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THE EFFECTS OF BLUEBERRY CONSUMPTION ON SATIETY AND GLYCEMIC CONTROL By Elijah James Magrane B.S. Johnson & Wales University, 2007 A THESIS Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science (in Food Science and Human Nutrition) The Graduate School The University of Maine August, 2009 Advisory Committee: Mary Ellen Camire, Professor of Food Science and Human Nutrition, Advisor Alfred A. Bushway, Professor of Food Science and Human Nutrition Richard A. Cook, Associate Professor of Food Science and Human Nutrition THE EFFECTS OF BLUEBERRY CONSUMPTION ON SATIETY AND GLYCEMIC CONTROL By Elijah Magrane Thesis Advisor: Dr. Mary Ellen Camire An Abstract of the Thesis Presented in Partial Fulfillment of the Requirements for the Degree of Master of Science (in Food Science and Human Nutrition) August, 2009 The prevalence of type 2 diabetes and obesity is increasing in the United States and other nations. Highly digestible carbohydrates may promote a high glycemic response, possibly contributing to obesity-related diseases. Anthocyanins have been found to exert in vitro a-glucosidase inhibitory effect, suggesting that foods containing anthocyanins may improve glycemic control. Wild Maine lowbush blueberries, Vaccinium angustifolium Ait., are a rich source of anthocyanins and contain 6 grams of dietary fiber and 45 kcal per 140 grams. The objective of this study was to evaluate the effect of wild blueberries and their juice on post-prandial serum glucose and satiety. A randomized cross-over blinded study was conducted using 11 overweight (body mass index (BMI) 25-29.9 kg/m2) and 10 normal weight (BMI 18.5-24.9 kg/m2) subjects who were 25-50 years old. Subjects were provided a base meal of cornflakes, milk and orange juice after an overnight fast. Four meal types were tested. One treatment included one cup (140g) of lowbush wild Maine blueberries; another had 112 mL of 100% wild blueberry juice. A placebo beverage mimicked the equivalent volume, acidity, and sugars to that of blueberry juice, was the third treatment, and lastly, a control meal with added glucose and fructose to match the amount in the berry meal. All meals as well as the control were adjusted to provide the same amount of carbohydrates, simple sugars, and calories. Fasting serum triglycerides and glucose were measured at baseline, and at 30, 60, 90, and 120 minutes. Serum insulin was measured at baseline, 30 and 60 minutes. Serum triglycerides and glucose was evaluated with the Beckman clinical analyzer, while serum insulin was determined with a FLUOstar Omega plate reader. Satiety was measured utilizing a visual analog scale (VAS) at baseline, 15, 30, 45, 60, 90, 120, and 180 minutes. After each intervention, participants kept food journals for the remainder of the day. Area under the curve was evaluated for overall changes in satiety responses. Results were analyzed using SYSTAT analytical software. A repeated measure General Linear Model was utilized for analysis of control and treatment groups. Test meals had no effect on serum glucose levels, insulin, triglycerides, or energy intake. Satiety responses differed among subjects with overweight and normal BMIs. Overweight subjects were more satisfied (P=0.05) and full (P=0.002) when compared to their lower BMI counterparts throughout all treatments. More human research is needed in order to evaluate the mechanisms by which anthocyanins may affect glycemic control, as well as to determine optimal dose efficacy. ACKNOWLEDGMENTS The author would like to extend a gracious thank you to my advisor, Dr. Mary Ellen Camire. Dr. Camire has provided me with her guidance, support and encouragement and has helped me to improve as a researcher, a writer, and as an academic throughout this project. I would like to thank Dr. Alfred Bushway and Dr. Richard Cook for their support and guidance as committee members. Special thanks to Michael Dougherty, Judy Polyot, Dr. William Halteman, and Dr. Joseph Brito for all their technical help and support. The author would like to thank the Maine Technology Institute Seed Grant program and the Wild Blueberry Commission for funding this research project. A special thank you to the Wild Blueberry Association of North America, Wyman's, Van Dyk's, Tate & Lyle, and Jungbunzlauer who generously donated supplies and offered support needed to conduct this research. The author would also like to thank all twenty-one subjects who participated in the study. Lastly, I would like to thank my parents, friends, and family for all of their love and support. I would especially like to thank my girlfriend, Marissa, for her unwavering support and understanding throughout this process. Thanks to all. iii TABLE OF CONTENTS ACKNOWLEDGMENTS iii LIST OF TABLES vii LIST OF FIGURES viii Chapter 1. INTRODUCTION 1 Background Information 1 Glycemic Control 3 Satiety 7 Anthocyanins 10 Absorption, Bioavailability, & Metabolism of Anthocyanins 13 Reported Biological Activity of Anthocyanins 17 Wild Blueberries 22 Blueberry Composition 24 Objective 26 2. MATERIALS AND METHODS 27 Study Design 27 Preparation of Placebo 32 Weight 33 IV Phlebotomy 34 Serum Glucose 35 Serum Insulin 36 Serum Triglycerides 39 Serum Peptide YY3.36 41 Food Records 43 Statistics 43 3. RESULTS AND DISCUSSION 44 Subject Demographics 44 Blood Analysis 45 Screening 45 Serum Glucose 45 Serum Triglyceride 47 Serum Insulin 49 Serum Peptide YY3.36 51 Satiety Scores 51 Food Record Analysis 54 4. CONCLUSIONS 55 REFERENCES 57 APPENDICES 69 Appendix A. Recruitment Flyer 70 Appendix B. Informed Consent Form 72 Appendix C. Screening Questionnaire 74 v Appendix D. Informed Consent Form 76 Appendix E. Directions for Testing 78 Appendix F. Satiety Rating Scales and Questions 79 Appendix G. Food Record Instructions Handout 80 Appendix H. Individual Mean Satiety Scores: Food Consumption 82 Appendix I. Individual Mean Satiety Scores: Fullness 83 Appendix J. Individual Mean Satiety Scores: Hunger 84 Appendix K. Individual Mean Satiety Scores: Satisfaction 85 BIOGRAPHY OF THE AUTHOR 86 VI LIST OF TABLES Table 1. Common Anthocyanidins 12 Table 2. Nutritional Composition of Wild Blueberries (100 g) 25 Table 3. Inclusion/Exclusion Criteria 29 Table 4. Experimental Meals 32 Table 5. Placebo Formula 33 Table 6. American Heart Association Guidelines for Triglycerides 39 Table 7. Subject Demographics 44 Table 8. Fasting Serum Glucose at Screening: Grouped by BMI 45 Table 9. Mean Serum Glucose Levels: Grouped by Treatment and BMI 46 Table 10. Mean Area Under the Curve of Serum Glucose: Grouped by BMI 46 Table 11. Mean Serum Triglyceride Levels 48 Table 12. Mean Area Under the Curve of Serum Triglycerides: Grouped by BMI 48 Table 13. Mean Serum Insulin Levels: Grouped by Treatment and BMI 50 Table 14. Mean Area Under the Curve of Serum Insulin: Grouped by BMI 50 Table 15. Mean Area Under the Curve of Satiety: Grouped by and Treatment BMI 53 Table 16. Mean Energy Intake by BMI 54 VII LIST OF FIGURES Figure 1. Mechanisms of Glucose Homeostasis 4 Figure 2. The Gut-Brain Axis and Satiety 8 Figure 3. Basic Flavonoid Structure 10 Figure 4. Basic Anthocyanin Structure 11 Figure 5. Study Design 28 Figure 6. Satiety Rating Scales and Questions 30 Figure 7. Glucose Analysis Reaction 36 Figure 8. Triglyceride Analysis Reaction 40 vm Chapter 1 INTRODUCTION Background Information The World Health Organization (WHO) defines overweight and obesity as abnormal or excessive fat accumulation that presents a risk to health (World Health Organization, 2006). The WHO's latest projections indicated that globally in 2005, approximately 2.0 billion adults (age 15+ years) were considered either overweight or obese. Further projections by the WHO predict that by 2015 that number could reach as high as 3.0 billion. In the United States obesity has been on the rise over the past 25 years. According to the Centers for Disease Control (CDC), utilizing data from the most recent NHANES survey, approximately 66% of adults, or an estimated 97 million Americans between the ages of 20-74, are either overweight (Body Mass Index (BMI) between 25.0-29.9 kg/m2) or obese (BMI >30.0 kg/m ). Furthermore, the prevalence of obesity in adult men and women has made only marginal rises between 2003-2004 and 2005-2006. An additional NHANES found that between 1976-1980 and 2003-2006 the prevalence of obesity had increased. For children aged 2-5 years, prevalence increased from 5.0% to 12.4%; for those aged 6-11 years, prevalence increased from 6.5% to 17.0%; and for those aged 1219 years, prevalence increased from 5.0% to 17.6% (NCHS, 2008). Obesity develops as a result of a complicated interaction between a person's genes, environment, and lifestyle. Clinically, obesity is characterized by long-term energy 1 ^m. imbalance due to excessive caloric consumption, insufficient energy output resulting from a sedentary lifestyle, and /or a low resting metabolic rate. Overweight and obese individuals have an increased risk of morbidity from numerous health conditions. In an evidence report conducted by the National Institutes of Health's National Heart, Blood, and Lung Institute (NIH, 1998), higher morbidity was associated with overweight and obesity for hypertension, type 2 diabetes, coronary heart disease (CHD), stroke, gallbladder disease, osteoarthritis, sleep apnea and respiratory problems and some types of cancer (endometrial, breast, prostate, and colon). Obesity is also associated with complications of pregnancy, menstrual irregularities, hirsutism, stress incontinence, and depression. Due to the intimate connection obesity seems to have within other diseases, paradigms have started to shift in the way that physicians look at and treat other diseases. For example, the emphasis used to be on treatment of Type 2 diabetes, but within the last decade prevention has become the focus. To illustrate the point even further, in a review conducted by Astrup and Finer in 2000 the term "diabesity" was coined to demonstrate the relationship between diabetes and obesity. This relationship is so intertwined in fact, that a lifetime diabetes risk at 18 years of age may increase from 7.6 to 70.3% between underweight and very obese men and from 12.2 to 74.4% for women (Narayan et ah, 2007). Obesity and obesity-related diseases are also taxing our healthcare system. Obesityattributable health care spending between 1987 and 2001 contributed to 27% of the rise in inflation-adjusted per capita spending between the same time span. Furthermore, obesity 2 prevalence alone may have accounted for 12 percent of the growth in health care spending (Thorpe et ah, 2004). The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) estimated that for 2007, diabetes-related care alone cost the American economy 174 billion dollars. Despite high healthcare costs, obesity and obesity-related disease are still not getting any better. Wolfe/ ah, (2007) estimated that if an intensive lifestyle intervention model was used and successful, the health plan cost savings per individual would be $3,914 a year, representing a decrease in health plan payments of 34%. Glycemic Control Stability of blood glucose is a critical factor in lifelong health (DeFronzo et ah, 1992) yet abnormalities in glycemic control are increasingly common in adults and represent a central feature of many chronic diseases including type 2 diabetes, the metabolic syndrome and obesity (Baum et al., 2006). Blood glucose is maintained within a narrow physiologic range through a complex balance of dietary intake, de novo synthesis, glycogen storage and release, and insulindependent and noninsulin-dependent glucose uptake by tissues. Use of blood glucose as a fuel is relatively constant for tissues including the brain, nervous system, red blood cells, and kidneys (Figure 1). 3 Figure 1. Mechanisms of Glucose Homeostasis Figure 1: Under basal conditions, approximately 80% of circulating glucose is taken up by the brain. When food is ingested there is a parallel rise in blood glucose levels. This increase in blood glucose is sensed by the B-cells in the pancreas and as a result, insulin is secreted. Insulin circulates through the body and signals to the major insulin sensitive organs: muscle, liver, kidneys, and fat to increase their glucose intake (+). Insulin simultaneously leads to a reduction of glucose production from the liver and other organs (-). In this way the insulin counteracts the rise of glucose in the blood returning the system to its equilibrium (The Sanger Institute, 2008). 4 In normal subjects the extracellular supply of glucose is carefully regulated by insulin and glucagon (Gerich, 1988). As plasma glucose concentrations rise after a meal, glucose enters the pancreatic B-cells via the GLUT 2 and GLUT1 transporters in the cell membrane. In the cells, glucose is then phosphorylated to glucose-6-phosphate by an islet-specific glucokinase (Matschinsky et al, 1993). Insulin then acts to restore normoglycemia in three ways (Gerich, 1988): it decreases hepatic glucose production by diminishing both glycogenolysis and gluconeogenesis; it increases glucose uptake by skeletal muscle and adipose tissue by translocating glucose transporters from an intracellular pool to the cell surface (Kahn and Flier, 1990); and, via its antiproteolytic and antilipolytic actions, it diminishes the delivery of the gluconeogenic precursors, alanine and glycerol, to the liver. Insulin also inhibits glucagon secretion by direct inhibition of the glucagon gene in the pancreatic alpha-cells (Philippe, 1991), which further diminishes hepatic glucose production. Since diabetes is fundamentally a disorder of glucose metabolism, it is reasonable to ask whether different types of dietary constituents can influence glycemic control. Some highly-digestible carbohydrate foods for example, may promote a high glycemic response, possibly contributing to obesity. It has been theorized that foods that support high glycemic responses, or foods categorized as having a high glycemic index (GI), promote postprandial carbohydrate oxidation versus fat oxidation, thus altering metabolism in a way that might favor fat storage. Conversely, low GI foods may favor weight control because they promote satiety, minimize postprandial insulin secretion, and maintain insulin sensitivity (Brand-Miller et ah, 2002). 5 The glycemic index is based on the theory that the GI of a food will vary depending on the rate of digestion. The faster the digestion of a food's carbohydrates, the higher the GI value (a low GI value of food is considered <70). However, the rate of carbohydrate digestion is affected by a number of factors that might be difficult to quantify: the type of carbohydrate (glucose has a GI value of 138, while maltose, sucrose, and fructose have 105, 75, and 30, respectively); the fat and protein content of food; acidity (acidity affects gastric emptying and, hence, the GI of a food); the physical properties of food (i.e., particle size); the presence of viscous soluble fibers; ripeness, cooking, or processing that renders the carbohydrate, particularly starch, more digestible; and the presence of other factors (i.e., insoluble fiber as found in whole intact grains) that slow absorption of the carbohydrate (Radulian et al., 2009). There is some evidence that even when the kilocalorie intake is the same, low glycemic index food diets may stimulate more weight loss in obese people than do high glycemic index diets (Brand-Miller et al., 2002). One review highlighted the possible usefulness of low glycemic index foods in the management of obesity (Pawlak et al., 2002). However, there is controversy. In another review of the outcomes of appetite, food intake, energy expenditure and body weight, the authors concluded that there is currently no evidence that low glycemic index foods are superior to high glycemic index foods in regard to long-term body weight control (Raben, 2002). More recently, in a review conducted by the Cochrane Collaboration (Thomas, Elliott, and Baur, 2007); authors assessed the effects of low glycemic index or glycemic load diets in overweight or obese people. Six randomized controlled trials, involving 202 participants, were analyzed. Interventions ranged from five weeks to six months duration. Participants receiving the low glycemic 6 index or load diet lost a mean of one kilogram more than did those on comparison diets. Lipid profiles also improved more in participants receiving the low glycemic index or load diet. Currently, no consensus has been reached in terms of the efficacy of low GI foods as an effective treatment in obesity or obesity-related diseases such as diabetes and cardiovascular disease. Satiety An individual's satiation (or feeling of fullness) is comprised of a myriad of physiological and behavioral factors. Palatability, food composition, environment, and psychosocial factors all play a role. Hormones in the gut can also influence satiety. Gut hormones such as peptide YY induce satiety and reduce food intake, whereas ghrelin promotes feeding (Chaudhri et al., 2005). It is believed that gut hormones (along with other neural signals) may convey signals between higher brain centers and the gut, thus regulating meal initiation and termination (Murphy and Bloom, 2006). The hormones leptin, peptide YY 3-36, cholecystokinin, and ghrelin convey information regarding energy status to regulatory sites in the brain (Figure 2). Food intake increases concentrations of leptin, PYY3-36, and CCK while decreasing ghrelin concentrations. These hormonal responses decrease food intake, which will eventually increase ghrelin concentrations while decreasing leptin, PYY3-36, and CCK concentrations, thus perpetuating the feeding cycle (Orr and Davy, 2005). Evidence supports the existence of a system in the gut that senses the presence of food in the gastrointestinal tract and signals to the brain via neural and endocrine mechanisms to 7 regulate short-term appetite and satiety (Murphy and Bloom, 2006). The gut releases more than 20 peptide hormones in response to specific stimuli, and the release of a number of these hormones is sensitive to changes in gut nutrient content. Recent evidence has shown that specific gut hormones administered at physiological or pathophysiological concentrations can influence appetite in rodents and humans (Wren et al., 2000 and 2001; Batterham et al., 2002 and 2003; Cohen et al., 2003). Gut hormones have an important physiological role in postprandial satiety; however, the mechanisms that regulate shortterm, postprandial satiety are still being established. Figure 2. The Gut-Brain Axis and Satiety (Orr and Davy, 2005) [+] = Concentration Increase; [-] = Concentration Decrease 8 Dietary fiber contributes significantly to satiety. In a position paper by the American Dietetic Association (ADA), Marlett and colleagues (2002) emphasized that fiber-rich foods are digested more slowly. Furthermore, foods with high fiber contents tend to be less energy-dense but have a greater volume that may take longer to eat and bring on a feeling of satiety sooner. Wild blueberries, for example, contain 6 grams of dietary fiber (4 g insoluble and 2 g soluble fiber) and 45 kcal per 140 grams (about one cup) (based on information provided by the Wild Blueberry Association of North America [WBANA] (http://www.wildblueberries.com/health_benefits/nutrition.php). Despite the physiological and biological factors that contribute to satiety, satiety as it relates to appetite is very subjective. Due to that subjective nature, measuring satiety can be difficult. One popular method for measuring satiety is the visual analog scale (VAS). First developed in 1921 as a method for supervisors to rate their employees (Hayes and Patterson, 1921), the VAS quickly gained popularity and was used subsequently in the field of psychiatry to assess mood (Atiken, 1969). VAS is typically based on a 100 millimeter horizontal line with verbal anchors at each end. Several studies have evaluated the reproducibility and validity of the visual analog scale. Flint and colleagues (2000) determined that VAS scores were reliable for appetite research and inferred that they do not seem to be influenced by prior diet standardization. In a review of 4 clinical trials Parker et al. (2004) confirmed that food intake is related to perceptions of hunger and fullness as assessed by VAS in healthy older and young subjects. Conversely, Raben and others (1995) found it difficult to reproduce VAS scores in relation to satiety and palatability when subjects were fed identical meals on different days. In an attempt to explain his findings, Raben admitted that "it is likely that the variation in appetite ratings 9 is due both to methodological day-to-day variation and to biological day-to-day variation in subjective appetite sensations". Anthocyanins The term anthocyanin is derived from the Greek words meaning flower and blue. Anthocyanins are a group of natural occurring pigments belonging to the flavonoid family. Flavonoids share a common C6-C3-C6 configuration consisting of 2 aromatic rings linked by 3 carbons (Figure 3). They are responsible for the red-blue color of many fruits and vegetables and are the largest group of water-soluble plant pigments in the plant kingdom (Mazza and Miniati, 1993). Figure 3. Basic Flavonoid Structure Anthocyanins are present in nature mainly as heterosides. The basic anthocyanin structure is comprised of an aglycone or anthocyanidin, which is derived from the 10 flavilium ion or 2-phenylbenzopyrilium, and a sugar moiety usually attached at the 3position on the C-ring or the 5, 7-position on the A-ring (Figure 4) (Prior and Wu, 2006). Structurally, anthocyanins vary in the number of hydroxyl groups, the degree of methylation of these hydroxyl groups and position of attachment (Nicoue et al., 2007). In plants, anthocyanidins are linked to one or more glycosidic units, including glucose, galactose, arabinose, rhamnose, xylose, or fructose. Depending on the number and position of the hydroxyl and methoxyl substituents, over a dozen different anthocyanidins have been identified, of which six are commonly found in fruits and vegetables (Table 1) (Pascual-Teresa and Sanchez-Ballesta, 2008). Cyanidin, delphinidin, and pelargonidin are the most common anthocyanins in nature (Swain, 1976) with cyanidin glycosides reportedly being present in approximately 90% of all fruits (Prior, 2003). Anthocyanins can be found in very high concentrations in berry fruits ranging from 10 to 600 mg/100 g fresh weight. Figure 4. Basic Anthocyanin Structure 11 Table 1. Common anthocyanidins Rl* R2* H Anthocyanidin Pelargonidin OH H Cyanidin OH OH Delphinidin OH OCH3 Petunidin OCH3 H Peonidin OCH3 OCH3 Malvidin *Refers to substituting side groups that define the particular anthocyanidin Until recently, the only available data on anthocyanin intake by humans was that of Kuhnau (1976). Kuhnau estimated that human consumption of anthocyanins was between 180-215 mg/day in the United States. More recently Chun et al. (2007) calculated the mean daily anthocyanin intake based on 24-hour dietary recalls and the USDA database for the "Flavonoid Content of Selected Foods" (Agricultural Research Service, 2003), and arrived at a much lower figure of 3 mg/day. These figures are in more of an agreement with the value of 12 mg/day obtained by Wu et al, (2006) also for the U.S. population. However, it is of note that due to anthocyanins higher concentrations in certain foods, i.e., berries, red fruits, and wine, it can be assumed that there are wide 12 variations between individuals as well as populations. For example, in Finland, where the consumption of berries is common, the average intake of anthocyanins has been estimated to be 82 mg/day (Heinonen, 2001). Absorption, Bioavailability, & Metabolism of Anthocyanins Due to the ubiquitous and variable biochemistry of flavonoids, reported bioavailability differs widely. In addition to the basic flavonoid structures, several modifications can occur altering their chemical, physical, and biological properties. These alterations undoubtedly affect absorption, metabolism, and bioavailability. Other key aspects such as dietary source and the foods accompanying them upon consumption cannot be ignored as well (Manach et al., 2005). The combination of all above-mentioned factors that determines their biological functions and, therefore, dictates their health effects (Hollman andKatan, 1999). In the past, flavonoid glycosides were not believed to be absorbed by the gastrointestinal tract (Griffiths and Borrow, 1972) and hydrolysis of the glycoside was believed to be necessary for absorption. As no specific enzymes were known to selectively hydrolyze these glycosidic bonds, it was assumed flavonoids were poorly absorbed. Human studies within the last decade have proven this theory to be incorrect as the absorption of flavonoids has been documented (Hollman et ah, 1995; Williamson et al., 2000). However, the exact mechanisms involved in flavonoid absorption are a matter of much discussion. It has been suggested that most aglycone flavonoids are absorbed by passive diffusion (Hollman et al., 1997; Donovan et al., 2001), while another suggests active 13 transport or facilitated diffusion (Crespy et al., 1999). Yet an additional hypothesis infers that flavonoid glycosides must be deglycosylated by intestinal enzymes or colonic microflora in the intestine prior to absorption (Williamson et al., 2000; Manach et al., 2005). Research has shown evidence, however, of anthocyanin absorption from the stomach (Passamonti et al., 2003) and jejunum (Matuschek et al., 2006) of rats and mice, respectively. Further studies demonstrating glycosidases capable of hydrolyzing glycosidic bonds within the cells of the gastrointestinal mucosa confirmed the small intestine as a major site of flavonoid absorption (Hollman et al., 1995; Hollman et al., 1996; Scalbert and Williamson, 2000). In vitro studies have shown that anthocyanidin monoglucosides (3 -glucosides of cyanidin, malvidin, and peonidin) and diglucosides (3,5-diglucosides of cyanidin and malvidin, and cyanidin rutinoside) are deglycosylated by the action of colonic microflora in a 20 minute to 2 hour period, depending on their structure (Aura et al., 2005; Keppler and Humpf, 2005). Anthocyanin distribution in animal tissues has also been described. The accumulation of pelargonidin and some of its metabolites have been detected in stomach, liver, kidney, brain, and lung of rats after 2 hours post- ingestion (El Mohsen et al., 2006). Matsumoto and others (2006) have reported intact anthocyanins in several ocular tissues after administration of blackcurrant extract to rabbits or rats. In humans, anthocyanins are rapidly absorbed, with a maximum plasma concentration of 1.4 and 200 nM for anthocyanin doses of 10-720 mg. The maximum concentration is reached 45 minutes to 4 hours after ingestion of an anthocyanin-containing meal, 14 depending on conditions of the trial (Matsumoto et al., 2001; Cao et al., 2001). Plasma half-life for anthocyanins has been documented from 2 hours for cyanidin glucoside and sambubioside, to 3.3 hours for the rutinosides of cyanidin and delphinidin (PascualTeresa and Sanchez-Ballesta, 2007). Anthocyanins have been recovered from plasma not only as the original glycosides but also as glucuronidated and methylated metabolites. However, in one study, Cao et al., (2001) reported the presence of the aglycone cyanidin in plasma as well. In urine the maximum concentration of anthocyanins is reached 1.5 to 6 hours after ingestion (Felgines et al., 2003, 2005; Frank et al., 2005). The percentage of anthocyanins encountered in urine represents between 0.004 and 0.2% of the quantity ingested. Only in two known published studies is this percentage of anthocyanins in urine substantially higher, reaching 5% in the study conducted by Lapidot et al. (1998) and 1.80% in that of Felgines et al. (2003). Kay et al. (2004) reported that after ingestion of a high dose (1.3 g) of cyanidin glucosides, the original anthocyanins represented 75% of the total anthocyanins extracted from urine, while 10% where identified as methylated metabolites, and 15% was eliminated as glucuronidates. Felgines and colleagues (2005) produced different results after ingestion of cyanidin glycosides. In this study, monoglucuronides represented 64% of total anthocyanins excreted in urine, 19% as glycosides, 10% as aglycones, and 1.2% as diglucuronides. In general, bioavailability of anthocyanins can be affected by various factors, one of which is the nature and position of their glycosidic groups. For instance, it has been 15 shown that the 3-monoglucosides of anthocyanidins are less bioavailable than their corresponding rutinosides (McGhie et al, 2003; Nielsen et al, 2003). Absorption may be influenced by their structure too. Nielsen and colleagues (2003) did not find any differences between delphinidin and cyanidin, but McGhie and others (2003) did find differences between the galactosides of malvidin and petunidin and that of delphinidin. They suggested that an increase in the number of hydroxyl groups may decrease bioavailability. Although seemingly absorbed rapidly and eliminated rapidly, low concentrations of anthocyanin glycosides in human blood and urine have been documented extensively (Matsumoto et al, 2001; Murkovic, et al, 2001; Netzel et al, 2001; McGhie et al, 2003; Kay et al, 2004; Felgines et al, 2005). While most studies have focused primarily on the analysis of anthocyanin metabolites derived from glucuronidation and sulphation, it has been suggested that anthocyanins may be metabolized by intestinal microflora producing a set of new products that are often overlooked (Prior and Wu, 2006). For instance, in a rat study, Tsuda et al. (1999) reported a significant increase in plasma concentrations of protocatechuic acid, a byproduct derived from the breakdown of anthocyanins, following the oral administration of cyanidin-3-glucoside (C3G). The researchers proposed that C3G was first hydrolyzed in the intestine by P-glucosidase and that the aglycone produced is degraded to protocatechuic acid. Youdim et al. (2000) measured plasma levels of some anthocyanins following oral supplementation of blueberry skin extract mainly containing cyanidin-galactoside, C3G, Cy-3-arabinose, and the aglycone cyanidin. Data suggested that cyanidin-glycosides are incorporated from the digestive tract into the blood stream in their intact glycosylated form. 16 As reviewed, much work has been done in the field of anthocyanin analysis. Current knowledge suggests that there seem to be some important differences in the metabolism of anthocyanins, compared with those for other polyphenols. Anthocyanins' apparent lack of stability poses several difficulties for researchers as does the proposed limitations of analytical methods (McGhie and Walton, 2007). Studies on absorption and metabolism are still needed; especially those targeted at identifying the effect of gastrointestinal microflora on anthocyanin structure. Reported Biological Activity of Anthocyanins In recent years, several studies have eluded to the numerous health benefits provided by anthocyanins (Pascual-Teresa and Sanchez-Ballesta, 2008). The majority of the work investigating these activities focuses on the antioxidant properties of anthocyanins (Bohm et al., 1998; Kong et al., 2003; Galvano et al., 2004). This research has promoted focus on the functional components of certain fruits and vegetables, one of which is wild blueberries, due to their consistently higher levels of anthocyanins, total phenolics, and antioxidant capacity (Kalt et al., 2001; Prior et al., 1998). Molan and colleagues (2008) found that when rats were gavaged with a water-soluble blueberry extract, food intake decreased by 8.6% while weight gain decreased by 9.2% relative to the rats in the control group. According to the authors, this study for the first time demonstrated the reducing effect of blueberry extract premeals on subsequent food intakes. The exact mechanism of the results was not identified as part of the study. However, in a review, McDougall and Stewart (2005) concluded that the inhibition of 17 starch, protein and/or lipid digestion and their subsequent absorption by berry phenols, including anthocyanins may represent an important role for delivering health benefits. Alpha-glucosidase (AGH), which is a membrane-bound enzyme in the epithelium of the small intestine, catalyzes the cleavage of glucose from disaccharides (Hauri et al., 1982). The clinical significance of the enzyme is demonstrated by the fact that the prescription diabetes medications acarbose, miglitol and volgibose are widely used as therapeutic a-glucosidase inhibitors to delay glucose absorption from the small intestine (Goto et al, 1989; Odaka et al, 1992). Matsui et al. (2001) investigated the a-glucosidase inhibitory action of natural acylated anthocyanins on the AGH activity utilizing an in vitro rat intestinal assay. Twelve of the 16 anthocyanin extracts that were tested were found to give a significant free aglucosidase inhibitory effect contributing to post-prandial blood glucose suppression. Recent research demonstrated that anthocyanins from blueberries have shown comparable results of efficacy for glycemic control when compared to diabetic pharmaceuticals. Grace et al. (2009) established that anthocyanins from blueberries have the potency to alleviate symptoms of hyperglycemia in diabetic C57M/6J mice. Mice were gavaged (500 mg/kg body wt) with a phenolic-rich extract and an anthocyanin-enriched fraction formulated with labrasol (a pharmaceutically acceptable self-microemulsifying drug delivery system) had lowered elevated blood glucose levels by 33 and 51%, respectively. The hypoglycemic activities of these formulas were comparable to that of the known anti-diabetic drug metformin (27% at 300mg/kg). The extracts were not significantly hypoglycemic when administered without Labrasol, 18 demonstrating its bio-enhancing effect, most likely due to increasing the bioavailability of the administered preparations. The phenolic-rich extract contained 287.0±9.7 mg/g anthocyanins, while the anthocyanin-enriched fraction contained 595±20.0 mg/g (as cyanidin-3-glucoside equivalents). The greater hypoglycemic activity of the anthocyanin-enriched fraction compared to the initial phenolic-rich extract suggested that the activity was due to the anthocyanin components. Martineau et al. (2006) maintained that V. angustifolium may contain active in vitro principles with insulin-like and glitazone-like properties, while conferring protection against glucose toxicity. Results demonstrated that V. angustifolium ethanol extracts of root, stem, leaf, and fruit possess considerable insulin-like properties, as evidenced by enhancement of insulin-dependent and -independent glucose uptake in cell-based assays. Recently, Adisakwattana et al. (2009) investigated cyanidin-3-galactoside for its aglucosidase inhibitory activity. Researchers found that a low dose of cyanidin-3 galactoside showed a synergistic inhibition on intestinal a-glucosidase (maltase and sucrase) when combined with acarbose. A kinetic analysis showed that cyanidin-3galactoside gave a mixed type inhibition against intestinal sucrase. The results indicated that cyanidin-3-galactoside is indeed an a-glucosidase inhibitor and could be used in combination with acarbose for treatment of diabetes. Evidence suggests that anthocyanin-rich foods may also help prevent fat gain from high-fat meals. Tsuda and coworkers (2003) published data on the effects of anthocyanins from purple corn in the prevention of obesity and the amelioration of 19 insulin resistance is a mouse model. Mice were fed a control, cyanidin 3-O-P-Dglucoside purple corn color, high fat, or high fat plus the purple corn color diets for 12 weeks. Dietary purple corn color significantly suppressed the high fat diet induced increase in body weight gain and white and brown adipose tissue weights. In contrast, the induction did not incur in the group receiving the high-fat diet with purple corn color. A similar study investigated the effects of cyanidin 3-glucoside amelioration of hyperglycemia and insulin sensitivity. Sasaki et al. (2007) fed type 2 diabetic mice a control or the anthocyanin diet (2 g/kg) for 5 weeks. Dietary cyanidin 3-glucoside significantly reduced blood glucose concentrations and enhanced insulin sensitivity. This study suggests that the improved results can be attributed to increases in glucose transportation specifically known as glucose transporter 4 and decreases in inflammatory markers, such as retinol binding protein 4. Anthocyanins have also been examined for their effects on adipocyte function. Recent studies show adipocyte dysfunction is strongly associated with the development of obesity and insulin resistance (de Ferranti and Mozaffarian, 2008). Systemic mediators of adipocyte dysfunction include adipokines, free fatty acids, and inflammatory mediators. Adipokines, including adiponectin, leptin, resistin, and ghrelin, are circulating molecules produced by adipocytes that affect energy use and production and appear central to the pathophysiology of obesity and its systemic health effects, including, insulin resistance, atherosclerosis, and type 2 diabetes (Arita et al, 1999; Tilg and Moschen, 2006). In addition to their effects on energy use, adipokines influence production of inflammatory mediators. For instance, leptin plays a role in body weight regulation by suppressing 20 appetite and burning fat stores in adipose tissue (Escott-Stump, 2002). Adiponectin, one of the most important adipokines, is specifically and highly expressed in adipocytes (Scherer et ah, 1995). Adiponectin contributes to several metabolic processes. Adiponectin is excreted exclusively from adipose tissue into the blood stream and are found in decreased levels in the obese and insulin resistant state, while adiponectin administration improves insulin action accompanied by increased fatty acid oxidation (Yamauchi et ah, 2001). Tsuda and coworkers (2005) evaluated the gene expression profile in isolated rat adipocytes treated with cyandin-3-glucoside or cyanidin. C3G upregulated a total of 633 genes, while cyanidin upregulated 427. Between both treatments a >1.5 fold in genetic up regulation was observed. The up-regulated genes included lipid metabolism and signal transduction-related genes, however, 32% of the altered genes were somewhat different between the C3G and cyanidin treated groups. Some of lipid metabolism-related genes (uncoupling protein2, acylCoA oxidase 1 and perilipin) also significantly induced in both the C3G or cyanidin treatment groups. Based on the gene expression profile, upregulation of hormone sensitive lipase and enhancement of the lipolytic activity were demonstrated by the treatment of adipocytes with C3G or Cy. In a follow-up study, human preadipocytes were obtained, cultured, and treated with anthocyanins. Based on the gene expression profile, significant changes of adipocytokine expression (upregulation of adiponectin and down-regulation of plasminogen activator inhibitor-1 and interleukin-6) took place (Tsuda et ah, 2006). In response to these results, Prior and Wu (2006) maintained that "although this data may have identified new responsive genes with potentially important functions [in adipocytes] additional investigation is needed. In 21 vivo, adipocytes are not likely to be exposed to the aglycone because of the instability of the aglycone." Jayaprakasam et al. (2005) have shown that anthocyanins are also able to stimulate marginal insulin secretion from rodent pancreatic beta-cells. Of all the anthocyanins the delphinidin-3-glucoside and cyanidin-3-galactoside were the most effective insulin secretagogues among the anthocyanins and anthocyanidins tested. In another study, utilizing anthocyanins and ursolic acid from cornelian cherries {Cornus mas) Jayaprakasam and associates (2006) fed mice a high-fat diet for 4 weeks and then switched to a high-fat diet containing anthocyanins (1 g/kg of high-fat diet) and ursolic acid (500 mg/kg of high-fat diet) for an additional 8 weeks. The high-fat diet induced glucose intolerance, and this was prevented by an anthocyanin and ursolic acid fed diet. The anthocyanin-treated mice showed a 24% decrease in weight gain. These mice also showed decreased lipid accumulation in the liver, including a significant decrease in liver triacylglycerol concentration. Anthocyanin and ursolic acid treated mice exhibited extremely elevated insulin levels. Both treatments, however, showed preserved islet architecture. Wild Blueberries The wild or lowbush blueberry, Vactinium angustfolium Ait., is grown commercially in eastern Canada, provinces including New Brunswick, Nova Scotia, and Quebec, and in the northeastern U.S. state of Maine. The berries are a member of the Ericaceae family 22 and include many species, most notably of which is the highbush blueberry (Vaccinium corymbosum L) and the rabbiteye blueberry {Vaccinium ashei Reade). The state of Maine is without doubt the largest producer of wild blueberries in the world. Approximately 60,000 acres are dedicated to production. According to the United States Department of Agriculture, preliminary reports suggest that Maine produced more than 85 million pounds of wild blueberries in 2008. The crop value exceeded $50 million dollars, of which both wild and cultivated are included (Yarborough, 2009). Wild blueberries have one of the highest recorded in vitro antioxidant capacities among various fruits and vegetables studied. A direct correlation (r=0.85) has also been observed between the total oxygen radical absorbance capacity (ORAC) and the total phenolics content of several Vaccinium species (Prior et al., 1998). When compared to highbush blueberries, wild blueberries were higher in total antioxidant capacity (TAC), total phenolics, and anthocyanins, independent of the method of extraction (Kalt et al., 2001). Five major anthocyanins were identified in the lowbush blueberry as cyanidin, delphinidin, malvidin, peonidin, and petunidin. Anthocyanins in wild blueberries have been reported as the 3-glucosides, galactosides and arabinosides of delphinidin, cyanidin, petunidin, peonidin and malvidin and have been shown to occur both as non-acylated and acetylated forms (Gao and Mazza, 1995). Chlorogenic acid is the major phenolic acid in lowbush blueberries, while other major organic acids include citric, malic and quinic acids (Kalt and McDonald, 1996). Human blueberry consumption has been associated with increases in total antioxidant capacity (Mazza et al., 2002). Likewise, Kay and Holub (2002) concluded that "wild 23 blueberry, a food source with high in vitro antioxidant properties, is associated with a diet-induced increase in ex vivo serum antioxidant status." The antioxidants of wild blueberries and other Vaccinium species have been associated with several beneficial health effects including the ability to limit the development and severity of certain cancers and vascular diseases including atherosclerosis, ischemic stroke, and neurodegenerative diseases (Neto, 2007). Blueberry Composition According to Medallion Labs (Minneapolis, MN), 100 grams of wild blueberries contains 45 kilocalories, 0.00% protein, 1 kilocalorie from fat, 13.2% carbohydrate, and 4.4% fiber (Table 2). Wild blueberries also contain a wide variety of micronutrients in the form of vitamins and minerals. Wild blueberries can differ chemically depending upon clone, maturity as well as genetic variation, environmental factors, and other outside influences (Kalt et al, 1996; Clarke et al, 2002). 24 Table 2. Nutritional Composition of Wild Blueberries (per l00g) Component Amount Units Component Amount Unit Calories 45 Calories/100g Calcium 17.4 mg/100 Calories from Fat Total Fat 1 Calories/100g Iron 0.577 mg/100 0.16 % Vitamin E 0.386 IU/lOOg Saturated Fat 0.03 % Vitamin B1 0.030 mg/lOOg 0.02 % Vitamin B2 O.010 mg/lOOg Monounsatu rated Fat Polyunsaturated Fat Sodium 0.09 % Vitamin B6 0.020 mg/lOOg 2.57 mg/l00g Phosphorus 12.9 mg/lOOg Potassium 67.6 mg/l00g Magnesium 6.5 mg/lOOg Total Carbohydrates Total Dietary Fiber 13.2 % Zinc 0.667 mg/lOOg 4.4 % Moisture 85.8 % 3.0 % Ash 0.187 % Insoluble Fiber Soluble Fiber 1.4 % Folic Acid 26.6 mg/lOOg Total Sugar 7.04 % Niacin 0.610 mg/lOOg Protein 0.00 % Manganese 2.87 mg/lOOg Total Beta Carotene 57.6 IU/lOOg Vitamin C 2.01 mg/lOOg In conclusion, current research suggests that anthocyanins reduce blood glucose levels, increase insulin sensitivity, and enhance regulation of key adipokines that may, in turn, 25 increase weight loss and glycemic control. The data that this proposed pilot study will provide will be an invaluable tool in order to better understand the effects of anthocyaninrich foods such as blueberries on satiety and glycemic control. Objective The objective of this study was to demonstrate that consumption of wild blueberries as part of meal makes a person feel full sooner, potentially leading to weight loss, and slows the release of glucose and insulin into the blood, thus reducing risks for diabetes and obesity. Whole berries are expected to be more satiating than the juice, and persons of normal weight are expected to have different responses to the treatments compared with overweight subjects. 26 Chapter 2 MATERIALS AND METHODS Study Design The design and protocol for this study was approved by the University of Maine's Institutional Review Board for the Protection of Human Subjects (IRB). Twenty-one subjects participated in this randomized cross-over research design (Figure 5). Subjects were primarily recruited through postings on the University of Maine's First Class email system (Appendix A) in alumni, student, and faculty folders. A University of Maine press release was issued to increase awareness of the study in the community and facilitate the recruitment process. A Bangor television station broadcast the press release, leading to several potential subjects. Screening criteria were designed to exclude persons with health conditions that might affect study outcomes (Table 3). The investigator screened persons who indicated an interest in the study via telephone. Subjects who met the criteria where then invited to campus to learn about the study requirements. Participants were asked to participate in a minimum ten-hour fast prior to the screening blood draw, sign an informed consent (Appendix B), answer questions pertaining to their eating habits, life style, and present and past health (Appendix C), and have their height and weight measured. A blood draw was taken to ensure the absence of diabetes or other form of hyperglycemia. In the event that the results indicated a possible diabetes risk by a fasting blood glucose >126 mg/dL (American Diabetes Association, 2009) subjects were contacted privately by telephone within 24 hours, and told to contact their healthcare provider for further evaluation. 27 Figure 5. Study Design Recruitment 1 Total Responses (44) I Initial visit: informed consent, health questionnaire, & blood draw (31) Subjects meeting criteria (24) I Subjects are randomized into treatment meal groups & baseline phlebotomy is ta|<en Blueberry / Blueberry Juice \\ Subjects failing to meet criteria (7) I Subjects told they are not eligible for the study Control Placebo Beverage Subjects that failed to complete the study (3) • 1 due to phlebotomy discomfort 1 due to scheduling conflicts 15, 30, 45, 60, & 90 minutes after meal: Phlebotomy and VAS scales 120 & 180 minutes after meal: last phlebotomy and satiety scales Completed Study (21) 28 Table 3. Inclusion/Exclusion Criteria Inclusion Criteria Exclusion Criteria 25-50 years of age Diagnosed with Type 1 or Type 2 diabetes Good physical health Family history of diabetes Eats breakfast "regularly" (at least 6 days a week) Body Mass Index between 18.5 to 29.9 (kg/m2) Not in athletic training Smokers Currently trying to lose weight or lost >15 lbs in the last 3 months Women who were pregnant and/or lactating During the active phase of the research, subjects came to campus on four separate occasions after an overnight fast. Baseline data each day were obtained from subjects who rated their hunger and fullness using 100-millimeter visual analog scale (VAS) (Figure 6) and by initial phlebotomy prior to consuming their test meals. After eating, volunteers were asked to complete subsequent VAS scales at 15, 30, 45, 60, 90, 120, and 180 minutes after completion of meals. Blood draws, consisting of no more than 5-10 mL (1-2 teaspoons), were made at baseline, 30, 60, 90, and 120 minutes. A cannula was placed during the first blood draw, for increased comfort, allowing the subjects to experience one prick per treatment session. Subjects and their cannulas were then closely monitored by the phlebotomist, and adjusted if necessary. Participants were allowed to drink water as necessary but were asked to avoid all other foods and beverages. Volunteers were also asked to write the name and quantity of everything they ate and drank for the rest of the day in a food diary that was provided to them. A stipend of $200 was given to each participant after all obligations were completed and paperwork received. 29 Figure 6. Satiety Rating Scales and Questions How hungry do you feel right now? Not Hungry at all 0 Extremely 100 hungry How satisfied do you feel? Completely 0 empty Cannot eat 100 another bite How much food do you think you could eat right now? Nothing at all 0 A large 100 amount How full do you feel right now? Not full at all 0 _ Extremely 100 full Once sufficient subjects were recruited and divided into their respective BMI categories, volunteers were randomly assigned to four initial treatment groups. Each subject consumed and tested each of the four breakfast meals but they were scheduled at a minimum of two weeks apart to ensure an adequate washout period as well as time to recuperate from the phlebotomy. Throughout this study subjects acted as their own controls. After a ten-hour minimum overnight fast and baseline blood work and VAS scales were completed, the volunteers left the Clinical Nutrition Laboratory and went to the Consumer Testing Center where they were served a base meal consisting of 28 g of 30 cornflakes cereal (Hannaford Brand), 118.29 raL of skim milk (Oakhurst, Portland, ME) and 112.38 mL of 100% orange juice, not from concentrate (Hannaford "Premium" Brand). The control and placebo meals were adjusted to the sugar content of 140 grams of frozen wild blueberries, based on information provided by Wild Blueberry Association of North America. The wild blueberries utilized for this study were obtained from the Wild Blueberry Association of North America (WBANA) through Jasper Wyman & Son (Cherryfield, ME). WBANA collected a composite sample from the 2007 crop to compensate for genetic and geographic variations among berry clones. The whole blueberry meal consisted of 140 grams of wild blueberries and the base meal. The juice treatment included a serving of 112 mL of Van Dyk's 100% wild blueberry juice (Queens, Nova Scotia, Canada) that also contained 10 grams of sugar (equal parts of glucose and fructose) along with remaining items from the meal (cornflakes, skim milk, and orange juice). The placebo treatment consisted of a placebo beverage that provided a similar taste and aesthetic appeal to the blueberry juice. The control meal was comprised of the aforementioned base meal that was adjusted to provide the same amount of carbohydrates as the other meals by the addition of an equivalent amount of fructose and glucose to the orange juice (Table 4). During treatment sessions, subjects were asked to consume the entire meal within 10-15 minutes of being served and to leave no food or beverage leftovers. 31 Table 4. Experimental Meals' Variable 140 grams (1 cup) frozen wild blueberries from the composite sample to represent all growing regions Base Meal cornflakes, milk, orange juice 112 mL of Van Dyke's 100% wild blueberry juice Placebo 112 mL of a beverage formulated with beverage and matched for acidity, flavor and color of the juice Control 5 grams of fructose, 5 grams of glucose added to the orange juice a All meals were controlled for sugar content cornflakes, milk, orange juice cornflakes, milk, orange juice Treatment Blueberry Blueberry juice cornflakes, milk, orange juice Preparation of Placebo The placebo formula was prepared the day before each meal was to be served in the Consumer Testing Center kitchen. All ingredients were weighed on a Denver Instrument Co. scale (Model XL 500, Denver, CO) (Table 5) First, 190 grams of distilled water were placed in a 1000 mL beaker which was then placed on a stir plate (PC 353, Corning, Corning, NY) with a spin bar and set on low. Each ingredient was slowly incorporated into the water vortex. Ingredients were incorporated according to descending mass. After all ingredients were well-mixed the sample was transferred into a 473.17 mL, safety coated, amber, wide mouth jar and stored, in the consumer testing lab kitchen, at +2°C to +5°C. All ingredients were handled and stored according to manufactures instructions. 32 Table 5. Placebo Formula Weight (g) 190 Percentage of total 0.867% KRYSTAR 300 Crystalline Fructose STALEYDEX 333 Dextrose 11.48 0.052% 11.48 0.052% Ascorbic Acid 0.54 0.002% Citric Acid Anhydrous (E # E330) DL Malic Acid (CAS # 617-481) Trisodium Citrate Dihydrate (E #E331) Red Dye (NO. 07003 FD&C Red # 3 Powder) Blue Dye (NO. 05601 FD&C Blue # 1 Powder FDA/EC) Cranberry Flavor (F915068) 0.54 0.002% 0.33 0.0015% 0.33 0.0015% 1.44 0.0065% TATE & LYLE, Decatur, IL TATE & LYLE, Decatur, IL Jungbunlzauer, Newton Centre, MA Jungbunzlauer Inc, Newton Centre, MA Jungbunzlauer Inc, Newton Centre, MA Jungbunzlauer Inc, Newton Centre, MA Sensient Colors Inc 0.08 0.00078% Sensient Colors Inc 1.00 0.0045% Frutarom USA Inc Blueberry Flavor (F91781) 1.80 0.0082% Frutarom USA Inc Xantham Gum (Food Grade; FCC/NF CAS #11138-66-2) Total 0.03 0.00019% Jungbunzlauer Inc, Newton Centre, MA 219.05 100% Ingredient Distilled Water Manufacturer Weight Body weight was measured in kilograms as part of the screening criteria using a digital readout scale (Model # 8431, Detecto Scales, Brooklyn, NY), with a maximum capacity of 200 kg. Subjects were weighed in street clothes without shoes, coats, and sweaters. Height was measured utilizing the height rod of the Health-O-Meter medical scale (Model # 402KL, Bridgeview, IL) and confirmed by the volunteer. BMI was calculated 33 according to formula of weight in kilograms divided by the square of the height in meters: BMI = weight (kg) +- height (m2) (World Health Organization. 1995). Phlebotomy All blood draws were completed in the Clinical Nutrition Laboratory located in 201 Hitchner Hall at the University of Maine, Orono. Joseph Brito, M.D. acted as the phlebotomist and drew all samples. The phlebotomist primarily used the 23G 3A x 12" Vacutainer Brand Safety-Lok Blood collection set in combination with a BD Blood Transfer Device (Ref 367283) and BD lOmL Syringe. Approximately 5mL of blood was retrieved from each volunteer per draw and subsequently collected in one BD Vacutainer Plus Blood Collection Tube (Franklin Lakes, NJ). Tubes were immediately centrifuged at 3000 rpm for fifteen minutes in a ClinaSeal-Sealed Technology Centrifuge (CS6C, Grandview, MO). Once blood samples were centrifuged and separated, serum was transferred using a VWR 100-1000 ul pipette with Fisher Scientific brand general purpose disposable tips into 2-3 0.5 ml sample cups (Micro Polystyrene non-sterile, Fisher Healthcare, Houston TX). Each sample cup was labeled with subject's corresponding code, date, and treatment number. Cups were capped using Evergreen Scientific Caps (CAT # 02-544-134, Fisher Healthcare, Houston, TX) for automated analyzers. All samples that were not being analyzed immediately were frozen at -80C in an Ultima II, Revco freezer (Kendro Laboratory Products, Asheville, NC). Serum glucose and triglycerides were analyzed in duplicate by the Beckman-Coulter CX4 PRO Clinical Analyzer (Brea, CA). Insulin, ORAC, and PYY levels were measured manually in the Healthy Foods laboratory. 34 Control standards for the Beckman-Coulter CX4 PRO Clinical Analyzer were run daily using the Synchron Multilevel Comprehensive Chemistry Control Serum (Fullerton, CA) with Synchrons 1, 2, and 3. Serum Glucose Measurement of the amount of glucose in the blood, whether after an overnight fast (minimum often hours) or post-prandial, is an invaluable tool. Serum glucose provides a current view of glycemia and according to the American Diabetes Association (2009) it is considered the best method for diagnosing and treating diabetes due to its simplicity, accuracy, and reproducibility. Analyses of both post-prandial and fasting glucose levels were measured using a Beckman-Coulter CX4 PRO Clinical Analyzer (Brea, CA). Glucose parameters were calibrated to an analytical range of 0.3-38.8 mmol/L (5700mg/dL) every 14 days using the Synchron CX Multi Calibrator (Fullerton, CA). No preparations were required for this calibration. The opened calibrators were stored at +2°C to +8°C and were stable until date of expiration. Quality controls were also run with each calibration to assure accuracy. Fresh serum samples were run in duplicate and measured utilizing Synchron Systems Glucose Reagent (Fullerton, CA) with an automatic proportion of one part sample to 100 parts reagent. The glucose reagent is used to measure glucose concentration via the time endpoint method. In this enzymatic based reaction, the transfer of the phosphate group from adenosine triphosphate (ATP) was catalyzed by hexokinase to glucose to form adenosine diphosphate (ADP) and glucosesphosphate. Glucose-6-phophate was then oxidized to 6-phosphogluconate with the concomitant reduction of (3-nictotinamide adenine dinucleotide (NAD) to reduce p- 35 nictotinamide adenine dinucleotide (NAD+H ) by the catalytic action of glucosesphosphate dehydrogenase (Figure 7). Finally, the glucose concentration of the sample was directly proportional to the reaction absorbance measured at 340nm. Figure 7. Glucose Analysis Reaction Glucose + ATP HK G-6PO4 + NAD+ G6PDH >G-6PQ1 + ADP » 6-phosphogluconate + NADH + H+ HK= Hexokinase G6DPH= Glucose-6-phosphate dehydrogenase G-6P04= Glucose-6-phospahte Serum Insulin Insulin is a peptide hormone composed of 51 -amino acids that is synthesized, packaged, and secreted in pancreatic beta cells (Escott-Stump, 2002). Disorders of insulin homeostasis such as insulin resistance are believed to play a large role in the pathogenesis of the metabolic syndrome, obesity, Type 2 diabetes, and cardiovascular disease (Farag et ah, 2007). Normal insulin values are between 5-35 mmol/L (30-210 pmol/L). Baseline, 30 and 60 minutes serum samples were analyzed. Human Insulin ELISA Immunoassay kit (Cat. # EZHI-14K) was purchased from Millipore (St. Charles, MO). Sample absorbance was read utilizing the BMG Labtech, Fluostar Omega Plate Reader (Offenburg, Germany). 36 The test kit included a human insulin ASF ELISA plate coated with monoclonal insulin antibodies, 2 adhesive plate sealers, 1 OX HRP wash buffer concentrate, ASF human insulin standards (2, 5, 10, 20, 50, 100, and 200 uU/mL), ASF quality controls 1 and 2, matrix solution, assay buffer, human insulin ASF detection antibody, enzyme dilution buffer, concentrated enzyme solution (streptavidin-horseradish peroxidase conjugate), 3, 3',5,5'-tetramethlybenzidine in buffer, and ELISA stop solution (0.3 M hydrochloric acid). To complete the protocol first all reagents and samples were brought to room temperature. Serum samples were initially frozen at -80°C before being analyzed. The frozen serum was first allowed to thaw and then vortexed utilizing a Fisher Scientific touch mixer (Model # 232, Pittsburg, PA) to ensure thorough mixing. Next the HRP wash buffer was diluted by mixing with it 450 mL of deionized water. The microtiter plate was removed from the foil pouch; each well was then filled with 300 uL of diluted HRP wash buffer, the plate was then left to sit for 5 minutes at room temperature, and subsequently decanted and tapped onto paper towels several times. Then 20 uL of assay buffer were added to the blanks and each sample well. Next twenty uL of the matrix solution were added to the blank, standard, and quality control wells. Twenty uL of human insulin were then added (in duplicate) to the appropriate wells in ascending order of concentration, 20 uL of quality control 1 and quality control 2 were added to the appropriate wells. Next 20 uL of each unknown sample were added, in duplicate, to the remaining wells. All described chemical additions were completed within 30 minutes. Each plate was covered with plate sealer and incubated at room temperature for 90 minutes on a Lab-Line Instruments, Inc. orbital shaker (Melrose Park, IL) set at 400-500 rpm. Next the plate sealer was removed and the liquid was decanted. The wells were washed 3 times with 37 300 uL of diluted washer buffer; liquid was decanted after each wash. Twenty uL of detection antibody were added to all wells. The plate was covered and incubated at room temperature for one hour. Next the plate sealer was removed and the liquid was decanted. The wells were washed with 300 uL of diluted washer buffer 3 times; the liquid was decanted after each wash. Next 100 uL of ASF detection antibody was added to each well; the plate was covered with the plate sealer and incubated with moderate shaking (400-500 rpm) at room temperature for 1 hour on the microtiter plate shaker. After alloted time the plate sealer was removed and the liquid was decanted. The wells were washed with 300 uL of diluted washer buffer 3 times; the liquid was decanted after each wash. Next 100 uL of the enzyme solution were added to each well; the plate was covered and incubated at room temperature, shaking at moderate (400-500 rpm) speed for 30 minutes. The sealer was removed and the liquid was decanted. The wells were washed 5 times with 300 uL of dilute washer buffer; the liquid decanted after each wash. Then 100 uL of substrate solution was added to each well; the plate was sealed and placed on the shaker for approximately 20-30 minutes until blue color was fully formed in standards well with intensity equal to that of the concentrations. The sealer was removed and 100 uL of stop solution was added to each well. The plate was shaken by hand until the blue turned to yellow. The absorbance was read at 450 nm within 5 minutes. The absorbance results were calculated utilizing Omega Data Analysis Software (Version 1.00, 2007) that came with the plate reader. A 4-parameter logistic function was used to fit the dose response curve. 38 Serum Triglycerides Triglycerides (TG) are the chemical form in which most fat exists in both food as well as the body. Postprandial hyperlipidemia is highly prevalent in individuals who exhibit impaired glycemic control with both normal (Chen et al., 1993) and elevated (Lewis et al., 1991 ; Syvanne et al., 1994) fasting TG concentrations. The American Heart Association (2009) considers a fasting TG levels below 150 mg/dL (1.7mmol/L) to be normal (Table 6). Table 6. American Heart Association Guidelines for Triglycerides Category Fasting serum triglyceride level Normal Less than 150 mg/dL Borderline-high 150 to 199 mg/dL High 200 to 499 mg/dL Very high 500 mg/dL or higher *A11 numbers are based on an 8-12 hour food and alcohol fast. Triglyceride levels were measured using the Beckman-Coulter CX4 PRO Clinical Analyzer (Brea, CA). Triglyceride levels were calibrated to an analytical range of 0.111.3 mmol/L (10-1000 mg/dL) using the Synchron CX Multi Calibrator (Fullerton, CA). Triglyceride levels were calibrated every 14 days using the Synchron System Calibrator (Fullerton, CA). There were no preparations required for this calibration. The opened calibrator was stored at +2°C to +8°C and was stable until the expiration date. 39 The open triglyceride reagent was stored at +2°C to +8°C and was stable for 30 days unless the expiration date was exceeded. The triglyceride reagent was prepared by transferring the contents of compartment C into compartment A of the analyzer. Quality control was run with each calibration to ensure accuracy with levels < 1.9 mmol/L. Individual subject samples were run in duplicate and measured using Synchron Systems Triglyceride Reagent (Fullerton, C A) with an automatic proportion of one part sample to 100 parts reagent. The sample triglycerides, catalyzed by lipase, are hydrolyzed to glycerol and free fatty acids. Oxidative coupling of 3, 5-dichloro-2hydroxybenzenesulfonic acid (DHBS) with 4-aminoantipyrine forms a red quinoneimine dye following a series of enzymatic reactions (Figure 8). The observed color change is observed at an absorbance of 520 nm and is directly proportional to the triglyceride concentration of the sample. To produce the red color glycerol is sequentially coupled with glycerol kinase, glycerophosphate oxidase (GPO), and horseradish peroxidase (HPO). Figure 8. Triglyceride Analysis Reaction (a) Triglycerides Cholestero1 Esterase» Glycerol + Fatty Acids (b) Glycerol + ATP GK Mg++ Glycerol-3-phosphate + ADP *" (c) Glycerol-3-phosphate + O2 ——•Dihydroxyacetone + H2O2 HPO (d) 2 H2O2 + 4-Aminoantipyrine + DHBS 2H 2 0 •Quinoneimine Dye + HCL + GK=Glycerol Kinase GPO= Glycerophosphate Oxidase HPO= Horseradish Peroxidase DHBS= 3, 5-dichloro-2-hydroxybenzenesufonic acid 40 Serum Peptide YY3.36 Peptide YY (PYY), a gut hormone produced by the intestinal L cells, is released into circulation after a meal and is reduced by fasting (Orr and Davy, 2005). Administration of PYY has been shown to significantly inhibit food intake in rodents and primates (Adams et al., 2004; Chelikani et al., 2005; Moran et al., 2005) as well as humans (Batterham et al., 2002 and 2003). Concentrations of PYY increase within 15 minutes of food ingestion and remain elevated for as long as 5 hours, peaking after approximately 2 hours (Adrian et al., 1985). Serum samples were analyzed at baseline and 60 minutes. The Human PYY (Total) ELISA Immunoassay kit (Cat. # EZHIPYYT66K) used was purchased from LINCO Research (St. Charles, MO). The absorbance was read utilizing the BMG Labtech, Fluostar Omega Plate Reader (Offenburg, Germany). The test kit included a Human PYY ELISA Plate coated with pretitered antibodies, 2 adhesive plate sealers, 10X HRP wash buffer concentrate, human PYY standards (10,40,100,200,500, 1000 and 2000 pg/mL), quality controls 1 and 2, matrix solution, assay buffer, human PYY capture antibody, human PYY detection antibody, blocking solution, concentrated enzyme solution (pre-titered streptavidin-horseradish peroxidase conjugate), 3, 3',5,5'-tetramethlybenzidine in buffer, and ELISA stop solution (0.3 M hydrochloric acid). Frozen serum was utilized in this assay, which was stored at -80°C for approximately 6 months before being analyzed. To complete the protocol first all reagents and samples were brought to room temperature. Next the HRP wash buffer was diluted by mixing with it 450 mL of deionized water. The microtiter plate was removed from the foil pouch; each well was then filled with 300 uL of diluted HRP wash buffer and subsequently decanted and tapped onto paper towels several times; the process was 41 repeated 3 times. Then twenty uL of the matrix solution were added to the blank, standard, and quality control wells. Next 20 uL of assay buffer were added to each blank and each sample well. Twenty uL of human PYY were then added (in duplicate) to the appropriate wells in ascending order of concentration, 20 uL of quality control 1 and quality control 2 were added to the appropiate wells. Next 20 uL of each unknown sample were added, in duplicate, to the remaining wells. Twenty uL of blocking solution was then added to each well. All chemical additions were completed within 30 minutes. The plate was covered with plate sealer and incubated at room temperature for 30 minutes on a Lab-Line Instruments, Inc. orbital shaker (Melrose Park, IL) set aat 400-500 rpm. Next the plate sealer was and 50 uL of the 1:1 mixture of capture and detection antibodies was added. The plate was recovered with sealer and incubated at room temperature for 1.5 hours on the orbital microtiter plate shaker at moderate speed (400500rpm). Next the plate sealer was removed and the liquid was decanted. The wells were washed with 300 uL of diluted washer buffer 3 times; the liquid was decanted after each wash. Twenty uL of detection antibody were added to all wells. The plate was covered and incubated at room temperature for one hour. Next the plate sealer was removed and the liquid was decanted. The wells were washed with 300 uL of diluted washer buffer 3 times; the liquid was decanted after each wash. Next 100 uLof the enzyme solution were added to each well; the plate was covered and incubated at room temperature, shaking at moderate 400-500 rpm) speed for 30 minutes. The sealer was removed and the liquid was decanted. The wells were washed 6 times with 300 uL of dilute washer buffer; the liquid decanted after each wash. Then 100 uL of substrate solution was added to each well; the plate was sealed and placed on the shaker for approximately 5-20 minutes until blue color 42 was fully formed in the standards well with intensity equal to that of the concentrations. The sealer was removed and 100 uL of stop solution was added to each well. The plate was shaken by hand until the blue turned to yellow. The absorbance was read at 450 nm and 590 nm within 5 minutes. The absorbance results were calculated utilizing the Omega Data Analysis Software (Version 1.00, 2007). A 4-parameter logistic function was used to fit the dose-repsonse curve. Food Records Twenty-four hour food records were collected for each volunteer after each treatment. Each record was analyzed using Nutritionist Pro (Version 2.4.1 First Databank Inc. 2008). The majority of the food items recorded by the participants were matched with foods available from the databank. Energy values were found on product websites and entered into the databank for those food items that could not be matched with foods all ready available in the data bank. Statistics All statistical analyses were completed using SYSTAT analytical software (Version 12.00.08, 2007). A repeated measure General Linear Model was used for analysis of control and treatment groups as was BMI. A probability level of 0.05 was selected for statistical significance Tukey's HSD (Honestly Significant Difference) test was used for means comparison. 43 Chapter 3 RESULTS AND DISCUSSION Subject Demographics Thirteen females and eight males completed the study in its entirety. Three subjects dropped out. All three left after completing one treatment session. One cited phlebotomy discomfort as a reason for leaving, while another reported scheduling conflicts. The last subject did not provide a reason and was unreachable after the first session. Subjects' ages ranged from 25 years to 50 years of age, with a mean age of 33.7±8.3 years. Body mass index (BMI) ranged from 18.5 kg/m2 to 29.9 kg/m2 with a mean BMI of 24.99±3.01 kg/m2. Eleven subjects were categorized into the normal BMI category based on their BMI weight of 18.5 to 24.9kg/m2. The normal group consisted of eight females and three males, with a mean age of 34.7±8.42 years and a mean BMI of 22.6±1.21. The overweight BMI category was comprised often subjects, five males and five females with a mean age of 32.3±8.69 years. BMI's ranged from 25.0-29.9k/m with a mean 27.7±1.93kg/m2 (Table 7). Reported average breakfast consumption for all subjects was 6.4 days per week, with a mean of 6.7 and 6.0 days per week for normal and overweight BMI groups, respectively. Table 7: Subject Demographics3 BMI Category N Age BMI (kg/m2) Normal (18.511 34.7±8.42 22.6±1.21 24.9kg/m2) Overweight 10 32.3±8.69 27.7±1.93 (25.0-29.9kg/m2) a= Mean ± standard deviation. M=Male; F=Female. 44 M F Mean Breakfast Intake (days per week) 3 8 6.7 5 5 6.0 Blood Analyses Screening All potential participants that met the inclusion criteria were subsequently screened to ensure they had normal glycemic control as evidence by fasting serum glucose level below 100 mg/dL (Table 8). Table 8: Fasting Serum Glucose at Screening3: Grouped by BMI BMI N Minimum Maximum Mean Normal 11 76.0 97.5 88.06±6.4 Overweight 10 79 99 90±8.0 a = Mean ± standard deviation; mg/dL, duplicate readings per person per blood draw. b= Normal BMI =18.5-24.9 kg/m2, Overweight BMI =25.0-29.9 kg/m2. Serum Glucose Treatment, BMI, and their interaction had no effect on serum glucose (Table 9). There were also no significant differences for area under the curve (Table 10). Although impaired glucose tolerance has been correlated with increases in BMI (Fall et al., 2008; Modan et al., 1986) little literature exists in relation to non-diabetic postprandial increases in glucose and BMI values. Recently, however, Erdmann and colleagues (2009) found that postprandial glucose response was 38-82% higher in people with increasing weight compared to normal weight individuals. 45 Table 9: Mean Serum Glucose Levels3: Grouped by Treatment and BMI Time (min) BMI 2 o Treatment Baseline 30 60 90 120 WB 85.6±7.4 111.0±30.1 86.6±17.6 82.4±10.6 81.7±7.6 BBJ 87.9±9.8 117.0±27.2 83.2±15.3 75.4±15.7 77.4±11.3 85.5±8.4 108.0±27.6 74.1±15.7 68.5±12.9 75.4±11.1 82.1±6.9 103.5±23.9 78.1±14.5 75.9±16.2 75.7±15.2 WB 89.2±5.7 132.1±25.8 92.4±24.6 79.2±9.9 81.1±7.5 BBJ 85.4±4.2 117.7±28.6 91.8±29.2 78.9±15.4 77.0±10.5 91.5±7.3 138.5±27.9 99.1 ±23.1 89.7±9.2 87.1±3.6 88.5±8.5 130.6±29.0 93.6±17.8 76.5±10.3 83.U8.1 P3 O < n . a = Mean ± standard deviation; mg/dL, duplicate readings per person per blood draw; (n=21) (normal BMI n=l 1; overweight n=10) b= Normal BMI =18.5-24.9 kg/m2, Overweight BMI =25.0-29.9 kg/m2. WB= Whole Blueberries; BBJ=Blueberry Juice; C= Control; P= Placebo. Table 10: Mean Area Under the Curve of Serum Glucose3: Grouped by BMI Treatment Normal BMI Whole Blueberries 570.82±104.78 Blueberry Juice 558.07±70.87 Control Placebo 518.13±59.40 548.81±57.30 555.69±84.00 604.0±65.55 Overweight 579.10±72.80 582.27±71.50 BMI a = mg/dL, duplicate readings per person per blood draw. (n=21) (normal BMI n=l 1; overweight n=10 b= Normal BMI =18.5-24.9 kg/m2, Overweight BMI =25.0-29.9 kg/m2 46 Serum Triglycerides There were no significant differences in serum triglycerides at specific times (Table 11) or for mean area under the curve (Table 12). A majority of the fasting triglycerides levels measured were well within the normal range of <150mg/dL, however, 2 participants were within the borderline high levels (150 - 199 mg/dL) consistently throughout the study. Furthermore, some variation was observed from week-to-week which contributed to a large fasting triglyceride standard deviations between subjects. While fasting "normal" triglyceride levels differ slightly between age and gender it may be that some subjects may not have complied with fasting instructions. However, the lack of postprandial increases among all treatments and subjects could be explained by the low fat content of all treatment and base meals. Prior and colleagues (2008) surmised that the lipid lowering effects of anthocyanins may only be effective in the context of purified anthocyanins. Researchers fed male C57BL/6 mice diets with either 10% kcal from fat, a high fat diet 45% or 60% kcal from fat. Plasma cholesterol and triglyceride levels were elevated with a high fat diet and decreased to control levels when purified anthocyanins from either whole strawberries or whole blueberries were included in the drinking water. In the first study the diets were prepared with or without freeze dried powders from whole blueberries or strawberries. In the second study, a low fat or high fat diet was fed and purified anthocyanins from strawberries or blueberries were added to the drinking water of the treatment groups fed the high fat diet. When whole strawberry or blueberry powder was included in the diet, plasma triglycerides were increased by feeding the HF diet but were elevated further when blueberry was included in the high fat diet. Mice fed the high fat diet plus purified 47 anthocyanins from blueberry in the water had lower body weight gains and body fat than the high fat fed controls without blueberry. Table 11. Mean Serum Triglycerides Levels3: Grouped by Treatment and BMI Time f min) BMI Treatment Normal WB BBJ C P Overweight WB BBJ C P Baseline 30 60 90 120 73.5±24.7 72.6±25.4 67.7±25.9 66.6±30.3 64.7±30.1 82.6±31.1 80.2±26.8 77.1±32.5 71.1±34.9 70.8±36.4 83.0±19.4 79.2±18.9 76.9±18.3 72.7±19.6 73.8±21.9 72.1±18.7 69.6±17.6 61.0±17.0 58.8±17.0 57.7±20.2 90.7±36.7 90.9±35.8 91.4±43.6 88.6±46.9 88.4±45.6 88.4±41.5 87.0±29.2 86.1±43.3 80.6±44.5 78.2±45.9 105.1±96.0 106.1±91.8 97.1±86.1 93.3±83.7 91.1±75.9 85.1±26.6 79.2±34.0 71.6±32.3 73.7±38.7 78.6±36.1 a = Mean ± standard deviation; mg/dL, duplicate readings per person per blood draw; (n=21) (normal BMI n=l 1; overweight n=10) b= Normal BMI =18.5-24.9 kg/m2, Overweight BMI =25.0-29.9 kg/m2. WB= Whole Blueberries; BBJ=Blueberry Juice; C= Control; P= Placebo. Table 12. Mean Area under the Curve of Serum Triglycerides: Group by BMIa BMI Placebo Control Whole Blueberries Blueberry Juice Normal 467.58±58.8 499.77±58.7 498.11±83.1 495.21±60.8 Overweight 473.65±79.4 519.47±62.7 526.98±72.7 502.18±94.4 a= Mean ± standard deviation (n=21) (normal BMI n=l 1; overweight n=10). Normal BMI =18.5-24.9 kg/m2, Overweight BMI =25.0-29.9 kg/m2 48 Serum Insulin Serum insulin was measured at baseline, 30, and 60 minutes. Area under the curve was then calculated to evaluate how subjects, overall, responded to each treatment meal. No difference was found when time intervals were compared by BMI, treatment, or crossed with treatment (Table 13). Treatment meals had no effect between the two BMI groups or on all 21 subjects as a whole or when BMI's were analyzed along with treatment groups. Observed insulin and satiety AUC relationships were not significant as well (Table 14). Holt and Brand-Miller (1995) found a significant negative association between the individual insulin and satiety AUC responses to certain ingested starches. The authors suggested that the increased rate of starch digestion and higher insulin responses are associated with lessened satiety. Jayaprakasam and coworkers (2005) found that the 3-glucosides of cyanidin and delphinidin effectively stimulated insulin production by 50% in rat pancreatic cells when rats were exposed to varying concentrations of glucose. 49 Table 13. Mean Serum Insulin Levels3: Grouped by Treatment and BMI Time (min) BMI Treatment Baseline WB BBJ Normal C P WB BBJ Overweight C P 30 60 100.9±63.9 323.5±130.2 177.1±121.4 80.2±55.6 402.4±172.9 263.7±115.4 91.9±101.1 388.3±133.2 166.7±73.2 72.2±50.7 339.7±192.2 203.7±67.4 52.8±30.3 423.9±179.0 161.5±95.9 60.7±37.0 435.2±313.0 197.6±98.5 73.5±21.1 480.7±182.4 228.5±125.6 56.1±26.9 504.9±425.2 223.7±120.7 a = Mean ± stanc ard deviation; pmol/L, duplicate readings per perse>n per blood draw. (n=21) (normal BMI n=l 1; overweight n=10) b= Normal BMI =18.5-24.9kg/m2, Overweight BMI =25.0-29.9kg/m2. WB= Whole Blueberries; BBJ=Blueberry Juice; C= Control; P= Placebo. Table 14. Mean Area Under the Curve of Serum Insulin: Grouped by BMIa BMI Blueberry Juice Control Placebo Normal Whole Blueberries 1367.2±1884.7 2160.41±2949.0 1639.29±1403.0 1434.61±960.7 Overweight 1844.1±1192.4 1858.51±1241.4 1292.84±280.2 2106.85±1279.1 a= Mean ± standard deviation (n=21) (normal BMI n=l 1; overweight n= 10).Normal BMI =18.5-24.9kg/m2, Overweight BMI =25.0-29.9kg/m2 50 Serum Peptide YY3.36 Unfortunately, approximately eighty percent of the Peptide YY3.36 (PYY) results were out of range in relation to the minimum standard. One possible explanation is that PYY has a relatively short half-life in vivo. PYY exhibited a functional half-life of only 3 hours (Shechter et al., 2005) but our study took samples within that time frame. Another is that the gold standard for serum PYY collections suggests a pre-chilled vacutainer containing EDTA as the anti-coagulant, then immediately adding 150 ul of ready-to-use aprotinin solution (Sigma #A6279), followed by placing the sample in an ice bath immediately, and finally centrifuging the blood within 10 minutes of collection by using a refrigerated centrifuge set at 4° C for 15 minutes at 1500 x g (ICTS, 2009). Unfortunately, utilizing frozen serum that was not treated with an anticoagulant or a protein degradation inhibitor, i.e., aprotinin, as well as not chilling the equipment, may have added to the results being out of range. Furthermore, while technical error in part contributed to less than precise results, standard curves, represented as R values, of all five PPY plates were well within sufficient ranges as were quality controls. Satiety Scores Satiety was assessed by calculating area under the curve for the change in rating from baseline, a larger value indicating greater satiation. A smaller value for hunger and perceived consumption and larger value for satisfaction and fullness indicate greater satiation. There were no significant differences between any of the scores when compared with the treatment or treatment crossed with BMI. There was, however, significant differences when subjects were divided into two groups based on BMI (P= 51 0.023) (Table 15). More specifically, differences were found between subject BMI for satisfaction (P= 0.05) and fullness (P= 0.002) satiety scores. Overweight subjects were more satisfied and full when compared to their lower BMI counterparts throughout all treatments. Treatment, BMI, and their interaction also had no effect on individual time points for each satiety score. Although not significant, subjects in both the overweight and normal BMI categories had higher satisfaction when the whole blueberry treatment was consumed compared to the control. The high standard deviations, which persisted consistently throughout all scores and treatments, can be explained by the subjective nature of satiety combined with the small sample size. Delgada-Aros and colleagues (2004) evaluated the association between BMI, satiety, and gastric volume. According to the authors, overweight and obese individuals had a delayed response to satiety, and consumed more calories before reporting maximum satiation. More recently, Arumugam and coworkers (2008) examined the effects of variations in postprandial glycemia and insulinemia on subjective satiety. Researchers altered the ingestion rate of overweight and obese women, utilizing a glucose beverage, to reproduce the effects of postprandial high-and low-glycemic meals. Subjective appetitive sensations were measured with visual analog scales, along with serum glucose and insulin, before and after meals. Higher ratings of hunger and the amount of food that could be consumed were reported by subjects in the rapid glucose beverage group versus the presumed delayed glucose group, at both 4 hours after breakfast and several hours after lunch. Serum glucose was more strongly correlated with VAS scores when the rapid glucose beverage was consumed when compared to the delayed beverage. 52 <ws Table 15. Mean Area Under the Curve of Satiety: Grouped by Treatment and BMI Treatment Amount of Food that could be Consumed Fullness Normal BMI Overweight BMI Normal BMI Overweight BMI WB 8135.8±3442.2 7858.5±3986.8 7021.9±3695.9 8904.4±3824.0 BBJ 8683.9±2984.1 8810.4±3155.2 5498.7±3055.6 7440.8±2016.8 Control 9158.8±4157.9 9322.7±3660.5 4963.4±3972.9 7043.9±3432.6 Placebo 8349.6±3878.2 7171.8±3350.6 5355.3±3687.6 9155.5±3678.5 Hunger Satisfied Treatment Normal BMI Overweight BMI Normal BMI Overweight BMI WB 7878.3±3938.2 7168.2±3669.1 5402.6±3051.5 8192.9±4317.1 BBJ 7877.4±3248.9 8923.8±3064.3 4430.1±2436.6 6352.2±3821.9 Control 8522.4±4123.6 9277.4±3906.1 2832.9±2635.1 5312.2±4381.4 Placebo 7943.3±4746.0 6907.8±3349.5 4515.9±3869.7 6727.7±5011.3 a= Mean ± standard deviation (n=21) (normal BMI n=l 1; overweight n=10) on a 0-100mm VAS scale; 0 = not hungry at all, extremely unsatisfied, unable to consume food, not full at all, and 100 = extremely hungry, extremely satisfied, able to consume a large amount of food, extremely full. WB= Whole blueberries; BBJ= Blueberry Juice. Normal BMI =18.5-24.9 kg/m2, Overweight BMI =25.0-29.9 kg/m2 53 Food Record Analysis Total calories, protein, carbohydrates, and fat calculated intake was not affected by treatment or subject BMI (Table 16). Observationally, participants consistently selected the same types of foods from week to week. However, variations of what seemed to be "cookout food" were observed in many subjects throughout the entire study. Furthermore, no subject consumed excess amounts of foods that might be considered high in anthocyanins. Baseline data was obtained to assess typical dietary patterns. Although not statistically significant, normal BMI subjects who were treated with the whole berry meal consumed 200 kilocalories less than those who were treated with the control. If these effects could be sustained in the long-term, potential weight loss efficacy could be drastically increased. A two-hundred kilocalorie a day deficit translates into a weight loss of a pound of body fat approximately every two and a half weeks or 20 pounds a year. Table 16. Mean Energy Intake by BMIa Treatment Energy(kilocalories) Normal BMI Overweight BMI Whole Blueberry 1699±483 1527±843 Blueberry Juice 1632±335 1789±690 Control 1484±341 1907±791 Placebo 1534±421 1636±633 a= Mean ± standard deviation (lower BMI: n=l 1 [3 males, 8 females]); upper BMI: n=8 [7 males, 1 female]). Normal BMI =18.5-24.9 kg/m2, Overweight BMI =25.0-29.9 kg/m2. 54 Chapter 4 CONCLUSIONS The results of this pilot study indicated there were no significant differences in glucose levels, triglyceride levels, insulin levels, energy intake, and satiety scores when subjects were grouped by treatment alone or in combination with BMI. Statistically significant differences could be seen among BMI groups in both the Hunger and Fullness scores of the satiety surveys as well as postprandial blood glucose values. Overweight individuals appeared to be more satisfied and fuller despite the treatment meal. This effect is troubling since only subjects who consumed breakfast on a regular basis were recruited, so as to avoid satiety based merely due to breakfast consumption. Significant differences could also been seen between the two BMI categories and glucose levels at baseline, 30, 60, and 120 minutes. This effect may merely be due to differences in BMI. The small sample size of this pilot study lacked power, possibly contributing to a type II error. Power calculations are an analysis that estimates the number of subjects needed in a study to observe statistically significant differences between groups. No power calculation were made for this pilot study. As mentioned above, the lack of significant difference in this study could be related to too few subjects. Further confounding the results may be that the initial 24 subjects, although quantitatively homogenous in relation to glycemic control, may possess subtle homeostatic glycemic differences brought about by age and gender. Under-reporting energy intake when 24 hour food recalls is used is prevalent and well established (Johanssen et al., 2001) which may have contributed to erroneous insignificant results. 55 Another possible cofounder as suggested by Prior et al. (2007 and 2008) is the reported difference in biologically activity between whole blueberries and whole blueberry powder when compared to purified anthocyanins. In both studies, researchers reported that anthocyanins when fed as part of the diet, to male C57BL/6 mice, as the whole berry did not prevent obesity. As a result of the extensive recruiting process, the study started in late May and continued throughout early August. Furthermore, due to scheduling conflicts the majority of the study was conducted on Saturdays and Sundays. This time frame may have contributed to abnormal eating patterns, as research suggests Americans consume more calories on weekend days when compared to week days (Haines et al., 2003). One can also presume that the summer months may provide an increased frequency of social gatherings where high calorie/high fat foods are more readily available, possible contributing to caloric irregularities. To the best of this author's knowledge, this study is the first to investigate the beneficial potential of frozen blueberries and 100% blueberry juice on glycemic control and satiety in humans. The study design and participant involvement may need to be more selective for age, gender, and lifestyle. 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Incorporation of the elderberry anthocyanins by endothelial cells increases protection against oxidative stress. Free Radic Biol Med, 29(l):51-60. 68 APPENDICES 69 APPENDIX A RECRUITMENT FLYER Volunteers needed for a Research Study: The Effects of Breakfast on Appetite and Blood Chemistry Volunteers will be asked to eat a complete breakfast, provided by the Food Science and Nutrition Department on 4 different days, every 2 weeks for an 8 week period. Volunteers will be provided with $200 after completion of the study. Persons interested in participating must: Be 25-50 years old Have a BMI between 18.5-29.9 (see chart below) In good health Regularly eat breakfast Do not smoke Are not pregnant or lactating Participants must not be diabetic or have any family history of diabetes Not currently trying to lose weight. Not in athletic training If you are interested in taking part in this study or have any questions please contact Elijah Magrane at 581- 8434 or via email ([email protected]) 70 BMI Height (inches) 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 Body Weight (pounds) 58 59 60 91 96 100 105 110 115 119 124 129 134 138 143 148 153 158 162 167 | 61 100 106 111 116 122 127 132 137 143 148 153 158 164 169 174 180 185 ; 62 104 109 115 120 126 131 136 142 147 153 158 164 169 175 180 186 191 ; 63 107 113 118 124 130 135 141 146 152 158 163 169 175 180 186 191 197 | 64 110 116 122 128 134 140 145 151 157 163 169 174 180 186 192 197 204 | 65 114 120 126 132 138 144 150 156 162 168 174 180 186 192 198 204 210 j 66 118 124 130 136 142J148 155 161 167 173 179 186 192 198 204 210 216 | 67 121 127 134 140 146 153 159 166 172 178 185 191 198 204 211 217 223 i 68 125 131 138 144 151 158 164 171 177 184 190 197 203 210 216 223 230 69 128 135 142 149 155 162 169 176 182 189 196 203 209 216 223 230 236 70 132 139 146 153 160 167 174 181 188 195 202 209 216 222 229 236 243 | 94 99 104 109 114 119 124 128 133 138 143 148 153 158 163 168 173 97 102 107 112 118 123 128 133 138 143 148 153 158 163 168 174 179 71 136 143 150 157 165 172 179 186 193 200 208 215 222 229 236 243 250 1 72 140 147 154 162 169 177 184 191 199 206 213 221 228 235 242 250 258 | 73 144 151 159 166 174 182 189 197 204 212 219 227 235 242 250 257 265 | 74 148 155 163 171 179J186 194 202 210 218 225 233 241 249 256 264 272 | 75 152 160 168 176 184 192 200 208 216 224 232 240 248 256 264 272 279 i 76 156 164 172 180 189 197 205 213 221 230 238 246 254 263 271 279 287 i 71 APPENDIX B INFORMED CONSENT FORM The Effects of Breakfast on Satiety and Related Changes in Blood Chemistry You are invited to participate in a screening for a research project being conducted by Elijah Magrane, a graduate student and supporting faculty advisor, Dr. Mary-Ellen Camire, in the Department of Food Science and Human Nutrition at the University of Maine. The purpose of this session is to see if you qualify to be in a research study on the effects of breakfast on the feeling of fullness and related changes in blood chemistry. What Will You Be Asked to Do? If you decide to take part in the session, you will be asked not to consume any food or beverages overnight (at least 10 hours) prior to the visit. A sample of blood will also be needed to ensure that you do not have diabetes. A trained technician will draw your blood (5-10mL per draw-1-2 teaspoons). Next a series of questions dealing with your current eating habits, lifestyle, and current and pass health, such as "how many days a week do your normally eat breakfast?" and "do you have any food allergies or intolerances?" Your weight and height will also be measured. The session should take approximately 3045minutes. • • • • • Risks Risks associated with having blood drawn are slight but may include: Excessive bleeding Fainting or feeling light-headed Hematoma (blood accumulating under the skin) Infection (a slight risk any time the skin is broken) The risks involved are minimal, and not expected to be greater than those encountered from typical blood donating occasions. Benefits Possible early warning of diabetes. Confidentiality All information will be destroyed immediately if you are not eligible or choose to not participate in the study. If you are eligible and choose to take part in the study all information with be stored in locked file cabinet in a locked limited access office. The study can be expected to be completed within one year of the start date. After which, all items will be destroyed within 2 years of the study's completion. 72 Voluntary Participation is voluntary. You many choose not to participate in this screening now or can stop at any time during the screening session. You may skip any questions. If you are eligible, participating in the screening does not obligate you to participate in the study. Compensation There is no compensation for participation in the screening exercise. Contact Information If you have any questions about this study, please contact Elijah Magrane at 581-8434 or by email at [email protected]. If you have any questions about your rights as a research participant, please contact Gayle Anderson, Assistant to the University Of Maine Protection Of Human Subjects Review Board, at 581-1498 (or e-mail [email protected]). Your signature below indicates that you have read and understand the above information. Signature Date 73 APPENDIX C SCREENING QUESTIONAIRE Height Do you smoke? Yes Weight cm .kg No_ Would you be willing to avoid consuming alcohol and recreational drugs 24 hours before and after the cereal testing? Yes No How many days a week do your normally eat breakfast? Never 1-2 3-4 5-6 7 5-6 7 How many days a week do you normally exercise? Never 1-2 3-4 Do you have any food allergies (specifically blueberries) or intolerances? If so, to what foods? Do you have a family history of diabetes or hypoglycemia? Are you currently trying to lose weight? In the previous 3 months have you lost more than 15 pounds? Are you currently in athletic training? 74 Are you currently taking any prescription drugs or supplement? If so which ones? Do you currently suffer from any gastrointestinal problems? You will be notified either by email or phone within a week to let you know whether or not you qualify to take part in the study. Thank you again for your help. 75 APPENDIX D INFORMED CONSENT FORM The Effects of Breakfast on Satiety and Related Changes in Blood Chemistry You are invited to participate in a screening for a research project being conducted by Elijah Magrane, a graduate student and supporting faculty advisor, Dr. Mary-Ellen Camire, in the Department of Food Science and Human Nutrition at the University of Maine. The purpose of this session is to see if you qualify to be in a research study on the effects of breakfast on the feeling of fullness and related changes in blood chemistry. What Will You Be Asked to Do? If you decide to participate, you will be required to come to the testing center for a total of 4 visits (2-3 weeks apart). Before each visit you will be asked not to consume any food or beverages, except water, for 10 hours. During the first visit, the researcher will go over the criteria for the study and show you how to keep your food records. You will then be asked to try different breakfasts, which you will be expected to eat entirely. At each session you will be asked rate your level of hunger before consuming the meal and then again after 15, 30, 45, 60, 90, 120 and 180 minutes. A sample of blood will also be needed before the meal and then again 30, 60, 90, and 120 minutes after. A trained technician will draw your blood (5-10 mL per draw-1-2 teaspoons) and insert a small tube in your arm before your meal so you will only receive on prick throughout the day. It should take approximately 3 hours for each testing at the resting center (total 12 hours). This time can be spent reading, watching television, or conducting work quietly in Hitchner Hall. After leaving the testing center each testing day you will be asked to keep a record of everything you eat and drink for the rest of the day. Risks • • • • If you have any known food allergies or intolerances (corn, milk, or orange juice) please do not participate in this study. Risks associated with having blood drawn are slight but may include: Excessive bleeding Fainting or feeling light-headed Hematoma (blood accumulating under the skin) Infection (a slight risk any time the skin is broken) The risks involved are minimal, and not expected to be greater than those encountered from typical blood donating occasions. There is a chance of possible mild gastrointestinal discomfort (mild stomach pains, bloating and gas). The risks involved are minimal, and not expected to be greater than those encountered from typical eating occasions. 76 Benefits The information obtained from the study will used help understand how food could be modified to aid in weight-loss. Compensation You will receive $200 if you complete the entire study. There will be no compensation for partial participation in the study except for the free breakfasts provided. Confidentiality Your name will not be on any of the documents. A code number will be assigned to you at the beginning of the study that you will use for the entire study to protect your identity. The principal investigator will be the only person with access to the key containing names and codes. All data will be stored in a locked file cabinet in the Consumer Testing Center office; computer data will be held in a password-protected computer in a limitedaccess locked room. All data will be destroyed two years after the study has been completed. Voluntary Participation is voluntary. You may choose not to participate in this study now or may stop at any time during the study. However, you will not receive a gift card if you do not complete the study. Contact Information If you have any questions about this study, please contact Elijah Magrane at 581-8434 or by email at [email protected]. If you have any questions about your rights as a research participant, please contact Gayle Anderson, Assistant to the University Of Maine Protection Of Human Subjects Review Board, at 581-1498 (or e-mail Gayle. Anderson(g),umit. maine. edu). Your signature below indicates that you have read and understand the above information. Signature Date 77 APPENDIX E DIRECTIONS FOR TESTING • Please do not consume alcohol and recreational drugs within 24 hours prior of the testing session. You will also be asked to not consume these substances during the time you are recording what you are eating after the testing. • Please do not eat or drink anything except water after 10 p.m. the night before the test session until you arrive at the test center the next morning. • Please try to eat the same or similar meals the night before each test session • On test days please try to arrive a few minutes early, so we can begin the testing on time. You are scheduled to come on 158 Hitchner Hall. date&time The testing will be held in If you have any questions or concerns feel free to contact Elijah Magrane at 581-8434 or through email at [email protected] 78 APPENDIX F SATIETY RATING SCALES AND QUESTIONS Questions to Rate Satiety How hungry do you feel right now? Not Hungry at all 0 Extremely 100 hungry How satisfied do you feel? Completely empty 0 Cannot eat 100 another bite How much food do you think you could eat right now? Nothing _ at all 0 A large 100 amount How full do you feel right now? Not full _ at all 0 Extremely 100 full 79 APPENDIX G FOOD RECORD INSTRUCTIONS HANDOUT Directions for Food Records • Write down everything you put in your mouth, including beverages and water. • Include all extras, such as butter, jelly, salad dressings, and sauces. • Listing them as soon as possible, preferably immediately after you have eaten. Do not depend on your memory. • Record the time at which you eat the foods. • Record only one food per line in the record booklet. • Record only the food amount that you actually ate. • Record amounts in household measurements, such as cups, teaspoons, tablespoons, ounces or units. For example-1 cup of 2% fat milk, 1 medium Mcintosh apple, 2 slices of Nissen white bread. For items such as pizza, meats, and lasagna use inches. For example, 1 slice of a 9 inch cheese pizza. • Include method that was used to prepare each food item- example: fresh, frozen, stewed, baked, broiled, fried, or canned. • Write down brand names, if you know them. For items that are individual packaged, record the weight or volume on the package for items, such as a bag of peanut, or a can of soda. • For mixed dishes write down the major ingredients and amounts. • Record items such as crackers, French fries and baby carrots by number. For example, 5 reduced-fat Triscuit crackers; 8 baby carrots. If you have any questions at all, please contact Elijah at 581-8434 or by email ([email protected]). 80 Time Subject Code Number Date Food Item and Method of Preparation 81 Amount Eaten APPENDIX H INDIVIDUAL MEAN SATIETY SCORES: FOOD CONSUMPTION Time (min) BMI Trt Normal WB BBJ C P Overweight WB BBJ C P 0 15 30 45 60 90 120 17.8±26.6 34.8±25.9 51.2±28.2 43.6±28.7 39.4±24.9 27.1±20.4 25.3±17.9 12 13.1±19.0 41.1±17.2 36.3±16.0 33.9±16.1 27.4±20.1 26.2±24.5 21.9±23.0 11 13.1±20.9 19.5±18.3 18.7±16.8 18.6±14.5 16.3±14.2 14.5±13.6 13.3±19.6 15 14.6±16.5 29.5±26.5 29.1±24.6 32.1±25.8 31.4±25.6 25.0±26.7 24.6±29.0 15 22.4±32.4 51.8±28.3 53.8±27.7 49.2±27.0 53.9±23.8 43.9±24.5 40.7±25.8 44 24.3±31.9 43.2±23.7 39.8±20.8 37.3±24.4 43.0±20.8 36.2±26.1 33.5±26.6 25 27.9±27.8 34.8±25.3 29.8±27.8 26.9±25.3 28.5±25.1 32.2±25.3 30.1±25.7 2 30.5±32.2 41.5±28.3 42.3±29.2 43.5±28.8 39.0±29.6 38.7±28.2 35.2±29.2 3 a= Mean ± standard deviation (n=21) (normal BMI n=l 1; overweight n=10) on a 0-100mm VAS scale; 0 = unabl and 100 = able to consume a large amount of food. WB= Whole blueberries; BBJ= Blueberry Juice; C= Control; BMI =18.5-24.9 kg/m2, Overweight BMI =25.0-29.9 kg/m2. APPENDIX I INDIVIDUAL MEAN SATIETY SCORES: FULLNESS Time (min) BMI Trt Normal WB BBJ C P Overweight WB BBJ C P 0 15 30 45 60 90 120 10.5±14.1 59.6±24.3 58.4±24.6 55.1±25.9 44.7±25.3 37.8±22.9 34.1±26.3 22 7.7±8.5 51.7±14.4 40.8±15.3 40.8±14.2 37.8±23.0 33.9±24.1 25.4±22.6 15 18.2±29.2 44.6±22.6 36.9±21.9 36.1±23.7 29.3±25.2 27.8±26.1 20.3±26.4 2 9.0±9.7 41.8±19.8 40.7±22.9 37.7±21.6 35.0±25.5 30.5±27.2 30.4±26.9 13 15.6±18.8 66.9±21.8 62.2±21.1 57.6±23.8 58.5±19.7 53.1±22.6 41.1±25.8 40 11.7±12.8 52.3±16.8 55.5±15.6 57.6±14.8 51.3±15.3 49.5±13.5 31.3±13.8 2 13.1±16.3 58.3±24.4 50.9±27.7 49.2±23.4 47.8±23.0 36.2±21.6 35.5±19.4 2 19.3±25.9 61.3±23.4 62.U21.2 60.5±20.2 59,4±20.5 51.3±24.7 47.1±23.8 4 a= Mean ± standard deviation (n=21) (normal BMI n=l 1; overweight n=10) on a 0-100mm VAS scale; 0 = not fu able to extremely full. WB= Whole blueberries; BBJ= Blueberry Juice; C= Control; P= Placebo. Normal BMI =1 Overweight BMI =25.0-29.9 kg/m2. APPENDIX J INDIVIDUAL MEAN SATIETY SCORES: HUNGER Time (min) BMI Trt 0 Normal WB BBJ C P Overweight WB BBJ C P 15 30 45 60 90 120 57.6±26.7 22.8±22.5 28.2±23.0 32.4±24.5 36.4±22.7 48.4±25.7 47.8±26.3 58 61.4±28.7 30.4±23.1 32.2±22.0 31.8±22.0 35.4±22.1 37.8±22.99 46.9±22.7 71 58.9±27.9 30.5±27.8' 33.6±26.8 41.6±25.4 40.7±25.3 44.7±30.3 54.5±25.4 59 61.0±25.9 31.5±25.9 32.1±27.8 34.6±28.1 36.4±30.3 44.5±30.0 46.5±31.2 58 67.8±32.3 16.8±9.9 32.9±22.4 35.7±22.3 33.6±18.7 38.4±20.9 42.9±25.3 32 70.3±18.2 33.3±19.7 34.6±14.8 37.0±17.6 36.7±18.8 44.2±20.0 58.6±24.6 67 60.3±31.5 28.8±22.1 40.4±25.6 40.9±26.9 44.5±23.6 57.3±24.5 57.9±23.1 6 64.5±25.4 27.1±18.3 30.2±21.9 33.6±21.7 31.2±19.3 39.2±22.1 44.1±26.8 44 a= Mean ± standard deviation (n=21) (normal BMI n=l 1; overweight n=10) on a 0-100mm VAS scale; 0 = not h extremely hungry. WB= Whole blueberries; BBJ= Blueberry Juice; C= Control; P= Placebo. Normal BMI =18.5Overweight BMI =25.0-29.9 kg/m2. APPENDIX K INDIVIDUAL MEAN SATIETY SCORES: SATISFACTION Time (min) BMI Trt Normal WB BBJ C P Overweight WB BBJ C P 0 15 30 45 60 90 120 17.8±24.6 34.8±25.9 51.2±28.1 43.6±28.7 39.4±24.9 27.1±20.4 25.3±17.9 12 13.1±19.0 41.1±17.2 36.3±16.1 33.9±16.0 27.4±20.1 26.2±24.5 21.9±23.1 11 13.1±20.8 19.5±18.3 18.7±16.9 18.6±14.5 16.3±14.2 14.5±13.6 13.3±19.6 15 14.6±16.5 29.5±26.4 29.1±24.6 32.1±25.8 31.4±25.6 25.0±26.7 24.6±29.1 15 22.4±32.5 51.8±28.3 53.8±27.8 49.2±27.1 53.9±23.8 43.9±24.6 40.7±25.8 44 24.3±31.9 43.2±23.7 39.8±20.8 37.3±24.4 43.0±20.8 36.2±26.1 33.5±26.6 25 27.8±27.8 34.8±25.4 29.8±27.8 26.9±25.3 28.5±25.1 32.2±25.3 30.1±25.7 2 30.5±32.2 41.5±28.3 42.3±29.2 43.5±28.9 39.0±296 38.7±28.2 35.2±29.2 3 a= Mean ± standard deviation (n=21) (normal BMI n=l 1; overweight n=10) on a 0-100mm VAS scale; 0 = extrem 100 = extremely satisfied. WB= Whole blueberries; BBJ= Blueberry Juice; C= Control; P= Placebo. Normal BM Overweight BMI =25.0-29.9 kg/m2. BIOGRAPHY OF THE AUTHOR Elijah Magrane was born in Boston, Massachusetts on March 10, 1981. He was raised just outside of Cape Cod, Massachusetts, where he graduated from Wareham High School in 1999. He attended Johnson and Wales University and graduated in 2002 with an Associate's degree in Culinary Arts. He worked as a successful Chef throughout Southeastern Massachusetts and Rhode Island before returning to Johnson and Wales University where upon he decided to study nutrition; soon after he graduated in 2007 with a Bachelor's in Culinary Nutrition. He then decided to continue his education at the University of Maine and entered the Dietetic Internship and Masters program in the fall of2007. After receiving his degree, Elijah is scheduled to continue his education and pursue his doctoral degree at McGill University. He is a candidate for the Master of Science degree in Food Science and Human Nutrition from The University of Maine in August, 2009. 86