications of the HEI 2005 ................................ ................. 35 3 METHODOLO GY ................................ ................................ ................................ ... 47 Participants ................................ ................................ ................................ ............. 47 Procedures ................................ ................................ ................................ ............. 48 Height and Weight ................................ ................................ ............................ 49 Waist Circumference ................................ ................................ ........................ 49 Blood Pressure and Heart Rate ................................ ................................ ........ 49 Blood Analysis ................................ ................................ ................................ .. 50 Physical Activity ................................ ................................ ................................ 50 Dietary Intake and HEI 2005 ................................ ................................ ............ 51 Statistical Tests ................................ ................................ ................................ ....... 52

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6 4 RESULTS ................................ ................................ ................................ ............... 53 Demographic Description of Participants ................................ ................................ 53 Anthropometric Description ................................ ................................ ..................... 53 Clinical and Dietary Characteristics, by Gender ................................ ...................... 54 Clini cal and Dietary Characteristics, by Race ................................ ......................... 54 Clinical and Dietary Characteristics, by BMI Percentile Distribution ....................... 54 Descriptive Statistics for HEI 2005 Score ................................ ............................... 55 HEI 2005 Scores by Gender ................................ ................................ ............ 55 HEI 2005 Scores by Race ................................ ................................ ................ 55 HEI 2005 Scores by BMI Percentile Distribution ................................ .............. 55 Adherence to Federal Physical Activity Guidance ................................ .................. 56 Does the Diet Quality, via the HEI 2005, of this Overweight Study Population Adhere to the DGA 2005? ................................ ................................ ................... 56 Association between Total HEI 2 005 Score and Disease Risk Factors .................. 57 Association between HEI 2005 Component Score and Disease Risk Factors ....... 57 Exploratory Analysis: Association between Total/Component HEI 2005 Score and Disease Risk Factors ................................ ................................ .................... 57 5 DISCUSSION ................................ ................................ ................................ ......... 70 Does the Diet Q uality, via the HEI 2005, of this Overweight Study Population Adhere to the DGA 2005? ................................ ................................ ................... 71 Association between Total HEI 2005 Score and Disease Risk Factors .................. 71 Association between HEI 2005 Component Scores and Disease Risk Factors ...... 72 Exploratory Analysis: Association between Total/Component HEI 2005 Score and Disease Risk Factors ................................ ................................ .................... 73 Limitations ................................ ................................ ................................ ............... 73 Implications for Future Research ................................ ................................ ............ 74 APPENDIX: BLOCK KIDS FOOD FREQUENCY QUESTIONNAIRE 2004 .................. 76 LIST OF REFERENCES ................................ ................................ ............................... 84 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 96

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7 LIST OF TABLES Table page 2 1 Body weight status for children and adolescents ................................ ............... 39 2 2 Task Force on Blood Pressure Control in Children Guidelines of Hypertension classification of Blood Pressure (BP) ................................ ............ 39 2 3 Original Healthy Eating Index (original HEI) components and standards for scoring ................................ ................................ ................................ ................ 40 2 4 2005 Healthy Eating Index components and standards for scoring .................... 41 2 5 Estimated Daily Calorie Needs for Children ages 4 13 years old ....................... 42 2 6 Recommended Daily Amounts of Food from Eac h Group for children ............... 43 2 7 Estimated 2005 Healthy Eating Index Component and Total Scores, United States, 1994 96 and 200 1 02 ................................ ................................ ............. 44 2 8 Estimated mean 2005 Healthy Eating Index total and component scores for children and adolescents ages 2 to 17, United States, 2003 04 ......................... 45 4 1 Participant demographic results, by gender, n=178 ................................ ........... 59 4 2 Characteristics of the sample, n=178 ................................ ................................ 59 4 3 Participant anthropometric results, by gender, n=178 ................................ ........ 60 4 4 Mean values for physical and blood parameters, by gender, n=178 .................. 60 4 5 Clinical and Dietary Characteristics, by Race, n=178 ................................ ......... 61 4 6 Mean values for physical and blood parameters, by BMI Percentile Distribution, n=178 ................................ ................................ ............................. 62 4 7 Comparison of Energy Consumption a nd Total and Component HEI 2005 Scores by Gender, n=178 ................................ ................................ ................... 63 4 8 Comparison of Age, Energy Consumption and Total/Component HEI 2005 Scores by Race, n=178 ................................ ................................ ...................... 64 4 9 Comparison of Total/Component HEI 2005 Scores by BMI Percentile Distribution, n=178 ................................ ................................ ............................. 65 4 10 Physical Activity of Participants, n=94 ................................ ................................ 65 4 11 Percent of participants meeting DGA 2005 goals, n=178 ................................ ... 66

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8 4 12 Association between Total HEI 2005 Scores and Disease Risk Factors, n=178 ................................ ................................ ................................ ................. 67 4 13 Association of HEI 2005 Components and Disease Risk Factors, n=178 .......... 67 4 14 Exploratory: Association of Total HEI 2005 and Disease Risk Factors, by BMI percentile groups, n=178 ................................ ................................ .................... 68 4 15 Exploratory: Association of HEI 2005 Components and Disease Risk Factors, by BMI percentile groups, n=178 ................................ .......................... 69

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9 LIST OF FIGURES Figure page 2 1 Prevalence of overweight among US children and adolescents ......................... 37 2 2 Prevalence of Obesity in US Ma les and Females Aged 2 t hrough 19 Years ...... 38 2 3 2005 Healthy Eating Index Component Scores for children and adolescents from 1999 2004 NHANES data. ................................ ................................ ......... 46

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10 LIST OF ABBREVIATION S AHEI Alternate Healthy Eating Index Block Kids FFQ B lock Kids Food Frequency Questionnaire 2004 BP Blood Pressure BMI Body Mass Index BMI z score Body Mass Index z score CVD Cardiovasc ular Disease CSFII Continuing Survey of Food Intakes by Individuals DGA 2005 2005 Dietary Guidelines for Americans DGOV Dark Green and Orange Vegetables E FLIP for Kids Extension Family Lifestyle Intervention Program for Kids FGP Food Guide Pyramid FGS Food Guide System HEI 2005 2005 Healthy Eating Index HRQOL Healthy Related Quality of Life HDL cholesterol High Density Lipoprotein Cholesterol HbA1c Glycosylated Hemoglobin HTN Hypertension LDL cholesterol Low Density Lipoprotein Cholesterol Original HEI Original Healthy Eating Index NHANES National Health and Nutrition Examination Survey SoFAAS Solid Fat, Alcohol and Added Sugar TC Total Cholesterol TG Triglycerides T2D Type 2 Diabetes

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11 USDA CNPP United States Department of Agriculture Center for Nutritio n Policy Promotion YHEI Youth Healthy Eating Index

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12 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science THE RELATIONSHIP BETWEEN DI ET QUALITY, AS ASSESSED BY THE HEALTHY EATING INDEX 2005, AND DISEASE RISK FACTORS IN OVERWEIGHT CHILDREN By May 201 2 Chair: Anne E. Mathews Major: Nutritional Sciences Objective: To evaluate the relationship between diet quality (2005 Healthy Eating Index (HEI 2005)) and disease risk factors in overweight children living in rural areas of north central Florida. Background: E stimates show that approximately 32% of children and adolescents, ages 6 11, are overweight, 17% are obese and 11% are extremely obese. Reducing childhood obesity is important because it predicts chronic diseases which are seen as early as childhood, as well as in adulthood. Even though there is existing literature assessing the diet quality of overweight child ren, limitations exist (i.e. there is little to no research on disease risk factors and its associations with diet quality, in overweight children). Methods: This cross sectional study presents baseline data from the Extension Family L ifestyle Interventio n Program. One hundred seventy eight overweight 7 12 year olds completed the Block Kids Food Frequency Questionnaire 2004 (FFQ) and underwent various physical measurements of height, weight, waist circumference, blood pressure (BP), and finger prick blood collection to determine hemoglobin A1c (HbA1c), triglycerides (TG), low density lipoprotein cholesterol (LDL C), high density lipoprotein cholesterol (HDL C) and total cholesterol (TC) levels. Data from

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13 the FFQs were used to calculate the HEI 2005 score fo r each participant. Results: The majority of participants were female (56%), Caucasian (65%), and >97 th percentile weight for height (82%). The mean total HEI 2005 score was 61.210.6. Only 2% of the participants met the government recommendations (80/100 ) for total HEI 2005 score. Participants scored poorly for Whole Grains, Sodium and calories from Solid Fat and Added Sugar component scores in addition to several that needed improvement. Results revealed several associations for children in the 85 th to 95 th percentile weight for height group: Total Cholesterol (TC) (p<0.01,r= 0.76) and LDL Cholesterol (p<0.01,r= 0.74) were inversely associated with Total HEI 2005 score; TC was inversely associated with Total Fruit (p=0.05,r= 0.57), Total Vegetables (p=0 .05,r= 0.57), Saturated Fat (p<0.01,r= 0.74) and Sodium (p<0.01,r= 0.77) component scores. Conclusions: Results of this study suggest that future weight management interventions should be designed to focus on improving dietary quality components in additio n to decreasing caloric intake, as this also reduces chronic disease risks in overweight children

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14 CHAPTER 1 INTRODUCTION An emergent concern about the increase in type 2 diabetes (T2D) and cardiovascular disease (CVD) in the young American population, over the past few decades, has occurred. Risk factors for T2D and CVD include behavioral factors (diet and physical activity) genetic factors (family history), and environmental factors. These same risk factors can also contribute to obesity. In addition to obesity and an imbalance of energy consumption with energy expenditure, overall diet quality, and the excess or limited in take of some nutrients and food groups may also contribute to the development of chronic diseases such as T2D and CVD. While literature does exist evaluating the association between diet quality and disease risk factors in adults [ 1 ] little research has inv estigated this association in overweight children. This project is a sub study of the ongoing behavioral weight management intervention project, the Extension Family Lifestyle Intervention Program for Kids (E FLIP for Kids) and was conducted prior to the s tart of the intervention. The purpose of this project was 3 fold: 1) to evaluate the overall diet quality, in terms of adherence to the 2005 Dietary Guidelines for Americans (DGA 2005), among overweight children, ages 7 12 years, using the HEI 2005. I hyp othesized that the children would not meet the recommended dietary guidelines; 2) to investigate the relationship between diet quality and disease risk factors in the same overweight study population. I hypothesized that children with poorer diet quality ( lower HEI 2005 scores) would have a larger waist circumference, higher BP, higher glycosylated hemoglobin (HbA1c), higher serum TG, higher serum total cholesterol (TC), higher serum low density lipoprotein cholesterol (LDL Cholesterol) and 3) to explore the association of

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15 specific dietary components of the HEI 2005 with disease risk factors in overweight children. I hypothesized that higher Total Fruit Score, Whole Fruit Score, Total Vegetables Score, Dark Green and Orange Vegetables (DGOV) Score, Total Grain Score and Whole Grain Score will be associated with lower serum TC and LDL cholesterol levels; higher Dairy Score and higher Sodium Score will be associated with lower systolic blood pressure (SBP) and diastolic blood pressure (D BP ) ; higher Saturat ed Fat Scor e will be associated with lower serum TC and LDL cholesterol levels; and higher Solid Fat, Alcohol and Added Sugar (SoFAAS) Scor e will be associated with lower serum TG and serum HbA1c levels.

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16 CHAPTER 2 LITERATURE REVIEW Childhood Obesity Childhood obesity is a major health problem in America. There are many negative health outcomes associated with childhood obesity that were previously thought to only affect adults. These outcomes include cardiovascular dise ase, type 2 diabetes dyslipidemia, hypertension (HTN), asthma, pulmonary disease, musculoskeletal disease that prevents exercise, as well as social and psychosocial consequences and even mortality [ 2 7 ] Since the early 1960 s, the National Center for Health Statistics has u sed their findings from ongoing surveillance studies to determine the prevalence of major diseases and risk factors for these diseases [ 8 ] The name has evol ved over the years from the National Health Examination Survey to the National Health and Nutrition Examination Survey (NHANES). NHANES conducted by National Center for Health Statistics is a survey research program that assesses the health and nutriti onal status of adults and children in the United States (US) [ 8 ] Since t he early 1970 s, overweight has become a major public health concern for children and adolescents in th e United States [ 9 ] Figure 2 1 illustrates the prevalence of overweight children from 1963 70 until 1999 [ 10 ] As sh own, the prevalence of overweight did increase over that 30 year span. In America, the terms for childhood obesity have evolved over the years. The report from the US Health and Human Services (HHS) used the term overweight to define gender and age specifi c BMI greater than or equal to the 95th percentile [ 9 ] Currently, the term overweight is more popularly defined as having a body mass index (BMI) greater than or equal to the 85th percentile, specific for age and gender. And, the term obesity is defined as having a BMI greater than or equal to the 95th percentile

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17 specific for age and gender. In 2012, Ogden et al. conducted a study with 2 19 year olds, using data from 1999 2010 NHANES (Figure 2 2) [ 11 ] The encouraging news demonstrated by Ogden et al [ 11 ] is the prevalence of boys and girls having a hig h BMI, this consistent prevalence confirms that the childhood obesity epidemic has failed to improve. The investigators also reported that 32% of children and adolescents a ges 6 11 years old were overweight, 16% were obese and 11% were extremely obese [ 11 ] For the remain der of this thesis, overweight and obesity will be defined as Ogden et al. has defined it. The literature indicates that overweight children may become overweight adults [ 12 13 ] In 1995, McPherson a nd colleagues (1995) [ 14 ] reported that the prevalence of overweight in children was more common than the prevalence of underweight and growth retarded children, including among low income children in America. This prevalence of obesity appe ars to carry over into adulthood. Whitaker et al. (1997) published a retrospective study where the purpose was to determine the probability of obesity in young adulthood in relation to the presence/absence of obesity at various times throughout childhood a [ 12 ] They found that parental obesity significantly alters the risk of ob esity in adulthood for both obese and non obese children, especially those under the age of 10 years old. Freedman et al. (2001) [ 13 ] published a prospective study where the purpose was to determine the relationship of childhood obesity to coronary hea rt disease risk factors in adulthood. The participants were initially examined between the ages of 2 to 17 and

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18 then re examined between the ages of 18 to 37. Along with the other findings, Freedman et al. found that 25% of obese adults were overweight as c hildren and 22% had childhood BMIs between 85th and 94th percentile This information leads one to believe that childhood obesity is a vicious cycle, and that children are now seeing the negative health and psychological effects that tend to carry over in to adulthood. Childhood obesity causes a wide range of serious complications, and increases the risk of premature illness and death later in life, raising public health concerns. Some of the chronic diseases that obesity is a risk factor for include CVD su ch as coronary artery disease and hypertension, T2D, metabolic syndrome, arthritis, liver and kidney disease, cancer, asthma, sleep apnea and even, mortality. Research continues to show that body weight status is extremely important to health status. Body Weight Status The body weight status of children is categorized as underweight, healthy weight, overweight, obese, and morbidly obese ( Table 2 1 ) [ 15 ] Awareness of body weight status is important because studies have found that increased body weight is associated with poor er physical health and various studies have shown that with greater degrees of obesi ty, health tends to deteriorate [ 16 18 ] Children and adolescents growth and BMI z score is plot ted using growth charts for gender and age [ 15 ] BMI z score expresses body weight st or below the mean [ 19 ] BMI is calculated as weight, in kilograms, divided by the square of the height, in meters, [ 15 ]

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19 Contributing Factors of Obesity When more calories are consumed than expended, the result is increased body weight. Over time, this increase in body weight can result in overweight and obesity. The childhood obesity epidemic is multi factorial. Genetics, environment and behavioral factor s are all thought to contribute to the imbalance between calories consumed and expended in overweight and obese children. Genetic Factors Genetics is the study of heredity. Heredity is the passing of characteristics fr om one generation to the other [ 20 ] An indivi due to certain genetic characteristics [ 21 22 ] For example, a clinical feature of Prader Willi syndrome a rare genetic disorder is obesity [ 23 ] A characteristic of Prader Willi syndrome is early childhood onset hyperphagia and obesity from age 2 8 years [ 24 ] Other genetic disorders that are characterized by obesity include Bardet Biedl Cohen, and Alstrom syndromes [ 25 ] The risk factors for various diseases are higher for certain races. For example, African Americans, Hispanic, Native Americans, Asian Americans and Pacific Islanders are at a higher risk for developing T2D than their Caucasian counterparts [ 26 ] Weight is 30 70% dependent on genetics [ 27 28 ] drastically within a few decades, but interestingly enough the prevalence of obesity has tripled among school aged children during that time. Ultimately, the rapid increase in prevalence of pediatri c obesity cannot be solely attributed to genetics. Environmental Factors In addition to genetics, environmental factors influence weight gain, overweight and obesity in children. Two types of environment exist: 1) Food environment and 2)

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20 Physical environm ent. A great amount of evidence has recognized the influence that the food environment has on body weight and dietary behavior in children [ 29 ] The food environment is the source of food and beverages and the circumstances around which they are acquired and consumed [ 30 ] For children, the food environment includes home, food stores, restaurants and schools [ 31 ] Research shows that lower BMI is associated with supermarkets present in local neighborhoods, especially for low income Americans [ 29 ] When the geographic density of fast food restaurants and convenience [ 29 ] The relationship between inadequate physical activit y and weight gain is strong and consistent [ 32 33 ] In s pite of national recommendations for greater physical activity, American children engage in low levels of physical activity [ 32 ] There is increasing evidence that certain features of physical and social environ ment influence levels of physical activity [ 34 35 ] A sense of safety in the neighborhood appears to be one important environmental determinant [ 34 ] Adults who perceive their neighborhoods to be unsafe are substantially more likely to be physically inactive, along with their kids, than are adults who perceived their neighborhoods as safe [ 36 ] Outdoor safety is especially important for children, because time spent outdoors is strongly associated with physical activity [ 32 ] Outdoors, children have the opportunity to engage in vario us games that incorporate physical activity [ 37 ] Parents rank safety as the most important factor in deciding whether to let their young children p lay in a given location [ 32 ] Lovasi et al. [ 38 ] found that there was a strong support for the importance of food stores, exercise facilities and safety, as it related to obesity related effects of built environment characteristics. Lovasi et al. [ 38 ] also concluded that there could be a reduction in

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21 obesity related health dispa rities if there was an increase in the access to supermarkets, more places to exercise, and safety. Behavioral Factors Finally, behavior is another factor that contributes to the obesity epidemic. Even though there is not one explicit behavior that trigger s overweight and obesity, there are particular behaviors that contribute to poorly regulated energy balance and thus to increased adiposity and obesity. It is ideal that for normal growth and development, metabolism, immunity and cognitive function, child ren and adolescents consume a diet that includes the proper amounts of nutrient rich foods and beverages from all the major food groups [ 29 ] In order for children and adolescents to maintain a healthy weight and healthy weigh t gain, a proper balance of total caloric (energy) intake and energy expenditure is essential [ 29 ] While there is very little support on particular foods that play a part in unjustifiable energy intake in children, hypotheses include: an increase in energy density of the diet [ 39 ] ,an increase in consumption of fruit juices [ 40 ] an increase in portion sizes of food and beverages [ 41 ] an increase in the frequency of meals consumed away from the home, especially fast food consumption, [ 42 43 ] a decrease in the frequency of breakfast consumption [ 29 44 ] a low intake of fruits, vegetables and fiber [ 45 ] and an increase in the consumption of beverages with add ed sugar [ 46 ] Guthrie et al. [ 47 ] mentioned that the amount of foods purchased away from the home has increased from 14% in 1977 78 to 32% in 1994 96. Consuming meals away from the home is associa ted with larger portion sizes, compared to the same foods prepared at home, thus foods prepared outside of the home usually contain more calories [ 42 ]

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22 Research has proven that engaging in regular ph ysical activity reduces certain disease risk factors as well as achieving and maintaining a healthy body weight [ 48 ] The DGA 2005 recommends a minimum of 60 minutes of physical activity, in addition to usual activity in the home or at school most days of the week, for children [ 48 ] Physical inactivity is another behavior that leads to overweight and obesity in children and adolescents. Caspersen and colleagues [ 49 ] define physical activity activity is very important because it helps build healthy bones [ 50 ] and muscles [ 51 ] helps control weight [ 52 ] reduces anxiety [ 53 ] and stress [ 54 ] increases self esteem [ 55 ] and may improve BP [ 56 ] and cholester ol levels [ 57 ] Alternatively, the opposite behaviors are associated with decreased adiposity in children. Another behavior that is assoc includes television, computer, video games, etc [ 58 ] Adding physical activity in the day of a child should not solely be the responsibility of the parent or child because children spend a good amount of time at school, where the opportunity of being physically active is increased. Frightfully, Eaton et al. [ 59 ] found that daily participation in school physical education among adolescents has dropped from 42% in 1991 to 28% in 2003 for various reasons. Also, children spend a considerable amount of time engaged in sedent ary behavior [ 58 ] Roberts and colleagues [ 60 ] found that children aged 8 18 years spend a little more than 3 hours per day watching TV, videos, DVDs, and movies. Consequences of Obesity Physical Health Consequences Childhood Obesity causes a wide range of serious complications, and increases the risk of premature illness and death later in life, raising public health concerns.

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23 Pediatric obesity is not only associated with adverse short term health effec ts but many studies suggest that overweight children and adolescents become overweight and obese adults and thus increase their risk for developing a wide range of chronic diseases [ 12 13 61 63 ] Some of the chronic diseases that obesity is the risk factor for include T2D [ 64 ] CVD [ 65 ] metabolic syndrome [ 63 66 ] arthritis [ 67 68 ] liver disease [ 69 70 ] sleep apnea [ 71 72 ] asthma [ 4 73 ] and certain cancers [ 74 75 ] For the purposes of this research, T2D, CVD and cancers, and the risk factors associated, will b e discussed from this point forward. Goran et al. [ 76 ] advise that there are seve ral risk factors that contribute to the development of CVD and T2D. The risk factors of T2D and CVD include increased body and abdominal fat, insulin resistance, ethnicity and onset of puberty [ 76 ] African American, Hispanic, and Native American children are at the highest risk for developing both CVD and T2D [ 76 ] Strauss and Pollack [ 77 ] explain that the prevalence of African American and Hispanic children that are above the 85 percentile, in 1988, was 35% compared to Caucasian children who were at just above 20%. According to Freedman et al. [ 13 ] 25% of children ages 5 to 10 years have relatively high cholesterol, high BP, or other early warning sign for heart disease. Type 2 Diabetes. Type 2 Diabetes occurs when the body develops a resistance to [ 78 ] The metabolic defects that contribute to the development of T2D may vary from individual to individual. Possible causes include blood glucose [ 78 ] and/or a defect in the receptors that allow insulin to bind to the surface of cells [ 79 ] deficiency of insulin results in a chronic el evation of serum glucose (fasting glucose

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24 [ 80 ] Children whose BMI is above the 85 th percentile for age, are at an increased risk for developing T2D [ 78 ] A potential reason that T2D is associated with obesity is because adipose tissue is extremely resistant to insulin in comparison to other bodily tissues [ 81 ] Diabetes is one of the more common chronic conditions in school aged children in the United States [ 78 ] T2D becomes increasingly more common in children after age 10, with higher rates in minority groups in comparison to non Hispanic whites, those who are obese, have family history of diabetes, and those who experience insulin resistance [ 78 ] The highest rates of T2D are seen in American Indian children and adolescents [ 78 ] Mahler and Adler have shown that the more children and adolescents weigh, the more risk of T2D there is through insulin resistance, which is the same effect seen in adults [ 82 ] The Bogalusa Heart Study, 1972 2005, has been described as the longest, most detailed study of biracial children in the world [ 13 ] This study looked at the history of cardiovascular (CV) risk factors, coronary heart disease (CHD), T2D and hypertension [ 13 ] The investigators observed that an increased BMI in childhood is associated with ctor for T2D [ 13 ] Both weight status and weight gains are risk factors for T2D [ 83 ] Bhargava et al. [ 84 ] followed 1,492 adolescents and found that children with low weight at age 2, but then with an exponential increase in BMI between ages 2 12 were at an increased risk for developing impaired glucose tolerance and diabetes. Cardiovascular D isease. Cardiovascular disease (CVD) is the class of diseases that involve the heart, blood vessels and arteries [ 85 ] An important determinant of CVD is obesity [ 85 ] As stated by Freedman an d colleagues (2001), overweight children are

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25 more likely to become obese adults [ 13 ] Only few studies, to date, have followed children the appropriate amount of time needed to determine the relationship between weight status and adult chronic diseases [ 7 ] The Harvard Growth Study included 508 men and women who were between the ages of 13 and 18 years old between 1922 and 1935 [ 68 ] These adolescents were both lean and overweight [ 68 ] classified as having a BMI between the 25 th and 50 th p ercentile and the term th percentile compared to their counterparts of the same age and gender [ 68 ] The results of this study are that overweight adolescents were at higher risk for experi encing coronary events in adulthood than their peers [ 68 ] One of the most profound effects that obesity has on CVD is hypertension even though overweight and obesity are the most important modifiable risk factors [ 86 ] Moreover, the Bogalusa Heart Study, mentioned above, found that the major etiologies of adult heart disease, atherosclerosis, coronary heart disease and essential hypertension began in childhood [ 13 ] Hypertension. B lood P ressure is the force of blood that p ushes against the arteries as the heart pumps [ 79 ] A balance between ca rdiac input and vascular resistance is what determines BP [ 87 ] If one of these variables increases without the other decreasing equally, average BP tends to rise [ 87 ] Gruskin [ 87 ] determined different factor s that affect BP. The factors that affect cardiac output include baroreceptors, extracellular volume, effective circulating volume, and the sympathetic nervous system [ 87 ] The factors that affect vascular resistance have two categories: 1) Pressors and 2) Depressors. Electrolyte homeostasis is very crucial to the balance between cardiac output and vascular resistance [ 87 ] Changes sodium, potassium,

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26 calcium and magnesium, especially, affect cardiac output and vascular resistance [ 87 ] For example, when homeostasis is normal, sodium consumed is nearly equal to sodium excreted in the urine thereby resulting in the constancy of extracellular sodium volume is retained, the extracellular volume increases, increasing BP. But a host of physical and hormonal mechanisms take place and triggers change in glomerular filtration rate and tubular reabsorption of sodium, resulting in excretion of excess sodium, thereb y restoring normal sodium levels. Also, when potassium consumption increases, the production and release of rennin is suppressed [ 87 ] Increased potassium ingestion also induces natriuresis [ 88 ] Natriuresis is the removal of sodium in the urine by action of the kidneys [ 88 ] This excretion of sodium from the blood causes BP to decrease since blood volume decreases as a result of osmotic forces that removes water from the blood as it follows sodium out of blood circ ulation and into the urine [ 88 ] If BP is consistently elevated damage to various parts of the body can follow [ 89 ] High BP or HTN can lead to coronary artery disease, heart failure, stroke and renal disease, amongst other conditions [ 89 ] Pediatric hypertension (PH) is more common tod ay than ever and the long term effects of PH are very substantial [ 90 ] Throughout the day, BP rises and falls. For example, when asleep, BP falls and when awake, BP rises [ 89 ] BP also rises when excited, nervous and du ring activities [ 89 ] Only when BP remains high throughout the day, is one at risk for health problems. According to the Task Force on Blood Pressure Control in Children, commissioned by the National Heart, Lung, and Blood Institute (NHLBI), there are five ca tegories of BP levels in children (Table 2 2) [ 91 ] Average BP is considered normal when both systolic

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27 and diastolic values are at the 90 th gender and height. Average BP is diagnosed as pre hypertensive when either systolic or diastolic values are above the 90 th percentile, but below the 95 th percentile. Average BP is diagnosed as Stage I hypertension when either systolic or diastolic values are from the 95 th percentile t o the 99 th diagnosed as Stage II hypertension when either systolic or diastolic values are greater than the 99 th percentile plus 5 mmHg. In developed countries, hypertension (HTN) is the leading ca use of death [ 92 ] Leupker et al. completed a study with 10 14 year olds and found that between 1986 and 1996 there wa s a concordant increase in BMI and systolic BP [ 93 ] Similarly, Sorof et al. found that as BMI percentile increased from the 5 th to the 95 th percentile, the greater the prevalence of systolic hypertension [ 94 ] This investigator found BMI to be the strongest association with hypertension [ 94 ] The Dietary Approaches to Stop Hypertension (DASH) trial has proven that certain dietary patterns have shown to be effective in the prevention of hypertension [ 95 ] ere randomized to one of three diet groups: 1) control diet (four servings of fruits and vegetables daily and a macronutrient profile that was representative of American consumption); 2) Fruit and Vegetables diet (ten servings of fruit and vegetables daily that was rich in potassium, magnesium and fiber); and 3) Combination diet (emphasis on fruits, vegetables, low fat dairy, whole grains, poultry, fish, nuts, reduced red meat, fat, sweets, and sweetened beverages) [ 95 ] The combination diet also was rich in potassium, magnesium, calcium, fiber, reduced in saturated fat, total cholesterol, and

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28 total fat [ 95 ] The sodium content was maintained at 3g per 2100 kcal, which is slightly below the average American daily intake [ 95 ] The dietary patterns include incre ased consumption of fruits, vegetables, and low fat dairy foods and a reduction of total and saturated fat consumption [ 95 ] Various studies have proven that salt intake is directly related to BP levels in adults and the reduction of salt is related to a red uction in BP [ 96 99 ] The DASH Sodium Trial is a follow up study to the DASH Trial Instead of examining the range and consistency of group average BP, this trial examined the average systolic BP for individuals. Researchers have introduced the concept of salt sensitivity, which shows that the BP of some individuals rise with increased sa lt intake and falls with the reduction of salt intake, while yet the BP of others do not [ 87 100 101 ] This trial used a study sample of 412 men and women [ 101 ] A total of 204 were assigned to the control diet, similar to what Americans consume [ 101 ] The remaining 188 participants were assigned to the three feeding periods, which were 30 day feeding periods of salt intake at three clinically relevant levels (High, Medium, and Low) [ 101 ] The researchers found that categorizing individuals as salt responders is very difficult [ 101 ] Thus, the results support the current recommendations for lower sal t intake for [ 101 ] A study, in 2005, by Alonso et al. investigated whether whole fat, low fat or total dairy consumption was associated prospectively with hyperten sion risk [ 101 102 ] This study was conducted in 5880 students in Spain > 20 years old who were not hypertensive or had any cardiovascular disease at the start of the study. The investigators used validated semi quantitative food frequency questionnaires to assess

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29 dairy consumption. The finding was that in this cohort low fat da iry consumption, but not whole fat dairy consumption was associated with lower risk of incident hypertension. Cancer. Micozzi [ 103 ] mentioned that cancer experienced in adulthood is associated with childhood and adolescent overweight and obesity. Must et al. [ 68 ] conducted a long term follow up study and found that men who were overweight ad olescents had a higher prevalence of colorectal cancers. Various studies have been consistent in showing that there are associations between obesity and increased risk of endometrium, kidney, gallbladder cancer in women, and breast cancer in postmenopausal women [ 104 ] Though most of t hese studies have found associations of adult obesity with cancers, it is well known that childhood obesity may persist into adulthood [ 12 13 ] Social and Psychological Consequences Not only do children experience physical health consequences of obesity, they are als o subject to more social and psychosocial consequences. Although physical health consequences become apparent in adulthood, many of the social and psychosocial consequences have an immediate effect [ 25 105 106 ] Some of these consequences may include eating disorders and disordered eating [ 105 ] teasing and bullying [ 107 ] decreased self esteem [ 108 ] negative body image [ 109 ] increased depression [ 110 ] and overall decrease in quality of life [ 3 ] Fairburn and Brownell [ 111 ] suggest that eating disorders include not only anorexia nervosa and bulimia nervosa, but food avoidance emotional disorder, selective eating and pervasive refusal syndrome. Tanofsky Kraff et al. [ 112 ] observed that higher eating disordered cognitions and behaviors were experienced among 82 non treatment seeking overweight children compared to 80 normal wei ght children aged 6 13 years old. A school based study by

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30 Neumark Sztainer et al. [ 113 ] found that, among 7 th 1 2 th graders, overweight participants self reported binge eating more often than their normal weight peers. Among 106 children and adolescents aged 5 18 years, Schwimmer et al. [ 3 ] observed that obese children and adolescents, compared with hea lthy children and adolescents, reported significantly lower health related quality of life (HRQOL). In fact, the obese children and adolescents were more likely to have impaired HRQOL, similar to children and adolescence diagnosed with cancer [ 3 ] Further evidence of the bias against overweight individuals by children comes from Hayden Wade et al. [ 107 ] who studied 70 overweight and 86 non overweight children aged 10 14 years old from southern California and suburban New York City. The children were asked to complete questionnaires that assessed various components of HRQOL, which included: loneliness, self esteem, weight concerns, and preferences for active vs. sedentary and social vs. isolative activities, teasing and an eating disorder psychopathology and body image disturbance qu estionnaire [ 107 ] When many factor s frequency, intensity, emotional impact and stigmatized content were examined, findings indicated that teasing proved to more severe for overweight children when compared to all other components [ 107 ] Healthy Eating Index Background Every five years since 1980, the Department of Healt h and Human Services (HHS) and the United States Department of Agriculture (USDA) have jointly provided nutrition recommendations to the American public (children and adults) and since this time, the guidelines have become more comprehensive [ 48 ] This joint effort has ensured that the Federal government speaks with one voice about nutritional issues [ 48 ] These

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31 recommendations are called the Dietary Guidelines for Americans (DGA), which provides scientific based guidance in defining healt hful diets and how consuming a healthful diet can reduce major disease risks [ 114 ] These guidelines are recommended for individuals 2 years and older [ 114 ] The Healthy Eating Index (HEI) was originally developed in 1995 by the USDA in an attempt to assess how well Americans conformed to Federal Dietary Guidance (FDG) and the ori ginal Food Guide Pyramid (FGP) and is termed the original HEI [ 114 ] All in all, the HEI is a composite tool that assesses overall diet quality in the American population. Because it is an index, the HEI measures the degree to which individuals are following the 2005 Dietary Guidelines for Ame ricans (DGA 2005) and FGP. The original HEI was issued in October 1995 by the USDA Center for Nutrition and Policy Promotion (CNPP) and has been used to measure the adherence to the FDG for the 1989 1990, 1994 96, and 1998 Continuing Survey of Food Intakes by Individuals (CSFII) as well as the 1999 2000 NHANES data [ 29 ] Original HEI Scoring The original HEI was composed of 10 dietary components, equally weighted, which provided one single score out of a possible 100 points (Table 2 3) [100] A diet [ 115 ] When the recommended servings are reached, a maximum score of 10 is given. Likewise, when no portion of the component is consumed, a score of zero is given [ 115 ] Intakes between the minimum and maximum levels are scored proportionately [ 115 ] Components 1 5 of the original HEI measures the degree to which individuals Milk (dairy) and Meat (and beans), based on gender and age [ 115 ] Component six

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32 through nine assessed asp ects of the diet that should be limited or consumed in moderation Component six is a score that evaluates total daily sodium intake [ 116 ] Component seven represents sa turated fat consumption as a percentage of total food intakes. Component score eight represents total fat consumption as a percentage for total of energy intake. Component nine reflects daily TC intake and the last component estimates the extent of variet Modifications to the original HEI. Investigators have modified the original HEI in an attempt further define what constitutes the most healthful diet for Americans. These modified versions of the original HEI include the Youth Health y Eating Index (YHEI), Alternate Healthy Eating Index (AHEI) and the 2005 Healthy Eating Index (HEI 2005). Feskanich and colleagues, in 2004, modified the original HEI to create the YHEI, in an attempt to better address dietary issues that are specific to older children and adolescents [ 117 ] Feskanich and colleagues [ 117 ] developed the YHEI and used it to score food consumption and address dietary behaviors, such as eating breakfast, attending dinner with family, and avoiding snack foods and soft drinks that are pivotal to healthy chil dhood and adolescent growth and development. These behaviors were given scores and added to components of the original HEI totaling 100 points [ 11 7 ] The study by Hurley et al. is a cross sectional study that compared HEI and YHEI scores for two samples of low income African American urban middle school adolescents [ 118 ] This group of investigators also compared the associations between the pair of indexes and health indicators to assess the relative strength of each measure to predict the dietary risks for chronic disease [ 118 ] The investigators found that lower HEI scores, not YHEI, was associated with higher percent body/abdominal fat [ 118 ] Even though

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33 both indices are useful for assessing diet quality, only HEI was inversely associated with body composition, chronic disease predictors, and accounted for gender differences in the DGA. In 2006, McCullough and colleagues created a modified version of the original HEI. The AHEI a 9 component design sought to associate reduced disease risks to target food choice s and macronutrient sources [ 114 ] Thi s study examined 2 healthy up Study [ 114 ] During the 8 12 year follow up, these investigators found that the AHEI predicted chronic disease risk better than the ori ginal HEI, possibly because a strong inverse association with CVD [ 114 ] Another revision of the original HEI is to the HEI 2005, created in November 2007 by Guenther and colleagues [ 115 ] This revision of the original HEI occurred because of the new DGA 2005 which placed more emphasis on specific components of a quality diet [ 115 ] Good diet quality includes but is not limited to: increased consumption of whole grains, different types of fruits and vegetables, appropriate amounts of specific [ 116 ] Discretionary calories ting calories from Solid Fat (including fat in milk products), Alcohol and Added Sugar. The DGA 2005 suggests that Americans should reduce their SoFAAS intake which provides most of the non essential calories that Americans consume [ 48 ] Americans consume 35% of their total calories from SoFAAS, which is currently too high [ 48 ] It is recommended that Americans consume 20 to 30 percent of their total calories from SoFAAS [ 48 ] An increased consumption of SoFAAS

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34 increases the amount of Saturated fat and cholesterol intake which decreases the amount of dietary fibe r and other nutrients consumed [ 48 ] HEI 2005 S coring The HEI 2005 complies with the DGA 2005 and the new food intake patterns developed for the MyPyramid Food Guidance System (FGS). The original HEI was modified for two reasons [ 116 ] Firstly, the HEI 2005 takes into account the fact that energy needs vary among individuals by age, gender and activity levels [ 116 ] Secondly, the HEI 200 5 is based on an energy density approach [ 116 ] This approach is to distinguish dietary quality from dietary quantity [ 116 ] The food group standards are expressed per 1,000 kcal (Table 2 4.) [66] The food group standards are Total Grains, Whole Grains, V egetables, Dark Green and Orange Vegetables and Legumes (cooked dry beans and peas), Total Fruits (including 100% juice), Whole Fruits (forms other than juice), Milk (all milk products and soy beverages), Meat and Beans (meat, poultry, fish, eggs, soybean products other than beverages, nuts, and seeds); and Oils (non hydrogenated vegetable oils and oils in fish, nuts, and seeds) [ 116 ] Standard nutrients and discretionar y energy are based on percent total energy [ 116 ] The remaining three components, for which moderation is recommended, are Saturated Fat; Sodium; and extra calories from SoFAAS [ 116 ] Higher intakes of these components reveal poorer intakes and thus are scored lower [ 116 ] Like the original HEI, when the recommended servings are reached, a maximum score is given with the HEI 2005 [ 116 ] Likewise, when no portion of the individual component is consumed, a score of zero is given and intakes between the minimum and maximum levels are scored proportionately [ 116 ]

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35 Current Use and I mplications of the HEI 2005 The estimated amounts of calories needed to maintain energy balance differ for gender and age groups at different levels of physical activity (Table 2 5) [ 29 ] The U SDA CNPP has also suggested amounts of various foods from basic food groups, sub groups and oils to meet recommended nutrient intakes at 12 different calorie levels (Table 2 6) [ 29 ] One study that has estimated the HEI 2005 s cores (Table 2 7) [29] in different populations was conducted by Guenther et al [ 119 ] Th ese scores are representative of Americans, looking at two surveys in particular, the CSFII 1994 96 and the NHANES 2001 02 [ 119 ] Reviewing the data from these two sets of years show that the HEI [ 119 ] For example, whole fruits, total vegetables, and whole grains statistically decreased in 2001 02 compared to 1994 96 [ 119 ] The components milk, oil, and sodium all statistically increased from 1994 96 to 2001 02 [ 119 ] One can argue that the quality of the diet has not improved much for Americans [ 119 ] Not only does the diet quality of Table 2 8 dem onstrates the diet quality of children ages 2 5, 6 11, and 12 17 [ 120 ] 2005, in 2003 04 [ 120 ] Eve n though all of the scores are under 60, children ages 2 5 years old had a tendency to have a diet that was of more quality than any of the other age groups [ 120 ] ity of diets in children and adolescents ages 2 17 years, even though the scores were higher in 2 5 year olds compared to 6 11 and 12 17 year olds [ 120 ] No recent data has been documented, using the HEI 2005, estimating how well children and adolescents adhere to the DGA 2005 [ 120 ]

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36 Studies have used the HEI 2005 to determine how children and adolescents adhere to the DGA [ 12 1 125 ] In school aged children and adolescents, 5 1 8 years, some concerns were raised from the 1999 2004 NHANES data [ 126 ] Some of the key concerns were that the children and adolescents scored low on many of the HEI 2005 components (Figure 2 3) [120] For instance, the children and adolescents scored low with their intake of vegetables and fruits, especially whole fruits; children had a very low percentage of intake of dark green and orange vegetables and legumes; children had a ver y low intake of whole grains; there was a high intake of discretionary calories from SoFAAS, sodium and saturated fat among these school age children and adolescents, represented by the lower percentage scores [ 126 ] To our knowledge, the relation of diet quality and disease risk factors has not been studied in children. In this study, it is significant that the HEI 2005 is used, as opposed to using other methods of assessing diet quality, since the HEI 2005 is comprised of the different components of the diet rather than just one [ 116 ] Evaluating single nutrients may prove to be insufficient information, since there are complicated interactions that occur among nu trients. For example, iron absorption is enhanced in the presence of Vitamin C. The goal of this research was to evaluate diet quality in terms of adherence to the 2005 DGA and FGP among overweight children, 8 12 years old, living in rural counties via the HEI 2005. The index scores were compared with markers of disease risks, such as high BP, increased waist circumference, high serum TG, high serum HbA1c, low serum HDL cholesterol, high serum TC and serum LDL cholesterol levels. For the purpose of this st udy, overweight was defined as being at or above the 85 th percentile for BMI for gender and age.

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37 Figure 2 1. Prevalence of overweight among US children and adolescents

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38 Figure 2 2. Prevalence of Obesity in US Males and Females Aged 2 t hrough 19 Years

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39 Table 2 1. Body weight status for children and adolescents Category BMI for Age Percentile Range Underweight Less than the 5 th percentile Healthy weight 5 th percentile to less than the 85 th percentile Overweight 85 th percentile to less than the 95 th percentile Obese Equal to or greater than the 95 th percentile Table 2 2. Task Force on Blood Pressure Control in Children Guidelines of Hypertension classification of Blood Pressure (BP) Category Systolic BP (mmHg) Diastolic BP (mmHg) Normal th percentile And th percentile Pre hypertensive 90 th to 95 th percentile Or 90 th to 95 th percentile Hypertension, Stage 1 95 th to 99 th percentile Or 95 th to 99 th percentile Hypertension, Stage 2 th percentile Or th percentile

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40 Table 2 3. Original Healthy Eating Index (original HEI) components and standards for scoring Component Maximum Value Standard for maximum score Standard for minimum score of zero Total Fruit 10 2 4 servings (approx, 1 2 cups 1 ) 0 servings Total Vegetables 10 3 5 servings (approx. 1.5 2.5 cups 1 ) 0 servings Total Grains 10 6 11 servings (approx. 6 11 oz eq 1 ) 0 servings Milk 10 2 3 servings (2 3 cups 2 ) 0 servings Meat (and beans) 10 2 3 servings (approx. 5.5 7.0 oz eq. 1 ) 0 servings Sodium 10 Saturated Fat 10 Total Fat 10 Cholesterol 10 Variety 10 3 days 3 in 3 days Total 100 1 Acc ording to gender and age 2 According to age 3 In 1994 96 and 1999 2000, 8 o r more different foods in 1 day

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41 Table 2 4. 2005 Healthy Eating Index components and standards for scoring Component Maximum points 1 Standard for maximum score Standard for minimum score of zero Total Fruit (includes 100% juice) 5 equiv. per 1,000 kcal No Fruit Whole Fruit (not juice) 5 equiv. per 1,000 kcal No Whole Fruit Total Vegetables 5 equiv. per 1,000 kcal No Vegetables Dark Green and Orange Vegetables and Legumes 2 5 equiv. per 1,000 kcal No Dark Green or Orange Vegetables or Legumes Total Grains 5 per 1,000 kcal No Grains Whole Grains 5 per 1,000 kcal No Whole Grains Milk 3 10 equiv. per 1,000 kcal No Milk Meat and Beans 10 per 1,000 kcal No Meat or Beans Oils 4 10 1,000 kcal No Oil Saturated Fat 10 energy 5 Sodium 10 5 1,000kcal 1,000 kcal Calories from Solid Fat, Alcohol and Added Sugar (SoFAAS) 20 energy Total 100 1 The maximum total HEI 2005 score is 100 2 Legumes counted as vegetables only after Meat and Beans standard is met. 3 Includes all milk products, such as fluid milk, yogurt, and cheese, and soy beverages. 4 Includes non hydrogenated vegetable oils and oils in fish, nuts, and seeds. 5 Saturated Fat and Sodium get a score of 8 for the intake levels that reflect the 2005 Dietary Guidelines, <10% of calories from saturated fat and 1.1 grams o f sodium/1,000 kcal, respectively.

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42 Table 2 5. Estimated Daily Calorie Needs for Children ages 4 13 years old Variable Calorie Range Females 4 8 years 9 13 1,600 Males 4 8 years 9 13

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43 Table 2 6. Recommended Daily Amounts of Food from Each Group for children Calorie level 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000 3200 Fruits 1 cup 1 cup 1.5 cups 1.5 cups 1.5 cups 2 cups 2 cups 2 cups 2 cups 2 cups 2.5 cups 2.5 cups Vegetables 1 cup 1.5 cups 1.5 cups 2 cups 2.5 cups 2.5 cups 3 cups 3 cups 3.5 cups 3.5 cups 4 cups 4 cups Grains 3 oz eq 4 oz eq 5 oz eq 5 oz eq 6 oz eq 6 oz eq 7 oz eq 8 oz eq 9 oz eq 10 oz eq 10 oz eq 10 oz eq Meat and Beans 2 oz eq 3 oz eq 4 oz eq 5 oz eq 5 oz eq 5.5 oz eq 6 oz eq 6.5 oz eq 6.5 oz eq 7 oz eq 7 oz eq 7 oz eq Milk 2 cups 2 cups 2 cups 3 cups 3 cups 3 cups 3 cups 3 cups 3 cups 3 cups 3 cups 3 cups Oils 3 tsp 4 tsp 4 tsp 5 tsp 5 tsp 6 tsp 6 tsp 7 tsp 8 tsp 8 tsp 10 tsp 11 tsp Discretionary calorie allowance 165 171 171 132 195 267 290 362 410 426 512 648

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44 Table 2 7. Estimated 2005 Healthy Eating Index Component and Total Scores, United States, 1994 96 and 2001 02 Component (Maximum Score) 1 1994 96 Score 2 2001 02 Score 2 Total Fruit (5) 3.1 3.1 Whole Fruit (5) 4.5 3.4* Total Vegetables (5) 3.6 3.2* Dark Green and Orange Vegetables and Legumes (5) 1.4 1.4 Total Grains (5) 5.0 5.0 Whole Grains (5) 1.2 1.0* Milk (10) 5.9 6.3* Meat and Beans (10) 10.0 10.0 Oil (10) 6.0 6.8* Saturated Fat (10) 6.5 6.4 Sodium (10) 3.2 4.1* Calories from Solid Fats, Alcoholic beverages, and Added Sugars (20) 7.8 7.5 Total HEI 2005 score (100) 58.2 58.2 1 The maximum total HEI 2005 score is 100 2 Sources of data: Continuing Survey of Food Intakes by Individuals, 1994 96, and National Health and Nutrition Examination Survey, 2001 02 Significantly different (p<0.05).

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45 Table 2 8. Estimated mean 2005 Healthy Eating Index total and component scores for children and adolescents ages 2 to 17, United States, 2003 04 Component (maximum score) 1 Age 2 5 years 2 (n=763) Age 6 11 years 2 (n=900) Age 12 17 years 2 (n=1,632) Total Fruit (5) 5.0 2.9* Whole Fruit (5) 4.3 2.7* Total Vegetables (5) 2.2 2.3 2.4 Dark Green and Orange Vegetables and Legumes (5) 0.6 0.5 0.6 Total Grains (5) 5.0 5.0 5.0 Whole Grains (5) 0.8 0.9 Milk (10) 10.0 8.7* Meat and Beans (10) 7.3 7.8 Oil (10) 5.5 6.6 Saturated Fat (10) 4.7 5.2 5.4 Sodium (10) 4.8 4.5 Calories from Solid Fats, Alcoholic beverages, and Added Sugars (20) 9.4 7.7* Total HEI 2005 score (100) 59.6 54.7 54.8 1 The maximum total HEI 2005 score is 100 2 Number of servings depends on recommended Food Guide Pyramid servings *Age 2 5 versus 6 11 (significantly different, p<0.05). 5 versus 12 17 (significantly different, p<0.05).

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46 Figure 2 3. 2005 Healthy Eating Index Component Scores for children and adolescents from 1999 2004 NHANES data.

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47 CHAPTER 3 METHODOL OGY This study is a cross sectional evaluation of diet quality and the association between diet quality and disease risk factors in an overweight pediatric population. The data from this study was gathered from the first two waves of the Extension Family Lifestyle Intervention Project (E FLIP for Kids) Study. Participants Subjects for this sub analysis included children recruited in the first four waves of the E FLIP for Kids Study. The E FLIP for Kids Study is prospective, randomized controlled study tha t evaluates the influence of two behavioral lifestyle interventions, Parent Only (PO) and Family Based (FB) compared to a Health Education Control (HEC) group. Participants were recruited from rural counties in North Central Florida through direct solicita tion methods. These methods included distributing information to households, health care providers, churches, social organizations, and community events using brochures, presentations, and Cooperative Extension Service faculty. All potential candidates wer e screened over the telephone. The E FLIP for Kids Study was approved by the Institutional Review Board of University of Florida. To participate in E FLIP for Kids Study, children met the requirements of being between the ages of 8 12 and met weight requir th percentile for their age and gender) prior to study initiation. Participants were excluded from the E FLIP for Kids Study if they had: any medical restrictions that contraindicated participation in the program which included a reduc ed energy intake and increased physical activity ; resting BP exceeding 140/90 mm Hg; medication regimes including, but not limited to, monoamine oxidase inhibitors, antibiotics for HIV or TB, or the use of prescription weight loss drugs taken within the

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48 past six months; and or conditions that affect the E FLIP for Kids Study including inability to consent, inability to read English, major psychiatric disorder, or a child with a developmental delay, pattern of aggressive or oppositional behavior. Demograph ic data for this sub analysis included age, gender and Race. Race was classified as African American, Caucasian, Bi Racial or Unknown/No Response. Anthropometric data included height, weight, and waist circumference. Height and weight were used to calcula te BMI, BMI z score and BMI percentile category. For this th to <95 th th to <97 th percentile, and th percentile because this study only included participants who were above the 85 th percentile and three groups was the best way to categorize the participants. Clinical characteristic data included finger pricks which was used to measure serum HbA1c, serum TC, serum HDL Cholesterol, serum LDL Cholesterol, and serum TG. Procedures Parents who provided consen t and children who assented to participate in the E FLIP for Kids Study completed assessment measures across two assessment visits. At heart rate BP and blood analysis was collected to determine eligibility. If eligible the child and primary care giver returned for a baseline data collection visit within three weeks of the study initiation. At the second visit, height, weight and waist circumference was measured. The SenseWear armband accelerometer was given to the child, at the second visit, to be worn for 7 days. Data for this analysis was taken from the baseline data collection visit. Anthropometric measures were collected by a trained nurse or nurse technician. Questionnaire data was collected with the assistance of a trained research assistant or parent of the child. Participants with missing data for

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49 disease risk indicators, including systolic and diastolic blood pressure, serum total cholesterol, LDL Choleste rol, HDL Cholesterol, HbA1c and TG, as well as anthropometric measures were excluded. Height and Weight Height was measured to the nearest 0.1 centimeter using a Harpendon stadiometer, without shoes. Weight was measured to the nearest 0.1 kilogram using a certified digital scale, with one layer of clothes, without shoes and emptied pockets. Both height and weight were used to calculate BMI, BMI percentile category, and BMI z score, specific for age and gender. BMI was calculated as weight in kilograms div ided by height in meters squared. Age and gender specific BMI z scores were calculated according to Centers for Disease Control and Prevention growth charts [ 127 ] Waist Circumference To determine the waist circumference, the upper hip bone was located and a measuring tape was placed around the abdomen, ensuring that the tape measure was horizontal. The tape measure was snug but did not cause compressions on the skin and the measurement was taken. Blood Pressure and Heart Rate The resting systolic and diastolic BP and resting heart rate was measured using manual blood pressure cuff Before the measurements were taken, there was a 5 minute resting period where the participant was devoid of distractions, seated and feet lying flat on the floor. The measurements were taken a total of three times with two minutes between ea ch reading. The first reading was discarded while the last two were averaged together. Participants were excluded from the study if their resting BP was above the 95 th percentile for height, weight, and gender.

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50 Blood Analysis Glycosylated hemoglobin (Hb A1c), TG, serum TC, LDL cholesterol and HDL cholesterol were measured under aseptic conditions, using approximately 2 3 drops of blood. The Cholestech LDX System and Cholestech GDX System A1c (Invernes Medical, 2010) were the devices used to analyze the ch olesterol blood samples and HbA1c, respectively. Physical Activity The participants were asked to wear a SenseWear armband accelerometer, which was worn for 24 hours a day for 7 consecutive days, with the exception of when bathing or swimming. Only participants who wore the SenseWear armband accelerometer for a minimum of 16 hours were included in the analysis. A t otal of 4 days of data were used for this analysis. The first 3 weekdays and first 1 weekend day, that the participants wore the SenseWear armband accelerometer the accepted amount of time, was used in this analysis. The data gathered from the acceleromet er examined, objectively, total energy expenditure, total physical activity energy expenditure and MET equivalents of physical activity for the 7 day period. MET equivalents, measured by the SenseWear armband accelerometer, examine the intensity of physi cal activity. Percent active energy expenditure (%AEE) was calculated as measured active energy expenditure divided by total energy expenditure multiplied by 100. Multiple studies have defined sedentary individuals as expending < 10% of their daily energy expenditure in activities [ 128 129 ] but that definition was not used in this study because of the paucity of data to support their va lidity in children. Percent AEE was coded into three tertiles, following the direction of Manini et al. [ 130 ] For our

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51 analysis, the three tertiles of %AEE are defined as low: < 15%; medium: 15 24%; high: >24%. Dietary Intake and HEI 2005 The Block Kids FFQ was completed to assess overall diet, was designed by Dr. Gladys Block and contains 77 food/beverage items for children and adolescents aged 8 17 years (Appendix A). The food list originated from the NHANES 1999 2002 dietary recall data. The Block Kids FFQ asks about the frequency of foods consumed within the p revious week. The participants received help with completing the Block Kids FFQ, from either a parent of a research staff member, if they needed the assistance. There were six response categories, which ranged from none to every day. The quantities of the foods and beverages consumed were estimated with either three or four categories related to the type of food. For example, foods like fruits or vegetables were assessed in cups ( i.e. c, c, 1 c, and 2 c). For items like tacos, responses bowls, were presented in a hand out to assist in portion size determination. We calculated the HEI 2005 score for each participant by using their responses to the Block Kids FFQ. Nutri tionQuest, first, analyzed the Block Kids FFQ responses. NutritionQuest used the food items from the Block Kids FFQ to assign to the appropriate food groups. Recipes and foods, from the NutritionQuest nutrient database, were separated into their component parts and individual foods were assigned to the appropriate food groups. NutritionQuest totaled the daily quantity of servings of foods servings consumed for the following food groups: fruit, vegetables, grains, dairy, meat, and discretionary oils and fat; estimated intake of calories and nutrients, sugar intake,

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52 and the percent of calories from solid fat and sweets. We used the information from NutritionQuest to then, calculate the HEI 2005 component scores and total score. Only food and recipe constituents that contributed toward the 8 food groups were counted when calculating average daily number of servings. Participants were excluded if they reported extreme energy intakes o f <500 or >5,000 kcal/day (n=7) [ 117 ] Statistical T ests Data were analyzed using JMP8 statistical software (SAS Institute, Inc, Cary, NC). To test the association between overall HEI 2005 score and the disease risk factor variables, simple pair wise correlation analyses were performed. We also tested the association between physical activity and the disease risk factor variables as activity is known to influence disease risk factors as well. If indicated, physical activity was used in a multiple linear regression analysis to further elucidate the independent relationship of diet quality (HEI 2005) with these disease risk factors. For the analy ses, we controlled for gender, race, and weight status. Similar analyses were then performed with the individual component scores identified in the hypotheses and the disease risk factors to determine any potential relationship. A significance level of p < 0.05 was set for each statistical test.

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53 CHAPTER 4 RESULTS Demographic Description of Participants Table 4 1 lists the participant demographic data. One hundred seventy eight children participated in the study including 78 males and 100 females. The mea n age of the total sample of the participants was 9.94 (1.4) years. The mean age for boys and girls was 10.2 (1.4) years and 9.8 (1.4) years respectively. The racial distribution of participating children included 29 (16%) African American, 115 (65%) Caucasian, 23 (13%) Bi racial, 10 (5.5%) Unknown/No response and 1 (0.05%) Asian American. From this point forward, whenever comparisons are made between ethnic groups, the one Asian American and 12 Unknown/No Response children were excluded from the anal ysis. Anthropometric Description Of the 178 partici pants, t he distribution of BMI of study participants was 12 (7%) above the 85 th percentile, 21 (12 %) above the 95 th percentile, and 145 (81%) greater than the 97 th percentile. Fifty three percent of the Caucasian participants 14% of African American participants and 11% of the Biracial participants were in the >97 th percentile group ( Table 4 2 ) The average height and weight for the study population was 148.4 (10.4) cm and 64.5 (19 .1) kg, respec tively ( Table 4 3 ) The mean weight for height was 28.8 (5.8), mean BMI z score was 2.2 (0.4), mean waist circumference was 94.1 (14.9) cm, mean systolic blood pressure was 96.7 (11.2) mmHg, and mean diastolic blood pressure was 64.9 (8.4) mmHg.

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54 Clin ical and Dietary Characteristics, by Gender Table 4 4 illustrates the clinical and dietary characteristics of the study participants, by gender. At baseline, the mean clinical characteristics were all within normal ranges for all characteristics except for serum TG. For both boys and girls, the mean serum TG levels were in the borderline range, 163.3 (72.1) and 171.8 (79.7) mg/dl, respectively (p=0.46). The normal serum TG level, for both boys and girls in this age group, is less than 150 mg/dL. The mean energy intake for this study population was 1329.1 (602.2) kcal. The minimum reported energy intake was 503.69 kcal and macronutrient levels fell between normal levels. Gi rls had significantly lower % kcal from protein compared to boys (p=0.05). The participants in this study consumed 33.3 (6.0) % of calories from fat. The participants in this study consumed an average of 54.3 (7.6) %from carbohydrates. The mean fiber con sumption was 10.5 (5.9) g, daily. Clinical and Dietary Characteristics, by Race The mean clinical and dietary characteristics, by Race, are found in Table 4 5. African Americans had a significantly higher BMI (p=0.002), BMI z score (p=0.04) and HbA1c (p=0 .002) compared to Caucasians. African Americans also had significantly lower triglycerides compared to both Caucasian and Bi Racial participants (0.003). There were no other significances observed between the races. Clinical and Dietary Characteristics, b y BMI Percentile Distribution The mean values for physical and blood parameters by BMI percentile distribution are found in Table 4 6. The >97 th percentile group had significantly higher body fat (p<0.0001), BMI (p<0.0001), BMI z score (p<0.0001), waist ci rcumference (p<0.0001), systolic BP (p<0.0001), diastolic BP (p=0.002) and triglycerides (p=0.03). The 85 th to

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55 95 th percentile group reported consuming significantly higher (p<0.01) energy compared to the other percentile groups. Descriptive Statistics f or HEI 2005 Score HEI 2005 Scores by Gender Table 4 7 presents the energy consumption and mean total and individual component HEI 2005 scores for all participants and by gender. On average, the energy consumption was 1339.7 (596.6) kcal. The mean total HE I 2005 score was 61.2 out of a possible 100. Total HEI 2005 scores were similar for boys (62.7 11.2) and for girls (60.1 10.0), (p=0.10). The mean daily energy intakes, as assessed by the FFQ, for 7 12 year old boys and girls in the E FLIP for Kids S tudy were 1334.9 (574.8) and 1340.16 (615.9) kcal (p=0.93), respectively. Boys scored significantly higher on the SoFAAS component compared to girls (p=0.001). Neither boys nor girls met the government recommendation for the following HEI 2005 component s: Total Vegetables, DGOV, Whole grains, Dairy, Oil, Sodium, Saturated fat, SoFAAS and total HEI 2005 score. HEI 2005 Scores by Race Table 4 8 presents the comparison of energy consumption and total/component HEI 2005 scores by Race. No significant differe nces were seen between racial groups and HEI 2005 component scores. HEI 2005 Scores by BMI Percentile Distribution Table 4 9 presents the comparison of total/component HEI 2005 scores by BMI percentile distribution. According to the food frequency questionnaire analysis, the 85 th to 95 th percentile group scored a significantly lower score compared to the 95 th to 97 th percentile distribution group for the Meat & Beans component (p=0.02). The 95 th to 97 th

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56 percentile group scored significantly higher than the other groups for the Whole Grains component (p=0.05). The 85 th to 95 th percentile group scored significantly higher for the Sodium component compared to the other BMI percentile groups (p=0.05). Adherence to Federal Physical Activity Guidance On ly 94 of the 178 study participants had physical activity data that met these criteria ( Table 4 10 ) This study population spent an average of 120 minutes per day being physically active, 114 minutes in moderate intensity (3 6 METS) physical activity and 6 minutes in vigorous intensity (>6 METS) physical activity. According to the federal physical activ ity guidance, this study population was meeting the recommended goals for physical activity. When the participants were categorized into the three tertiles for %AEE, 32 participants were in the low tertile, 32 participants were in the medium tertile, and 3 0 participants were in the high tertile. After adjustment for physical activity, no significant associations were observed. Does the Diet Q ual ity, via the HEI 2005, of this O verw eight Study Population A dhere to the DGA 2005? This study population failed t o meet the government recommendations for the total HEI 2005 score (61.2 /100.00). For this study population, the mean HEI 2005 component scores were below the maximum possible score for every component ( Table 4 7 ) The federal government recommends that t he total and component HEI 2005 scores are greater than or equal to 80% of the score, indicating that the quality of the maximum score) for the following HEI 2005 components: Total Fruit, Whole Fruit, Total include: Total Vegetables, DGOV, Dairy, Oil, and Saturated Fat. The HEI 2005

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57 component scores were particularly poor (less than or close to 50% o f the maximum score) for Whole Grains, Sodium, and SoFAAS. Only 2% (n=3) of the study participants the Ca ucasian group ( Table 4 11 ) Association between Total HEI 2005 Score and Disea se Risk Factors After adjusting for physical activity and weight, there were no associations detected. Table 4 12 presents the data for the association between the HEI 2005 Score and disease risk factors. Neither serum TC (p=0.12, r=0.00), serum LDL chole sterol (p=0.12, r=0.01), serum HbA1c (p=0.65, r= 0.00) Systolic BP (p=0.45, r=0.00), Diastolic BP (p=0.02, r= 0.02) nor was waist circumference (p=0.72, r= 0.03) significantly associated w ith mean total HEI 2005 score. Association between HEI 2005 Compone nt Score and Disease Risk Factors The hypothesis evaluating the association of specific dietary components of the HEI with disease risk factors was tested using pair wise correlations. After adjustment for physic al activity there were significant differe nces in the HEI 2005 components scores and d isease risk factors Total Fruit score was inversely associ ated with TC (p=0.02, r= 0.16) ( Table 4 13 ). Exploratory Analysis: Ass ociation between Total/C omponent HEI 2005 Score and Disease Risk Factors When adjus ting for weight, various expected associations were observed. When analyzing the association between total HEI 2005 score and disease risk factors, there was a strong association observed in the 85 th to 95 th percentile group. TC ( p =0.004, r= 0.76) and LDL C ( p =0.005, r= 0.74) was significantly associated with total HEI 2005 score When analyzing the association between HEI 2005 component scores and

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58 disease risk factors, associations were observed. In the 85 th to 95 th percentile group, Total Fruit ( p =0.05, r = 0.57), Total Vegetable ( p =0.05, r= 0.57), Saturated Fat ( p =0.006, r= 0.74) and Sodium ( p =0.003, r=0.77) were significantly associated with TC. In the same group, Saturated Fat ( p =0.007, r= 0.73) was associated with LDL C. In the 95 th to 97 th percentile group, Sodium (p=0.03, r=0.47) was significantly associated with TC. No th percentile group. There were no specific associations observed when the componen t scores were analyzed by Race ( Table 4 14 ) Wh en adjusting for weight, various expected associations were observed. When analyzing the association between total HEI 2005 score and disease risk factors, there was a strong association observed in the 85 th to 95 th percentile group. TC and LDL C was signi ficantly associated with total HEI 2005 score. In the 85 th to 95 th percentile group, the association between Total Fruit score and TC disappeared. An unexpected association was observed between the SoFAAS score and TG with a positive correlation. No expec th percentile group. There were no specific associations observed when the componen t scores were analyzed by Race ( Table 4 15 )

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59 Table 4 1. Participant demographic results, by gender, n=178 Variable All participants Boy s Girls Mean Age Standard Deviation (yr) 9.94 ( 1.4) 10.2 ( 1.4) 9.8 ( 1.4) Number of participants 178 78 100 Race African American 29 (16%) 12 17 Caucasian 115 (65%) 52 63 Bi Racial 23 (13%) 8 15 Asian American 1 (0.05%) 1 0 Unknown/No Response 10 (5.5%) 5 5 Table 4 2. Characteristics of the sample, n=178 Asian American African American Caucasian Bi Racial No Response Total Boys 85 th to 95 th Percentile 1 (0.05%) 0 (0.00%) 3 (1.7%) 1 (0.05%) 0 (0.00%) 5 95 th to 97 th Percentile 0 (0.00%) 1 (0.05%) 6 (3.4%) 0 (0.00%) 0 (0.00%) 7 >97 th Percentile 0 (0.00%) 11 (6.2%) 43 (24.2%) 7 (3.9%) 5 (2.8%) 66 Total 1 12 52 8 5 78 Girls 85 th to 95 th Percentile 0 (0.00%) 1 (0.05%) 3 (1.7%) 1 (0.05%) 2 (1.1%) 7 95 th to 97 th Percentile 0 (0.00%) 3 (1.7%) 9 (5.1%) 1 (0.05%) 1 (0.05%) 14 >97 th Percentile 0 (0.00%) 14 (7.9%) 51 (28.7%) 13 (7.3%) 2 (1.1%) 79 Total 0 18 63 15 5 100

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60 Table 4 3. Participant anthropometric results, by gender, n=178 Anthropometric Measurements All Participants (n=178) Boys (n=78) Girls (n=100) Mean Height, cm 148.4 ( 10.4) 149.0 ( 9.5) 147.8 ( 11.0) Mean Weight, kg 64.5 ( 19.1) 65.1 ( 18.4) 63.9 ( 19.7) BMI 28.8 (5.8) 28.9 (5.9) 28.7 (5.8) BMI z score 2.2 (0.4) 2.17 (0.4) 2.1 (0.4) Waist Circumference (cm) 94.1 (14.9) 94.4 (14.2) 93.9 (15.6) Systolic BP (mmHg) 96.7 (11.2) 97.6 (12.7) 96.1 (9.8) Diastolic BP (mmHg) 64.9 (8.4) 65.9 (9.4) 64.3 (7.5) Table 4 4. Mean values for physical and blood parameters, by gender, n=178 Characteristic All Participants (n=178) Boys (n=78) Girls (n=100) P value Mean (SD) Biochemical Data Hemoglobin A1c (%) 5.5 (0.3) 5.6 (0.4) a 5.5 (0.3) a 0.27 Cholesterol level (mg/dl) TC 160.5 (27.5) 160.3 (26.7) a 160.7 (28.2) a 0.91 HDL Cholesterol (mg/dl) 39.1 (10.0) 39.5 (10.6) a 38.8 (9.6) a 0.63 LDL Cholesterol (mg/dl) 87.6 (23.7) 87.8 (24.0) a 87.4 (23.5) a 0.91 TG (mg/dl) 168.1 (76.4) 163.3 (72.1) a 171.8 (79.7) a 0.46 Dietary Nutrient Intake Energy (kcal) 1329.1 (602.2) 1334.9 (574.8) a 1324. 5 (625.3) a 0.91 % kcal from Fat 33.3 (6.0) 33.2 (6.0) a 33.5 (6.0) a 0.79 % kcal from Carbohydrates 54.3 (7.6) 53.9 (7.2) a 54.5 (8.1) a 0.62 % kcal from Protein 14.1 (2.7) 14.5 (2.4) a 13.7 (2.8) b 0.05 Total Fiber (g) 10.6 (5.9) 10.9 (5.9) a 10.2 (6.0) a 0.44 ab Means not sharing the same superscript letter (a,b) are significantly different ( P

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61 Table 4 5. Clinical and Dietary Characteristics, by Race, n=178 Characteristic African American (n=29) Caucasian (n=115) Bi Racial (n=23) P value Mean (SD) Age (years) 10.2 (1.3) a 9.9 (1.5) a 10.2 (1.4) a 0.45 Anthropometric Measurements BMI 32.1 (7.7) a 28.0 (4.9) b 29.3 (6.3) ab 0.002 BMI z score 2.3 (0.4) a 2.1 (0.4) b 2.2 (0.4) ab 0.04 Waist Circumference (cm) 98.5 (17.3) a 93.6 (14.8) a 92.5 (12.8) a 0.25 Physical Assessment Systolic BP (mmHg) 98.0 (13.0) a 97.1 (11.2) a 94.3 (8.6) a 0.45 Diastolic BP (mmHg) 63.64 (8.28) a 65.25 (8.58) a 66.59 (7.12) a 0.32 Clinical Characteristics HbA1c (%) 5.7 (0.5) a 5.5 (0.3) b 5.6 (0.3) a 0.002 Cholesterol level (mg/dl) TC 153.1 (26.1) a 163.6 (28.5) a 159.1 (24.6) a 0.18 HDL C 41.2 (11.1) a 38.9 (9.9) a 38.3 (9.2) a 0.49 LDL C 86.8 (23.5) a 88.7 (25.5) a 84.3 (16.7) a 0.69 Triglycerides (mg/dl) 125.1 (50.6) a 177.3 (79.0) b 188.4 (106.5) b 0.003 Dietary Nutrient Intake Energy (kcal) 1365.3 (686.7) a 1330.8 (579.4) a 1334.9 (580.1) a 0.96 % kcal from Fat 33.6 (4.3) a 33.9 (6.5) a 31.9 (5.2) a 0.36 % kcal from Carbohydrates 54.1 (5.2) a 53.5 (8.3) a 56.5 (7.3) a 0.23 % kcal from Protein 13.8 (2.0) a 14.3 (2.8) a 13.2 (3.0) a 0.16 Total Fiber (g) 11.3 (7.4) a 10.6 (5.7) a 10.4 (5.3) a 0.83 ab Means not sharing the same superscript letter (a,b) are significantly different ( P <0.05)

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62 Table 4 6. Mean values for physical and blood parameters, by BMI Percentile Distribution, n=178 Variable (Max Score) 85 th to 95 th percentile (n=12) 95 th to 97 th percentile (n=21) >97 th percentile (n=145) P value Mean (SD) Age (years) 10.5 (1.5) a 9.9 (1.4) a 9.9 (1.4) a 0.42 Anthropometric Measurements BMI 22.2 (1.8) a 23.3 (1.5) a 30.1 (5.6) b < 0.0001 BMI z score 1.4 (0.08) a 1.7 (0.06) b 2.3 (0.02) c < 0.0001 Waist Circumference cm) 79.4 (8.4) a 81.6 (7.6) a 97.0 (14.6) b < 0.0001 Systolic BP (mmHg) 87.8 (6.1) a 87.7 (6.3) a 98.7 (11.1) a <0.0001 Diastolic BP (mmHg) 58.3 (4.5) a 62.2 (4.0) ab 65.9 (8.8) b 0.002 Clinical Characteristics HbA1c (%) 5.4 (0.3) a 5.5 (0.3) a 5.5 (0.3) a 0.33 Cholesterol level (mg/dl) TC 150.6 (7.9) a 160.5 (6.2) a 161.3 (2.3) a 0.43 HDL C 39.2 (7.9) a 41.7 (15.2) a 38.7 (9.3) a 0.48 LDL C 85.6 (23.2) a 90.6 (22.5) a 87.3 (24.0) a 0.81 Triglycerides level (mg/dl) 129.3 (43.4) a 140.9 (67.1) a 175.0 (78.2) b 0.03 Dietary Nutrient Intake Energy (kcal) 1725.9 (906.3) a 1401.9 (541.7) a 1286.8 (571.5) a 0.04 % kcal from Fa t 31.0 (3.9) a 31.7 (5.5) a 33.7 (6.1) a 0.13 % kcal from Carbohydrates 57.7 (4.9) a 56.5 (7.0) a 53.7 (7.8) a 0.07 % kcal from Protein 13.0 (1.6) a 13.7 (2.4) a 14.2 (2.7) a 0.29 Total Fiber (g) 12.5 (6.4) a 12.3 (5.7) a 10.2 (5.8) a 0.17 ab Means not sharing the same superscript letter (a,b) are significantly different ( P <0.05)

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63 Table 4 7. Comparison of Energy Consumption and Total and Component HEI 2005 Scores by Gender, n=178 Variable (Max Score) All Participants (n=178) Boys (n=78) Girls (n=100) P value Mean(SD) Energy consumption (Kcal) 1339.7 (596.6) 1334.9 (574.8) a 1343.4 (615.9) a 0.93 Total HEI 2005 (100) 61.2 (10.6) 62.7 (11.2) a 60.1 (10.0) a 0.10 Total Fruit (5) 4.4 (1.3) 4.67 (0.76) a 4.57 (0.90) a 0.45 Whole Fruit (5) 4.0 (1.6) 3.9 (1.6) a 4.1 (1.5) a 0.33 Total Vegetables & Legumes (5) 3.1 (1.4) 3.2 (1.5) a 3.0 (1.4) a 0.53 DGOV & Legumes (5) 2.9 (2.0) 3.2 (2.0) a 2.7 (2.0) a 0.14 Total Grains (5) 4.4 (0.8) 4.51 (0.8) a 4.3 (0.8) a 0.11 Whole Grains (5) 1.1 (0.8) 1.1 (0.9) a 1.1 (0.8) a 0.59 Dairy (10) 6.6 (2.5) 6.8 (2.5) a 6.4 (2.6) a 0.24 Meat & Legumes (10) 8.4 (2.5) 8.1 (2.9) a 8.6 (2.1) a 0.24 Oil (10) 6.7 (2.7) 6.7 (2.8) a 6.7 (2.6) a 0.98 Sodium (10) 3.7 (2.3) 3.5 (2.2) a 4.0 (2.3) a 0.18 Saturated Fat (10) 5.8 (2.6) 6.1 (2.3) a 5.6 (2.7) a 0.22 SoFAAS (20) 10.1 (5.0) 11.4 (5.2) a 8.9 (4.5) b 0.001 ab Means not sharing the same superscript letter (a,b) are significantly different ( P <0.05)

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64 Table 4 8. Comparison of Age, Energy Consumption and Total/Component HEI 2005 Scores by Race, n=178 Variable (Max Score) African American (n=29) Caucasian (n=116) Bi Racial (n=23) P value Mean (SD) Energy consumption (Kcal) 1365.3 (686.7) a 1319.9 (586.8) a 1307 (587.0) a 0.93 Total HEI 2005 (100) 68.0 (14.5) a 69.3 (18.4) a 61.53 (12.3) a 0.85 Total Fruit (5) 4.2 (1.6) a 4.3 (1.4) a 4.7 (0.6) a 0.24 Whole Fruit (5) 4.1 (1.7) a 3.9 (1.6) a 4.2 (1.2) a 0.68 Total Vegetables & Legumes (5) 3.2 (1.2) a 3.2 (1.5) a 3.0 (1.3) a 0.84 DGOV & Legumes (5) 3.0 (1.8) a 3.0 (2.0) a 2.9 (2.0) a 0.98 Total Grains (5) 4.6 (0.8) a 4.4 (0.8) a 4.2 (0.7) a 0.34 Whole Grains (5) 1.3 (0.9) a 1.0 (0.9) a 1.0 (0.7) a 0.44 Dairy (10) 6.7 (2.3) a 6.7 (2.5) a 5.7 (2.9) a 0.23 Meat & Legumes (10) 8.7 (1.8) a 8.5 (2.5) a 8.3 (2.4) a 0.89 Oil (10) 6.8 (2.4) a 6.8 (2.7) a 6.7 (2.9) a 0.97 Sodium (10) 3.0 (2.1) a 3.8 (2.4) a 4.3 (2.1) a 0.15 Saturated Fat (10) 5.7 (2.6) a 5.5 (2.6) a 6.5 (2.4) a 0.32 SoFAAS (20) 10.0 (4.1) a 10.3 (5.3) a 8.3 (3.9) a 0.18

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65 Table 4 9. Comparison of Total/Component HEI 2005 Scores by BMI Percentile Distribution, n=178 Variable (Max Score) 85th to 95th percentile (n=12) 95 th to 97th percentile (n=21) >97th percentile (n=145) P value Mean (SD) Total HEI 2005 (100) 57.2 (11.1) a 65.3 (11.9) a 60.9 (10.3) a 0.08 Total Fruit (5) 4.5 (1.0) a 4.3 (1.4) a 4.4 (1.3) a 0.89 Whole Fruit (5) 4.0 (1.5) a 4.2 (1.6) a 4.0 (1.6) a 0.88 Total Vegetables & Legumes (5) 2.5 (1.4) a 3.1 (1.4) a 3.2 (1.4) a 0.27 DGOV & Legumes (5) 1.7 (1.9) a 3.0 (2.0) a 3.0 (2.0) a 0.07 Total Grains (5) 4.2 (0.9) a 4.6 (0.7) a 4.4 (0.8) a 0.43 Whole Grains (5) 0.9 (0.6) a 1.5 (1.1) b 1.1 (0.8) a 0.05 Dairy (10) 7.7 (2.1) a 6.9 (1.9) a 6.4 (2.6) a 0.26 Meat & Legumes (10) 7.0 (2.9) a 8.6 (1.9) b 8.5 (2.5) b 0.02 Oil (10) 5.5 (2.4) a 7.0 (2.7) a 6.8 (2.7) a 0.26 Sodium (10) 5.3 (1.5) a 3.5 (2.3) b 3.7 (2.3) b 0.05 Saturated Fat (10) 5.9 (2.4) a 5.2 (2.1) a 5.7 (2.6) a 0.39 SoFAAS (20) 8.2 (1.4) a 11.7 (5.2) a 9.9 (4.9) a 0.13 ab Means not sharing the same superscript letter (a,b) are significantly different ( P T able 4 10. Physical Activity of Participants, n=94 Variable All Participants Average minutes of PA (>3METS) 119.9 Minutes in Vigorous activity (>6 METS) 5.9 Minutes in Moderate activity (3 6 METS) 114.4 Minutes in Sedentary activity (<3METS) 1225 % AEE Low 32 Medium 32 High 30

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66 T able 4 11. Percent of participants meeting DGA 2005 goals, n=178 Variable n % Total Fruit Score Yes No 141 37 79 21 Whole Fruit Score Yes No 119 59 67 33 Total Vegetables & Legumes Score Yes No 61 117 34 66 DGOV & Legumes Score. Yes No 78 100 44 56 Total Grains Score Yes No 127 51 71 29 Whole Grains Score Yes No 1 177 0.56 99.4 Dairy Score Yes No 60 118 34 66 Meat & Legumes Score Yes No 128 50 72 28 Oil Score Yes No 67 111 38 62 Sodium Score Yes No 7 171 4 96 Saturated Fat Score Yes No 49 129 27 73 SoFAAS Score Yes No 29 149 16 84 Total HEI 2005 Score Yes No 3 175 2 98

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67 Table 4 12. Association between Total HEI 2005 Scores and Disease Risk Factors, n=178 1 TC Serum Total Cholesterol 2 LDL C Serum LDL cholesterol 3 HbA1c Serum Hemoglobin A1c 4 SBP Systolic Blood Pressure 5 DBP Diastolic Blood Pressure 6 WC Waist Circumference Table 4 13. Association of HEI 2005 Components and Disease Risk Factors, n=178 Variable Total Cholesterol LDL Cholesterol Total Fruit Score p=0.20 r=0.10 p=0.35 r=0.07 Whole Fruit Score p=0.82 r= 0.02 p=0.77 r=0.02 Total Vegetables Score p=0.72 r= 0.03 p=0.32 r=0.07 DGOV Score p=0.54 r= 0.05 p=0.67 r=0.03 Total Grains Score p=0.67 r= 0.03 p=0.45 r= 0.06 Whole Grains Score p=0.41 r=0.06 p=0.45 r=0.06 Saturated Fat Score p =0.33 r=0.07 p=0.72 r=0.03 Variable S ystolic B lood P ressure D iastolic B lood P ressure Dairy Score p=0.25 r=0.09 p =0.34 r=0.07 Sodium Score p=0.70 r=0.03 p =0.75 r=0.02 Variable T riglycerides Hemoglobin A1c SoFAAS Score p =0.80 r=0.01 p = 0.66 r= 0.03 Variable TC 1 LDL C 2 HbA1c 3 SBP 4 DBP 5 WC 6 HEI 2005 Score p = 0.33 r=0.07 p = 0.38 r=0.07 p = 0.65 r= 0.04 p = 0.69 r=0.03 p = 0.07 r=0.13 p =0.75 r=0.02

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68 Table 4 14. Exploratory: Association of Total HEI 2005 and Disease Risk Factors, by BMI percentile groups, n= 178 1 TC Serum Total Cholesterol 2 LDL C Serum LDL cholesterol 3 HbA1c Serum Hemoglobin A1c 4 SBP Systolic Blood Pressure 5 DBP Diastolic Blood Pressure 6 WC Waist Circumference Variable TC 1 LDL C 2 HbA1c 3 SBP 4 DBP 5 WC 6 85 th to 95 th percentile HEI 2005 Score p =0.004 r= 0.76 p =0.005 r= 0.74 p =0.44 r= 0.25 p =0.32 r=0.26 p = 0.85 r=0.06 p =0.22 r=0.39 95 th to 97 th percentile HEI 2005 Score p = 0.11 r=0.36 p = 0.15 r=0.33 p = 0.83 r= 0.05 p = 0.85 r=0.05 p = 0.51 r=0.15 p =0.47 r=0.17 >97 th percentile HEI 2005 Score p = 0.36 r=0.08 p = 0.31 r=0.08 p = 0.78 r= 0.02 p = 0.36 r=0.08 p = 0.07 r=0.15 p =0.81 r=0.02

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69 Table 4 15 Exploratory: Association of HEI 2005 Components and Disease Risk Factors, by BMI percentile groups, n=178 Total Cholesterol LDL Cholesterol Variable 85th to 95th percentile 95th to 97th percentile > 97th percentile 85th to 95th percentile 95th to 97th percentile > 97th percentile Total Fruit Score p=0.05 r= 0.57 p=0.25 r=0.26 p=0.83 r=0.02 p=0.19 r= 0.40 p=0.34 r=0.22 p=0.77 r=0.02 Whole Fruit Score p=0.07 r= 0.53 p=0.22 r=0.27 p=0.21 r=0.11 p=0.17 r= 0.42 p=0.14 r=0.33 p=0.44 r=0.06 Total Vegetables Score p=0.05 r= 0.57 p=0.22 r=0.27 p=0.88 r=0.01 p=0.08 r= 0.52 p=0.33 r=0.22 p=0.26 r=0.09 DGOV Score p=0.06 r= 0.54 p=0.40 r= 0.19 p=0.38 r= 0.07 p=0.13 r= 0.46 p=0.36 r=0.21 p=0.65 r=0.04 Total Grains Score p=0.33 r= 0.31 p=0.13 r= 0.34 p=0.80 r=0.02 p=0.28 r= 0.34 p=0.75 r= 0.07 p=0.68 r= 0.04 Whole Grains Score p=0.42 r= 0.26 p=0.25 r=0.26 p=0.66 r=0.04 p=0.35 r= 0.30 p=0.45 r=0.17 p=0.54 r=0.05 Saturated Fat Score p=0.006 r= 0.74 p=0.52 r=0.15 p=0.15 r=0.12 p=0.007 r= 0.73 p=0.66 r=0.10 p=0.42 r=0.07 Systolic Blood Pressure Diastolic Blood Pressure Variable 85th to 95th percentile 95th to 97th percentile > 97th percentile 85th to 95th percentile 95th to 97th percentile > 97th percentile Dairy Score p=0.24 r=0.36 p=0.83 r=0.05 p=0.08 r=0.15 p=0.88 r= 0.05 p=0.47 r=0.17 p=0.17 r=0.12 Sodium Score p=0.003 r=0.77 p=0.03 r=0.47 p=0.92 r=0.00 p=0.14 r=0.45 p=0.75 r=0.07 p=0.56 r=0.05 Triglycerides Hemoglobin A1c Variable 85th to 95th percentile 95th to 97th percentile > 97th percentile 85th to 95th percentile 95th to 97th percentile > 97th percentile SoFAAS Score p=0.41 r=0.26 p=0.30 r= 0.33 p=0.75 r= 0.03 p=0.30 r= 0.33 p=0.86 r=0.04 p=0.76 r=0.03

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70 C HAPTER 5 DISCUSSION The HEI 2005 is a tool, created by the USDA, used to assess how adherent the American population ages 2 years and older, is to the DGA 2005 [ 115 ] The DGA 2005 is meant to help maintain good health and diminish the risk of chronic diseases, although it is unclear in how these are defined in children and therefore difficult to know whether the HEI 2005 is successfully accessing health in children ages 7 12 years old [ 115 ] Mean energy intake was low in this sample. Underreporting of energy intake has been seen consistently in overweight individuals, including children [ 131 133 ] Heitmann and Lissner (1995) found that dietary reporting is influenced both qualitatively and quantitatively, depending on the degree of obesity [ 134 ] The current study is consistent with Heitmann and Lissner (1995) and other research studies [ 135 137 ] The 85th to 95th percentile group reported consumin g significantly more than the other groups. This likely underestimate of energy intake may also be a reflection of the method used to assess intake, a food frequency questionnaire [ 134 ] There were no significant gender differences in overall energy intake, although gender dif ferences were observed for one nutrient (% kcal from protein). The DGA 2005 recommends a minimu m fiber intake of the age of 14 g of fiber per 1,000 kcal [ 48 ] The participants in this stud y are expected to consume 18.76 g of fiber given their mean energy consumption of 1339.7 kcal. The DGA 2005 recommends that children 4 18 years old should keep fat consumption between 25 to 35% of calories, t heir carbohydrate consumption between 45 and 65% of calories and protein consumption 10 to 30% of calories. The participants, in this study, consumed an

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71 average of 33.11% of calories from fat, 54.6% of their calories from carbohydrates, and 14.13% of calor fell between normal levels. Does the D iet Q uality, via the HEI 2005, of this O verweight S tudy P opulation A dhere to the DGA 2005? The mean HEI 2005 score of the study participants was 61.2, indicating that the 2005 scores [ 91 ] Particularly, these children need to increa se the ir consumption of total vegetables and legumes, dark green and orange vegetables and legumes, whole grains, dairy and oils. On the other hand, this study population needs to decrease their consumption of sodium, saturated fat, and calories from solid fat and added sugar. For this study population, the overall mean HEI 2005 score was seven points higher than the national averages of 54.7 (6 11 years old) and 54.8 (12 17 year olds) [ 75 ] Our findings are consistent with reports comparing the diet quality of children [ 75 119 138 139 ] While still not reaching the level of a participants may be explained in part by their willingness to volunteer for a long term study aimed at improving lifestyle behaviors such as nutrition intake. Thes e children and more healthful diet that are not seen in the general population at the time of data collection Association between Total HEI 2005 Score and Disease Risk F actors The expected associations between total HEI 2005 score and disease risk factors were not observ ed with this study population. The reason may be explained by m ost of

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72 the children in th is study not exhibit ing elevations in serum lipids, TG, HbA1c or b lood pressure. This may be simply due to the young age of the participants and that elevations in these measures may not occur until later in life. A study completed in obese adults defined individuals who were obese but had favorable metabolic profiles as Metabolically Healthy but Obese (MHO) [ 140 ] This study proved that the prevalence of MHO individuals decreased with aging. Another potential explanation why no associations were observed may be related to the fact that many of the study participants were taking specific medications that t reated certain diseases like hypertension, type 2 diabetes, and hypercholesterolemia. Additionally, we may not have observed a correlation because the maximum score for the HEI 2005 makes full evaluation difficult. The components have maximum scores that b ecame the ceiling. So, children that surpassed the recommended amounts of dietary component were grouped with children who barely met the recommendations. All in all, the HEI 2005 should be further tested to evaluate its usefulness in predicting disease ri sk factors in longitudinal studies with a larger study population. Association between HEI 2005 Component Scores and Disease Risk Factors When all of the participants were analyzed together, controlling for physical activity, no associations were observe d The association seen with the SoFAAS score and HbA1c as well as the association seen with the Total Grains score and LDL cholesterol were expected. The associations seen with the Total Vegetable component and TC and LDL cholesterol were much unexpecte d. The results are interpreted as the higher the Total Vegetable or DGOV Score, the higher the serum TC or LDL cholesterol. A possible reason why we observed this relationship was because when NutritionQuest analyzed the Block Kids

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73 FFQ, they included fried vegetables in the total vegetables. The DGOV and LDL cholesterol association observed was also unexpected because of the positive correlation, but since preparation of the food items were not collected and the number of food items were limited for the Bl ock Kids FFQ, knowing what all was served on the vegetables is not known. Exploratory Analysis: Association between T otal/ C omponent HEI 2005 Score and Disease Risk Factors The strong relationship of the HEI 2005 component scores with disease risk factors, when controlling for weight, suggests that higher scores on this index may translate to reduced risk for chronic diseases in overweight and from rural counties in Florida. These results prove that the greater degree of overweight and obesity can c ontribute to disease risk factors. Also, obesity may override any benefits of quality diets. Limitations In addition to the many strengths of this study, there were several limitations. A limitation of this study is that both the original HEI and HEI 2005 were designed to be measured using a single 24 hour recall [ 1 120 141 ] but this study measured diet quality using a FFQ. Many studies have used FFQs to successfully calculate diet quality [ 142 144 ] The relative reliability and validity of the Block Kids FFQ among youth aged 10 to 17 years has been tested [ 145 ] The results from the study imply that the Block Kids FFQ is valid for some nutrients but not for many of the food groups that were difference was noted between the 24 hour recall and the Block Kids FFQ which included Energy Intake, %Energy from Carbohydrates, %Energy from Protein,

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74 Cholesterol, Fiber, Calcium, Sodium, Vegetables, Grains, and Milk, yogurt, and cheese. In a large epidemiological study, FFQs are financially appealing c ompared to 24 hour alternative dietary assessments tools and settled with the food frequency questionnaire because using a food frequency questionnaire would cost $1.2 million c ompared to $25 million for three 24 hour recalls and $23.2 million for 3 day food records. Another drawback is the underreporting on the Block Kids FFQ, commonly observed among overweight and obese individuals [ 131 133 ] This might have resulted in a concomitant underreporting of energy intake and HEI 2005 components. In addition to mentioned limitations of the HEI 2005, Guenther et al. (2008) lists further limitations, such as not having component scores that assess the intak es of food groups, total fat, trans fats, cholesterol, or oils [ 116 ] The HEI 2005 does, however, have one component for saturated fats and one for oils. The only oil s accounted for in the Oil component are from fish, nuts and non hydrogenated vegetable oils [ 116 ] Stated previously, the HEI 2005 has truncated scores. Although steps were taken the address the undesirable floor and ceiling effects, these were observed in this study [ 116 ] The floor effect is when the scores bunch at the low end of t he scale (score of 0) and the ceiling effect is when the scores bunch at the high end of the scale (scores of 5, 10, or 20 depending on the component) [ 116 ] Completing a study evaluating associations between the HEI 2005 total score and component scores is very difficult because no linear relationship can be observed with a ceiling or floor in the scoring. Implications for Future Research The present study found a n umber of areas where the diets of overweight children, ages 7 12 years, in rural counties in Florida need improvement, which could put them at

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75 increased risk for chronic diseases. Conventionally weight management interventions have focused on decreasing c aloric intake by focusing on increasing fruits and vegetables while decreasing calories from fat, in an effort to achieve weight loss. Traditionally, little attention has been given to the overall diet quality in weight management interventions and specifi cally to the intake of the recommended amounts of sodium, dairy and saturated fats I propose that since there is a direct correlation between certain dietary components and disease risk, future weight management interventions should be designed to focus o n improving diet quality, in addition to decreasing caloric intake. Based on our results, specific focus should be given to th e recommended intakes of vegetables, whole grains, dairy, oils, saturated fat, sodium and calories from solid fat and added sugars Consequently, utility of the HEI 2005 to predict disease risk should be confirmed in a longitudinal study with a larger population. Short term, future research is needed to: 1) evaluated change in diet quality due to intervention assignment within E FLIP and 2) examine the relationship between nutrient/food group intake and di sease risk factors. Long term future research is needed to incorporate a focus on diet quality into existing intervention materials and evaluate its impact.

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76 APPENDIX BLOCK KIDS FOOD FREQ UENCY QUESTIONNAIRE 2004

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96 BIOGRAPHICAL SKETCH Woods was born in Winter Garden, Fl orida She attended Tildenville Elementary, Lakeview Middle and Maynard Evans High School. In May 2009, she graduat degree from the University of Florida. Her major was n utrit ional s ciences with a m inor in c hemistry. Up on graduation, Alexis became a m University of Florida. She receive d her Master of Science degree in Nutritional Sciences i n the spring of 2012 Alexis was admitted into the Health Education and Behavior department to complete her doctorate.