Download THE EFFECTS OF BLUEBERRY CONSUMPTION ON SATIETY

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. Furthermore, avoiding a twenty-four hour food recall and
providing an additional "in house" meal, i.e., a buffet, after an intervention could be a
valuable tool, allowing the researcher to observe and control energy intake far better.
56
REFERENCES
Adams SH, Won WB, Schonhoff SE, Leiter AB, Paterniti JR. (2004). Effects of
PYY[3-36] on short-term food intake in mice are not affected by
prevailing plasma ghrelin levels. Endocrinology, 145(11):4967-4975.
Adisakwattana S, Charoenlertkul P, Yibchok-Anun S. (2009). Alpha-glucosidase
inhibitory activity of cyanidin-3-galactoside and synergistic effect with acarbose.
J Enzyme Inhib Med Chem, 24(l):65-69.
Arumugam V, Jung-Sheng L, Nowak JK, Pohle RJ, Nyrop JE, Leddy JJ, Pelkman CL.
(2008). A high-glycemic meal pattern elicited increased subjective appetite
sensations in overweight and obese women. Appettie, 50(2):215-222.
Agricultural Research Service. (2003). Database for the flavonoid content of selected
foods. USDA. Available from:
http://www.nal.usda.gov/fnic/foodcomp/Data/Flav/flav.pdf [cited May 2009].
American Diabetes Association. How to tell if you have Pre-Diabetes. Available from:
http://www.diabetes.org/pre-diabetes/pre-diabetes-symptoms.jsp. [cited May
2009 ].
American Heart Association. Triglycerides,]. Available from:
http://www.americanheart.org/presenter.jhtml?identifier=4778. Copyright 2009.
[cited May 2009].
Arita Y, Kihara S, Ouchi N, Takahashi M, Maeda K, Miyagawa J, Hotta K, Shimomura
I, Nakamura T, Miyaoka K, Kuriyama H, Nishida M, Yamashita S, Okubo K,
Matsubara K, Muraguchi M, Ohmoto Y, Funahashi T, Matsuzawa Y. (1999).
Paradoxical decrease of an adipose-specific protein, adiponectin, in obesity.
Biochem Biophys Res Commun. 2, 257(l):79-83.
Astrup A, Finer N. (2000) Redefining Type 2 diabetes: 'Diabesity' or 'obesity dependent
diabetes mellitus'? Obesity Reviews l(2):57-59
Atiken RCB. (1969). Measurement of feelings using visual analogue scales. Proc Roy
Soc Med, 62(\0):9S9-995.
Aura AM, Martin-Lopez P, O'Leary KA, Williamson G, Oksman-Caldentey KM,
Poutanen K, Santos-Buelga C. (2005). In vitro metabolism of anthocyanins by
human gut microflora. Eur JNutr, 44(3):133—142.
Batterham RL, Cowley MA, Small CJ, Herzog H, Cohen MA, Dakin CL, Wren AM,
Brynes AE, Low MJ, Ghatei MA, Cone RD, Bloom SR. (2002). Gut hormone
PYY(3-36) physiologically inhibits food intake. Nature, 418(6898):650-654.
57
Batterham RL, le Roux CW, Cohen MA, Park AJ, Ellis SM, Patterson M, Frost GS,
Ghatei MA, Bloom SR. (2003). Pancreatic polypeptide reduces appetite and food
intake in humans. J Clin Endocrinol Metab, 88(8):3989-3992.
Baum JI, Layman DK, Freund GG, Rahn KA, Nakamura MT, Yudell BE. (2006). A
Reduced Carbohydrate, Increased protein diet stabilizes glycemic control and
minimizes adipose tissue glucose disposal in rats. J. Nutr, 136(7): 1855-1861.
Bohm H, Boeing H, Hempel J, Raab B, Kroke A. (1998). Flavonols, flavone and
anthocyanins as natural antioxidants of food and their possible role in the
prevention of chronic diseases. Z Ernahrungswiss, 3 7(2): 147-163.
Brand-Miller JC, Holt SHA, Pawlak DB, McMillan J. (2002). Glycemic index and
obesity. AmJClin Nutr, 76(1):281S-285S.
Cao G, Muccitelli HU, Sanchez-Moreno C, Prior RL. (2001). Anthocyanins are absorbed
in glycated forms in elderly women: a pharmacokinetic study. Am J Clin Nutr,
73(5):920-926.
Chaudhri OB, Small CJ, and. Bloom SR. (2005). The gastrointestinal tract and the
regulation of appetite. Drug Discovery Today: Disease Mechanisms, 2(3):289-94.
Chelikani PK, Haver AC, Reidelberger RD. (2005). Intravenous infusion of peptide
YY(3-36) potently inhibits food intake in rats. Endocrinology, 146(2):879-888.
Chen Y-DI, Swami S, Skowronski R, Coulston A, Reaven GM. (1993). Differences in
postprandial lipemia between patients with normal glucose tolerance and
noninsulin-dependent diabetes mellitus. J Clin Endocrinol Metab, 76(1):347351.
Chun OK, Chung SJ, Song WO. (2007). Estimated dietary flavonoid intake and major
food sources of U.S. adults. J Nutr, 137, 1244-1252.
Clarke JR, Howard L, Talcott S. (2002). Variation in phytochemical composition of
blueberry cultivars and breeding selections. Acta Hort, 574(7):203-207.
Cohen MA, Ellis SM, le Roux CW, Batterham RL, Park A, Patterson M, Frost GS,
Ghatei MA, Bloom SR. (2003). Oxyntomodulin suppresses appetite and reduces
food intake in humans. J Clin Endocrinol Metab, 88(10):4696-4701.
Crespy V, Morand C, Manach C, Besson C, Demigne C, Remesy C. (1999). Part of
quercetin absorbed in the small intestine is conjugated and further secreted in the
intestinal lumen. Am J Physiol, 277(1 Pt 1):G120-126.
58
de Ferranti S and Mozaffarian D. (2008). The perfect storm: obesity, adipocyte
dysfunction, and metabolic consequences. Clin Chem, 54(6):945-955.
DeFronzo RA, Bonadonna RC, Ferrannini E. (1992). Pathogenesis of NIDDM. A
balanced overview. Diabetes Care, 15(4):318-368.
Delgaro-Aros S, Cremonini F, Castillo JE, Chial HJ, Burton DD, Ferber I, Camilleri M.
(2004). Independent influences of body mass and gastric volume on satiation in
humans. Gastreology, 125(4):432-440.
Donovan JL, Crespy V, Manach C, Morand C, Besson C, Scalbert A, Remesy C. (2001).
Catechin is metabolized by both the small intestine and liver of rats. JNutr,
131(6):1753-1757.
El Mohsen MA, Marks J, Kuhnle G, Moore K, Debnam E, Kaila Srai S, Rice-Evans C,
Spencer JP. (2006). Absorption, tissue distribution and excretion of pelargonidin
and its metabolites following oral administration to rats. BrJNutr, 95(l):51-55.
Erdmann J, Mayr M, Oppel U, Sypchenko O, Wagenpfeil S, Schusdziarra V. (2009).
Weight-dependent differential contribution of insulin secretion and clearance to
hyperinsulinemia of obesity. Regul Pept, 152(1 -3): 1 -7.
Esscot-Stump S. (2002). Nutrition and Diagnosis-Related Care 5th ed. Lippincott
Williams & Wilkins Philadelphia, PA.
Fall CH, Sachdev HS, Osmond C, Lakshmy R, Biswas SD, Prabhakaran D. (2008).
Adult metabolic syndrome and impaired glucose tolerance are associated with
different patterns of BMI gain during infancy: Data from the New Delhi Birth
Cohort. Diabetes Care, 31(12):2349-2356.
Farag A, Karam J, Nicasio J, McFarlane SI. (2007). Prevention of Type 2 diabetes: an
update. Curr. Diab Rep, 7(3):200-207.
Felgines C, Talavera S, Gonthier MP, Texier O, Scalbert A, Lamaison JL, Remesy C.
(2003). Strawberry anthocyanins are recovered in urine as glucuro- and
sulfoconjugates in humans. JNutr, 133(5):1296—1301.
Felgines C, Talavera S, Texier O, Gil-Izquierdo A, Lamaison JL, Remesy C. (2005).
Blackberry anthocyanins are mainly recovered from urine as methylated and
glucuronidated conjugates in humans. JAgric Food Chem, 53(20):7721-7727.
Flint A, Raben A, Blundell JE, Astrup A. (2000). Reproducibility, power and validity of
visual analogue scores in assessment of appetite sensations in single test meal
studies. Int J Obesity, 24(l):38-48.
59
Frank T, Sonntag S, Strass G, Bitsch I, Bitsch R, Netzel M. (2005). Urinary
pharmacokinetics of cyanidin glycosides in healthy young men following
consumption of elderberry juice. Int J Clin Pharmacol Res, 25(2):47-56.
Galvano F, La Fauci L, Lazzarino G, Fogliano V, Ritieni A, Ciappellano S, Battistini
NC, Tavazzi B, Galvano G.(2004). Cyanidins: metabolism and biological
properties. JNutr Biochem, 15(1):2-11.
Gao, L. and Mazza, G. (1995). Characterization of acetylated anthocyanins in lowbush
blueberries. J. Liquid Chromatogr, 18(2):245-259.
Gerich JE. (1988). Lilly lecture 1988. Glucose counterregulation and its impact on
diabetes mellitus. Diabetes, 37(12):1608-1617.
Goto Y, Toyoda T and Oikawa S. (1989). Long term treatment of Bay g 5421 (acarbose)
inNIDDM. Yakuri to Chiryo, 17(1):387-401.
Grace MH, Ribnicky DM, Kuhn P, Poulev A, Logendra S, Yousef GG, Raskin I, Lila
MA. (2009). Hypoglycemic activity of a novel anthocyanin-rich formulation
from lowbush blueberry, Vaccinium angustifolium Aiton. Phytomedicine,
16(5):406-415.
Griffiths LA and Barrow A. (1972). Metabolism of flavonoid compounds in germ-free
rats. Biochem J, 130(4):1161-1162.
Haines PS, Hama MY, Guilkey DK, Popkin BM. (2003). Weekend eating in the United
States is linked with greater energy, fat, and alcohol intake. Obes Res, 11(8):945949.
Hauri H, Wacker H, Rickli E, Meier B, Quaroni A, Semenza G. (1982). Biosynthesis of
sucrase-isomaltase. J Biol Chem, 257(8):4522-4528.
Hayes M, Patterson D. (1921). Experimental development of the graphic rating method.
Psycchological Bulletin, 18(2):98-99.
Heinonen M. Anthocyanins as dietary antioxidants. In: Voutilainen S, Salonen JT, eds.
Third international conference on natural antioxidants and anticarcinogens in
food, health, and disease (NAHD), June 6-9, (2001), Helsinki, Finland. Helsinki:
Kuopion Yliopisto, 2001:25.
Hollman PC and Katan MB. Health effects and bioavailability of dietary flavonols.
(1999). Free Radic Res, 31(Suppl):S75-80.
Hollman PC, de Vries JH, van Leeuwen SD, Mengelers MJ, Katan MB. (1995).
Absorption of dietary quercetin glycosides and quercetin in healthy ileostomy
volunteers. Am J Clin Nutr, 62(6): 1276-82.
60
'SUBfc.
Hollman PC, vd Gaag M, Mengelers MJ, van Trijp JM, de Vries JH, Katan MB. (1996).
Absorption and disposition kinetics of the dietary antioxidant quercetin in man.
Free Radic Biol Med, 21(5):703-07.
Holt SHA and Brand-Miller J. (1995). Increased insulin responses to ingested foods are
associated with lessened satiety. Appetite, 24(l):43-54.
Institute for Clinical and Translation Science at the University of Iowa. (2009). Peptide
YY (PYY) Sample Collection. Available from: http://icts.uiowa.edu/PeptideYY(PYY). [Cited on July 20, 2009].
Jayaprakasam B, Olson LK, Schutzki RE, Tai MH, Nair MG.(2006). Amelioration of
obesity and glucose intolerance in high-fat-fed C57BL/6 mice by anthocyanins
and ursolic acid in Cornelian cherry (Cornus mas). J Agric Food Chem,
54(l):243-248.
Jayaprakasam B, Vareed SK, Olson LK, Nair MG. (2005). Insulin secretion by bioactive
anthocyanins and anthocyanidins present in fruits. J Agric Food Chem, 53(1 ):2831.
Johansson G, Wikman A, Ahren AM, Hallmans G, Johansson I. (2001). Underreporting
of energy intake in repeated 24-hour recalls related to gender, age, weight status,
day of interview, educational level, reported food intake, smoking habits and area
of living. Public Health Nutr, 4(4):919-927.
Kahn BB, Flier JS. (1990). Regulation of glucose-transporter gene expression in vitro
and in vivo. Diabetes Care, 13(6):548-564.
Kalt W and McDonald JE. Chemical composition of lowbush blueberry cultivars.
(1996). J Am Soc Hortic Sci, 121(1): 142-146.
Kalt W, Ryan DA, Duy JC, Prior RL, Ehlenfeldt MK, Vander Kloet SP. (2001).
Interspecific variation in anthocyanins, phenolics, and antioxidant capacity
among genotypes of highbush and lowbush blueberries (Vaccinium section
cyanococcus spp.). J Agric Food Chem, 49(10):4761-4767.
Kay CD and Holub BJ. (2004). The effect of wild blueberry (Vaccinium angustifolium)
consumption on postprandial serum antioxidant status in human subjects, Br J
Nutr, 88(4):389-398.
Kay CD, Mazza G, Holub BJ and Wang J. (2004) Anthocyanin metabolites in human
urine and serum. Br J Nutr, 91(6): 93 3-942.
61
Keppler K and Humpf HU. (2005). Metabolism of anthocyanins and their phenolic
degradation products by the intestinal microflora. Bioorg Med Chem,
13(17):5195-5205.
Kong JM, Chia LS, Goh NK, Chia TF, Brouillard R. (2003). Analysis and biological
activities of anthocyanins. Phytochem, 64(5):923-933.
Kuhnau J. The flavonoids. (1976). A class of semi-essential food components: their role
in human nutrition. World Rev Nutr Diet, 1 (24): 117-191.
Lapidot T, Harel S, Granit R, Kanner J. (1998). Bioavailability of red wine anthocyanins
as detected in human urine. JAgric Food Chem, 46(10):4297-302.
Lewis Gf, O'Meara NM, Cabana VG, Blackman JD, Pugh WL, Druetzler AE, Lukens
JR, Getz GS, and Polonsky KS. (1991). Postprandial triglyceride response in
Type 1 (insulin-dependent) diabetes mellitus is not altered by short-term
deterioration in glycaemic control or level of postprandial insulin replacement.
Diabetologia, 34(4):253-259.
Manach C, Williamson G, Morand C, Scalbert A, Remesy C. (2005). Bioavailability and
bioefficacy of polyphenols in humans. I. Review of 97 bioavailability studies.
Am J Clin Nutr, 81(1 Suppl):230S-242S.
Marlett JA, McBurney MI, Slavin JL. (2002). Position of the American Dietetic
Association: health implications of dietary fiber. J Am Diet Assoc, 102(7):9931000
Martineau LC, Couture A, Spoor D, Benhaddou-Andaloussi A, Harris C, Meddah B,
Leduca C, Burt A, Vuonga T, Lea PM, Prentkie M, Bennettd SA, Arnasonc JT,
Hadda PS. (2006). Anti-diabetic properties of the Canadian lowbush blueberry
Vaccinium angustifolium Ait. Phytomedicine, 13(9-10):612-623.
Matschinsky F; Liang Y; Kesavan P; Wang L; Froguel P; Velho G; Cohen D; Permutt
MA; Tanizawa Y; Jetton TL. (1993). Glucokinase as pancreatic beta cell glucose
sensor and diabetes gene. J Clin Invest, 92(5):2092-2098.
Matsui T, Ueda T, Oki T, Sugita K, Terahara N, Matsumoto K. (2001). a-glucosidase
inhibitory action of natural acylated anthocyanins. 1. survey of natural pigments
with potential inhibitory activity. J Agric Food Chem, 49(4): 1948-1951.
Matsumoto H, Inaba H, Kishi M, Tominaga S, Hirayama M, Tsuda T. (2001). Orally
administered delphinidin 3-rutinoside and cyanidin 3-rutinoside are directly
absorbed in rats and humans and appear in the blood as the intact forms. J Agric
Food Chem, 49(3): 1546-1551.
62
Matsumoto H, Nakamura Y, Iida H, Ito K, Ohguro H. (2006). Comparative assessment
of distribution of blackcurrant anthocyanins in rabbit and rat ocular tissues. Exp
Eye, 83(2):348-356.
Matuschek M, Hendriks W, McGhie T, Reynolds G. (2006). The jejunum is the main
site of absorption for anthocyanins in mice. JNutr Biochem, 17(l):31-33.
Mazza G and Miniati E. (1993). Anthocyanins in fruits, vegetables, and grains. Boca
Raton, FL:CRC Press.
Mazza G, Kay CD, Cottrell T, Holub BJ. (2002). Absorption of anthocyanins from
blueberries and serum antioxidant status in human subjects. JAgric Food Chem,
50(26):7731-7737.
McDougall GJ and Stewart D. (2005). The inhibitory effects of berry polyphenols on
digestive enzymes. Biofactors, 23(4): 189-195.
McGhie TK and Walton MC. (2007). The bioavailability and absorption of
anthocyanins: Towards a better understanding. Mol Nutr Food Res, 51(6):702713.
McGhie TK, Ainge GD, Barnett LE. (2003). Anthocyanin glycosides from berry fruit are
absorbed and excreted unmetabolized by both humans and rats. JAgric Food
Chem, 51(16):4539^548.
Medallion Labs, Minneapolis, MN. (2005). Frozen wild blueberries analyzed for Maine
Wild Blueberry Company, Cherryfield, Maine.
Modan M, Karasik A, Halkin H, Fuchs Z, Lusky A, Shitrit A, Modan B. (1986). Effect
of past and concurrent body mass index on prevalence of glucose intolerance and
type 2 (non insulin-dependent) diabetes and on insulin response. The Israel study
of glucose intolerance, obesity and hypertension. Diabetologia, 29(2):82-89.
Molan AL, Lila MA, Mawson J. (2008). Satiety in rats following blueberry extract
consumption induced by appetite-suppressing mechanisms unrelated to in vitro or
in vivo antioxidant capacity. Food Chemistry, 107(3): 1039-1044.
Moran TH, Smedh U, Kinzig KP, Scott KA, Knipp S, Ladenheim EE. (2005). Peptide
YY(3-36) inhibits gastric emptying and produces acute reductions in food intake
in rhesus monkeys. Am J Physiol Regul Integr Comp Physiol, 288(2):R384-R338.
Murkovic M, Mulleder U, Adam U, Pfannhauser W. (2001). Detection of anthocyanins
from elderberry juice in human urine. JSci Food Agric, 81(9):934-937.
63
Murphy KG and Bloom SR. (2006). Gut hormones and the regulation of energy
homeostasis. Nature, 444(14):854-859.
Narayan KMV, Boyle JP, Thompson TJ, Gregg EW, Williamson DF. (2007). Effect of
BMI on lifetime risk for diabetes in the U.S. Diabetes Care, 30(6): 1562-1566.
National Institute of Diabetes and Digestive and Kidney Disease (NIDDK). National
Diabetes Statistics Fact Sheet: National Estimates on Diabetes in the United
States, (2007). U.S. Dept of Health and Human Services, National Institute of
Health, Bethesda, MD.
NCHS. (2008). Prevalence of overweight, obesity and extreme obesity among adults:
United States, trends 1976-80 through 2005-2006. Hyattsville, Maryland: Public
Health Service. Available from:
http://www.cdc.gov/nchs/products/pubs/pubd/hestats/overweight/overweight_adu
ltht [cited May 2009].
Neto CC. (2007). Cranberry and blueberry: Evidence for protective effects against cancer
and vascular diseases. Mol. Nutr. Food Res, 51(6):652-664.
Netzel M, Strass G, Janssen M, Bitsch I, Bitsch R. (2001). Bioactive anthocyanins
detected in human urine after ingestion of blackcurrant juice. J Environ Pathol
Toxicol Oncol, 20(2):89-95.
Nicoue EE, Savard S, Belkacemi K. (2007). Anthocyanin in wild blueberry of Quebec:
extraction and identification. J Agric Food Chem, 55(14):5626-5635.
Nielsen IL, Dragsted LO, Ravn-Haren G, Freese R, Rasmussen SE. (2003). Absorption
and excretion of black currant anthocyanins in humans and Watanabe heritable
hyperlipidemic rabbits. J Agric Food Chem, 51(9):2813-2820.
National Institute of Health (NIH). (1998). Publication. No. 98-4083. NHLBI Clinical
Guidelines on the Identification, Evaluation, and Treatment, of Overweight and
Obesity: The Evidence Report. Bethesda, MD.
Odaka H, Miki N, Ikeda H, Matsuo T. (1992). Effect of a disaccharidase inhibitor, AO128, on postprandial hyperglycemia in rats. J Japan Soc Nutr Food Sci, 45(1):2731.
Orr J and Davy D. (2005). Dietary influences on peripheral hormones regulating energy
intake: potential applications for weight management. J Am Diet Assoc,
105(7):1115-1124.
64
Parker BA, Sturm K, Macintosh CG, Feinle, M Horowitz M, Chapman IM. (2004).
Relation between food intake and visual analogue scale ratings of appetite and
other sensations in healthy older and young subjects. Euro J Clin Nutr,
58(2):212-218.
Pascual-Teresa S and Sanchez-Ballesta MT. (2008). Anthocyanins: from plant to health.
Phytochem Rev, 7(2):281-299.
Passamonti S, Vrhovsek U, Vanzo A, Mattivi F. (2003). The stomach as a site for
anthocyanins absorption from food. FEBS Lett, 544(1-3):210-213.
Pawlak DB, Ebbeling CB, Ludwig DS. (2002). Should obese patients be counseled to
follow a low-glycaemic index diet? Yes. Obes Rev, 3(4):235-243.
Philippe J. (1991). Insulin regulation of the glucagon gene is mediated by an insulinresponsive DNA element. Proc Natl Acad Sci U.S., 88(16):7224-7227.
Prior RL, Cao G, Martin A, Sofic E, McEwen J, O'Brien C, Lischner N, Ehlenfeldt M.,
Kalt W, Krewer G, Mainland CM. (1998). Antioxidant capacity as influenced by
total phenolic and anthocyanin content, maturity, and variety of Vaccinium
species. JAgric Food Chem, 46(7):2686-2693.
Prior RL. (2003). Fruits and vegetables in the prevention of cellular oxidative damage.
Am J Clin Nutr, 78(3):570-578.
Prior RL, Hoang H, Gu L, Wu X, Bacchiocca M, Howard L, Hampsch-Woodill M,
Huang D, Ou B, Jacob R. (2003). Assays for hydrophilic and lipophilic
antioxidant capacity (oxygen radical absorbance capacity (ORAC FL) of plasma
and other biological and food samples. J Agric Food Chem, 51(11):3273-3279.
Prior RL, Wu X. (2006). Anthocyanins: structural characteristics that result in unique
metabolic patterns and biological activities. Free Radic Res, 40(10):1014-1028.
Prior RL, Wu X, Hagar T, Hagar A, Howard, L. (2007). Anthocyanins: Do they really
prevent obesity? The FASEB Journal, 21(6):225-227.
Prior RL, Wilkes S, Wu X. (2008). Correction of dyslipidemia resulting from high fat
diets with purified anthocyanins from blueberry or strawberry but not in context
of the complete berry. The FASEB Journal, 22(2):460-462.
Raben A, Tagliabue A, Astrup A. (1995). The reproducibility of subjective appetite
scores. Brit J Nutr, 73(4):517-530.
Raben A. (2002). Should obese patients be counselled to follow a low-glycaemic index
diet? No. Obesity Reviews, 3(4):245-256.
65
Radulian G, Rusul E, Dragomir A, Posea M. (2009). Metabolic effects of low glycaemic
index diets. NutrJ, 84(5):1365-1373.
Sasaki R, Nishimura N, Hoshino H, Isa Y, Kadowaki M, Ichi T, Tanaka A, et al.
Cyanidin 3-glucoside ameliorates hyperglycemia and insulin sensitivity due to
downregulation of retinol binding protein 4 expression in diabetic mice. (2007).
Biochem Pharmacol, 74(11):1619-1627.
Scalbert A, Williamson G. (2000). Dietary intake and bioavailability of polyphenols. J
Nutr, 130(8S Suppl):2073-2085.
Scherer PE, Williams S, Fogliano M, Baldini G, Lodish HF. (1995). A novel serum
protein similar to Clq, produced exclusively in adipocytes. J Biol Chem,
270(45):26746-26749.
Shechtera Y, Tsuberya H, Mironchika M, Rubinsteinc M, Fridkin M. (2005). Reversible
PEGylation of peptide YY3-36 prolongs its inhibition of food intake in mice.
FEBSLetters, 579(11):2439-2444.
Swain T. (1976). Flavonoids: Chemistry and Biochemistry of Plant Pigments. London:
Academic Press.
Syvanne M, Rosseneu M, Labeur C, Hilden H, Taskinen MR. (1994). Enrichment with
apolipoprotein E characterizes postprandial TG-rich lipoproteins in patients with
non-insulin-dependent diabetes mellitus and coronary artery disease: a
preliminary report. Atherosclerosis, 105(11):25-34.
Thomas D, Elliott EJ, Baur L. Low glycaemic index or low glycaemic load diets for
overweight and obesity. (2007). Cochrane Database of Systematic Reviews, Issue
3. Art. No.: CD005105. DOI: 10.1002/14651858.CD005105.pub2.
Thorpe KE, Florence CS, Howard DH, Joski P. (2004). The impact of obesity on rising
medical spending. Health (Millwood); suppl web exclusives: W4.480-486. DOI:
10.1377.
Tilg H and Moschen AR. (2006). Adipocytokines: mediators linking adipose tissue,
inflammation and immunity. Nat Rev Immunol. 6(10):772-783.
Tsuda T, Horio F, Osawa T. (1999). Absorption and metabolism of cyanidin 3-OP-Dglucoside in rats. FEBS Lett, 449(2-3): 179-182.
Tsuda T, Ueno Y, Kojo H, Yoshikawa T, Osawa T. (2005). Gene expression profile of
isolated rat adipocytes treated with anthocyanins. Biochim Biophys Acta, 1733(23):137-47.
66
Tsuda, T., Horio, F., Uchida, K., Aoki, H., Osawa, T. (2003). Dietary cyanidin 3-O-betaD-glucoside-rich purple corn color prevents obesity and ameliorates
hyperglycemia in mice. J Nutr, 133(7):2125—2130.
Tsuda T, Ueno Y, Yoshikawa T, Kojo H, and Osawa T. (2006). Microarray profiling of
gene expression in human adipocytes in response to anthocyanins. Biochemical
Pharmacology, 71(8):1184-1197.
Welcome Trust Sanger Institute [WTSI]. (Last modified Jan 30th 2008). Obesity and
Type 2 diabetes. Available from:
http://www.sanger.ac.uk/Teams/Team35/diabetes.shtml [cited May 2009].
Wild Blueberry Association of America (WBANA). Health Benefits, Nutrition Facts.
Available from: http://www.wildblueberries.com/health_benefits/nutrition.php
[cited May 2009].
Williamson G, Day AJ, Plumb GW, Couteau D. (2000). Human metabolic pathways of
dietary flavonoids and cinnamates. Biochem Soc Trans, 28(2): 16-22.
Wolf A, Siadaty M, Yaeger B, Conaway M, Crowther J,Nadler J, Bovbjerg V. Effects of
Lifestyle Intervention on Health Care Costs: Improving Control with Activity and
Nutrition (ICAN). J Am Diet Asso, 107(81):1365-1373.
World Health Organization. Obesity and Overweight. Available from:
http://www.who.int/mediacentre/factsheets/fs311/en/index.html [cited May 2009]
World Health Organization. (1995). Physical Status: The Use and Interpretation of
Anthropometry. Technical Report Series, 854, p. 9. Available from:
http://whqlibdoc.who.int/trs/WHO_TRS_854.pdf [cited June 2009].
Wren AM, Seal LJ, Cohen MA, Brynes AE, Frost GS, Murphy KG, Dhillo WS, Ghatei
MA, Bloom SR (2001). Ghrelin enhances appetite and increases food intake in
humans. J Clin Endocrinol Metab, 86(12):5992-5995.
Wren AM, Small CJ, Ward HL, Murphy KG, Dakin CL, Taheri S, Kennedy AR, Roberts
GH, Morgan DG, Ghatei MA, Bloom SR. (2000). The novel hypothalamic
peptide ghrelin stimulates food intake and growth hormone secretion.
Endocrinology 141(11):4325^1328.
Wu X, Beecher GR, Holden JM, Haytowitz DB, Gebhardt SE, Prior RL. (2006).
Concentrations of anthocyanins in common foods in the United States and
estimation of normal consumption. J Agric Food Chem, 54(11):4069-4075.
67
Yamauchi T, Kamon J, Waki H, Terauchi Y, Kubota N, Hara K, Mori Y, Ide T,
Murakami K, Tsuboyama-Kasaoka N, Ezaki O, Akanuma Y, Gavrilova O,
Vinson C, Reitman ML, Kagechika H, Shudo K, Yoda M, Nakano Y, Tobe K,
Nagai R, Kimura S, Tomita M, Froguel P, Kadowaki T. (2001). The fat-derived
hormone adiponectin reverses insulin resistance associated with both lipoatrophy
and obesity. Nat Med, 7(8):941-946.
Yarborough D. (2009). Wild Blueberry Fact Sheet: Crop Statistics, 1914-2008. Available
from: http://wildblueberries.maine.edu/PDF/Miscellaneous/CropStatistics.pdf
[cited May 2009].
Youdim KA, Martin A, Joseph JA. (2000). 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