the university of natural medicine high fructose corn...

THE UNIVERSITY OF NATURAL MEDICINE

HIGH FRUCTOSE CORN SYRUP AND CHILDHOOOD OBESITY IN THE UNITED

STATES: AN INVESTIGATION OF A CAUSAL RELATIONSHIP

DISSERTATION SUBMITTED TO

THE FACULTY OF THE DEPARTMENT OF NATURAL HEALTH SCIENCES

IN CANDIDACY FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

BY

ANITA DEHLINGER DELPRETE

ALBUQUERQUE, NM

SEPTEMBER 2011

High Fructose Corn Syrup and Childhood Obesity p. 2

Copyright © 2011 by Anita Dehlinger DelPrete All rights reserved.


Dedicated to my grandmother Dr. Jean R. Dehlinger who planted the first seed many years ago.


TABLE OF CONTENTS

Introduction 5 Chapter 1 Obesity rates in the United States

Adiposity Determination 8 Trends and Prevalence 15 Pediatric Trends & Health Impacts 19 Economic Cost 29

Chapter 2 Sugar Consumption and Metabolism

Proteins 34 Carbohydrates 36 Glucose and Fructose Metabolism 41 Glycolysis 42 Glycogen, Glycogenesis, de novo Lipogenesis 45 Metabolic Hormones 47 Lipids 51 Metabolic Regulatory Theories 54 Leptin 55 Metabolic Imbalances & Disorders 59

Chapter 3 High Fructose Corn Syrup Discovery, Use & Prevalence 63 Consumption Trends 70 Metabolism and Adiposity 78 Economic Benefits 96 Genetically Modified Foods (GMO/GE) 98

Chapter 4 Other Contributing Factors

Portion size & Increased Caloric Intake 106 Physical Activity 109 Television, Computer & Video Games 111 Family Mealtime 114 Fast Food and Fat Consumption 115 Soda Consumption 117 Genetics 119

Conclusion 121

Appendix 128 End Notes 129

Bibliography 135


Introduction

There is a global epidemic occurring and it is threatening to be one of the most

costly epidemics the world has experienced…obesity. Ten years ago the World Health

Organization (WHO) declared this epidemic to be “the biggest unrecognized public

health problem in the world”1 and sadly it has expanded since that declaration. The

health risk factors associated with obesity and excess adiposity have catapulted obesity

and overweight to one of the top health risk factors in the United States. According to the

Mayo Clinic (2011), these include are heart disease, stroke, cancer, type 2 diabetes,

chronic respiratory diseases, and accidents.2 Of these top health threats, six have been

directly associated with obesity and excessive adiposity which is precisely why health

professionals (adult and pediatric) are growing more concerned.

Current projections estimate that by 2030 half of all Americans will be obese, not

overweight but obese!3 This epidemic has traversed socio-economic, ethnic, racial,

gender, geographic and age boundaries and demarcations. While some groups or

classification of individuals may have a slightly higher preponderance of occurrence, no

population has escaped unscathed by this health crisis. According to the most recent

estimates of the Centers for Disease Control and Prevention (2011), 17% of all children

and adolescents in the United States are obese. Unfortunately, the CDC estimate does not

include those children who are overweight and/or borderline obese thus omitting a

significant population who may be at risk of developing the same health risks as those

who are classified “obese”. Some estimates report that an additional 25% of U.S.

children and adolescents are overweight.4 Thus cumulatively, these estimates suggest

that, at minimum, 42% of all U.S. children and adolescents are overweight or obese. The


predominant questions for researchers is 1) what is/are causing these alarming rates of

overweight and obesity and 2) what can be done to stop and/or reverse these trends?

The impetus for deriving an answer to these pertinent questions is based in part

to averting future medical crises. Adverse health conditions that once only effected the

adult population are now manifesting in children and adolescents. Cardiovascular

disease, hypertension, high blood pressure, hyperlipidemia, type 2 diabetes, insulin

resistance, and sleep apnea, conditions that were once reserved to the “adult” population,

are becoming more prevalent among our youth. Increased prevalence of medical

conditions translates into increased medical cost and economic burden, especially for

those receiving government-funded medical care (Medicaid and Medicare). In 2000, the

economical cost associated with obesity in the United States was estimated to be $117

billion dollars.5

Interestingly, the surge of high fructose corn syrup (HFCS) consumption in the

United States parallels the child and adolescent overweight/obesity rates trajectory

spawning the investigation of a causal relationship between HFCS consumption and

obesity. Because of the prevalence of HFCS in beverages, soft drinks in particular,

several studies have investigated the role of carbonated beverages and obesity. However,

what has not been investigated is the cumulative dietary intake of HFCS consumed via

beverages (carbonated and non-carbonated juices) and food in relation to adiposity.

Similarly, certain aspects of HFCS and its metabolic processes, as well as glucose,

fructose and sucrose metabolism, have been investigated however, the sum total of all the

experimental “parts” (i.e. findings) have not been cumulatively analyzed. Because

HFCS contains fructose as well as glucose, several studies have researched metabolic and


blood profile differences and/or similarities between the various sweeteners.6 Other

research has specifically investigated effects of HFCS consumption on weight gain and

fat mass7. Some studies have looked at HFCS consumption and satiety8 while others

have investigated effects on insulin, leptin and ghrelin levels.9 There has been research

on the efficacy of genetically modified foods as well.10 The relevance of this latter

research is that the vast majority of HFCS is derived from GMO/GE corn. Additionally,

HFCS has been shown to contain trace amounts of mercury11raising concerns as studies

have revealed toxic effects of mercury ingestion on the liver12 (the primary organ for

carbohydrate and fat metabolism), kidneys and brain tissue.

While seemingly unrelated, each experiment, each research investigation

provides information about a specific aspect of HFCS, but in order to determine the true

relationship between HFCS and obesity a critical analysis of all the information is

necessary. This research investigates the causal relationship between HFCS consumption

and excess adiposity and obesity among U.S. children and adolescents.


CHAPT. 1- Obesity in the United States

ADIPOSITY DETERMINATION

In traditional “medical” terms, obesity is defined simply as an excess of body

fat,13 however, what defines “excessive” remains somewhat nebulous and often

subjective. Some contend that obesity falls into the classification of a non-communicable

disease (NCD)14 while others contend that obesity itself is not a “disease” but rather the

result of metabolic processes from which subsequent diseases may develop as a result of

being obese.15 The World Health Organization (WHO) includes harmful health

ramifications in their definition of obesity stating that obesity is “the condition of having

abnormal or excessive body fat accumulation that may impair health.”16 Some of these

deleterious health impairments will be discussed in detail later in this chapter. For

adults, excess adiposity is traditionally measured via a height-weight index known as the

Body Mass Index (BMI) [also known as the Quételet index named after creator and

statistician Lambert Adolphe Jacques Quételet] which is a ratio of weight in kilograms to

the square of height in meters, BMI= !"#$!! (!")!!"#!! ! !

; when converting BMI into pounds and

inches this calculation becomes !"#$!! !" ! !"#!!"#!! !" !

.17 In the United States, an adult with a

BMI ≥ 25 (but < 30) is classified as “overweight”, a BMI ≥ 30 (but < 40) is “obese” and a

relatively new term of “super obese” applies to those with a BMI >40.18 However, in

their 2000 Report of Consultation regarding obesity, WHO further delineated levels of

obesity into three categories or sub-classifications: BMI 30-34.9 as Obese Class I, BMI

35-39.9 as Obese Class II and BMI ≥ 40 as Obese Class III.19 It should be noted that the

BMI index is not an exact measurement of adiposity, nor the only way to estimate or


determine adiposity, but rather an agreed upon universal guideline of appropriate height-

weight ratio and approximation of general adiposity.

One criticism of the BMI is that it generally has a low sensitivity and high

specificity in detecting excess adiposity.20 In clinical testing, sensitivity refers to the true

positive rate, that is, it reliably identifies all the individuals with a specific condition. For

example, if 100 people are tested for condition X and all 100 people actually have

condition X, but the test only identified 75 as having condition X then the test would

have a sensitivity of 75%. For 15 people the test showed a false negative; these

individuals thought that they were fine when in fact they had condition X. A test with a

low sensitivity is unreliable because is will fail to identify individuals who actually have

specific conditions. Conversely, specificity of a test refers to the number of false

positives. For example, if 100 people are screened for disease Y and only 75 actually

have that disease but all 100 test positive, the test has a low specificity rate. A test with a

low specificity is unreliable because it will give false positives. In this scenario, fifteen

people think they have disease Y when in fact they do not. Ideally, a test will have a high

sensitivity rate as well as a high specificity so as to accurately identify those individuals,

and only those individuals, with a specific condition.

Muscle tissue weighs more than adipose tissue (i.e. fat) therefore, it is possible

for a professional athlete with a low body fat percentage to score a high BMI based on

density of the muscle and smaller stature. A “real life” example of a specificity error

would be that a 5’6” male athlete weighing 173lbs., with a 29” waist and a true body fat

percentage of 10% would register a BMI of 28 thus falling into the upper end of the

“overweight” classification. Clearly, this individual is not overweight, yet because of his


muscle density he falls into almost borderline “obese” category. Conversely, a sensitivity

error with respect to BMI would be a 5’9” female weighing 135 lbs. with a 36” waist who

registers a “normal” BMI of 20 yet has a true body fat percentage of 35%. Sole reliance

on the BMI index would indicate she is well within “normal” (which is often perceived as

“healthy”) limits. However, an elevated body fat percentage of 35% coupled with the

location of the excess adipose tissue, primarily the abdomen, potentially places her at risk

for developing adverse health conditions. Some researchers have coined the term

“metabolically obese but normal weight (MONW)”21 to classify individuals meeting this

criteria. While prevalence ranges between 5%-45% depending upon specific criteria and

BMI cut off, individuals who fall into this classification exhibit higher abdominal and

visceral adipose tissue, higher blood pressure, lower insulin sensitivity, higher risk for

developing NIDDM (type 2 diabetes) and cardiovascular disease (CVD).22

Another criticism of the BMI is that it does not identify individuals at risk for

developing adverse health conditions.23 While BMI is better at estimating subcutaneous

(a.k.a. peripheral) adipose tissue, waist circumference measurements are better at

estimating visceral (a.k.a. central) adipose tissue. Subcutaneous/peripheral fat is a soft,

pliable (or “mushy”) fat that lies just underneath the skin. Subcutaneous fat is

predominantly found in the lower trunk, hips, thighs and buttocks. In the trunk area it

lies outside on the abdominal wall and has no harmful [health] effects except to perhaps a

woman’s dream of wearing a size 2. Conversely, visceral/central fat lies deep inside the

abdomen surrounding vital organs such as the liver, kidneys, intestines, stomach, and

heart. Unlike subcutaneous fat, visceral fat is hard and associated with increased risk of

developing many adverse health conditions and diseases such as cardiovascular disease


(CVD), high blood pressure, NIDDM (type 2 diabetes), arterial stiffening, colon cancer,

breast cancer, gallstones, sleep apnea and Alzheimer’s.24 In the previous example of the

5’9” female, her “normal” BMI rating masks the real health risk posed by her excess

abdominal fat. Clearly, BMI is not the most accurate method of determining adiposity

and the low sensitivity is a potential limitation25, nevertheless the BMI index is a

convenient, easy-to-interpret barometer for evaluating obesity26 and the most widely used

nationally and internationally.27

Because of studies linking visceral adipose to health risks and metabolic

disorders such as hyperlipidemia, hyperinsulinemia, glucose intolerance and insulin

resistance,28several health and medical professionals contend that waist circumference

measurements should additionally be used when calculating adiposity because of its

capability of estimating visceral adiposity. Savva et al. (2000) compared BMI, waist

circumference and waist-to-height ratio (WHtR) as predictors of cardiovascular disease

risk factors in children, specifically, high blood pressure, lipid and lipoprotein plasma

levels. Their findings concluded that waist circumference and WHtR measurements are

better predictors of the presence of cardiovascular risk factors than BMI. Results from

regression analysis revealed that waist circumference was the best predictor of all three

risk factors. Following waist circumference measurement, WHtR measurement was the

next best determinant while BMI was the least. Although BMI adequately predicts high

blood pressure, it fails to predict lipid and lipoprotein levels. Elevated blood lipid and

lipoprotein levels can evolve into hyperlipidemia and are considered to be significant

contributing risk factors for cardiovascular disease (CVD). A failure to predict these risk

factors could result in serious, and perhaps deadly, health consequences later on. Other


studies similarly conclude that waist circumference is more efficient in predicting health

risks, specifically cardiovascular disease, stroke, elevated blood lipids, hypertension, and

metabolic disorders, than BMI.29

Another popular method of determining adiposity is the skinfold thickness

measurement. Skinfolds are compressed subcutaneous fat that can be measured by

calipers at designated areas, specifically, triceps, subscapular and suprailiac sites.30

Researchers conclude that skinfold thickness is predictive of overall adiposity, especially

in children and adolescents.31 However, some research suggests that skinfold thickness is

also a predictor of certain risk factors. In a cross sectional analysis of boys between the

ages of 10 and 15 years old, Morrison et al. (1999) concluded that overweight boys had

greater skinfolds, lower HDL, higher LDL and triglyceride levels, and higher blood

pressure (both systolic and diastolic). While non-invasive and a good barometer of

general adiposity, some contend that skinfold testing is no longer the most optimal form

of measurement due to potential increase for human error when taking the

measurements.32 It is impossible to ensure that every physician and healthcare personnel

taking these measurements will place the calipers in the exact location on every person

every time. There is simply a level of human variability that cannot be escaped.

A relatively recent addition to the adiposity determination assessment toolbox is

dual energy x-ray absorptionmetry (DXA). Traditionally used to determine bone density,

DXA has recently been used to determine adiposity by differentiating body weight into

bone mineral, lean soft tissue and fat soft tissue masses.33 While touted by some to be a

quick and accurate method of determining adiposity, it requires a full body x-ray scan

which subjects are not quite as enthusiastic about especially on young children. Add to


that a cost of $25,000 - $30,000 per unit and it becomes less desirable.

The last method of determining adiposity discussed is hydrostatic weighting

(a.k.a. densitometry34). This is one of the most accurate methods of determining overall

adiposity however, like BMI it does not predict potential or existing health risk factors.

While the accuracy is within an impressive 1.5% according to Georgia State University’s

Department of Kinesiology and Health Body Composition (2011), 35 it is also one of the

most costly and least convenient forms of determining body fat percentage. It is based

upon Archimedes’ principle of water displacement and requires submersion in a

hydrostatic tank that is both expensive and difficult to find. From a clinical perspective

this is obviously a more preferable method of determining true adiposity because of the

accuracy, but unfortunately one that does not translate into real life applications.

Additionally, it does not delineate between visceral and peripheral adipose tissue which is

crucial when analyzing potential health risk factors.

Children have traditionally been measured using pediatric growth charts that

factor weight, length, age and head circumference, however, these were not standardized

until 1977, with modifications made in 1978 and 2000. This creates a challenge when

estimating child overweight and obesity rates and prevalence prior to 1977. In 1977, the

National Center for Health Statistics (HCHS) Growth Chart Task Force researched and

constructed the 1977 HCHS growth charts for children 2-18 years and alternative charts

for children birth to 36 months.36 These growth charts were based upon aggregate data

collected in the National Health Examination Survey (NHES) Cycle II and Cycle III, the

National Health and Nutrition Examination Survey (NHANES) 1971-74 for children ages

1-18 years, and data from the Fels Research Institute from 1929-1975. The NHANES is


a national survey that began in the 1960’s. Survey data consists of interviews including

demographic, socioeconomic, dietary, and health-related questions as well as a physical

examination that includes medical, dental, physiological measurements and laboratory

tests. In 1978, these growth charts were modified to allow for standard deviation

calculations and were adopted for use by the World Health Organization (WHO). 37 One

admitted flaw of utilizing the Fels Institute’s longitudinal study data in creating the

standardized growth charts was its biased infant-toddler sample.38 While comprehensive

in duration (46 years), the Fels Institute data was a single longitudinal study of white,

middle class infants that were primarily formula fed and resided in the small geographic

town of Yellow Springs, Ohio, not exactly a representative sample of the United States.

Another limitation of the 1977 and 1978 growth charts is that they excluded extreme

percentile ranges and limited the ability to analyze and chart anything beyond the 5th and

97th percentiles which would include those children who were extremely underweight as

well as those who were extremely overweight/obese. Nevertheless, this data in

combination with the NHES and NHANES, became the predominant guidelines

regarding healthy growth/weight curves for children.

In 2000, an expert panel from the Centers for Disease Control and Prevention

convened to review and revise the standardized growth charts. The results were the 2000

CDC Growth Charts that included the addition of the age and gender specific Body Mass

Index (BMI) for children 2-20 years as well as addition of 3rd and 97th percentiles. A

criticism of the growth charts has been that they do not provide standards for growth

patterns of healthy children but rather are a set of approximated percentiles based upon

national survey data.39 Just because there is an identified trend does not necessarily mean


it is a beneficial one, healthy one or one to aspire to. These critics maintain that these

trends should not translate into national health measurement standards. Several advisory

groups have recommended using BMI as a barometer for measuring adolescent adiposity.

40 The 2000 CDC Growth Charts classify children with a BMI ≥ 85th percentile as “at

risk of overweight” and those children with a BMI ≥ 95th percentile are classified as

“overweight”. However, in 2005, the Institute of Medicine issued a report that conveyed

the “seriousness, urgency and medical nature of childhood obesity, as well as the need to

take action.”41 One subsequent action step was the redefinition (or reclassification) of

children with a BMI ≥ 95th percentile as “obese” instead of “overweight”. Two years

later, a committee of pediatric and health experts not only endorsed the Institute of

Medicine’s recommendation, but further suggested that children with a BMI ≥ 85th

percentile (but < than 95th) be classified as “overweight” instead of “at risk of

overweight”. They noted that “at risk” is vague at best and inaccurate at worst. Because

there are no BMI references for children birth to 2 years of age, weight classification is

based on the weight-for-length/stature index. Children birth to two years with a weight-

for-length > 95th percentile are currently considered “overweight”.42

OBESITY TRENDS & PREVALENCE

In a 2005 press release, the World Health Organization (WHO) estimated that

there were one billion people globally who were overweight and/or obese and that, if the

rate continued as current trends predicted, that number would grow to 1.5 billion people

by 2015.43 The world’s current estimated population is 6.8 billion, thus according to

WHO estimates currently one out of every seven people (14%) are overweight and/or


obese.44 This is astonishing given that 1980 global obesity rates were 5% of men and 8%

of women. In roughly 30 years, rates almost tripled for men and doubled for women

globally. In the 2005 WHO estimates, over 75% of women over the age of thirty were

overweight in countries such as Egypt, Barbados, Malta, Mexico, Turkey, South Africa

and the United States. Similar rates for men were found in the nations of Germany,

Argentina, Greece, Kuwait, New Zealand, Samoa and the United Kingdom. A recent

report (2011) in the United Kingdom estimates that if current trends continue, by 2030

obesity rates among British men and women will be 40-48% and 35-43% respectively.

Similarly, they predict by that time 50% of all individuals in the United States will be

obese, not overweight, obese!45

In a 2007 meta-regression analysis of obesity in the United States, Drs. Youfa

Wang and May Beydoun from Johns Hopkins Bloomberg School of Public Health

predicted that by 2015, an alarming 75% of adults will be overweight and/or obese and

41% will be obese.46 These researchers analyzed trends and disparities among the

participants of the NHES and NHANES surveys with respect to gender, age, socio-

economic status (SES), geographical differences (urban vs. rural), race, ethnicity and

education however, for the purpose of this research further analysis of those findings

outside of age and gender disparities will be reserved for another time.

They compared data from the National Health Examination Survey (NHES I)

1960-1962 and the National Health and Nutrition Examination Surveys (NHANES) for

1971-1974 (NHANES I), 1976-1980 (NHANES II), 1988-1994 (NHANES III), 1999-

2000 (NHANES) and 1999-2002 (NHANES). They defined overweight as having a BMI

of ≥ 25 and obesity as a BMI ≥ 30. At the time of NHES I (1960-1962); 49.5 % of men


were overweight while 40.2% of women were overweight thus combined, 44.8% of men

and women over the age of 20 years were overweight. Those numbers increased slightly

by the NHANES II (1976-1980) to 52.9% for men and 42.0% for women for a

cumulative total of 47.4% for men and women. The most significant increases occurred

between the NHANES II (1976-1980) to the NHANES III (1988-1994), and the

NHANES III (1988-1994) to the NHANES (1999-2000) with an 8.6% increase and 8.5%

increase respectively. [Fig.1]

Similar trends were found in the obesity category. The NHES I (1960-1962) data

reveal 13.3% of adults were obese; 10.7% of men were obese and 15.7% of women were

obese. Collectively (men and women), there was a slight increase to 14.6% by the

NHANES I (1971-1974), and another slight increase to 15.1% by the NHANES II (1976-

1980). However, there was a substantial increase to 23.3% (an 8.5% increase) by the

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

NHES I 1960-‐1962

NHANES I 1971-‐1974

NHANES II 1976-‐1980

NHANES III 1988-‐1994

NHANES 1999-‐2000

NHANES 1999-‐2002

U.S. Adult Overweight Rates 1960-‐2002

Men

Women

Combined

Fig 1. U.S. Adult Overweight rates 1960-‐2002. Data Source: Wang and Beydoun (2007) National Health Examination Survey (NHES I) and National Health and Nutrition Surveys (NHANES) I-‐III and NHANES 1999-‐2000, NHANES 1999-‐2002. Graphic created by author.


NHANES III (1988-1994) and another significant (7.6%) increase to 30.9% by the

NHANES (1999-2000). By 2000, 30% of all adults were not merely overweight but

obese. Interestingly, women across all surveys had a higher percentage in the obesity

category (BMI ≥ 30) than men, whereas men had a higher percentage in the overweight

category (BMI ≥ 25) category than women. [Fig.2]

What was not analyzed and should be looked at in further studies is the

breakdown of these BMI ranges. For example, of the NHES I 13.3% obesity rates, were

the majority of individuals hovering around the 31-32 range or were they in the 40-45

range? Has the degree of adiposity increased as well as the prevalence? Similarly, is

there a greater prevalence of extreme or morbid obesity now than twenty, thirty or forty

years ago? Recently, morbid obesity has been added as a subcategory of obesity, this

refers to a BMI ≥ 40 and/or the individual is 100 pounds overweight. Unfortunately this

will be difficult to ascertain, as BMI specificity was not a category of the NHANES

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

NHES I 1960-‐1962

NHANES I 1971-‐1974

NHANES II 1976-‐1980

NHANES III 1988-‐1994

NHANES 1999-‐2000

NHANES 1999-‐2002

Adult Obesity Rates 1960-‐2002

Men

Women

Combined

Fig 2. U.S. Adult Obesity rates 1960-‐2002. Data Source: Wang and Beydoun (2007) National Health Examination Survey (NHES I) and National Health and Nutrition Surveys (NHANES) I-‐III and NHANES 1999-‐2000, NHANES 1999-‐2002. Graphic created by author.


surveys.

The health implications of this global epidemic are alarming. Dr. Catherine

LeGalès Camus, WHO Assistant Director-General of Non-communicable Disease and

Mental Health, warns, “The sheer magnitude of the overweight and obesity problem is

staggering. The rapid increase of overweight and obesity in many low and middle

income countries foretells an overwhelming chronic disease burden in these countries in

the next 10 to 20 years, if action is not taken now.”47 Currently, WHO estimates that

over 2.6 million people die each year as a result of being overweight and/or obese.48

Sadly, this epidemic does not exclude some of the most vulnerable and least self-

sufficient…our children.

PEDIATRIC OBESITY TRENDS & HEALTH IMPACTS

In 2010, an appalling WHO estimate stated that over 42 million children under

the age of five were overweight; of these, 35 million were children living in developing

nations.49 What used to be an isolated condition affecting the affluent, obesity has

traversed economic boundaries and proliferated the homes of the middle-class as well as

the poor and economically disadvantaged. Sharron Dalton, Associate Professor in the

Department of Nutrition, Food Studies and Public Health at New York University states,

“The forces of globalization have put these relatively cheap foods and drinks- high in

calories, low in nutrients-within reach of almost anyone, anywhere, giving childhood

obesity a foothold in even the poorest countries.”50 Dalton boldly contends that,

“childhood obesity is arguably the most pervasive and serious threat to children’s health

today.”51


Obesity is associated with many significant health problems plaguing both adult

and pediatric populations. Sadly, what were once considered “adult” diseases and

disorders are becoming increasingly prevalent among children and adolescent

populations. Obesity-related cardiovascular conditions now affecting children and

adolescents include cardiovascular disease (CVD),52 hypertension,53

hypercholesterolemia, and dyslipidemia.54 A study that has provided a wealth of

knowledge and clinical insight into cardiovascular disease and children is the Bogalusa

Heart Study.

Sponsored by the National Heart, Lung, and Blood Institute (NHLBI), the

Bogalusa Heart Study (1972-2002) was and is the longest biracial study of children

investigating the early natural history and etiology of cardiovascular disease and

hypertension.55 Conducted by Tulane University School of Medicine, the study consisted

of all children and young adults, approximately 22,000 subjects, residing in the town of

Bogalusa Louisiana.56 Data surveys were conducted in 1973-74, 1976-77, 1978-79, 1981-

82, 1988-89 and 1988-2991 and consisted of anthropometric data (height, weight, length

of body segments and body segment masses), health history, hemoglobin, blood pressure,

serum lipids and lipoprotein levels, skinfold thickness, heat rate, salt intake, smoking,

alcohol use and dieting habits.57 Two parallel cohorts of children ages 7 to 9 years old

were identified, one in 1973 and the other in 1984, and reexamined throughout the

duration of the study into adulthood. The study clearly revealed that the etiology for

CVD, hypertension, and atherosclerosis begins in childhood. Freedman et al. (2004)

were specifically interested in data from the Bogalusa Heart Study surrounding the

relationship between BMI, skinfold measurements and adult adiposity. Analysis


confirmed their presumptions that child BMI and triceps skinfold measurements are

positively associated with adiposity later in life. They also found skinfold measurement

to have a slightly stronger association with adult adiposity than did BMI, nevertheless

they concluded that both were reliant predictors of adiposity into adulthood.58

In addition to cardiovascular health risks, conditions such as non-insulin-

dependent diabetes mellitus (NIDDM or type 2 diabetes mellitus), insulin resistance and

hyperinsulinemia are also prevalent among overweight and obese children. Endocrine

system health is also adversely affected, reproductive health (menstrual irregularities),59

mental health (depression, oppositional defiant disorder),60 musculoskeletal health61 and

sleep and respiratory health (sleep apnea, asthma)62 are other areas adversely affected by

excessive adiposity.

Since 1980, the number of overweight adolescents has tripled.63 Today, one in

three American children are overweight or at risk of becoming overweight and/or obese,

that is one third of the adolescent population. Dr. Cynthia Ogden and Margaret Carroll

(2010) analyzed the National Health Examination Surveys (NHES) and National Health

and Nutrition Examination Surveys (NHANES) with respect to pediatric and adolescent

obesity. Their findings were similar to those of Wang and Beydoun. Based on expert

committee recommendations and the 2000 CDC BMI-for-age-growth charts, Ogden and

Carroll’s obesity cutoff criteria were individuals who were ≥ 95th percentile of the sex-

specific BMI-growth charts.64 Their analysis of the NHES II (1963-1965) & III (1966-

1970) and NHANES I (1971-1974), NHANES II (1976-1980), NHANES III (1988-1994)

and NHANES 1999-2000, 2001-2002, 2003-2004, 2005-2006 and 2007-2008 concluded

that for children 2- 5 years of age obesity more than doubled between 1976-1980 and


Fig 3b. Obesity rates among U.S. Children and adolescents. Data Source: Ogden et al. (2010) National Health Examination Surveys II (ages 6-‐11) III (ages 12-‐17) and National Health and Nutrition Surveys (NHANES) I-‐III and NHANES 1999-‐2000, 2001-‐2001, 2003-‐2004, 2005-‐2006, 2007-‐2008. Graphic created by author.

2007-2008 increasing from 5.0% to 10.4% respectively. Similarly, among children 6-11

years of age the rate tripled from 6.5% (1976-1980) to 19.6% (2007-2009). For

adolescents aged 12-19, the percentage of those that were obese tripled as well, from

5.0% (1976-1980) to 18.1% (2007-2008).65 [Fig. 3a & 3b]

Fig 3a. Obesity rates among U.S. Children and adolescents. Data Source: Ogden et al. (2010) National Health Examination Surveys II (ages 6-‐11) III (ages 12-‐17) and National Health and Nutrition Surveys (NHANES) I-‐III and NHANES 1999-‐2000, 2001-‐2001, 2003-‐2004, 2005-‐2006, 2007-‐2008. Graphic created by author.

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0

1971-‐1974 1976-‐1980 1988-‐1994 1999-‐2000 2001-‐2002 2003-‐2004 2005-‐2006 2007-‐2008

NHANES NHANES NHANES NHANES NHANES NHANES NHANES NHANES

Percen

tage

Prevalence of Obesity among U.S. Children 2-‐19 years

Total (2-‐19 yrs.)

0.0

5.0

10.0

15.0

20.0

25.0

Percen

tage th

at are Obe

se

Prevalence of Obesity among U.S. Children

Total (2-‐19 yrs.)

2-‐5 yrs.

6-‐11 yrs.

12-‐19 yrs.


They also discovered significant ethnic and gender disparities. In NHANES III

(1988-1994), there was not a significant difference between Mexican-American and non-

Hispanic White boys, 14.1% and 11.6% respectively (although some would argue

regarding health conditions 3% is significant enough). However, by NHANES 2007-

2008 those rates has increased to 26.8% and 16.7% respectively; Mexican-American

boys had a 61% greater prevalence of obesity than non-Hispanic White boys. While not

highlighted in their analysis, the data also showed a significant increase in the prevalence

of obesity in non-Hispanic Black boys. In 1998-1994, non-Hispanic Black boys had the

lowest prevalence of obesity at 10.7% (compared to non-Hispanic White boys at 11.6 and

Mexican-American boys at 14.1%). However, by NHANES 2007-2008, while still

trailing behind Mexican-American boys (26.8%), non-Hispanic Black boys (19.8%) had

surpassed non-Hispanic White (16.7%) boys by three percentage points. [FIG. 4]

Among adolescent girls, the trend between Mexican-American girls and non-

Hispanic Black girls was reversed. In NHANES III (1988-1994), the prevalence of

obesity among adolescent girls was 8.9% among non-Hispanic White girls, 16.3% among

non-Hispanic Black girls, and 13.4% among Mexican-American girls. By NHANES

2007-2008, those rates had increased to 14.5%, 29.2% and 17.4% respectively. [FIG. 5]

The prevalence of obesity among non-Hispanic Black girls increased an alarming 80%

between 1988-1994 and 2007-2008. While there was an overall lower prevalence of

obesity among non-Hispanic White boys and girls, Mexican-American boys and non-

Hispanic Black girls had the highest prevalence of obesity among all ethnicities.

Between these two reporting periods, non-Hispanic White boys and Mexican-American

girls had the lowest percentage point increase, 5.1 and 4.0 respectively; non-Hispanic


Black girls and Mexican-American boys had the highest percentage point increase of

12.9 and 12.7 respectively. [FIG 6] Given cultural and ethnic differences this data is

fascinating as it is usually presumed/assumed that trends occur within ethnicities

collectively. For example, Asians typically have smaller frames than Samoans. The

significant discrepancies of obesity prevalence between genders of the same ethnicity

warrants future research.

Fig 4. Percentage of obesity in U.S. boys between 1988-‐1994 and 2007-‐2008 categorized by ethnicity. Data Source: Ogden et al. (2010) National Health Examination Surveys II (ages 6-‐11) III (ages 12-‐17) and National Health and Nutrition Surveys (NHANES) I-‐III and NHANES 1999-‐2000, 2001-‐2001, 2003-‐2004, 2005-‐2006, 2007-‐2008. Graphic created by author.

0

5

10

15

20

25

30

non-‐Hispanic White boys

non-‐Hispanic Black boys

Mexican American boys

Prevalen

ce of o

besity

Prevalence of Obesity in U.S. boys 1988-‐1994 and 2007-‐2008

1988-‐1994

2007-‐2008


0

2

4

6

8

10

12

14

non-‐Hispanic white non-‐Hispanic black Mexican American

Percen

tage points

Percentage Point increase of obesity prevalence between 1988-‐1994 and 2007-‐2008

Boys

Girls

It is estimated that one third of obese preschool children and half of obese

school-age children will become obese adults66 putting them at risk for developing

serious and sometimes fatal health conditions such as: asthma, cardiovascular disease

0 5 10 15 20 25 30 35

non-‐Hispanic White girls

non-‐Hispanic Black girls

Mexican American girls

Percen

tage of o

besity

Prevalence of Obesity in U.S. girls 1988-‐1994 and 2007-‐2008

1988-‐1994

2007-‐2008

Fig 6. Percentage of increase of obesity in U.S. Children and adolescents between 1988-‐1994 and 2007-‐2008 categorized by ethnicity. Data Source: Ogden et al. (2010) National Health Examination Surveys II (ages 6-‐11) III (ages 12-‐17) and National Health and Nutrition Surveys (NHANES) I-‐III and NHANES 1999-‐2000, 2001-‐2001, 2003-‐2004, 2005-‐2006, 2007-‐2008. Graphic created by author.

Fig 5. Percentage of obesity in U.S. girls between 1988-‐1994 and 2007-‐2008 categorized by ethnicity. Data Source: Ogden et al. (2010) National Health Examination Surveys II (ages 6-‐11) III (ages 12-‐17) and National Health and Nutrition Surveys (NHANES) I-‐III and NHANES 1999-‐2000, 2001-‐2001, 2003-‐2004, 2005-‐2006, 2007-‐2008. Graphic created by author.


(CVD), hyperlipidemia, high cholesterol, hypertension/high blood pressure, high

cholesterol, stroke, type 2 diabetes (NIDDM), musculoskeletal disorders such as

osteoarthritis, depression, anxiety, behavioral problems, menstrual irregularities, sleep

disorders and even certain cancers.67 In a follow up study of the Harvard Growth Study of

1922 to 1935, researchers from the USDA Human Nutrition Research Center on Aging at

Tufts University (Must et al., 1992) concluded that mortality from coronary heart disease,

atherosclerosis, stroke and colorectal cancer was greater among adult men who were

overweight adolescents. Similarly, women who were overweight adolescents were eight

times more likely to have difficulty with “activities of daily living” such as climbing

stairs and lifting and had a higher incidence of arthritis than women who were not

overweight in their adolescents.68 These health risks and subsequent financial costs will

be discussed later in detail.

Using multinomial logistic regression models of the Early Childhood

Longitudinal Study-Birth Cohort data [ECLS-B, a national study that provides data on

children’s status birth through kindergarten] for children 9 months of age and 2 years of

age, researchers Brian Moss and William Yeaton (2011) estimate that one third of

children in the United States were either at risk or obese at nine months age (31.9%) and

at two years of age (34.3%).69 Other studies have confirmed a positive correlation

between weight gain within the first few months of life and adult overweight and

obesity.70 In addition, some research reveals that the age at which excess adiposity begins

has a significant impact on adiposity in adolescence and adulthood bringing with it

subsequent health risks. 71 In a 2000 cohort study, Ekelund et al. analyzed a ten percent

sample of the 14,000 participants of the 1991-92 Avon Longitudinal Study of Pregnancy


and Childhood (ALSPAC) to determine postnatal catch-up growth and its correlation

with obesity at five years old. Catch-up growth refers to a rapid growth rate (i.e. weight

gain) that occurs between the first and second year of life. Children who experienced

catch-up growth in either weight or length between the years of zero and two years of age

were heavier and taller at five years of age than other children who had not experienced

any “catch-up” growth. In addition, these children had a greater body mass index, higher

body fat percentage, higher total fat mass and higher central fat distribution (i.e. visceral

fat) than other children thus putting them at an increased risk of developing certain health

and metabolic risks later in adulthood and young adulthood.

A typically developing child will steadily gain body mass (i.e. weight) from birth

until the period of time between the ages of 4 to 7 years old. During this phase of

growth, adipose tissue (both size of and number of adipocytes) increases at a steady rate

and then levels out. At the 4 to 7 year benchmark, the rate of adipose tissue growth

diminishes but the height growth continues thus resulting in a sudden decline in weight

and body fat to a minimum set point. Upon reaching this set point the child will then

resume gaining weight into adulthood where it will [hopefully] stabilize at yet another set

point. The descending set point is called the “adiposity rebound” as it (adipose tissue

growth) suddenly but expectedly drops and then rebounds upward.72 [Illus.1]

Set point of adiposity rebound

Set point

Age of rebound: 1 2 3 4 5 6 7 8 9 1 0

Illus. 1 Author’s Schematic.


What is of great interest to researchers is when the rebound occurs. In a

longitudinal study of 151 children, Rolland-Cachera et al. (1984) found a positive

correlation between the age of adiposity rebound and degree of adiposity later in life.

They determined that an early adiposity rebound (< 5.5 years old) is followed by a

“significantly higher” adiposity level later on than that of a later occurring adiposity

rebound (< 7 years old). The results from their 1984 study concluded that 1) children

obese at one year old who experience an early adiposity rebound will remain obese, 2)

children obese at one year old who experience a late adiposity rebound will eventually

join the “average” group, 3) children who are not obese at 1 year old who experience a

normal or delayed rebound will remain average or even a lower weight and 4) children

who are not obese at one year old who experience an early or “advanced” rebound will

reach their higher percentiles and their overweight will be detected later in their

adolescence. Whitaker et al. (1998) also demonstrated a clear correlation between early

adiposity rebound and higher BMI and obesity rate in young adulthood. They further

concluded that the increased risk of adult obesity associated with an early adiposity

rebound is independent of both the BMI at the time of the rebound and parent obesity.

This suggests that the age (time) that the rebound occurs is a better predictor and/or

bigger risk factor for adult overweight/obesity than the child’s BMI or even the parents’

weight status.

Researchers (Gordon-Larsen et al., 2009) examined the incidence and trends of

obesity among 12-21 year old adolescents during a 12-14 year period into their 20’s and

30’s. Data was obtained from the National Longitudinal Study of Adolescent Health and


consisted of a sample size of nearly 21,000 youth. Gordon-Larsen et al. discovered that

13.3% of all adolescents were obese in 1996; by 2008, that number had surged to 36.1%.

In addition, 90% of those individuals who were obese as adolescents remained obese into

their 30’s.73 Studies have clearly shown a significant correlation between childhood

overweight/obesity and adult overweight/obesity. Simply, children who are overweight

and/or obese have a greater chance of remaining overweight into adulthood than do non-

overweight/obese children bringing with them substantial health and economic costs.

ECONOMIC COSTS OF OBESITY

Given the preponderance of medical conditions resulting from excess

adiposity, that often- if not always- require medical treatment, the economic burden of

obesity has become evident. In addition to the adverse health consequences, there are

also adverse economic consequences of the increasing obesity epidemic. The economic

burden of health care costs for the treatment of obesity-associated diseases is a tangential

epidemic whose rise and acceleration parallels the obesity trajectory. Unlike smoking or

alcohol consumption, there are very few studies that attempt to quantify the economic

costs associated with the obesity epidemic. In the 2000 Report of Consultation, the

World Health Organization (WHO) reported that the total economic cost of obesity is

comprised of three components: direct costs, opportunity costs and indirect costs. Direct

costs are defined as the cost to the individual as well as the cost to the service provider

treating the individual; in short, the medical costs. Opportunity costs refer to the social

and personal loss associated with obesity and obesity related diseases primarily resulting

from premature death and/or disability. Finally, indirect costs refer to the


workplace/workforce loss due to diminished production resulting from absenteeism or

premature death. WHO’s analysis of global economic costs of obesity range from 2-7%

of total health care costs in developed nations.74 In 1992, Dr. Graham Colditz, an

epidemiologist and Associate Director for Prevention and Control at the Alvin J. Siteman

Cancer Center, Washington University School of Medicine and Barnes-Jewish Hospital,

analyzed the economic cost of obesity in the United States for the year 1986. Colditz and

his team used a prevalence-based “cost of illness” analysis to determine the total cost of

five prevalent obesity-related illnesses: non-insulin-dependent diabetes mellitus

(NIDDM- also known as type 2 diabetes), cardiovascular disease (CVD), gall bladder

disease, hypertension and cancer (colon and postmenopausal breast cancer specifically).

Prevalence-based cost of illness analysis identifies the costs incurred by an individual

with a particular illness within a specific time frame irrespective of the severity or “stage”

of the illness. This approach is appropriate for estimating the cost of an illness and/or

disease on an annual basis versus estimating costs over the lifetime (or duration) of the

illness/disease, the latter estimate approach is an incidence-based analysis.75

According to Colditz and his team, $11.3 billion dollars, or 57%, of all costs

associated with NIDDM were directly attributed to obesity. With respect to CVD, they

discovered that among the obese, 70% of CVD was directly attributable to obesity and

that 19% of the total costs of CVD were attributable to obesity, equating to $22.2 billion

dollars. Least expensive were the associated costs of cancer, hypertension and

gallbladder disease. They estimated that 23% of all breast cancers and 42% of all colon

cancers were attributable to obesity. The estimated cost of breast cancer that was

attributable to obesity accounted for 1.4% of all cancer costs and estimated cost of colon


cancer attributable to obesity accounted for 1.1% of all cancer costs. Thus combined, the

2.5% of obesity-attributable cancer costs equaled $1.9 billion dollars. Hypertension and

gallbladder disease had economic costs of $1.5 billion and $2.4 billion respectively thus

bringing the cumulative cost of all five conditions to $39.3 billion dollars. In 1996, Wolf

and Colditz published revised estimates of economic costs of obesity and concluded that

approximately $68.8 billion dollars were spent on obesity-related diseases. 76 In just four

years, the costs of obesity related diseases increased $28.9 billion dollars; that is a 73.5%

increase in 48 months. Similar trends were found with adolescents specifically.

Drs. Guijing Wang and William Dietz (2002) analyzed the trend of obesity-related

diseases among youth (ages 6 to 17 years) and subsequent economic costs for the periods

1979-1981 and 1997-1999. Using data of the national Hospital Discharge Survey

(NHDS) collected by the National Center for Health Statistics, they analyzed the

incidence and hospitalization costs of obesity-related illnesses, specifically sleep apnea,

diabetes (NIDDM, type 2), obesity and gallbladder disease. Their research concluded

that both incidence of obesity related illness as well as the cost of treatment had increased

dramatically from 1979 to 1999.

The prevalence of NIDDM diabetes diagnoses increased from 1.43% (1979-1981)

to 2.36% (1997-1999), obesity diagnoses increased from 0.36% to 1.07%, sleep apnea

increased from 0.14% to 0.75% and gallbladder disease increased from 0.18% to 0.75%.

In addition to the increase in the number of diagnoses, the duration of hospitalization

increased as well. They estimated that the “total days of care” directly associated with

obesity increased from 152,000 in 1979-1981 to 310,000 in 1997-1999. They also

concluded that the percentage of total hospital costs for obesity related illnesses increased


as well, from 0.43% (1979-1981) to 1.70% (1997-1999). These estimates translate into

an economic cost of approximately $35 million dollars during 1979-1981 and $127

million dollars in 1997-1999, a more than threefold increase of $92 million dollars.

Wang and Dietz suggest that their estimates err on the side of being fiscally

conservative for several reasons. First, they did not include the financial costs associated

with medication(s), follow up physician visits etc. in their analysis, only inpatient

hospitalization costs. Second, only four obesity-related diseases were included in the

sample (sleep apnea, diabetes, obesity and gallbladder disease); there are other obesity-

related diseases requiring treatment that were not included in their research. Finally and

perhaps most significantly, they only looked at children and adolescents with a primary

or secondary ICD-9 diagnosis of “obesity”, citing that many individuals with obesity-

related illnesses may not have obesity listed as a primary or secondary diagnosis thus

being left out of the sample despite the obvious associated medical costs. Woo et al.

(2009) also issued caution when estimating inpatient utilization through ICD-9 diagnosis

codes, specifically with “obesity diagnosis”. Out of a sample of 29,352 discharges, 5989

children between the ages of 2 and 20 years had a BMI of ≥ 95% yet only 512 (1.7%) had

a diagnosis of obesity. They determined that research only using obesity diagnosis (i.e.

ICD-9 codes) “may significantly underestimate the magnitude of utilization and

economic impact of inpatients with BMI ≥ 95th percentile”, concluding that “using

impatient diagnoses of obesity in children greatly underestimates the total health care

utilization by obese children and misidentifies patterns of specific inpatient care in this

population.”77


CHAPT. 2- Sugar Intake and Metabolism

In order to understand how and why obesity rates are increasing we must first

understand the physiological process of metabolism and all the variables involved. Since

obesity is specifically related to adipose tissue (i.e. body fat), it is crucial to understand

how the body produces, utilizes and stores energy and what the mechanism(s) for storing

fat is. To answer these questions we must take a brief journey through the annals of

chemistry and human physiology and review the systems and processes involved in

digestion and metabolism. These two functions are symbiotic processes that are essential

for life and health.

The standardized definition of digestion is “the process of making food absorbable by

dissolving it and breaking it down into simpler chemical compounds that occurs in the

living body chiefly through the action of enzymes secreted into the alimentary canal.“78

At the risk of over simplification, foods are first digested (i.e. broken down into smaller

compounds) before they are metabolized for use throughout the body. Tortora et al.’s

physiology textbook defines metabolism as “an energy-balancing act between anabolic

(synthesis) and catabolic (degradative) reactions.”79 Anabolic reactions create (i.e. build)

complex molecules from simple substances conversely, catabolic reactions break down

complex molecules into simple ones. Catabolic reactions are necessary for energy

production as the breaking of bonds between molecules results in energy release in the

form of adenosine triphosphate (ATP). [This energy release will be discussed later in this

chapter.] As we look closer at the metabolic (i.e. anabolic and catabolic) processes that

occur during digestion, it will become evident that the foods we eat and beverages we


drink determine our health and in some instances our mortality.

Humans derive energy from external food that is ingested then subsequently

digested. Food molecules are comprised of six nutrient categories and perform one of

three primary functions: 1) supply energy for life sustaining functions such as DNA

replication, nerve impulse conduction, active transport, protein synthesis and muscle

contraction; 2) synthesis of other molecules such as hormones, enzyme and muscle

proteins; and 3) storage of energy for future use. 80 The latter is the focus of attention for

this research. The six categories of nutrients are proteins, lipids (fats), carbohydrates,

vitamins, minerals and water. Although all of these nutrients are essential for life, since

carbohydrates and fats are directly involved in energy production and fat storage, they

will be the primary focus of this chapter. Proteins, lipids and carbohydrates are

catabolized in the gastrointestinal tract into their primary building blocks: amino acids,

fatty acids, glycerol, monoglycerides, and monosaccharides. Once in their primary form,

these compounds are then metabolized at a cellular level.

PROTEINS

Proteins are complex organic compounds containing carbon, hydrogen, oxygen,

nitrogen and in some cases, sulfur. They have a variety of functions in the body. Some

proteins have a structural function such as building and maintenance of muscle tissue,

connective tissue (collagen), and skin, hair and fingernails (keratin). Some proteins

provide a transportation function such as hemoglobin, a protein that is responsible for

transporting oxygen (O2) and carbon dioxide (CO2) in the blood. Then there are proteins

that have a regulatory function such as insulin and parathyroid hormones, or an

immunological function such as various antibodies.81 The primary building blocks of


proteins are amino acids. For any kind of utilization in the body, proteins must be

catabolized into smaller peptides (dipeptides and tripeptides) and amino acids for

absorption to occur. When amino acids link together the attachment juncture, or the bond

adjoining them together, is called a peptide bond. When two amino acids combine the

result is a dipeptide molecule, when three amino acids combine the result is a tripeptide

molecule, and when four or more amino acids combine the result is a polypeptide

molecule. Dehydration synthesis is an anabolic process in which two molecules

combine to create a larger molecule, during this process a water molecule (H2O) is

released. In the example below, two amino acids undergo dehydration synthesis and

form a dipeptide molecule and a molecule of water. [Illus. 2]

This process in reverse is the catabolic process of hydrolysis whereby water (H2O) [often

in tandem with enzymes] is utilized to break chemical bonds of molecules reducing them

into smaller molecules. Hydrolysis is the predominant process utilized in digestion.

Below is an illustration of hydrolysis of a dipeptide. [Illus.3]

Dipeptide Amino Acid

Dehydration Synthesis Amino Acid

H2O

Dipeptide Amino Acid Hydrolysis (H2O) Amino

Acid




When food is first chewed, salivary glands excrete saliva that contains the α-

amylase enzyme ptyalin and thus begins the digestive process. Once the masticated food

(protein) enters the stomach, the enzyme pepsin begins to breakdown the large protein

molecules into the smaller molecules: polypeptides, peptones and proteoses. As the

digestion process continues, these molecules enter the small intestine where they combine

with the pancreatic enzymes trypsin, chymotrypsin and carboxypoly-peptidase (a.k.a.

carboxypeptidase). These enzymes catabolize the polypeptides peptones, and proteoses

into smaller polypeptides and amino acids. These smaller molecules continue to travel

through the intestinal tract brushing up against the epithelial cells of the intestinal lumen.

Microvilli line intestinal epithelial cells comprising the “brush border” and contain the

digestive enzymes aminopolypeptidase and dipeptidase. As the polypeptides and amino

acids brush along these microvilli, the peptidase enzymes breakdown the polypeptides

into smaller dipeptides, tripeptides and amino acids. These dipeptides, tripeptides and

amino acids are then able to enter the epithelial cells where the final stage of digestion

occurs. Inside the cytosol of the epithelial cell, peptidases break down the dipeptides and

tripeptides into amino acids that are then able to enter the bloodstream for utilization. A

similar process occurs for the digestion and metabolism of carbohydrates.

CARBOHYDRATES

While carbohydrates have been demonized in recent years (spawning the low-carb,

no-carb diet craze), they are essential to life [for example, 2-deoxyribose is a

carbohydrate sugar that forms the backbone of deoxyribonucleic acid (DNA)] and

metabolic processes. A carbohydrate is an organic compound consisting of carbon,

hydrogen and oxygen molecules in with a hydrogen-carbon ration of 2:1. Carbohydrates


are divided into three principle groups: monosaccharides (a.k.a. simple sugars),

disaccharides (two sugars), and polysaccharides (a.k.a. starches). While many

monosaccharides exist, the monosaccharides involved in metabolic processes that are of

great relevance to this research specifically are glucose and fructose (others include

galactose, ribose and deoxyribose). Disaccharides are two monosaccharides joined

together, again via dehydration synthesis, to comprise a larger sugar molecule and a

subsequent molecule of water. Just like the formation of dipeptides discussed earlier,

monosaccharides combine to form disaccharides. When the monosaccharides fructose

(C6H12O6) and glucose (C6H12O6) undergo dehydration synthesis the result is a

disaccharide sucrose (C12H22O11) molecule (table sugar) and a water (H2O) molecule.

[Illus. 4]

Note that while fructose and glucose share the same number of carbon, hydrogen and

oxygen atoms, their structural composition is different thereby resulting in two different

molecules. The diagram below illustrates this slight, yet monumental difference. [Illus. 5]

Glucose (C6H12O6) Fructose (C6H12O6)

Sucrose C12H22O11

Glucose C6H12O6

Dehydration Synthesis Fructose C6H12O6

H2O

CH2OH I C I H H

I C I OH

H I C I OH

OH I C I H

H I C I HO

O CH2OH I C I H

H I C I OH

HO I C I H

HO I C I CH2OH

O


Illus. 5 Author’s schematic of glucose and fructose molecules reference from Whitney et al. and Tortora et al.



We will discover later that these structural differences, while seemingly inconsequential,

yield significant differences in the body, especially with respect to metabolism and

energy production. The difference between these two molecules is an exemplary

example of how each and every individual molecule- down to the placement of individual

atoms- carries an important role in the cellular function of the body and the chemical

reactions that result.

Returning to disaccharides, sucrose, maltose and lactose are the most common

disaccharides in the body. Disaccharides are too large to pass through the cell membrane

wall and must be hydrolyzed into the monosaccharide form in order to be absorbed into

the blood stream. Again, hydrolysis is a catabolic reaction that utilizes water (H2O) to

break chemical bonds thereby releasing chemical energy in the form of single (simple)

sugars. Using the previous example, when a disaccharide sucrose molecule hydrolyzes,

the result is two monosaccharides: a fructose molecule and a glucose molecule. [Illus. 6]

These monosaccharides are metabolized for cellular fuel, however the primary

monosaccharide unit is not the only unit of fuel. Polysaccharides are long chains of

monosaccharides linked together, and like disaccharides, polysaccharides must be broken

down into the primary monosaccharides for cellular absorption. These large

carbohydrates can contain from tens to hundreds of monosaccharides and their large

molecular shapes are ideal for storing energy. Glycogen is one such polysaccharide.

Glycogen is the most abundant polysaccharide in the body; comprised of many glucose

Sucrose C12H22O11

Glucose C6H12O6

Hydrolysis (H2O) Fructose C6H12O6


molecules, glycogen is the body’s principle energy storage unit. Because of its role in

energy storage, glycogen’s metabolic process will be discussed in detail shortly.

DIGESTION & METABOLISM

As mentioned previously during the section on protein digestion, the first step of

digestion involves the salivary α-amylase enzyme ptyalin. Secreted by the parotid

glands, ptyalin breaks down food starches into disaccharides and other glucose polymers.

[A polymer is a molecule comprised of many molecules of similar structure called

monomers]. Because food remains in the oral cavity for a relatively short time (one to

three minutes depending upon how thorough the mastication is), only a small percentage

of the carbohydrates overall are broken down into disaccharides. Once swallowed, the

food bolus enters the stomach where it remains for approximately an hour mixing with

gastric secretions. The high acid pH of the stomach further digests the food and almost

40% of the starches are hydrolyzed into maltose and other glucose polymers by the time

the food substance is ready to exit the stomach. From the stomach, the food enters the

small intestine where it combines with the α-amylase pancreatic enzyme; this enzyme is

compositionally similar to salivary α-amylase but more potent. Within 30-40 minutes,

all of the starches have been hydrolyzed into disaccharides and other glucose polymers.

However, a disaccharide molecule is still too large to pass through the epithelial

membrane and must be broken down further into readily absorbable components,

specifically monosaccharides. The epithelial cells of the small intestine contain the

enzymes lactase, sucrase, maltase and α-dextrinase; these enzymes are all capable of

degrading disaccharides into their primary monosaccharide units. As the disaccharides


brush against the epithelial cells lining the intestinal lumen, the enzymes hydrolyze the

disaccharides into primary monosaccharides. For example, the enzyme sucrase

hydrolyzes the disaccharide sucrose into the monosaccharides glucose and fructose.

These monosaccharides are absorbed into the capillary villi of the small intestine and

deposited into the liver via the hepatic portal vein where they are either used for

immediate energy or stored for future use.

While glucose and fructose are both monosaccharides and have the same

molecular/chemical formula, they are metabolized differently. In the epithelial cells of

the upper intestine, glucose is absorbed via active transport (technically, secondary active

transport of glucose) by a sodium-glucose co-transporter whereas fructose is absorbed

via facilitated diffusion further down in the duodenum and jejunum (lower intestines) by

way of a non-sodium dependent transporter process.82 It is believed that the carrier

glucose protein (i.e. transporter protein) has a receptor for a glucose molecule as well as a

sodium ion and that unless both receptors are simultaneously filled and activated the

transport process will not occur. Like glucose, fructose also requires a transporter carrier

(protein) to cross the cell membrane, however it does not require an additional sodium

ion for this diffusion to occur. Once inside the cell, glucose diffuses through the

basolateral membrane into the cytoplasm/extracellular fluid via facilitated diffusion and

is then released into the bloodstream. The difference in these transporter processes

affects the rate of absorption; in fact, fructose is absorbed half as rapidly as glucose is.

While both glucose and fructose require transporter proteins to facilitate diffusion across

the cell membrane, they differ in which protein is utilized. The transporter (carrier)

proteins are commonly referred to as the glucose transporter (GLUT) family. Several


GLUT transport proteins, GLUT1-4, can transport glucose but only GLUT5 can transport

fructose. The difference in the GLUT transporters leads to another fundamental

difference between the two monosaccharides: unlike glucose, fructose does not stimulate

insulin secretion which some surmise is because insulin producing β cells in the pancreas

do not contain the GLUT-5 transporter protein or receptor.83 Insulin plays a significant

role in blood sugar regulation, fat metabolism and food intake (functions that will be

discussed later) so its stimulation or lack thereof is an essential component of

metabolism.

GLUCOSE VERSUS FRUCTOSE METABOLISM

In the liver, hepatic cells convert most of the fructose and galactose into glucose,

which comprises approximately 90-95% of all monosaccharides in the body. Because of

its role in the adenosine triphosphate (ATP) energy cycle, glucose is considered the

primary source of cellular fuel in the body and is essential for energy production. ATP, a

compound present in every cell, is comprised of adenine, ribose and three (tri) phosphate

radicals; the phosphate radical bonds are “high energy” and subsequently release a lot of

energy when they are broken (12,000 calories of energy to be exact84). From the

bloodstream, glucose enters into the cells via facilitated diffusion. Integral carrier

proteins within the cell wall bind with the glucose molecule and transport it from the

exterior of the cell into the interior of the cell. Once inside the cell, glucose undergoes a

ten step degradation processes (e.g. glycolysis) ultimately resulting in the release of ATP

(i.e. energy) and pyruvate molecules. Glycolysis (a.k.a. the glycolytic pathway) is how

energy is produced and released in the body; it literally means sugar breakdown (glyco =


sugar and lysis = breakdown). It is the metabolic process by which a [six carbon] glucose

molecule is broken down into two [three carbon] pyruvate molecules releasing molecules

of ATP (energy) in the process. Once glucose enters the cell it is phosphorylated (it

combines with a phosphate group) into glucose 6-phosphate by the enzyme glucokinase.

Glucose 6-phosphate is then converted (or more accurately, rearranged) into fructose 6-

phosphate by phosphoglucoseisomerase. It is at this point of the glycolytic pathway that

free fructose can enter for metabolic degradation [to be discussed shortly]. The enzyme

phosphofructokinase then converts fructose 6-phosphate into fructose 1,6-biphosphate.

Fructose biphosphate aldolase splits fructose 1,6-biphosphate into two triose sugars:

dihydroxyacetone phosphate and glyceraldehyde 3-phosphate. These sugars are then

merged (or interconverted) by triosephosphate isomerase and dehydrated by

glyceraldehyde phosphate dehydrogenase to form 1,3-biphosphoglycerate. The enzyme

phosphoglycerate kinase converts 1,3-biphosphoglycerate into 3-phosphoglycerate and

forming and two ATP molecules (i.e. releases energy) in the process. 3-

phosphoglycerate is converted into 2-phosphoglycerate by phosphoglycerate mutase.

Enolase converts 2-phosphoglycerate into phosphoenolpyruvate that is then converted

into two pyruvate molecules by the enzyme pyruvate kinase. Additionally, two more

ATP molecules are released during this final step. If oxygen (O2) is present, pyruvate is

converted into acetyl coenzyme A (CoA) in the mitochondria of the cell and the Krebs

cycle (a.k.a. citric acid cycle, tricarboxylic cycle) begins. Thus the pyruvate molecule

links glycolysis (glucose metabolism) with the energy producing Krebs cycle. 85 [Illus. 7]


Unlike glucose, fructose metabolism is independent of the phosphofructose kinase

regulatory pathway. Once inside the cell, fructose is phosphorylated into fructose-1

phosphate by the enzyme fructokinase. Just as biphosphate aldolase splits fructose 1,6-

biphosphate in glycogen synthesis, aldolase B splits fructose 1-phosphate into

glyceraldehyde and dihydroxyacetone phosphate. Glyceraldehyde has two separate

conversions: 1) the enzyme triokinase binds with glyceraldehyde to produce

glyceraldehyde 3-phosphate and 2) the enzyme glycerol dehydrogenase converts

glyceraldehyde into glycerol. [Illus. 8] Glycerol forms the backbone of phophsolipids

and triglycerides. 86

Glucose glucokinase Glucose 6-‐phosphate

Phosphoglucose isomerase

Fructose 6-‐phosphate

Phosphofructo-‐kinase

Fructose 1, 6-‐biphosphate

Biphosphate aldolase

Glyceraldehyde-‐ 3-‐phosphate

Dihydroxi-‐acetone phosphate

Glyceraldehyde 3-‐phosphate

dehydrogenase

1,3-‐biphospho-‐ glycerate

3-‐phospho-‐glycerate

ATP

phospho-‐glycerate kinase

2-‐phospho-‐glycerate

Phosphoenol-‐ pyruvate

Pyruvate (pyruvic acid) Pyruvate

kinase

ATP

Enolase

Phospho-‐glycerate mutase

Triose phosphate isomerase

Illus. 7 Author’s schematic of glycolysis reference from Whitney et al. and Tortora et al.

Mitochondria Krebs Cycle


These two processes (i.e. pathways) have been discussed in detail to emphasize the

complexity of the chemical reactions that occur continually in our bodies, and to illuminate

the importance of understanding what effect(s) chemical formulations [especially man-

made] can have on these reactions.

Most dietary glucose passes through the liver and is metabolized into ATP, H20 and

CO2 in skeletal muscle cells as well as hepatic cells. Additionally, it is metabolized into

glycerol phosphate in fat cells for use in triglyceride synthesis. On the other hand,

Fructose Fructokinase Fructose 1-‐phosphate

Aldolase B Dihydroxi-‐acetone phosphate

Aldolase B

Glyceraldehyde

Glyceraldehyde-‐ 3-‐phosphate Triose phosphate

isomerase

triokinase

Illus. 8 Author’s schematic of glycolysis reference from Heinz et al. and Tortora et al.

glycerokinase Glycerol-‐3-‐P

Triglyceride (VLDL)

Fatty Acids

esterfication

Glycerol

glycerol dehydrongenase


virtually all fructose is metabolized in the liver (hepatic cells) and results in the formation

of glycerol and fatty acids. Another significant difference between fructose and glucose

metabolism is the absence of ATP production either during the metabolism of fructose or

as an end product. Because fructose metabolism is not regulated by phosphofructose

kinase, its uptake by the liver and its metabolism into fatty acids is independent of

cellular ATP and citrate levels.87 These differences are why fructose consumption is of

great interest to metabolic scientists and researchers. While glucose and fructose share

the same chemical formula and some similar metabolic pathways, the entire process for

metabolism of these two monosaccharides is different. The question among researchers

is how significant is this difference?

Glycogen, Glycogenesis and de novo Lipogenesis

Whether glucose is utilized for immediate fuel or stored for future use depends

upon the needs and requirements of the body at that moment. For example, if energy is

required immediately, cells can oxidize glucose thereby releasing adenosine triphosphate

(ATP) energy for immediate use. If blood sugar levels are sufficient (or even elevated),

the liver converts excess glucose into the polysaccharide glycogen for storage via a

process called glycogenesis. Glycogen is basically a little storage pocket of energy

within the cell itself. If the glycogen stores within the cell are saturated, the liver

converts remaining glucose into fatty acids and glycerol, forming the long-term storage

lipid triglyceride. These triglycerides are then deposited into adipose tissue cells that

have a virtually unlimited storage capacity. This long-term storage unit is commonly

(and for many un-affectionately) known as “body fat”, and in large quantities results in


obesity. This is how sugar, if not metabolized (burned) for energy, can ultimately be

converted into fat and why diets with a high sugar content [without a simultaneously high

caloric/energy expenditure] can lead to excessive weight gain and ultimately obesity as

well as metabolic disorders.

Glycogen is stored in the liver and skeletal muscle cells and is a considered “long

term” energy storage unit, versus adenosine triphosphate (ATP), which is “short term”

energy storage. When blood sugar levels drop excessively, the hepatic cells hydrolyze

glycogen back into glucose (a process called glycogenolysis) thereby releasing the

glucose molecules into the blood stream. While both skeletal muscle cells and liver cells

can store glycogen, only hepatic cells contain the enzyme glucose phosphatase necessary

to release glucose back into the blood.

As mentioned earlier, when the cellular glycogen storage units are saturated, liver

and fat cells can convert the remaining glucose into glycerol and fatty acids to form

triglycerides, a process called de novo lipogenesis (a.k.a. hepatic de novo lipogenesis,

lipogenesis, or DNL; lipo = fat and genesis = creation). Unlike glycogenesis, de novo

lipogenesis is a “one way street” in that fat cannot be hydrolyzed back into glucose for

utilization. Some studies have shown that overconsumption of carbohydrates can result

in anywhere from a two to threefold increase88 up to a six to tenfold increase89 of de novo

lipogenesis. In a study of long-term consumption of fructose and glucose on de novo

lipogenesis, dyslipidemia, insulin resistance and visceral adiposity, Stanhope et al. (2009)

discovered that hepatic de novo lipogenesis increased during prolonged fructose

consumption but remained unchanged during (and after) prolonged glucose

consumption.90 Test results confirmed that increased de novo lipogenesis generates fatty


acids which are ultimately used in the formation of triglycerides. While both fructose-fed

and glucose-fed groups experienced weight gain (specifically, adipose tissue), they

differed in types and location of adipose tissue distribution. Their data suggests that

fructose consumption may “specifically promote” visceral adipose tissue development

whereas glucose consumption appears to favor development of subcutaneous adipose

tissue.

Metabolic Hormones: Glucagon & Insulin

Glucose metabolism and its conversion into glycogen and triglycerides for storage

has been discussed however, the blood glucose regulator insulin and its counterpart

glucagon have not. As referenced earlier, insulin is a hormone comprised of two amino

acid chains thus classified in the protein category. Insulin is just one of several hormones

secreted by the pancreas. The pancreas is comprised of two types or sections of tissue:

pancreatic acini, the exterior tissue which is responsible for secreting digestive enzymes

discussed earlier, and the islets of Langerhan’s, the more interior tissue that secretes

regulating hormones. Within the islets of Langerhan’s there are three major types of cells

cell classifications: alpha (α), beta (β) and delta (δ) cells. Alpha cells secrete glucagon,

the hormone responsible for increasing blood glucose levels; beta cells secrete insulin,

the hormone responsible for decreasing blood glucose levels; and delta cells secrete

somatostatin, a polypeptide which is able to suppress both insulin and glucagon release.

Glucagon is a large polypeptide with two primary functions in the liver: 1) breaking

down glycogen into glucose via glycogenolysis and 2) increasing gluconeogenesis, the

process of manufacturing glucose from amino acids, fats, and other substances that are

not carbohydrates. When blood glucose levels fall below normal, the pancreas secretes


this hormone setting off a domino effect of chemical reactions resulting in the

degradation of stored liver glycogen. Once glycogen is catabolized into glucose, it is

released into the bloodstream and blood glucose levels rise. Gluconeogenesis occurs if

all of the glycogen in the liver has been expended and glucagon is still being secreted.

The hormone antithesis of glucagon is insulin. This unique polypeptide hormone

interacts with the three primary nutrients previously discussed: carbohydrates, proteins

and lipids. Glucagon is responsible for increasing blood glucose levels whereas insulin is

responsible for decreasing blood glucose levels. Insulin plays an essential role in

carbohydrate metabolism and utilization and/or storage of energy. Insulin causes excess

dietary carbohydrates that are not immediately used for energy to be converted and stored

as glycogen in the liver and smooth muscle cells. Insulin is also involved in protein

metabolism via its inhibitory effect on gluconeogenesis and is instrumental in lipid

metabolism and storage.

When large amounts of carbohydrates are consumed in a short period of time, the

result is an elevation of glucose circulating in the blood. As the body always seeks to

maintain homeostasis, this elevated blood glucose triggers the pancreas to release a surge

of insulin. When insulin is secreted, it circulates throughout the bloodstream until it

binds with target GLUT receptors imbedded within the membrane wall. Once these

target receptors are activated, the cells (such as muscle cells and adipose cells)

immediately become highly permeable to glucose. Glucose is then transported into the

interior of the cell via active transport and typical carbohydrate metabolic functions occur

and ATP energy is released. As large amounts of insulin are secreted, target receptors are

stimulated, membranes become permeable, and the excess glucose is removed from the


blood as it is transported into the cells. Muscle cells metabolize or “burn” the energy

producing glucose via contraction of the muscle, i.e. exercise. If after several hours this

does not occur or if there is an excessive amount of glucose, then glucose is converted

into glycogen for storage in the hepatic and muscle cells for later use.

One of the primary functions of insulin is to facilitate the conversion of glucose to

glycogen in the liver. Insulin influences this process in three ways: 1) it inhibits the

hepatic enzyme phosphorylase that is, necessary for catabolizing glycogen into glucose,

2) it increases the enzymatic activity of glucokinase, the enzyme responsible for the

phosphorylation of glucose thereby essentially trapping the glucose in the cell

[preventing it from reentering the blood stream] and 3) it increases other enzymes

involved in glycogen synthesis, such as phosphofructokinase and glycogen synthase. The

relevance of this conversion process is that approximately two to three hours after a meal

blood glucose levels begin to drop and the body (i.e. cells) needs energy (glucose). When

this occurs the stored glycogen can be readily converted into glucose for immediate use.

The mechanism involved in the insulin-protein metabolism relationship is not as

well understood as with carbohydrate or lipid metabolism, however insulin does

positively influence protein integrity. That is, preservation of proteins. Scientists have

discovered that insulin decreases the rate at which amino acids are released from the

muscle cells thereby inhibiting protein catabolism. 91 Insulin also inhibits protein

degradation by hindering gluconeogenesis thus preserving amino acids that would have

otherwise been catabolized and converted into glucose. Without the presence of insulin,

protein degradation occurs and catabolized amino acids are released into the blood and

ultimately excreted via urine. One of the most dangerous effects of diabetes mellitus is


this dumping and excretion of protein that can lead to muscle wasting if not arrested.

In addition to decreasing blood glucose levels, increasing hepatic glycogen levels

and inhibiting protein degradation, insulin is intricately involved in lipid metabolism and

storage. If the body is busy pulling excess glucose from the blood to utilize for energy it

means that it is not catabolizing stored adipose tissue for energy production. Not only

does insulin, in essence, preserve current adipose (fat), it also promotes future adipose

deposits. As previously discussed, insulin secretion results in a surge of glucose to

entering cells, primarily hepatic and smooth muscle cells. If both cells and cellular

glycogen stores are saturated, glucose is then converted into fatty acids that are then

stored as triglycerides in adipose cells. It has been discovered that insulin has an

inhibitory effect on lipoprotein lipase, an enzyme residing in the capillary epithelial wall

of the liver and adipose tissue that hydrolyzes triglycerides into glycerol and fatty acids.

Thus, insulin actually promotes stored adipose preservation. In the absence of insulin,

lipoprotein lipase is activated and hydrolysis of triglycerides occurs resulting in surge of

fatty acids and glycerol molecules in the blood stream. The liver will convert some of

these excess fatty acids into cholesterol and phospholipids that are also released into the

bloodstream. This high blood lipid content can lead to a plaque build up (or thickening)

of the arterial wall ultimately resulting in atherosclerosis. This condition can seriously

impede healthy blood flow and greatly increase the risk of heart attack or stroke.

Research within the last few decades has also discovered that insulin affects leptin release

[discussed in detail shortly] and can acutely increase plasma leptin levels.92


LIPIDS

Lipids are a group of organic compounds containing hydrogen, carbon and oxygen

but unlike carbohydrates they do not contain a 2:1 hydrogen to oxygen ratio. Because

there is a lower O2 content, there are fewer polar covalent bonds thus resulting in

insolubility in polar solvents such as water. Hence the origin of the adage oil and water

don’t mix. Since they do not flow freely in high water content blood, lipids often

combine with proteins, specifically lipoproteins, for efficient transport throughout the

bloodstream. Lipids are essential to healthy function and have several sub-classifications

or sub-categories: triglycerides (a.k.a. triacylglycerols), phospholipids (lipids that contain

phosphorus), steroids (most notable, cholesterol), lipoproteins, eicosanoids and other

lipid substances including vitamins E and K.

Triglycerides are the most abundant lipid in the body and are known as “neutral

fats”. They are the body’s most highly concentrated source of energy and are the primary

unit for energy storage in adipose tissue. Triglycerides are comprised of a glycerol

molecule and three fatty acids (hence tri-glycerides). Depending upon bonds between

fatty acids, triglycerides can form saturated, mono-unsaturated or polyunsaturated fats.

Phospholipids form cell membranes and are found in high concentrations in nerves

and brain tissue. They are comprised of a polar “head” and two non-polar “tails”; the

non-polar tails line up touching ends and the polar heads face outwards. This formation

creates the membrane for every cell; healthy cells must have healthy membranes and

these membranes are comprised of lipids. Each steroid (cholesterol, vitamin D, sex

hormones and bile salts) has a different function in the body. Cholesterol comprises cell

membranes and is an essential precursor to vitamin D, bile salts and hormones. Vitamin


D is necessary for bone growth and repair as well as calcium regulation. Bile salts are

essential for digestion and emulsification of fat for absorption; in addition, they are

necessary for absorption of fat-soluble vitamins A, E, D and K. The sex hormones

estrogens, progesterone and testosterone are lipids that regulate and stimulate

reproductive organs and are essential for healthy function. Lipoproteins (discussed in

detail later in this chapter) are lipid-protein molecules that help transport lipids to the

liver and adipose tissue for storage as well transport cholesterol to cells and remove any

excess from the blood. Eicosanoids are lipid substances that are involved in an array of

processes from blood clotting and inflammation to stomach acid secretion and smooth

muscle contraction of the intestinal tract. As one can see, lipids are an integral part of

the body’s healthy functioning

The first step of the digestion of dietary fats is the emulsification process whereby

larger fat globules are broken into smaller ones so that enzymes can begin to break down

their surfaces. Bile salts and lecithin, both found in the liver, are required and responsible

for successful fat emulsification. The polar parts of bile salts and lecithin attach to the

polar portions of the on the surface of the fat globule dissolving the surface layer making

the molecule fragile and easily fragmented during peristalsis of the bowels. Once the

smaller, more fragile molecules enter the small intestine, pancreatic enzyme lipase breaks

down the emulsified fat molecules into fatty acids and two monoglycerides. [Illus. 6]

FAT Emulsified Fat

FATTY ACIDS

Bile & Peristalsis Pancreatic Lipase Mono-

glyceride Mono-

glyceride

Illus. 6 Author’s Schematic


Outside of the cell, monoglycerides and fatty acids are dissolved in the bile acid

and diffuse into the cellular fluids via the microvilli of the brush border; from there they

then diffuse through the intestinal epithelial membrane into the endoplasmic reticulum.

Once inside the endoplasmic reticulum of the cell, the fatty acids and monoglycerides

essentially re-combine to form “new” triglycerides. These new triglycerides aggregate

with cholesterol, phospholipids, β-lipoprotein to form a chylomicron. Chylomicrons are

then transported into the lymph system. 80-90 percent of all digested fat is metabolized

and absorbed this way and transported into the blood via chylomicrons and the lymphatic

system. Once all of the chylomicrons have been removed from the blood, the remaining

lipids in the blood plasma are in the form of lipoproteins. Lipoproteins are smaller than

chylomicrons but have a similar composition; they too contain cholesterol, phospholipids,

triglycerides and protein.

The function of lipoproteins is to transport various lipid components in the blood;

some transport cholesterol while others transport triglycerides. Lipoproteins are broken

into several classifications: Low Density Lipoproteins (LDL), Very Low Density

Lipoproteins (VLDL), and High Density Lipoproteins (HDL). Compositionally, LDLs

have a higher cholesterol level (55%) and lower triglyceride (20%) and protein (25%)

levels. LDLs deliver cholesterol to various cells in the body, smooth muscle fibers in

arteries for example. VLDLs have higher triglyceride levels (65%) and lower cholesterol

(25%) & protein (10%) levels. VLDLs transport triglycerides to the fat cells in adipose

tissue for long-term storage. After depositing the triglycerides they are then converted

into LDLs. Of the lipoproteins, HDLs contain the lowest level of cholesterol (13%), the

highest level of protein (50%), and a moderate level of triglycerides (37%). The function


of HDLs is to remove excess cholesterol from body cells and transport it to the liver for

elimination. This street sweeper function has earned them the name of “good

cholesterol” as they help clean the blood and prevent accumulation of cholesterol on the

arterial walls (a.k.a. the infamous “plaque”).

METABOLIC THEORIES

Over the years, several theories regarding metabolism and metabolic regulatory

pathways have emerged. While each have a different locus of control, they all include

the interaction of neurological pathways in the brain. The hypothalamus is the portion of

the brain that, among other activities of the central nervous system, is responsible for

regulating homeostasis. The principle functions of the hypothalamus are: control the

autonomic nervous system (ANS), regulate body temperature, regulate food intake,

regulate thirst, plays a role modulating the circadian rhythm (i.e. 24 hour sleep-awake

cycle), and is also associated with emotions of rage and aggression. The hypothalamus is

divided into four primary regions housing twelve neuron clusters. The two neuron

clusters actively engaged in caloric intake are: the lateral hypothalamic cluster (a.k.a. the

feeding/hunger center) and the ventromedial nuclei (a.k.a. the satiety center). Stimulation

of the lateral hypothalamic cluster causes animals (including humans) to eat heartily,

whereas stimulation of the ventromedial nuclei causes a cessation of food intake.

Scientists have studied the intricacies of the hypothalamus and its various functions

for decades; yet ironically, in the same year (1953) two different theories regarding the

hypothalamic regulation of energy consumption and expenditure emerged: the glucostatic

theory and the lipostatic theory. According to the glucostatic theory, changes in blood

glucose concentrations are detected by glucoreceptors in the hypothalamus and affect


energy intake (i.e. food consumption). An increase in blood glucose concentrations

results in increased feelings of satiety resulting in a cessation of consumption.

Conversely, a decrease in blood glucose concentrations diminishes the activity in the

hypothalamus [specifically the ventromedial nuclei] which in turn shuts down or inhibits

the satiety center thereby causing/spurring the individual to eat.93

The lipostatic theory regarding food intake regulation centers around lipid

metabolism rather than sugar (glucose) metabolism. The lipostatic theory posits that

hormones and other metabolic products resulting from fat metabolism (metabolic

byproducts so to speak) circulating in the blood signal the hypothalamus indicating how

much adipose tissue is in the body. It was hypothesized that in an effort to maintain the

body’s adiposity (i.e. maintain the same level of body fat) these substances inhibit the

satiety center in the hypothalamus resulting in a continuation of consumption. 94 During

this same period, geneticists were also investigating the role of genes and genetic markers

with respect to satiety, adiposity and energy expenditure lending to complex theories

regarding intricate neural networks, pathways and feedback loops. Leptin was one of

those discovered markers whose role in metabolic functions specific to food intake and

energy expenditure is still being investigated.

Leptin

Leptin (synonymously referred to in scientific literature as the “ob gene protein

product”, “ob protein” or “ob product”) is a hormone protein predominantly produced in

adipocytes of white fat tissue95 that, although not completely understood, is thought to be

a lipostatic signal “that contributes to body weight regulation through modulating feeding

behavior and/or energy expenditure”.96 In short, this hormone protein helps regulate


and/or inhibit food intake, increase energy expenditure (burning of fatty acids) and

subsequently reduce body fat. In 1950, while studying the genetic make up of

excessively voracious, obese mice, Dr. Margaret Dickie of The Jackson Laboratory

discovered the obese (ob) mouse gene. Dr. Dickie found that severely obese mice were

homozygous for a single gene mutation (named ob) increasing in weight until they were

nearly four times the size of a normal mouse.97 Homozygous ob/ob (also known as ob or

Lepob 98) mice have a mutation in the gene for the protein leptin 99; this mutation prevents

the ob/ob mouse from manufacturing leptin. Ob/ob mice are also hyperphagic (unable to

stop eating) and exhibit the diabetes-like syndromes of hyperglycemia, glucose

intolerance, elevated plasma levels and impaired wound healing.100 Shortly thereafter in

1966, Dr. Douglas Coleman (also of The Jackson Laboratory) initiated several

experiments building upon Dr. Dickie’s research. In 1972, he discovered the

homozygous db/db mutation (also known as db, Leprdb or the diabetes mutation101) and

postulated that the db/db mouse has a genetic defect in its satiety center. Db/db mice

have a mutation in the leptin receptor (LEP-R) resulting in elevated levels of leptin in

their blood. The LEP-R receptors are located in the gut as well as muscle tissue however,

the highest concentration lies within the ventromedial hypothalamus. In addition to being

obese, the mice are also polyphagic, polydipsic, polyuric, exhibit hyperplasia of the islet

β cells (pancreatic), hyperinsulinemia and exhibit impaired wound healing.102 According

to reports, Dr. Coleman’s theories eventually led to the successful cloning of the genes

behind the ob and db mutations by researchers at The Rockefeller University in the mid

1990s.

Dr. Jefferey Friedman and his colleagues at The Rockefeller University are credited


with discovering the protein hormone leptin while investigating the positional cloning

(i.e. DNA sequencing) of the mouse ob gene and its human counterpart in 1994.103 Leptin

deficiency has been directly associated with obesity (and other neuroendocrine

anomalies) in both mice and humans.104 Campfield et al. (1995) discovered that when

injected with leptin, ob/ob mice reduced food intake and body weight however, this

reaction did not occur with db/db mice leading them to surmise that leptin acts directly on

neural networks that control feeding and energy balance.105 Although how remained an

unknown.

In 2004, Dr. Friedman reported that experiments with human subjects revealed that

weight gain resulted from increased circulating plasma leptin levels. Conversely, both

obese and lean subjects lost adipose tissue when plasma leptin levels were decreased.106

Although not completely understood, scientists have concluded that leptin interacts with

neural receptors in the central nervous system, specifically in the hypothalamus, in some

sort of a negative feedback loop.

With respect to satiety and food regulation, researchers have since discovered that

leptin specifically influences two neurons located in the arcuate nucleus of the

hypothalamus: neuropeptide Y (NPY) and pro-opiomelanocortin (POMC). Leptin

suppresses the activity of NPY neurons while it simultaneously enhances the activity of

POMC neurons.107 NPY neurons affect feeding behavior, specifically by stimulating

appetite and food intake (as well as other roles not relevant to this paper) whereas POMC

suppresses appetite and food intake.108 Dr. Shirley Pinto and her colleagues at The

Rockefeller University discovered that leptin alters the number of neural connections that

either excite or inhibit NPY and POMC by altering the synaptic inputs of these


neurons.109 So leptin plays a key role in when and how much an individual consumes.

Leptin also influences glucose metabolism, which in turn, influences adipose production

and storage. Studies have also shown that in mice leptin can influence hepatic glucose

production by increasing gluconeogenesis while simultaneously decreasing

glycogenoloysis.110 Thus, leptin increases the manufacturing of glucose from amino

acids, fats, and other substances that are not carbohydrates while decreasing the

hydrolysis of stored glycogen into glucose. This means the stored glycogen that is not

hydrolyzed into glucose for utilization will eventually be converted into fat. Liu et al.

(1989) concluded that, “it is likely that these metabolic effects of leptin participate to the

regulation of hepatic glucose metabolism under physiological conditions.”111

In a human case study by Dr. I. Sadaf Farooqi and his colleagues (1999), a

morbidly obese nine-year-old girl with congenital leptin deficiency (a condition marked

by the inability to sense being full112) was injected with recombinant methionyl leptin

subcutaneously once a day for 12 months. At the onset of this trial, the young girl’s

weight registered in the 99.9th percentile weighing 94.4 kg (208 lbs.) at a height of 140

cm (4’8”). Of the 94.4 kg (208 lbs.), 55.9 kg (123 lbs.) of her weight was fat; almost

60% of her body was adipose tissue. After 12 months of therapy her total weight loss

was 16.4 kg (36 lbs.) of which adipose tissue (i.e. body fat) comprised 15.6 kg (34 lbs.).

During the trial, her energy expenditure remained constant thus the weight loss,

specifically the loss of adipose tissue, was directly attributed to the introduction of leptin

into her system.113 In a 2002 follow up study, Farooqi et al. reported similar findings

with three other children who also had congenital leptin deficiency.114 One caveat to Dr.

Farooqi’s studies is that all the individuals had the ob genetic mutation, the real question


is would he yield the same results in obese individuals without the genetic anomaly?

Emilsson et al. (1997) suggest that leptin may have an effect on pancreatic β-cells

inhibiting insulin secretion.115 This is important because maintaining proper blood

glucose levels is a continuous regulatory process essential to health and function.

Adverse conditions such as hypoglycemia, diabetes mellitus (NIDDM), hyperlipidemia,

insulin resistance and obesity can result from chronic irregularities.

METABOLIC IMBALANCES & DISORDERS

Hypoglycemia

Low blood glucose levels mark a condition called hypoglycemia. In hypoglycemic

individuals, often copious amounts of insulin are secreted by the pancreas resulting in a

rapid cellular uptake of blood glucose. This rapid cellular uptake of glucose results in a

rapid decrease of glucose circulating in the bloodstream. When blood glucose levels drop

precipitously, the adrenal glands secrete epinephrine, cortisol and other stress hormones

that stimulate the release of stored glycogen. Common symptoms of hypoglycemia are

weakness, dizziness, increased heart rate, hunger, anxiety and sweating. These symptoms

do not result so much from the drop in blood glucose but rather from the surge of stress

hormones released. Nevertheless, this condition is serious and if untreated can result in

more deleterious effects such as mental disorientation, convulsions and even shock.116

Insulin Resistance

The insulin resistance syndrome (a.k.a. Syndrome X, metabolic syndrome X and

Reaven Syndrome117) has traditionally been used to describe a clustering of metabolic

abnormalities that originally included glucose intolerance, insulin-stimulated glucose


uptake, hyperinsulinemia, hypertension, dyslipidemia marked by high triglycerides (TG)

and low HDLs, but has recently (2002) expanded to include small, dense LDLs, increased

uric acid concentrations, decreased levels of adiponectin (a hormone protein with similar

functions as leptin) and increased levels of plasminogen activator inhibitor 1 (inhibits the

activators which break down blood clots).118 In layman’s terms it is simply as the name

implies, the body becomes resistant to insulin. After prolonged and excessive sugar

consumption the adipose and muscle tissue cells become saturated with glucose

molecules. To prevent further overload, the cells reduce the number of active insulin

receptors by locking/inactivating them, a mechanism that although identified is still not

wholly understood. The pancreas then secretes even more insulin in an attempt to

override the cells’ resistance, a condition called hyperinsulinemia. In response to the

surge of insulin, the cells “lock” more insulin receptors and the cycle continues. Any

glucose circulating throughout the bloodstream is diverted into adipose tissue for long-

term storage but if those cells are saturated the glucose remains circulating in the

bloodstream which can ultimately lead to NIDDM (type 2 diabetes).119 In an analysis of

fructose, weight gain and the insulin resistance syndrome, Sharon Elliott and her

colleagues (2002) purport, that while a considerable amount of research still needs to be

done particularly with human subjects, elevated fructose consumption is clearly a

contributor “to nearly all the classic manifestations of the insulin resistance syndrome”

including hyperinsulinemia, hyperlipidemia, hypertension, and impaired glucose

tolerance.

Diabetes Mellitus

Perhaps the most serious condition associated with an insulin imbalance is the


metabolic disorder (which some argue is a “disease”) diabetes mellitus, a.k.a. diabetes.

Diabetes is classified into two types: Type 1 Diabetes, also known as insulin-dependent

diabetes mellitus (IDDM) or “juvenile diabetes” and Type 2 Diabetes, commonly

referred to as non-insulin-dependent diabetes mellitus (NIDDM) or “adult onset”.120

Type 1 diabetes most often occurs in young children and is s classified as an autoimmune

disorder wherein the immune system destroys the beta cells in the pancreas thus insulin is

never produced. For these individuals, insulin must be administered via injection to

ensure proper blood glucose levels. Approximately 5-10 percent of all diabetics have

type 1 diabetes which leaves an astounding 85-90 percent that fall in the latter category.

Type 2 diabetes (non-insulin-dependent diabetes mellitus or NIDDM) is a metabolic

disorder as opposed to an autoimmune disorder. In type 2 diabetes, the pancreas is able

to secrete insulin but the target cell receptors are less responsive to the insulin. This

decreased cellular sensitivity to insulin results in an inability to remove excess sugar from

the bloodstream. The primary difference between the two classifications of diabetes

mellitus is that type 1 diabetics have significantly decreased levels of insulin whereas

type 2 diabetics have normal to high levels of insulin but their cell receptors are resistant

to it. Another significant difference is that type 2 diabetes is environmentally induced

resulting in diminished metabolic function, whereas type 1 diabetes is predominantly

genetically/biologically induced, the individual is born with a physiological malfunction

of the pancreatic beta cells resulting in an inability to produce insulin. Although recent

research has shown that environmental factors also can also contribute to the onset of

type 1 diabetes as well. 121 Approximately 80-90 percent of type 2 (NIDDM) diabetics

are obese and it is the excess adiposity (and consumption of certain foods resulting in that


excess adiposity) that has caused the diabetic condition to emerge. Regardless of the

category, type 1 and type 2 diabetes share the same fundamental markers: 1) high blood

glucose levels (300-1200 mg/dl), 2) abnormal fat metabolism and 3) protein wasting all

of which are dangerous and, if not arrested, can become deadly.

Hyperlipidemia

Hyperlipidemia, as the name implies, is the condition of excessive blood lipids

(hyper = over, excessive and lipid = fat), also known as dyslipidemia,

hypercholesterolemia, or hyperlipoproteinemia. Excess blood lipids (cholesterol)

circulating in the blood can begin to line (or coat) the arterial walls eventually

diminishing, and in some cases blocking altogether, essential blood flow. Often this

condition is linked to diabetes and is a significant risk factor for cardiovascular disease

and stroke due to the influence on arterial plaque buildup.

A common denominator of all these conditions is that their origin ultimately

begins at phase I: mastication. High sugar foods translate into high sugar blood. These

are the most prevalent metabolic disorders that can result from metabolic imbalances and

erratic blood glucose levels. While not an exhaustive list, they provide a glimpse as to

how serious excess adiposity can become.


CHAPT. 3- High Fructose Corn Syrup

HISTORY & ORIGINS: HOW AND WHY HFCS WAS MADE

The chemical conversion of glucose to fructose [that is converting a glucose

molecule into a fructose molecule via induced chemical reactions], known as the Lobry

de Bruyn-Alberda van Ekenstein transformation, was first discovered in 1885 by Cornelis

Adriaan Lobry van Troostenburg de Bruyn and Willem Alberda van Ekenstein.122

Twentieth century scientists continued to experiment with various chemical reactions and

hopeful conversions. However, some incommodities of the chemical process are that it

produces non-metabolized substances (byproducts), yields less than 40% fructose and has

reduced sweetness and notable “off flavors”.123 Because of these disadvantages,

scientists began experimenting with other catalytic facilitators, namely enzymes.

In 1957, Richard Marshall and Earl Kooi discovered the enzymatic conversion of

glucose into fructose using xylose isomerase (a.k.a. D-Glucose/xylose isomerase, D-

xylose ketol isomerase; glucose isomerase124). In their endeavor to disprove the

prevailing hypothesis that xylose isomerase was unable to act on aldoses besides D-

xylose specifically, they discovered that not only was the enzyme able to act on other

aldoses (such as D-glucose), but that under specific conditions, it would actually

transform/convert glucose into fructose.125 They placed 90 g. of D-glucose in 500 ml of

arsenate (similar to phosphate) buffer containing 2.5 mmole of magnesium chloride

(MgCl2) and 5.0 g. of lyophilized (freeze-dried), xylose rich Pseudomonas hydrophila

cells and allowed the solution to rest in a sealed container for 48 hours. After the

incubation process, they discovered the solution yielded 29.2 g of D-fructose. They


concluded that, “present evidence warrants only speculation on the metabolic

significance of the isomerization of other sugars by this enzyme” and suggested future

investigation of other “d-glucose isomerizing activity in other microorganism, and on the

substrate specificity of the enzyme”.126 Japan’s Dr. Yoshiyuki Takasaki was one such

scientist to pursue that investigation.

For years, Dr. Takasaki studied sugar-isomerizing enzymes for Japan’s Agency of

Industrial Science and Technology, Ministry of International Trade and Industry.127 In

his research on Production and Utilization of Glucose Isomerase from Streptomyces sp

(1966), Takasaki noted that a few problems existed for the commercial use of glucose

isomerase enzymes, most notably the cost of cultivating the enzyme. 128 He discovered a

method for cultivating the enzyme from “cell-free extracts of a strain of Streptomyces sp.

isolated from soil”.129 This was a monumental (and subsequently profitable) discovery as

the enzyme was now able to be cultivated in a more economic medium, soil. Later,

Takasaki (1971) discovered the amount of fructose yielded was dependent upon the

sugar-borate ratio. He was able to convert 88-90% of glucose into fructose with a 1:1

sugar-borate ratio and a 7.5 pH, substantially more than Marshall and Kooi’s 40%.130 The

volume of yielded fructose was yet another significant step in the journey to commercial

mass production and thus he is credited with creating the industrialized process of

manufacturing high fructose corn syrup (HFCS).

The production of HFCS has three primary enzymatic “steps”: liquefaction,

saccharification and isomerization.131 (Illus. 7 chronicles this process) First, starch must

be extracted from the corn. Corn is soaked in hot water (approx. 140°) with sulfites,

caustic soda and hydrochloric acid for two days until the kernel swells and break into


four components: germ, starch, hull and protein. 132 The starch of the corn is composed

of the glucose-containing polysaccharides amylose and amylopectin (disaccharide and

polysaccharide) and requires significant heat and additional enzymes to hydrolyze into

the simple sugar glucose. Once the starch is extracted, the enzyme α-amylase (extracted

from Bacillus spp.133) is added and hydrolyzes the polysaccharides into shorter chained

dextrins and oligosaccharides [liquefaction step]. A second enzyme

glucoamylase/amyloglucosidase (extracted from the Apergillus fungi 134) is added and

hydrolyzes the dextrins and oligosaccharides into the simple sugar glucose

[saccharification step]. The result of these two enzymatic steps is glucose syrup,

commonly known as corn syrup. The third step, isomerization, is the most complex (and

costly) step which converts the glucose into fructose via enzymatic hydrolysis and liquid

chromotography. The use of glucose isomerase (also called D-glucose ketisomerase or

D-zylose ketoisomerase) is more intricate than the two previous enzymes. Whereas α-

amylase and glucoamylase are added directed to the mixture, in the isomerization step the

mixture (i.e. glucose syrup) is passed over a support structure containing glucose

isomerase (hence the glucose is stationary and “immobilized”). The immobilized glucose

isomerase is reused until most of the enzymatic activity is exhausted. The result of this

isomerization step is a liquid containing 90% fructose and 10% glucose, commonly

referred to as HFCS-90 or industrial HFCS. HFCS-90 is then blended with glucose

syrup to produce HFCS-55 (55% fructose 45% glucose) and HFCS-42 (42% fructose and

58% glucose).


Corn Starch

glucose isomerase & (enzyme)

chromatography step

The wet milling process has recently drawn attention as there are growing concerns

surrounding the use of mercury grade caustic soda (a.k.a. sodium hydroxide or lye) as a

primary medium during this phase of processing. “Caustic soda” is a mercury cell chlor-

alkali product known for its catalytic properties and widely used in manufacturing

processes. From a health and nutritional perspective, these products are concerning

because the process of making them requires electrolysis of sodium chloride via a

mercury cell and mercury is extremely toxic.135As with most chemical processes, there is

a transfer of compounds that occurs which is not necessarily desirable but often deemed

acceptable by the manufacturer. However, the problem occurs when the substance

transferred is a known toxin. In an investigation of measured mercury concentrations in

food, Renee Dufault and her colleagues (2009) discovered that in a 2005 field test of

twenty samples of HFCS (both 42 and 55), 45% contained mercury ranging from 0.00 to

Corn [wet milled]

α-‐amylase (enzyme)

oligosaccharides (dextrins &

maltodextrins)

glucoamylase/ amyloglucosidase

(enzyme)

Glucose (corn syrup)

HFCS 90 (industrial)

HFCS 42

Illus. 7 High Fructose Corn Syrup (HFCS) enzymatic production process-‐ author’s schematic.

HFCS 55


0.570 µg mercury/g HFCS. They estimated that that the average mercury exposure from

HFCS could range from zero to 28.4 µg.136 A now recognized toxin, mercury is able to

enter the endothelial cells of the blood-brain and placenta barriers (hence the FDA

warning pregnant women to refrain from eating seafood which often contains mercury

and other PCBs). Gradually, mercury can accumulate in the kidneys, liver and brain

tissue affecting both the central nervous system as well as key metabolic processes.137

According to Dr. Choong Yong Ung (2010), within 24 hours of ingestion or

inhalation, mercury is absorbed into the gastrointestinal tract, metabolized and distributed

throughout the body via red blood cells.138 Furthermore, Ung et al.’s research into

mercury-induced hepatotoxicity revealed that mercury can cause liver damage via

oxidative stress and cell death, as well as deregulating kinases (such as glucokinase)

responsible for gluconeogenesis and adipogenesis that “may eventually lead to

syndromes such as mitochondrial dysfunction, endocrine disruption and metabolic

disorders.”139 (italics added)

DISCOVERY, USE AND PREVALENCE

Due to the high fructose content and subsequent sweetness, HFCS-55 is used in

beverages such as carbonated drinks, energy drinks and fruit juices. Because it does not

crystalize like its sucrose counterpart, it makes it the ideal sweetener for beverages from a

manufacturing standpoint as well as a distribution standpoint (i.e. crystallization will not

occur once it is sitting on a shelf). HFCS-42 is not as sweet as HFCS-55 and therefore is

used in baked good, processed foods, condiments, dairy products such as yogurt,

pudding, and ice cream, sherbet and other frozen deserts. Some processed foods such as

desert items could probably get away with using HFCS-55 however, very few people (if


any) would want their macaroni and cheese or hotdog to taste as sweet as a Coke or

Pepsi.

Outside of the growing organic movement, whether food product or beverage,

HFCS has been the predominant sweetener used in the food and beverage industry in the

United States for the last 25 to 30 years. On a relative sweetness scale with sucrose (table

sugar) equaling 100, glucose has a sweetness of 70-80, fructose has a sweetness of 140,

and HFCS ranges from 120-160 (depending upon fructose concentration).140 The

intensity of sweetness is of great financial significance to food and beverage

manufacturers. Ounce per ounce, HFCS is sweeter than sucrose (cane/beet sugar) and

that means less of it is needed to achieve the same (if not higher) level of sweetness as

sucrose. Once people are accustomed to a certain level of sweetness it is difficult for

them to reduce it. The same behavior occurs with sodium chloride (table salt), people

become accustomed, or more accurately desensitized, to high levels of sodium (table salt

not naturally occurring sodium) added to food and thus when it is diminished or removed

from the food it tastes “bland”.

ECONOMIC EFFECTS

While Marshall and Kooi are credited with the discovery of enzymatic conversion,

Takasaki is often credited with the evolution and industrialization of this process for the

purpose of mass production. According to Bhosale et al., Clinton Corn Processing

Company introduced the production of glucose isomerization (GI) “on an industrial

scale” in 1967, however, GI was not officially commercially available in the United

States until 1974.141 By 1980, almost all starch-processing companies were utilizing GI

technology and it still commands the largest market share in the food industry to date. It


can be concluded from earlier discussion of Takasaki’s research that the economic cost

benefits were a motivational factor ushering in the technological advances for mass

production of HFCS. Serendipitously, at the same time that Takasaki and his team were

perfecting GI for mass production, the price of corn commodities was dropping

precipitously.142 According to Heather Schoonover and Mark Muller of the Institute for

Agriculture and Trade Policy (IATP) (2006), for manufacturers, HFCS was and is an

economical sugar substitute because, “Sugar is one of the few commodities for which a

government price support program still exists. To ensure fair price for farmers and to

maintain a domestic source, sugar prices are kept above a minimum price floor,

guaranteeing that sugar prices cannot fall below the cost of production. Replacing sugar

with a corn product, therefore, can represent a substantial cost savings to food

manufacturers.”143

In 1970, HFCS represented less than 1% of caloric sweeteners in the United States.

Today, it comprises over 40% of all sugar consumption in the U.S. and is a $2.6 billion

dollar industry.144 Using the U.S. Department of Agriculture food consumption tables

from 1967 to 2000, Bray et al. (2004) analyzed food consumption patterns in the United

States and discovered the consumption of HFCS increased more than 1000% between

1970 and 1990, “far exceeding the changes in intake of any other food or food group”.145

HFCS is the primary sweetener in both manufactured food and beverages, including but

not limited to: carbonated beverages, fruit juices, cereals, breads, cookies, biscuits, jams

& jellies, yogurt, ice cream, frozen deserts, canned foods, spaghetti sauce, lunch meat,

pizza and salad dressing.146 HFCS-42 was introduced into the U.S. market in 1967 for

use in foods (i.e. baked goods) as the sweetness was not enough to trump the flavor of the


food itself. The sweeter HFCS-55 followed ten years later in 1977, replacing sugar (cane

and beet sugar) and becoming the predominant sweetener used in beverages.147 By 2000,

HFCS-55 constituted 61% of all HFCS produced. There are several reasons HFCS has

replaced sugar in the food and beverage manufacturing industry: 1) HFCS is cheaper than

sucrose- 32 cents per pound versus 52 cents per pound, 2) HFCS is a liquid and therefore

easier to transport and use in beverages, 3) HFCS (both 42 and 55) is sweeter than

sucrose, 4) HFCS has a higher solubility than sucrose and 5) HFCS is acidic, containing

preservative properties and therefore maintains a longer shelf life under certain

conditions.148

CONSUMPTION TRENDS

In their analysis of consumption, prices and expenditures in the United States,

Judith Putnam and Jane Allshouse (1999) analyzed per capita consumption of major food

commodities in the United States over a twenty-seven year period, 1970-1997. Between

1982 and 1997, per capita consumption of sugar, primarily sucrose (been and cane sugar)

and HFCS, increased 34 pounds (roughly 28%) to a record average of 154 pounds per

annum, per person. That equates to 53 teaspoons of sugar per person, per day. A

teaspoon of table sugar yields 16 calories thus and additional 53 teaspoons of sugar a day

equates to an additional 848 calories a day. This is approximately half of the daily

caloric requirements of most adults [based upon 1500 kcal/day for women and 2000

kcal/day for men149].

Putnam and Allshouse discovered that not only was there a significant increase in

total sugar consumption, but also in types of sugar consumed. In 1970, sucrose was the

primary sugar sweetener used in the food and beverage industry yielding 83% of the


market share for calorie consumption, however, by 1997 that percentage had plummeted

to 43%. Conversely, HFCS’s total share of use and consumption rose from 16% (1970)

to 56% (1997) [Figure 5]. Not surprisingly, per person-per pound consumption of

sucrose and HFCS follow the same trajectory. In 1970, annual per capita use of sucrose

was 102 lbs., by 1997 that number had decreased to 60 lbs. per person. However,

individual consumption of HFCS per annum skyrocketed from 0.5 lbs. in 1970 to 62.4

lbs. per person in 1997 [Figure 6]

83%

43%

16%

56%

0%

20%

40%

60%

80%

100%

1970 1997

Percentage of Sucrose and HFCS used as primary caloric sweetener in the U.S.

Sucrose HFCS

Figure 5. The percentage of Sucrose and HFCS as primary caloric sweetener in the U.S. Resource: Putnam and Allshouse, 1999: Data Source from UDSA Economic Research Service. Statistical Bulletin No. 965. Graphic created by author.


Further analysis of the USDA Economic Research Service (ERS) data tables (tables

30, 51 & 52) reveal interesting trends, especially when cross-referenced with obesity

trends. Sucrose (table sugar) consumption experienced its sharpest decline between 1970

and 1986 decreasing from 72.5 lbs./yr. to 42.8 lbs./yr. respectively. Conversely, HFCS

experienced its sharpest rise in consumption within that same period from 0.4 lb./yr. to

32.3 lbs./yr. After 1986, HFCS consumption increased steadily until peaking at 44.2

lbs./yr. in 2002 while sucrose consumption rose only fractionally. [FIG 7] In 2003,

consumption for both sucrose and HFCS was at 43.4 lbs., however, from that point

forward sucrose consumption increased slightly while HFCS consumption decreased.

Since the majority of HFCS (61%) is earmarked for beverages, some suggest that the

increased availability of bottled water and diet drinks explains the HFCS consumption

decrease. Hodan Wells and Jean Buzby (2008), from the USDA Economic Research

Service (ERS), report that between 2000 and 2005 bottled water consumption increased

from 16.7 gallons per person to 25.4 gallons. In addition, consumption of diet beverages

102

60

0.5

62.4

0

20

40

60

80

100

120

1970 1997

Use of Sucrose and HFCS per pound per person

Sucrose HFCS

Figure 6 Use of Sucrose and HFCS per pound per person. Resource: Putnam and Allshouse, 1999: Data Source from UDSA Economic Research Service. Statistical Bulletin No. 965,. Graphic created by author.


increased 16 percent during that same time.150

United States Department of Agriculture (USDA) economists Stephen Haley, Jane

Reed, Biing-Hwan Lin and Annetta Cook (2005) analyzed the distribution of sweetener

consumption in the U.S. by demographic and product characteristics between 1994 and

1996. Data analyzed consisted of food intake surveys conducted by the USDA

Agricultural Research Service (ARS) which tracks household and individual food

consumption in the United States. Total data sample consisted of 20,862 individuals:

15,303 adults in the 1994-1996 Continuing Survey of Food Intakes by Individuals

(CSFII) and 5,559 children birth to nine years of age in the child-oriented 1998 CSFII.

For the purpose of their analysis, sweetener consumption was divided into several

categories: 1) sugar (defined as refined cane and beet sugar), 2) corn sweetener (defined

as corn syrup and HFCS), 3) others (inclusive of honey, maple syrup, maple sugar,

sorghum syrup and molasses) and 4) total sweeteners (all categories combined).151 With

respect to socio-economic status of sweetener consumption, their findings are a bit

surprising. CSFII household income brackets were based on Federal poverty guidelines:

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0

Poun

ds per person

Sucrose (Table Sugar) vs. HFCS ConsumpPon

Sucrose lb/yr

HFCS lb/yr

Fig 7. Sucrose (Table Sugar) and HFCS Consumption 1970-‐2010. Data Source: USDA ERS Briefing Room: Sugar and Sweeteners: Data Tables 51 (2011). Graphic created by author.


“low income” was defined as 130% or less of the poverty level, “middle income” was

131-350% of the poverty level and 350% and over the poverty level defined “high

income”. Low-income households had the lowest per pound, per capita consumption of

sweeteners totaling 99 lbs./year; 42.7 lbs. of sugar, 54.2 lbs. corn sweetener and 2.1 lbs.

other. Middle-income households had the highest per capita consumption at 105.8

lbs./year; 47.8 lbs. of sugar, 57.5 lbs. of corn sweetener and 0.5 lb. other. High-income

households were a hair behind middle-income households with a total sweetener

consumption of 102.1 lbs./year; 46.7 lbs. of sugar, 54.5 lbs. of corn sweetener and 0.9

other. According to their estimates, low-income household sugar consumption was 8.0%

less than the national average. In addition, they conclude that refined sugar consumption

(cane and beet) is more positively correlated with increasing levels of income than corn

sweetener (HFCS) consumption.152 That is to say that low income households consumed

more HFCS than refined [cane and beet] sugar. Not altogether surprising given the

relative cheapness of prepackaged, fast foods and “junk” foods. In the U.S. food and

beverage industry, the least expensive foods are unfortunately most often the highest in

sugar (specifically HFCS) and fat and lowest in nutrients. Their assertion would seem to

support the contention suggesting lower socio-economic households consume cheap,

manufactured foods due to their inability to pay for the more costly fresh fruits,

vegetables and whole grains.

Among Hispanic, non-Hispanic Black and non-Hispanic White ethnicities, non-

Hispanic Black individuals had the highest per capita consumption of sweeteners

consuming 45.0 lbs. of sugar, 58.2 lbs. of corn sweeteners and 3.1 lbs. other for a total

consumption of 106.3 lbs./year. Hispanic individuals had the lowest consumption of


sweeteners overall but the biggest differentiation between sugar and corn sweetener

consumption. Their total consumption was 94.1 lbs./year comprised of 38.2 lbs. of sugar,

55.5 lbs. of corn sweetener and 0.4 lb. of other. Non-Hispanic White individuals had the

second highest total consumption of sweeteners at 105.4 lbs./year. They had the highest

consumption of sugar at 48.6 lbs./year followed by 56.1 lbs. of corn sweetener and 0.7

other.

With respect to age variances, age categories were designated as follows: 2-11

years, 12-19 years, 20-39 years, 40-59 years and ≥ 60 years. Between genders, males

consumed more sweeteners in all categories. Average male consumption was 119.8 lbs.

of sweetener categorized into 51.8 lbs. of sugar, 66.4 lbs. of corn sweetener and 1.6 lbs.

of other. Average female consumption was a total of 86.9 lbs. of sweetener broken down

into 41.2 lbs. of sugar, 45.3 lbs. of corn sweetener and 0.4 lb. of other. What is

interesting about this data (although not terribly surprising) is that in both gender

categories, the highest total sweetener consumption occurred in individuals ages 12-19

years old. Furthermore, 12-19 year old males and females had the highest consumption

of corn sweetener among all age categories 58% for males and 57% for females. Males

(12-19 yrs.) consumed 159.8 lbs. of sweetener per year (40 lbs. more than the combined

average of all ages 2-60 and over); 65.7 lbs. of sugar, 93.0 lbs. of corn sweetener and 1.0

lb. other. Similarly, females (12-19 yrs.) consumed a total of 114.2 lbs. of sweetener;

49.5 lbs. of sugar, 64.6 lbs. of corn sweetener and 0.2 lb. other. [FIG 8]


With this level of consumption it is of no surprise that obesity and overweight rates have

increased dramatically among children and adolescents. A pertinent question arising out

of this data is: is it the increase in cumulative sugar consumption that is responsible for

the significant increase in child (and adult) adiposity or is it the type of sugar consumed

that is the decisive variable? At first glance, common sense would say certainly, more

sugar consumed = more calories = more fat, but sometimes things are not always as they

appear at face value.

When comparing the U.S. consumption rates of HFCS (USDA ERS 2011)

alongside the obesity rates of U.S. children and adolescents (Ogden et al. 2010) the

growth curves have striking similarities. [Fig 9] The period between 1972 and 1988

marks the most significant increase for both HFCS consumption and obesity rates. In

1972, the annual consumption of HFCS was 0.8 lb. per person and by 1988 that number

catapulted to 34.9 lb. per person. Similarly, the percentage of U.S. children 2-19 years of

age who were obese in 1972 was 5.0%, by 1988 that number had doubled to 10.0%.

Fig 8. U.S Sweetener consumption for adults and children per pound per person. Data Source: USDA ERS –Haley et al. 2005 Graphic created by author.

0 10 20 30 40 50 60 70 80 90 100

Males-‐ all ages

Males 12-‐19 yrs.

Females-‐ all ages

Females 12-‐19 yrs.

Poun

ds Per Person

Sweetener ConsumpPon among U.S. Adults (1994-‐1996) and children 12-‐19 years (1998)

Sugar (cane & beet)

Corn Sweetener (HFCS & corn syrup)


After 1988, both HFCS consumption and obesity rates continued to increase. HFCS

consumption peaked in 1999 with an annual per capita consumption of 45.4 lbs. and then

began a slow descent; currently the annual per capita rate of consumption is 35.1 lbs. per

person.153 Obesity rates, however, continued to climb until peaking in 2003, at which

point 17.1% of children and adolescents 2-19 yrs. old were obese. What is most alarming

about the obesity rates is that these numbers do not include children who were

overweight, only children who were categorized as obese. As mentioned earlier in the

discussion of BMI, and individual can be one or two pounds away from a label (or

diagnosis) of “obese” yet are still extremely overweight and at risk for severe health

complications.

This continued increase in obesity rates despite a decrease in HFCS consumption is not

necessarily surprising because the consumption of HFCS was still extremely high.

Obesity rates peaked in 2003 at 17.1% and at that time per capita HFCS consumption had

only decreased 2 lbs. down to 43.4 lbs. (from its peak at 45.4 lbs. in 1999). The data

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

1972 1978 1988 1999 2001 2003 2007

U.S. HFCS ConsumpPon and Child Obesity Rates

HFCS consumpgon per lb/per person

Percentage of children 2-‐19 yrs. Obese

Fig 9. U.S. HFCS Consumption 1970-‐2010 and U.S. Child and Adolescent obesity rates 1972-‐2007). Data Sources: USDA ERS Briefing Room: Sugar and Sweeteners: Data Tables (2011) and Ogden et al. (2010) National Health Examination Surveys II (ages 6-‐11) III (ages 12-‐17) and National Health and Nutrition Surveys (NHANES) I-‐III and NHANES 1999-‐2000, 2001-‐2001, 2003-‐2004, 2005-‐2006, 2007-‐2008. Graphic created by author.


from 1972 to 1999 appears to be a “slam dunk” for the HFCS-obesity correlation theory,

however data from 1999 to 2003 appears to negate that supposition. What must be taken

into account is the compound interest effect with respect to physiological processes. As

discussed in chapter 2, metabolic processes involved in glucose and fructose are very

complex. If a child was overweight (not obese thus not registering in the data) in 2001

and maintained (but did not increase) a high refined carbohydrate diet (one common

among fast food and prepackaged foods), it is quite possible that physiologically by 2003

all of his/her glycogen stores were saturated resulting in all calories not immediately

utilized being converted directly into triglycerides and stored as additional fat. In this

scenario, if a child reduced soda consumption (which contains HFCS) from three a day to

one a day, while that would be a significant reduction in HFCS consumption,

physiologically that may still be too much sugar for his/her body to utilize. What is

surprising about the data is the consumption pattern of sucrose and HFCS between 2003-

2007 compared to obesity rates. According to the data tables (table 51), sucrose

consumption began to increase during this period while HFCS consumption and obesity

rates both decreased. Some experts contend that HFCS is not responsible for the

[alarming] rise in obesity rates, claiming that the metabolic processes between sucrose,

glucose, fructose and high fructose corn syrup are not significantly different.154

METABOLISM AND ADIPOSITY

Several studies have looked at differences between glucose and fructose

consumption and their relationship with metabolic processes. 155 However, the majority of

studies investigate and measure the differences between sugars consumed via liquid


only156 and do not take into account differentiation of sugars present in food. For

example, in one experiment [that included food] the breakfast consisted of a bagel and

cream cheese.157 While a registered dietician designed the meal, what is not known (or

perhaps what is not stated in the reports) is whether or not the bagel contained any

additional sugars such as HFCS or sucrose. Hypothetically, if a bagel contained 2 g. of

HFCS and test subjects were given a liquid beverage containing 10 g. of sucrose then

total sugar consumption for that meal would be 12 g. with a breakdown of 2 g. of HFCS

and 10 g. of sucrose. Conversely, if the subjects were given a beverage containing 10 g.

of HFCS then while the total sugar consumption would be a constant 12 g., the

differentiation would be 12 g. of HFCS and 0 g. sucrose. Over time this could result in

significant metabolic differences. Nevertheless, the research performed thus far has shed

some light on metabolic differences and/or similarities between the various sugars, and

spawned further questions.

In a comparison study of dietary fructose and glucose on circulating insulin, leptin

and ghrelin levels, Dr. Karen Teff of the University of Pennsylvania School of Medicine

and her colleagues (2004) discovered that dietary fructose reduces circulating insulin and

leptin but increases ghrelin and triglycerides.158 Their subjects were twelve normal

weight women ages 19-33 each with a BMI within “normal” ranges. Experimental

testing consisted of two 48-hour periods a month apart. For one two day period, the

subjects were only allowed to consume meals whose simple-sugar carbohydrates were

derived from fructose (HFr), namely in the form of a fructose-sweetened beverage; an

additional liter of water was consumed throughout the day. Conversely, during the

second 48-hour period, meals consisted of simple-sugar carbohydrates derived from


glucose (HGl) in the form of a glucose-sweetened beverage; an additional liter of water

was consumed throughout the day. The research team administered each meal and each

sweetened beverage consumed. Blood samples were taken thirty minutes after each meal

as well as at other hourly intervals throughout the day for the purpose of analyzing the

following plasma concentrations: glucose, insulin, leptin, ghrelin, GIP (gastric inhibitory

polypeptide, an endocrine hormone now believed to induce insulin secretion and also

effect fatty acid metabolism through stimulation of lipoprotein lipase159), GLP-1

(glucagon-like peptide-1, a hormone that induces insulin secretion while suppressing

glucagon secretion and also appears to restore the glucose sensitivity of pancreatic β-

cells160), triglycerides (TG) and free fatty acids (FFA). As anticipated by the team,

plasma glucose levels were lower after HFr meals compared to HGl meals. There was a

significant decrease of 65% (± 5%) in insulin levels after HFr meals compared with HGl

meals; in addition, insulin secretion continued to be blunted throughout the day by

approximately 49% (± 5%) during the days of HFr consumption. There was a slight

difference between HGl meals and HFr meals with respect to plasma leptin levels. As

with insulin there was a significant difference in ghrelin levels. On the HGl days, plasma

ghrelin decreased by 30-35% after each meal; conversely, the suppression of ghrelin after

HFr meals was deemed “not significant”. Interestingly, it was what occurred during the

duration of the day (i.e. non-meal times) that caught researchers attention. During the

evening and early morning hours (i.e. fasting state; 11:00pm-3:00am), on HGl days,

ghrelin concentrations did not increase above baseline however, on HFr days, plasma

ghrelin levels were elevated above baseline. GIP levels were similar in the fasting state

(i.e. 11:00pm-3:00am) for both HGl and HFr days. However, GIP concentrations


increased more rapidly after HGl meals (within 30 minutes of consumption) than HFr

meals (within 60 minutes of consumption). In addition, overall plasma GIP levels

remained higher throughout the day on HGl days than on HFr days. While there was not

much differentiation between peak levels of GLP-1 after HGl meals versus HFr meals,

the GLP-1 levels remained elevated for a longer period of time (120 minutes) after HFr

meals than HGl meals. Plasma TG (triglyceride) levels increased (i.e. spiked) more

rapidly 4-5 hours after a HFr breakfast than after a HGl breakfast, in addition the TG

peak was higher with the HFr meal than the HGl meal. Plasma TG levels also remained

elevated throughout the 24 hour period on the HFr days whereas TG levels decreased

after peaks and remained below baseline levels during the night on HGl days. During the

morning prior to breakfast (9:00 am), plasma TG levels were markedly higher (approx.

35%) on HFr days than on HGl days. Plasma FFA levels were similar for both HFr days

and HGl days and they concluded that the differences were not statistically significant

and would not be expected to influence insulin sensitivity. Their results indicate that

consumption of HFr meals and beverages results in lower circulating plasma leptin and

insulin concentrations and higher ghrelin and triglyceride levels than consumption of HGl

meals and beverages. [Ghrelin is a hormone produced by the oxyntic glands of the

stomach that stimulates hunger161 as well as protects against chronic stress induced

depression, anxiety162 and enhances cognition163. Like glucagon is the counterpart of

insulin, ghrelin is considered to be the counterpart of leptin, in fact, ghrelin rivals NPY

for potency in stimulating appetite.164] Teff et al. surmise that because insulin, leptin and

ghrelin are participants with the central nervous system (CNS) regarding long-term

regulation of energy, prolonged consumption of diets high in fructose could lead to


weight gain and obesity. In addition, due to the elevated plasma TG levels upon

consumption of fructose, chronic consumption of fructose could also contribute to CVD

and atherogenesis.165

In a short-term study investigating endocrine and metabolic profiles after

consumption of different sugars, Stanhope et al. (2008) examined differences between

HFCS and sucrose when compared to glucose and fructose consumption. The format was

a somewhat similar to Teff et al. however the duration was shorter (24 hours versus 48

hours) and the participants were a mixed gender sample of 18 men and 16 women. Like

Teff et al.’s study, subjects were given prepared meals with an accompanying beverage

that was sweetened with either HFCS, sucrose, fructose or glucose. Their analysis

centered on the differences and/or similarities in blood profiles for the 24-hour testing

period. They reported no significant differences between HFCS and sucrose in plasma

glucose, leptin, ghrelin, TG or FFA concentrations.166 Insulin was slightly “but

significantly” increased with sucrose consumption versus HFCS consumption, however

the team deemed this increase to be age related as there was no reported significant

increase in subjects > 35 years old but only in subjects < 35 years old. While they

reported that plasma profiles during sucrose and HFCS consumption “were not different”,

their data tables state that plasma TG level during HFCS consumption was 1,043.5 mg/dL

whereas levels were 738.7 mg/dL during sucrose consumption, that’s 304.8 mg/dL

difference which may not be statistically “significant” but is certainly relevant, especially

over prolonged accumulation. Melanson et al. (2007) also report no significant

differences in blood glucose, insulin, leptin levels or appetite upon consumption of HFCS

or sucrose.167 However, this too was a short-term (48 hour) study and the team concluded


that further research was needed to see if similar results would occur in a longer study.

Studies like these are often the foundation for those who contend that HFCS is

metabolically equivalent to sucrose and that it is the recipient of unwarranted criticism and

“bad press”. However, obesity is not result of short-term consumption but rather long-

term over indulgence therefore, emphasis within the scientific community should be on

long-term studies.

Overall, there are very few studies isolating metabolic differences and/or

similarities between HFCS and sucrose. A major contention of HFCS supporters such as

White et al. (2010) is that HFCS is compositionally similar to sucrose (more so than to

straight fructose or straight glucose) and that the majority of studies examine the

metabolic differences between glucose and fructose168 rather than between HFCS and

sucrose.169 This is a valid contention. In fact, research investigating metabolic

differences and/or similarities between sucrose and HFCS is extremely limited and long-

term studies are almost non-existent. The majority of research designs have been

conducted within a 24-48 hour testing period which might be sufficient for analysis of

short-term effects on blood and metabolic profiles, but certainly is not sufficient to

project any potential (or probable) long-term effects or adverse health ramifications.

One of the few long-term studies with human subjects conducted thus far was by a team

of researchers from the Department of Nutrition at The University of California- Davis

who observed the effects of fructose consumption on blood lipid profiles during a ten-

week period. Swarbrick et al. (2008) concluded that long-term consumption of diets high

in fructose could potentially increase the risk of developing CVD.170 While their

conclusion sounds robust, their sample consisted of seven overweight and/or obese, post-


menopausal women and thus is neither a substantial nor representative sample for

conclusions to necessarily be projected onto the general public. However, as with other

studies investigating the effects of fructose consumption, their results clearly indicate that

fructose consumption significantly increases TG levels.171

Dr. White and his colleagues (2010) reported that it is the American Medical

Association’s (AMA) position that HFCS “does not appear to contribute” to obesity more

than any other sweetener. They also reported that the American Dietetic Association

(ADA) “have concluded that high-fructose corn syrup is not a unique cause of

obesity.”172 Both reports, the AMA’s Report 3 of The Council on Sciences and Public

Health: The Effects of High Fructose Syrup (2008) and the ADA’s Position on the

American Dietetic Association: Use of Nutritive and Nonnutritive Sweeteners, have been

thoroughly examined (and re-examined) yet those specific quotes remain elusive. The

AMA’s report actually concluded that, “because the composition of HFCS and sucrose

are so similar, particularly on absorption by the body, it appears unlikely that HFCS

contributes more to obesity or other conditions than sucrose. Nevertheless, few studies

have evaluated the potentially differential effect of various sweeteners, particularly as

they relate to health conditions such as obesity, which develop over relatively long

periods of time.”173(italics added) The council did not definitively conclude HFCS was

“not” a cause of obesity, nor that it was not a significant cause, only that the available

evidence was insufficient at that time to specifically restrict the use of HFCS or to require

the use of a warning label on food products containing HFCS.174 Furthermore, in their

Executive Summary, the Council on Science and Public Health also stated that, “only a

few small, short-term experimental studies have compared the effects of HFCS to sucrose


and most involved some form of industry support. Epidemiological studies on HFCS and

health outcomes are unavailable, beyond ecological studies, because nutrient databases

do not contain information on the HFCS content of foods and have only limited data on

added sugars in general.”175 (italics added) In addition, they recommended more

“independent research” (e.g. research sans ties-financial or otherwise- to the food &

beverage manufacturing industry) as well as long-term and epidemiological studies on

the health effects of HFCS. With respect to the American Dietetic Association (ADA),

again latitude may have been exercised by White et al.’s interpretation of the ADA’s

position. In the ADA’s 2004 Report White et al. cited, the context behind the ADA’s

position was/is that sweeteners (whether fructose, sucrose, or HFCS) “by themselves” are

not the sole reason for obesity or weight gain. The report does not state that sweeteners

were not a contributing factor to obesity…only that they were not the sole factor,

“existing evidence does not support the claim that diets high in nutritive sweeteners by

themselves have caused and increase in obesity rates or other conditions.”176 This position

was reached based on findings from David Lineback and Julie Jones’ (2003) summary

report of the 2002 Sugars and Health Workshop.177 The context behind their (Lineback

and Jones) report of findings was regarding the scarcity of research available overall, not

simply the scarcity of scientific research implicating HFCS and they concluded, “this is

not to say that sugars may not be involved in other health issues cited, particularly when

their overconsumption results in an energy imbalance with resulting weight gain, but that

currently evidence is not sufficient to validate a direct causative role for sugars

consumption.”178 They also reported that a major limitation of data collection regarding

specific sugar intake, as well as cumulative sugar intake, is that it is currently not possible


to “analytically distinguish” between added sugars and naturally occurring sugars.179 For

example, with Kraft Brand’s Capri Sun Grape Juice, there is no delineation between

naturally occurring fructose contained in the grape and pear juices and added HFCS, all

that is reported is the cumulative sugar content of 16 grams.180 Similarly, an apple pie

from McDonald’s contains 13 grams of sugar and contains: high fructose corn syrup,

sugar, dextrose and brown sugar.181 Aside from the notation that it contains less than 2%

of dextrose and brown sugar the remaining composition is unknown. Does it contain 11

grams of HFCS and 2 grams of sugar (i.e. sucrose) or does it contain 4 grams of HFCS

and 8 grams of sugar? For those investigating a potential correlation between sugar

consumption and obesity and specifically whether or not one sugar (such as HFCS)

increases adiposity over another sugar, this unknown is extremely relevant.

In another article espousing the “misconceptions about high-fructose corn syrup”,

Dr. White (2009) boldly proclaims that with respect to metabolic differences between

sucrose and HFCS, “in the relatively few studies in which the 2 have been compared, no

differences in metabolic markers of obesity or measures of satiety were observed.”182

Some noteworthy thoughts: first, the references cited were all short-term studies (24-48

hours) with one study having a testing period less than five hours. Second, “no

differences” is perhaps another liberal inference as one of the cited studies revealed a 300

point difference in plasma TG levels183 (perhaps statistically insignificant but a difference

nonetheless) while another study regarding uric acid levels concluded “the implication

for HFCS from these results is far from clear.”184 Finally, he claims that the markers of

serum glucose, insulin, ghrelin, triacyglycerols (triglycerides), uric acid, satiety and

hunger were all comparable between sucrose and HFCS.185 However, these results were


again after short-term trials, some only several hours in duration; hardly sufficient time to

make such a declarative conclusion with respect to HFCS and a condition that develops

over time such as obesity. It is also of some consideration that Dr. White, a well known

biochemist (some contend an expert in the field) who has published several papers in the

defense of HFCS as well as authored two reference books on enzymes, proteins and

peptides, is the president and founder of White Technical Research, an international

consulting firm for the food and beverage industry and proudly proclaims his affiliation

with the Corn Refiners Association, a national association representing the corn refining

(i.e. wet milling) industry.186 These associations cast a shadow on the purity or non-

biased nature of his assertions.

Four years after the ADA’s position cited by White et al. and in the same year the

AMA issued their report of finding regarding HFCS (note: findings that were based upon

scientific research only published through December 2007), a team of researchers

conducted one of the few studies regarding sugar consumption and adiposity specifically

(also one of the few with a lengthier duration). In a long-tern study involving Sprague-

Dawley rats, scientists from the University of West Virginia (Light et al., 2008) were

interested in adiposity with respect to consumption of fructose, glucose, sucrose and

HFCS-55 sweetened beverages. They accurately noted that while several animal studies

have investigated the differences between glucose and fructose consumption with respect

to adiposity, there lacks voluminous research comparing HFCS alongside fructose and

glucose. They also pointed out an important research design element: dosage of

sweetener used. They contend that previous animal studies used high doses of sweetener

that were “unlikely to be physiologically relevant to the amount of sugar consumed by


humans”.187

In their eight-week study, they specifically used a dose of sweetener comparable

to the amount that is found in a typical soft drink. Forty-four female Sprague-Dawley

rats were divided into five groups of 8-9 rats each. All rats consumed the same solid food

but different liquid solutions. Liquid solutions were either plain distilled water or water

sweetened with glucose, fructose, sucrose or HFCS-55. The concentration of added

sweetener was 13% w/v (weight/volume), again the equivalent of sweetener found in an

average soft drink. For eight weeks the rats were allowed to access food and liquid ad

libitum (at their leisure). Rats given the glucose-sweetened solution consumed the

greatest amount of liquid totaling 5154 mL, they were followed by rats given the sucrose-

sweetened solution consuming a total of 3403 mL. Fructose and HFCS-55 solution rats

followed and were relatively close in consumption volume at 2267 mL and 2795 mL

respectively. Those consuming the distilled water had the lowest liquid consumption at

1844 mL but had the highest food consumption at 968 g. Conversely, the glucose group,

while consuming the most liquid, had the lowest food intake at 597 g. The reduction of

solid food consumption could be attributed to the increased caloric consumption gained

via the liquid glucose solution and vice versa for those drinking the water. Results for

total caloric consumption is as follows: water = 3678 kcal, glucose = 4719 kcal, sucrose =

4247, fructose = 4224 kcal and HFCS-55 = 4140 kcal. Again, those consuming the

glucose solution consumed the most calories, water consumed the least (not surprisingly)

and sucrose, fructose and HFCS-55 had relatively similar total caloric consumption. If

one stopped analysis at this juncture these findings would certainly support White et al.

and others’ contention that HFCS and sucrose are metabolically identical. However, the


body weight and body composition results prove contrary and are quite damaging to the

champions of HFCS. Surprisingly, rats drinking the glucose-sweetened solution had the

least amount of total weight gain at 174 g. accompanied by 7.9 g. of gonadal adipose

tissue and 3.1 g. of retroperitoneal (abdominal) adipose tissue. Rats drinking water had

the second least amount of weight gain at 176 g., but had least amount of gonadal and

retroperitoneal adipose tissue at 6.3 g. and 2.9 g. respectively. Sucrose solution rats had a

total weight gain of 186 g. with gonadal and retroperitoneal adipose tissue being 9.8 g

and 4.2 g. respectively. Fructose solution rats experienced a 195 g. increase in weight

with gonadal adipose and retroperitoneal adipose tissue at 9.5 g and 4.0 g. respectively.

The group with the highest total weight gain, as well as highest amount of adipose tissue,

was the group fed the HFCS-55 solution. These rats increased body weight by 198 g. (24

g. more than the glucose group and 12 g. more than the sucrose group); in addition,

gonadal adipose was an 11.6 g. and retroperitoneal adipose was 5.0 g. While Light et al.

contend that these differences are not statistically significant they do conclude that the

type of sweetener added does, in fact, influence body weight and fat mass.188 However, it

is this author’s contention that these differences might very well become statistically

significant over a prolonged period of time, 52 weeks for example. While not

highlighted, their data indicates that the liver of the HFCS group weighed more (8.1 g.)

than those in the water, glucose, sucrose or fructose group (6.4 g., 7.0 g., 7.4 g., and 7.6

g. respectively). The liver is a key regulator of both glucose and fat metabolism (and

storage) therefore this increase could become a significant factor in a prolonged study.

Since HFCS-55 has replaced sucrose in the food and beverage industry as the preferred

sweetener, the liver weight differentiation between the groups (HFCS and sucrose in


particular) could be very important regarding human metabolism, adiposity and other

physiological conditions. Sometimes one point is a significant difference. For example,

in human blood analysis, (according to the 2010 standards of the American Diabetes

Association) an A1C rage of 5.7-6.4% classifies and individual as “increased risk” for

diabetes and anything over 6.5% is a diagnosis of diabetes.189 In this example, a tenth of

a percentage, which is usually deemed statistically insignificant, is quite significant. Light

et al.’s data clearly shows that rats consuming HFCS-55 for eight weeks not only

weighed more than the sucrose solution group (12 g. more), but also had more adipose

tissue as well, 16.6 g. and 14.0 g. (combined gonadal and retroperitoneal) respectively. If

there is no significant metabolic difference between sucrose and HFCS as proponents of

HFCS suggest then what is the explanation for the increased adiposity in the HFCS

group?

A valid criticism of the HFCS-Obesity correlation theory is that the total

consumption (HFCS and sucrose sugars combined) has increased over the years and

adiposity is an outcome of energy consumption versus energy expenditure. Again, more

energy (kcal) consumed than energy (kcal) expended results in accumulation of stored

adipose tissue (fat). Thus critics contend that it is the total consumption that is the

pivotal component in the purported correlation between sugar consumption and increased

adiposity and not the individual type of sugar, specifically, HFCS.190 According to the

USDA ERS data tables (2011), with an exception of a slight dip in 1975, total sugar

consumption per capita has increased steadily at an approximate rate of 1-2 lbs. per year

from 1970 until 1999; total consumption increasing from 72.9 lbs./yr. to 92.6 lbs./yr.

respectively. From 1999 to 2010 there has been a steady decrease again, averaging 1-2


lbs. per year to a present rate of 82.1 lbs./yr. per capita in 2010. While overall sugar

consumption increased, the most shocking finding is the differentiation in the types of

sugars that were being consumed. There is an inverse relationship between sucrose

consumption and HFCS consumption, which given the substitution of HFCS for sucrose

in the food and beverage industry is not surprising. As mentioned earlier, in 1970 per

capita sucrose consumption was 72.5 lbs./yr. while HFCS was a mere 0.5 lb./yr. per

capita. As sucrose consumption decreased steadily, HFCS consumption increased

peaking at 45.5 lbs./yr. in 1999. Between 2000-2002, both held relatively steady with

minor fluctuation of less than half a pound and by 2003 total consumption was split

evenly among them at 43.4 lbs./yr. After 2003, sucrose consumption began a slight

increase to 47.0 lbs. in 2010 and conversely, HFCS consumption began decreasing to

35.1 lbs. in the same year and most notably so did obesity rates. The period between

1978 and 1988 becomes interesting when adding the variable of child obesity rates.

As discussed previously, in 1972 the rate of child and adolescent obesity was 5.0%

and consumption of HFCS was a paltry 0.8 lb./yr., meanwhile the consumption of

sucrose was at 72.8 lbs./yr.191 By 1978, sucrose consumption decreased almost ten

pounds to 63.5 lbs./yr., HFCS consumption increased slightly to 3.5 lbs./yr., and child

obesity rates were at 5.5%. A decade later, sucrose consumption had decreased to 44.2

lbs./yr. and conversely, HFCS consumption had increased to 34.9 lbs./yr. at the same

time obesity rates had doubled. Between 1978 and 1988, total sugar consumption

increased 6.3 lbs./yr. but the breakdown of sugar consumption is interesting. In ten years

sucrose consumption decreased 20.9 lbs. while HFCS consumption increased 27.2 lbs.

and again, obesity rates doubled. Another significant period occurs between 2003 and


2007. In 2003, child obesity rates peaked at 17.1% but by 2007 those rates had decreased

to 15.5%. During this time total sugar consumption decreased from 86.8 lbs. (2003) to

83.7 lbs. (2007) similarly, HFCS consumption decreased from 43.4 lbs. to 40.1 lbs. but

sucrose consumption increased negligibly from 43.4 lbs. to 43.6 lbs. At first glance this

appears to confirm the contention that it is the quantity of sugar consumed that is the

smoking gun with respect to adiposity and not the quality (type) of sugar ingested.

However, you will recall from the previous section that studies have shown that there are

metabolic differences between different sugars, even between HFCS and sucrose.

Certainly, the quantity of any food substance ingested plays a role in overall weight as

well as lean muscle tissue to adipose tissue ratio. However once cannot dismiss the type

of substance either, 300 calories in a chicken breast is not metabolically equivalent 300

calories of chocolate cake or 300 calories of carrots.

If there is no significant metabolic difference [with respect to adiposity] between

sucrose and HFCS, then one would expect to find obesity rates consuming sucrose to be

similar to obesity rates consuming HFCS however, the data does not reveal that.

Between 1978 and 1988 sucrose consumption decreased (65.1 lb. to 44.2 lbs.

respectively) but obesity rates increased (5.5% to 10.0% respectively). During this same

time period HFCS consumption experienced its greatest surge from 7.7 lbs. to 34.9 lbs.

Opposition would contend that the rise in obesity during 1978-1988, despite the decrease

of sucrose consumption, was directly attributed to an overall increase in total sugar

consumption but that theory does not explain the period between 2003 and 2007. Again,

between 2003 and 2007 sucrose consumption increased but HFCS consumption and

adolescent obesity rates both decreased. [Fig 10]


Another weakness of the “cumulative sugar consumption” hypothesis is the time

period between 1999 and 2003. During this time, obesity rates steadily increased from

13.9 % (1999) to 17.1% (2003) yet total sugar consumption decreased from 92.6 lbs. to

86.8 lbs. In addition, both sucrose and HFCS consumption decreased from 45.4 lbs. and

47.2 lbs. (1999) to 43.4 lbs. and 43.4 lbs. (2003) suggesting there were other variables

that were also influential factors.

An admitted weakness of utilizing the USDA Data Tables for analysis of sucrose

consumption, HFCS consumption and child obesity rates is that while the obesity rates

are specific to children and adolescents, the sugar and HFCS consumption data is

cumulative of the U.S. population which means adults are included in that data sample. It

is highly improbable that a three or five year-old child consumed 78.2 lbs. of sugar

(sucrose) in 1972 or 45.4 lbs. of HFCS in 1999. However, it is also just as unlikely that

Fig 10. U.S. HFCS and Sucrose Consumption 1970-‐2010 and U.S. Child and Adolescent obesity rates 1972-‐2007). Data Sources: USDA ERS Briefing Room: Sugar and Sweeteners: Data Tables (2011) and Ogden et al. (2010) National Health Examination Surveys II (ages 6-‐11) III (ages 12-‐17) and National Health and Nutrition Surveys (NHANES) I-‐III and NHANES 1999-‐2000, 2001-‐2001, 2003-‐2004, 2005-‐2006, 2007-‐2008. Graphic created by author.

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

1972 1978 1988 1999 2001 2003 2007

U.S. Sucrose ConsumpPon, HFCS ConsumpPon and Child Obesity Rates

HFCS consumpgon per lb./per person

Percentage of children 2-‐19 yrs. Obese

Sucrose consumpgon lb./person


the same three or five year old child purchased and prepared the food he/she ate.

Obviously, the parents/caretakers are responsible for ensuring that their child eats. It

stands to reason that if the parent/caretaker is consuming large quantities sugar-laden

foods that those same foods are being fed to their children. Nevertheless, the data does

suggest a correlation between consumption of HFCS and increased adiposity among

children and adolescents.

Daily per capita total carbohydrate consumption rose 27% from 386 grams (1970)

to 491 grams (1994). Putnam and Allshouse attribute this increase to the increased use of

grains and sweeteners, as carbohydrates from sugars also increased 21% during the same

period (from 152 grams to 184 grams.). Between 1977-1994, consumption of grain

products such as pizza and lasagna (pasta) increased 115%; snack foods such as pretzels,

popcorn, crackers, and corn chips rose 200%; and consumption of “ready to eat” cereals

increased 60%. It should be noted that most all of these popular grain products often

contain added sugar in the form of HFCS (HFCS-42 specifically). However, while

overall consumption of grain products increased, they assert that whole grain

consumption remained below the ADA recommended daily allowance.

Overall caloric intake increased 500 calories (15%) between 1970 and 1994.

Again, at the risk of being repetitive, all calories are not metabolically and

physiologically the same. While they may contain [and release] the same kcal energy

unit, the other physiological responses outside of ATP production can vary. The same

500 kcal from protein such as a chicken breast will not yield the same physiological

response in the body as 500 kcal of sugar. Protein consumption remained consistent at

eleven percent (11%) of total calories consumed but what is of great interest are their


findings that while the proportion of calories yielded from carbohydrates increased (47%

to 51%), the proportion of calories derived from fats decreased (42% to 38%). This data

is contrary to those who hypothesize that obesity is not related to the increase of sugar

intake but rather an increase in overall dietary fat consumption.192 Dr. Walter C. Willett

(1998) of the Department of Nutrition, Harvard School of Public Health and professor of

medicine at Harvard Medical School, expressed concerns regarding the low-fat diet craze

in the 1990s, specifically that substituting carbohydrates for dietary fat consumption

could induce serious metabolic abnormalities [such as hyperlipidemia and

hypertricglceridemia] within a sedentary population that also exhibited a prevalence of

insulin resistance.

In an ecological study of dietary fat intake and its relationship to [excess] adiposity,

Dr. Willett concluded that dietary fat was not the “primary cause” of obesity. For

example, at the time of the study approximately 60% of South Africans were overweight

yet less than 22% of their caloric intake was from dietary fats, he found similar findings

in Saudi Arabia. He concluded that “compensatory mechanisms” within the body occur

such that in the long-term, fat consumption within the rate of 18-40% appears to have a

minimal effect on overall adiposity.193 Subsequently, limiting or removing it from one’s

diet, contrary to popular hypotheses of that time, did not result in shedding of unwanted

pounds (fat). However, some studies have contradicted Dr. Willett’s conclusion.

Gazzaniga and Burns (1993) examined the relationship between diet composition and

body fatness in preadolescent children (9-11 yrs.) and discovered that dietary fat

consumption, specifically saturated fats, were significantly correlated to body fat

percentage (BF%). In obese subjects, a greater portion of caloric intake was derived from


dietary fats and significantly less in carbohydrates than in non-obese subjects.

Additionally, obese children expended more energy per day than did non-obese children.

Gazzaniga and Burns surmised this increased energy expenditure resulted from the

additional body weight that was being carried; in short, it requires more energy to move a

larger mass. They concluded that a diet higher in fat and lower in carbohydrates may

contribute to obesity in preadolescent children. A significant limitation of this study is

that data was extrapolated from a 24-hour recall survey completed by the parents so

human error is a factor. Additionally, their data reveals that obese subjects consumed

more calories overall than did the non-obese subjects (9384 kcal versus 7056 kcal

respectively194) , another variable that could be a contributor to their adiposity.

Putnam and Allshouse also discovered that types of fats consumed changed as well

as the percentage/proportion of the overall caloric constitution. In 1970, animal fat

comprised 35% of dietary fat consumption while other fats and oils (primarily vegetable

oils) comprised 43%. By 1994, animal fat had decreased to 25% of all fat consumption

and other fats and oils rose to 52%. The increase in “other fats and oils” category was

most likely due to a significant increase in consumption of fast foods, snack-foods, salad

dressings and other foods laden with hydrogenated vegetable oils. For salad dressings

and cooking oils alone per capita consumption almost doubled between 1970 and 1997

from 15 to 29 pounds per person per year. 195

ECONOMIC BENEFITS

Some contend that the increase in sugar consumption and hydrogenated oil

consumption is inversely proportional to the decrease in price of crop/raw material for

manufacturers, specifically corn, soybean and wheat. The Institute for Agriculture and


Trade Policy (IATP) asserts (2006):

“The problem with the extensive use of these cheap commodities in food

products is that they fall into the very dietary categories that have been

linked to obesity: added sugars and fats. U.S. farm policies driving

down the price of these commodities make added sugars and fats some

of the cheapest food substances to produce. High fructose corn syrup

and hydrogenated vegetable oils- products that did not even exist a few

generations ago but now are hard to avoid- have proliferated thanks to

artificially cheap corn and soybeans. Whether by intention or not,

current farm policy has directed food industry investment into producing

low-cost, processed foods high in added fats and sugars.”196

An example of this influence is Kraft Brand’s recent switch from cane sugar

(sucrose) to HFCS in their popular children’s juice line Capri Sun. Due to the economic

cost of sugar (cane and beet) they have now converted to using HFCS to sweeten these

beverages. While they contend that the change was made “to help better manage costs

for consumers in today’s difficult economic environment”197there is no doubt they are

preserving the welfare of their own profit margin as well. Government support for

producing grain, corn and soybean (the latter two commonly referred to as oilseed crops)

takes many forms: money invested in universities and corporations for specific crop

research, direct subsidy payments to farmers to produce specific crops (to offset the

low/set crop prices), as well as agreements for future crop exports.198 These subsidies

indirectly affect the cattle/livestock industry as well since the majority of U.S. livestock

are grain-fed instead of a healthier and more natural grass-fed. The IATP contends that

by keeping crop prices for feed grains so low, the U.S. farm policy has created an unfair

market advantage favoring large, industrialized livestock production over “diversified

sustainable” (i.e. mixed crop and livestock) livestock production. Sadly, produce crops


(i.e. fruits and vegetables) on the other hand receive far less government support.

According to the IATP, it is this lack of support that creates a riskier economic

environment for produce farmers. Although fresh produce carries a higher price point,

the lack of support for growing these crops “makes growing vegetables a much riskier

proposition”.199

GENETICALLY MODIFIED FOODS (GMO/GE)

One of the largest areas of research today, estimated at $40 to $100 billion

dollars,200 is biotechnology and genetic engineering specifically, genetically modified

organisms (GMO, GM or genetically engineered, GE) crops. In an effort to increase crop

production and ultimately profitability, scientists have genetically modified crops to be

resistant to pesticides, fungi, bacteria and insects. To genetically modify a crop, scientists

splice together one or more genes into the crop’s genome using viral promoters,

transcription terminators, reporter genes and antibiotic resistant marker genes.201

Bacterium, fungi, and viruses are used as catalysts to graft in the desired gene product to

the host. The problem is that despite the industry driven propaganda, little scientific

research exists regarding the safety and efficacy of these GM products. Dr. Arpad

Pusztai and his colleague, Dr. Stanley W.B. Ewen, from the Rowett Research Institute

(RRI) in Scotland secured a multi-million dollar grant to investigate the impact, if any, on

genetically modified potatoes on animal and human health.202Once a staunch supporter of

GMO, his findings not only changed his stance but also eventually resulted in his

indefinite suspension at the institute and termination of research funding. After working

for RRI for 36 years and publishing almost 300 papers and nine books, Dr. Pusztai was

“removed from service, his research papers were seized, and his data confiscated”203 in


retaliation for comments made during a 1998 interview regarding his findings on the

effects of GM foods relative to their safety. Prior to splicing with the genetic marker

(gene product), Pusztai and Ewen first isolated the gene product (i.e. protein) lectin from

the snowdrop plant to determine its toxicity and/or effect on absorption of a normal diet

within the intestinal tract. Even at high doses, adding lectin by itself (administered via an

eyedropper) to normal (i.e. non-GM) food did not exhibit any adverse effects; the rats

were not harmed. However, this was not the case for rats fed GM “pest resistant”

potatoes spliced with lectin and the Cauliflower Mosaic Virus (CaMv). The rats fed GM

potatoes where the lectin had been added genetically via splicing suffered damaged

organs, intestinal tract and immune system.204 They concluded that the splicing process

somehow “destabilized the potato genome”205and those elements which made the potato

resistant to insects (i.e. substances that were toxic to the insect) were also making the

potato toxic. Furthermore, they discovered that the splicing process itself was

unpredictable. Their results revealed that genetic differences can occur between sibling

batches of GM foods despite being derived from the same root-stock and subjected to

indistinguishable conditions. It was assumed that these offspring batches would contain

an identical genetic composition however, Pusztai and Ewen’s nearly three year

experiment proved contrariwise. The GM batches were not the same. When discussing

the strains of GM spliced potatoes Dr. Pusztai recounts,

“We had two successful lines, both coming from the same genetic

transformation of the parent line at the same time. They were going

through the same laboratory tests and were growing in the fields for two

years down in the South of England. And when we looked at the two lines,

we found that against our expectations they were different. They were

different compositionally. For example, one of the lines contained exactly


the same amount of protein as the parent line but the other line, even

though it was as successful in protecting the plant against aphids

nematodes, it contained 20 percent less protein. Now this was a totally

unpredictable effect.”206

This unpredictability was most troublesome to the scientists and they realized that more

research was needed before deeming GE products as safe for human consumption, “We

don’t eat a lot of these things in GM foods that are now being sold. So it should be in our

interest to get it properly tested.”207 Mae-Wan Ho et al. (2000) reported that post-Pusztai

research has discovered that the CaMv promoter is susceptible to horizontal transfer (i.e.

it participates and potentially instigates unintended rearrangement of genetic coding) and

recombination hot spots (areas where the DNA break and then repair)208 thus contributing

to significant instability within the line. In addition, it has the potential for insertion

mutagenesis, insertion carcinogenesis and reactivation of dormant viruses or to create

new viruses in species it is transferred to.209 Dr. Ho, a geneticist, and her colleagues

recommend that “all transgenic crops containing CaMv 35S or similar promoters which

are recombinogenic should be immediately withdrawn from commercial production or

open field trials. All products derived from such crops containing transgenic DNA

should also be immediately withdrawn from sale and from use for human consumption

and animal feed.”210 Not surprisingly, this recommendation has met its share of criticism.

In many countries, lack of sufficient research regarding the safety and efficacy of

genetically modified foods has resulted in stringent restrictions and even moratoriums on

production. The United Kingdom (UK) has placed a moratorium on all GE foods

pending research on the environmental and human health effects. In 1997, Austria

banned the use of Bt corn. In 1999, Greece banned seven GE crops (including corn),


Italy banned GE corn and oil byproducts and Brazil banned the cultivation of GE soybean

and are now exporting GE-free soybeans. Germany banned the cultivation of GE corn

in 2000. Norway banned all GE foods and food manufacturers in Switzerland have

banned all GE ingredients. Japan has mandated testing on potential health risks of GE

foods and its two largest breweries have banned the use of all U.S. GE/GMO corn.211

Sadly, the U.S. is nowhere to be found in the list. Why? The answer is as disturbing as

Pusztai and Ewen’s results.

FDA APPROVAL

In 1992, in response to numerous inquiries regarding the safety and regulatory

oversight of foods produced through genetic modification such as recombinant DNA

techniques (i.e. gene splicing and cell fusion), the U.S. Food and Drug Administration

(FDA) issued a policy statement essentially deeming genetically modified foods to be

“functionally and physiologically equivalent” to normal, non-modified foods and

therefore fall under the GRAS (generally recognized as safe) purview as per sections

201(s) and 409 of the 1958 Federal Food, Drug and Cosmetic (FD&C) Act.212 According

to 201(s) of this Act, “any substance that is intentionally added to food is a food additive,

that is subject to premarket review and approval by FDA, unless the substance is

generally recognized, among qualified experts, as having been adequately shown to be

safe under the conditions of its intended use.”213 (italics added) Substances (foods, food

additives, chemicals etc.) that are recognized as GRAS do not require a formal premarket

review by the FDA. Instead, the process is voluntary. Manufacturers are encouraged,

although not mandated, to seek the FDA for guidance and “consultation”. Again, this

process is not mandated and somewhat akin to telling a ten year old child it would be nice


if they cleaned their room before watching TV but did not require them to. The GRAS

label is a very broad and somewhat nebulous term, particularly when applied to

genetically modified foods. According to the FDA, foods that are derived from new plant

varieties, despite how they are derived, “are not routinely subjected to scientific tests for

safety”.214 It is the “how” that is becoming a point of contention for many scientists,

consumers and regulating authorities. Proponents (from both private corporations as well

as government agencies) of genetic engineering contend that modifying the genetic code

of a food, such as introducing a protein from another food or plant, does not change the

constitution of the item and thus the original properties of that food are still intact and

thus safe. Because the food as a whole was not altered it is considered safe as the FDA

“has not found it necessary to conduct, prior to marketing, routine safety reviews of

whole foods derived from plants.”215 In the example of Puzstai’s potatoes, 20% less

protein is not a constitutional change because it is still by definition a potato. To add

further confusion to the GMO/GE safety and efficacy debate is the FDA’s stance that

food is deemed “adulterated” (and thereby rendered harmful and unlawful) if it “bears or

contains an added poisonous or deleterious substance that may render the food injurious

to health.”216(italics added) By FDA standards, GMO/GE foods do not have “added”

substances requiring pre-market research and approval. Apparently, microorganisms,

viruses, bacterium etc. added to a food substance via DNA recombinant techniques is not

really “added”, so there appears to be somewhat of a tap dance over terminology and

definitions. Additionally, there remains no guidance on the process by which these

substances, harmful or not, are added. As seen in Pusztai’s and Ewen’s research, the

recombinant process itself was unpredictable. In addition, there are no long-term studies


to prove or disprove the safety of these new biotechnological processes, nor the safety of

the resulting food product. Even scientists within the FDA have expressed concerns

regarding product versus process. In response to the 1992 Statement of Policy, Dr. Linda

Kahl (FDA compliance officer) stated in a memorandum to the FDA Biotechnology

Coordinator, “the document is trying to force an ultimate conclusion that there is no

difference between foods modified by genetic engineering and foods modified by

traditional breeding practices. This is because of the mandate to regulate the product, not

the process.”217 In 2001, the FDA stated that they no longer felt the “voluntary

consultation process” for genetically modified foods was sufficient to ensure the safety of

foods into the U.S. commerce supply.218 However, to date no definitive actions or

regulations have been implemented and the process remains voluntary.

GMO/GE PROCESSING: WHY THE CONCERN

The relevance (and importance) of understanding GE/GMO crops with respect to

HFCS is that virtually all of the corn used to produce HFCS is GE/GMO corn. A

popular strain of GE/GMO corn used in the U.S. is Bt-corn. It is derived from the soil

bacterium Bacillus thuringiensis (Bt) which has a delta endotoxin that is toxic to

caterpillars, specifically during the larvae stage. Industry scientists claim that the Bt delta

endotoxin is selective, “generally not harming insects in other orders”219and because of

this selectivity they deem it safe for humans and other animals. However, Pusztai’s

research has shown, it is not necessarily the gene product used that is harmful but rather

the recombinant DNA splicing process itself. In an examination of the possible

toxicological effects of three GM corn varieties on mammalian and human health, French

researchers Joël Spiroux de Vendômois and his colleagues (2009) discovered toxic,


adverse reactions affecting the liver, kidneys as well as the heart, adrenal glands, and

spleen in subjects fed GMO grain.220 de Vendômois et al. performed a comparative

analysis of three popular commercialized GM corn specifically, NK 603, MON 810 and

MON 863 (all manufactured by the Monsanto Corporation). NK 603 (Monsanto’s

Roundup Ready® Corn line) was created to be resistant to the pesticide Roundup®,

whereas MON 810 and MON 863 both contain Bt endotoxins. The data analyzed were

the pre-approval research trails (one per GMO product) Monsanto submitted to the FDA

that, resulting from a lawsuit, were obtained and made public by Greenpeace attorneys in

Denmark and Germany (MON 810 and MON 863) as well as the Swedish Board of

Agriculture (NK 603).221 According to de Vendômois et al., the sample size for each

clinical trial consisted of 200 male and 200 female Sprague-Dawley rats for a total of 400

test subjects, yet only ten were randomly selected and measured at the 5th and 14th week

mark. They rightly conclude that this extremely minute sample size of ten rats measured

only twice in 14 weeks was (and is) “insufficient to ensure an acceptable degree of power

to the statistical analysis performed and submitted by Monsanto.” In addition, they

discovered that Monsanto’s statistical analysis ignored gender differences and skewed

actual results in favor of not detecting a substantial effect by approximately 70%.222

Utilizing Monsanto’s own data they performed sex-specific analysis via the Shapiro test,

Bartlett test, Welch method, Kruskal-Wallis rank sum test as well as an ANOVA per sex,

per variable for each GMO. Results were substantially different from that reported to the

FDA.223

Male rats fed NK 603 were more sensitive than the counterpart female rats and had

relatively higher liver weights (11% increase at the end of the trial) than the non-NK 603


group. These rats (male and female) also exhibited “statistically significant” urine ionic

disturbances and kidney deficiencies that suggested potential renal leakage. MON 863

rats also exhibited diminished renal function however, in the MON 863 group renal

disturbances were specifically attributed to increased creatinine levels the development of

chronic interstitial nephropathy. In addition, female rats fed MON 863 exhibited

“statistically significant” differences in serum glucose and triglyceride levels (up to 40%

increase and a physiological state “indicative of a pre-diabetic profile”), elevated

creatinine, elevated blood urea nitrogen levels, increased liver weight and increased body

weight overall (3.7%). Males on the other hand experienced a decrease (7%) in kidney

weight as well as a chronic nephropathy and an overall decrease in body weight (3.3%).

MON 810 rats (male and female) displayed significant disturbances within the liver as

well as elevated blood urea nitrogen, increased adrenal gland, kidney and spleen weights.

Just as Pusztai (and others) has espoused the necessity of further testing, de Vendômois et

al. conclude that because these GMO/GE substances are not naturally occurring and

subsequently have not been a staple within the human diet (not until the last 10-12 years),

it is essential that long-term studies be performed as “the consequences for those who

consume them, especially over long time periods are currently unknown.” 224 The United

Nation’s Food and Agriculture Organization (2000) acknowledged the lack of studies

regarding long-term effects from consumption of GMO foods and recommended

monitoring changes in nutrient levels in foods derived from GMO products as well as

assessment of the nutritional status of the consumer population.225


CHAPT. 4- Other Contributing Factors to Childhood Obesity

PORTION SIZE & INCREASE IN CALORIC INTAKE

Analysis of patterns and trends of U.S. food portion sizes reveals that portion size,

both inside and outside of the home, has increased substantially since the 1970s.226

Young and Nestle (2002), from the Department of Nutrition and Food Studies at NYU,

report that per capita caloric intake contained 500 more calories in 1996 than in 1977

(similar to findings of Putnam and Allshouse). In addition, 1996 portion sizes exceeded

both USDA and FDA standards; cookies exceeded USDA standards by 700%, pasta

exceeded by 480%, muffins by 333% and steaks and bagels trailed at 224% and 195%

respectively.227 In the 1950’s there was one size of french fries (which is now considered

“small”) offered by McDonald’s whereas now, there is small, medium, large and extra-

large (a.k.a. “supersized”). Interestingly, these increases are not unilateral across the

globe. In 1999, an extra-large soda at McDonald’s in Rome, London and Dublin was the

equivalent of a large in the U.S. Similarly, a large order of french fries in the U.K.

yielded 446 calories per serving versus 610 calories per serving for the same “size” in the

U.S.

Their data indicates that the upward trend towards larger portion sizes began in the

1970, increased sharply in the 1980s, and has continued the ascent into the 1990s.

Researchers Samara Nielsen and Barry Popkin from the University of North Carolina

(Chapel Hill) found similar trends as well. They analyzed 63,380 surveys from the

National Food Consumption Survey 1977 (NFCS77), Continuing Survey of Food Intake

for Individuals 1989 and 1996 (CFSII89 and CFSII96) collectively and concluded that

portion sizes served inside and outside of the home increased between 1977 and 1996.


Their findings regarding the increase of portion sizes within the home suggests that new

patterns of behavior have also occurred. In addition to the increased consumption of high

density, low nutrient foods (e.g. fast foods and prepared foods), families (children and

adults) are simply consuming more. Researchers Fisher et al. (2003) further contend that

larger portion sizes “may constitute an obesigenic environmental influence” for

preschoolers.228 They discovered preschoolers consumed more calories when given large-

portion lunches irrespective of their level of hunger. Furthermore, they observed that

when given the larger portioned meal, the average bite size was larger as well. The larger

bite size was not determined to be sex specific only meal size specific, that being the

average bite size was smaller during the normal, age appropriate sized meals. They also

observed that children who ate more when served the large portion also had greater

intakes even in the absence of hunger. This observation would suggest that consumption

was not solely biological (i.e. a physiological response to the hunger-satiety feedback

mechanism) but behavioral as well.

Adults are also prone to consuming larger portions,229 subsequently consuming

more calories, irrespective of hunger.230 In a study investigating the relationship between

the size of a pre-packaged snack and caloric intake, researchers Rolls et al. (2003)

discovered that additional caloric consumption was correlational to the size of the

packaged snack. On five separate days 60 adults (34 women and 26 men) were served a

package of potato chips as an afternoon snack, three hours prior to dinner. Snack sizes

varied each day: 28 g., 42 g., 85 g., 128 g., or 170 g. Researchers discovered that

subjects consumed the contents of the larger package (170 g.) just as readily as the

smaller package (28 g.) and that during dinner caloric intake was not modified to offset


the additional calories consumed at snack-time.231 Clearly, hunger and satiety are not the

only variables governing consumption.

Nielsen and Popkin suggest that growth in the food industry sector, variety of new

products, an increase in people eating out, aggressive marketing as well as price

competition among manufacturers are possible contributors for this increase. Today,

corporations spend between $10 to $15 billion dollars annually on child/youth-targeted

advertising including brand licensing, product placement, contests, promotions, in-school

marketing, video games, mobile marketing (cell phone, ipod® etc.) and social networking

sites (Facebook®, Twitter® etc.), compared to just $100 million in 1983. 232 Contrary to

what one might think, this demographic (children and adolescents) constitutes a large

segment of consumers, a $200 billion dollar segment according to some estimates, of

which the majority is spent on candy, snack food, soda and cereal.233 WHO contends

evidence suggests that “the heavy marketing of these foods and beverages” to young

children bears some responsibility in the prevalence of obesity among children.234

The increase of portion size has resulted in an increase of overall caloric

consumption.235 However, studies also suggest that it is not portion size alone that is

responsible for excessive caloric intake but a combination of large portions of high-

density foods.236 High-density (a.k.a. energy density) food refers to the amount of energy

in a given weight of food and is dependent upon water, protein, carbohydrate and fat

content.237 For example, a salad is considered “low-density” due to the high fiber, high

water and low fat and low calorie (kcal) content, as the name implies it is less dense.

Conversely, a cheeseburger or Snicker’s bar is high-density due to the high fat, high

sugar, low water and low fiber content. Compositionally, these latter foods are


comprised of high k/cal compounds resulting in an overall high(er) k/cal yield.

Obviously, a pound of lettuce and a pound of cheese are not caloric equivalents despite

registering the same weight on a scale. Consequently, consumption of these high-

density/high-energy foods results in a higher caloric intake.238 Sadly, data reveals that as

consumption of high-density foods has risen, consumption of high fiber fruits and

vegetables has declined.239 Pediatricians and nutrition experts are rightfully concerned

that this decline has resulted in insufficient levels of vital nutrients such as iron, folate,

calcium and vitamin A.240 The cumulative effect of years of insufficient nutrient intake

can result in significant adverse health conditions.

DECREASED PHYSICAL ACTIVITY, INCREASED TELEVISION & COMPUTER USAGE Physical Activity

With respect to excess adiposity and obesity, diet and exercise are

inextricably intertwined. When caloric consumption exceeds caloric expenditure the

body converts the excess energy (calories) into fat. Physical activity is a cornerstone

foundation for health and wellbeing of both adults and children. Physical activity

strengthens the immune system, increases bone density, improves mental health and self

esteem, reduces stress, increases energy and protects against diseases/conditions such as

CVD, hyperlipidemia, hypertension, insulin resistance, certain cancers (breast, pancreatic

and colon) and osteoporosis.241 In addition, physical activity helps maintain a healthy

body weight and can reduce excess adiposity.242 In a 12 month randomized trial of 201

overweight, sedentary women researchers (Jakicic, et al. 2003) discovered that both

moderate and vigorous levels of exercise yield weight loss (8% to 10% of body weight

respectively) and improved cardiovascular fitness.243 This benefit applies to children as


well. A 29.6- week study of 292 elementary school students in Songhkla, Thailand

suggests that a long-term school-based exercise program can prevent weight gain and

may facilitate a “remission” of obesity in children. 244

In 2003, the Youth Risk Surveillance Survey (YRSS) reported that 62.6% of

students (ninth through 12th grade) nationwide met the recommended standards of

physical activity (≥ 20 minutes of rigorous activity ≥ 3 days/week) and 24.7% met the

standards for moderate physical activity (≥ 20 minutes of rigorous activity ≥ 3

days/week). Only 11.5% of youth did not engage in any type of physical activity, sadly

that number doubled (23.1%) by the 2009 Youth Risk Surveillance Survey.245 By 2009,

18.4% of students participated in ≥ 60 minutes of rigorous physical activity ≥ 7

days/week and 37.0% participated in ≥ 60 minutes of rigorous physical activity ≥ 5

days/week. Standards and definitions of physical activity were modified between 2003

and 2009 thus making definitive conclusions between the two data samples difficult to

draw and allowing only for observational estimates. Nevertheless, in 2003 87.3% of

students engaged in rigorous and moderate physical activity (categories combined)

whereas 55.4% of students in 2009 engaged in rigorous physical activity (again,

categories combined). Again, it is impossible to make a direct and completely accurate

comparison between the two sets of data because of dissimilar variables.

First, there is a significant difference between 20 minutes of exercise ≥ 3 days a

week and ≥ 60 minutes of rigorous physical activity ≥ 7 days/week. If there are only

three categories of reported physical activity to chose from (7 days a week, 5 days a week

or no activity) as in the 2009 survey, hypothetically there could be a significant number

of students who participated in rigorous exercise two to three days a week or who


exercise daily but for only 30 minutes not 60 and therefore did not fall into any

recognized category. Second, the 2003 “insufficient moderate physical activity”

category was removed in the 2009 survey. What is not known is whether or not that was

combined with the “no rigorous physical activity during the week” category. The 2003

survey reported “moderate” and “insufficient” exercise whereas the 2009 survey only

reported “rigorous”. If the categories were combined it might explain the increase in “no

rigorous physical activity” in 2009 especially taking into consideration the increase in

participation of physical education class (PE). In 2003, 55.7% of students reported

attending ≥ 1 day of PE and 28.4% reported attending ≥ 5 days of PE. By 2009, those

numbers had increased to 56.4% and 33.3 % respectively. Albeit not statistically

significant, it is an increase and somewhat surprisingly, not a decrease.

Television, Computers and Video Games

The YRSS surveys also reported daily television and computer usage. In 2003,

28.4% of students reported watching ≥ 3 hours of television per day. That number

decreased slightly in 2009 to 32.8% however, reported computer usage was 24.9% ≥ 3

hours per day. The 2003 survey did not collect data regarding computer usage (again,

dissimilarities between the 2003 & 2009 survey data) therefore any conclusion is purely

speculative. Despite the inconsistencies in data collected the 2003 and 2009 Youth Risk

Surveillance Surveys, these reports provide an overview of national trends in the

adolescent population. Television and computer usage are both sedentary activities

requiring minimal energy expenditure. There are no studies on other sedentary activities

such as board games and reading as they relate to overweight and obesity. However,

prior to the “computer age” obesity and overweight was not as pervasive prompting


researchers to investigate a possible connection between the two.

As of 1999, video games accounted for over 30% of the toy market in the United

States with approximately 97% of teenagers (12-17) playing either on a computer,

console, portable (hand held) unit or via the internet.246 Dr. Jean-Philippe Chaput, from

the Department of Pediatrics at the University of Ottawa, and his colleagues (2011)

discovered that during one hour of playing video games, energy expenditure was

significantly higher however, ad libitum energy intake (i.e. consumption) was

significantly increased during the resting state post-playtime.247As with other studies

regarding television viewing,248Chaput et al. discovered that post-play consumption was

not associated with appetite or hunger. There is some mildly good news regarding video

games and that is the emergence of “active” video games. These are video games that

require users to move their body to elicit a desired response on the monitor and game

genres range from sports and races to dance and yoga. Dr. Elaine Biddiss and Jennifer

Irwin (2010) found that active video games can facilitate light to moderate physical

activity in some cases increasing energy expenditure 100% and elevating heart rate

20%.249 Maddison et al. also discovered that active video games have a positive

influence on BMI and body fat composition in overweight and obese children, namely

reducing both.250 While this activity may not compare to activity expended during

participation in real athletic events, it is certainly a move (no pun intended) in the right

direction.

There appears to be a direct relationship between hours of television watched and

adiposity.251 In analysis of the California Teen Longitudinal Survey of 1993 and 1996,

Kaur et al. found that children who watched television ≥ 2 hours a day were twice a likely


to be overweight in the 1996 follow up study than those who watched < 2 hours a day.252

Similarly, an earlier study by Dietz and Gortmaker (1985) found that every one hour of

television viewing resulted in a 2% increase in prevalence of obesity among 12-17 year

olds.253 Dr. Ross Anderson et al. (1998) from Johns Hopkins School of Medicine

investigated the correlation between television and BMI specifically. They discovered

that children who watched television ≥ 2 hours a day had higher BMIs and greater body

fat than children who watched < 2 hours a day.254 They also surmised that repeated

exposure to food commercials prompt children to increase consumption regardless of

hunger or lack thereof, ultimately resulting in weight gain. A recent study (2011) found

that children who engage in high levels of television viewing are more responsive to

advertising geared towards food than non-food (e.g. toys).255 Additionally, “high

viewing” children migrated towards high-density, high carbohydrate and high fat food

selections even after watching commercials marketing toys, whereas the “low viewing”

children did not. Temple et al. (2007) found that television watching increased the time

spent eating, the amount consumed and caloric intake.256 There also appears to be an

inverse relationship to television at mealtime and consumption of whole grains, fruits,

and vegetables. Children from families that have the television on during two or more

meals a day consume less grains, green and yellow vegetables, non-fried potatoes, beans

and nuts than children from families who do not watch television during mealtime.257

This intertwines with another proposed contributor to child obesity: the reduction (and for

some families cessation) of traditional “family meal time”.


REMOVAL OF TRADITIONAL FAMILY MEALTIME

Family mealtime has been positively associated with adolescents making healthy

food choices, diminished consumption of fried foods, diminished frequency of eating

disorders, increased family connectedness and improved adolescent mental health.258

Family dinners are positively correlated with children eating breakfast as well as higher

consumption of fruits, vegetables and whole grains.259 Consistent family mealtime is also

positively correlated with healthy child and adolescent body weight. Children and

adolescents who never report eating family meals are significantly more likely to become

overweight and/or obese.260Despite the benefits of family meals, sadly, the prevalence of

regular family mealtime appears to be diminishing.

In 1991, only 27% of adolescents (12-17 years old) ate dinner with their family

every day, 47% ate with them 4 to 6 days a week and 27% ate together 1-3 days a week.

261 The percentage of younger children (< 12 years old) who eat as a family every day is

slightly better at 41%-to 45% but still abysmally small especially given the role it appears

to have with proper nutrient intake. In a comparative study of 16,862 children (9-14

years old) who ate dinner as a family never/some days, most days or every day, Gillman

et al. (2000) observed that children who ate meals with their family daily consumed more

vegetables and fruits (twice as much) and less fried foods and soda.262 These youth also

had a higher intake of essential nutrients such as calcium, iron, vitamin C, folate and

fiber. Furthermore, they consumed less saturated fat, trans fat and high glycemic foods.

What is interesting about their data is that children who ate meals with their family daily

consumed more calories than those in the never/some category (9294.6 k/cal. and 8677.2

k/cal. respectively), however they had a lower BMI than those who ate never/some days.


Surprisingly, children who ate family meals daily had slightly less physical activity than

those who never or some times ate with the family. To summarize, children who ate

meals with family daily consumed more calories, more nutrients, and had less physical

activity than children who never or some times ate family meals, yet they had a lower

BMI. This suggests that what is consumed plays a key role in weight and adiposity in

children and adolescents.

FAST FOOD & FAT CONSUMPTION

Changes in fast food consumption patterns also play a role in obesity and share

similar trajectories. Contrary to most expectations, dietary fat consumption has

decreased among children and adolescents over the last 20 to 30 years.263 In an analysis

of food intake trends from 1965-1996, Cavadini et al. (2000) discovered a significant, and

somewhat surprising, dietary “shift” among U.S. adolescents (ages 11 to 18 years). In an

analysis of 12,498 Nationwide Food Nutrition Surveys (NFCS), researchers discovered

that total energy intake among adolescents decreased between 1965 and 1996 as did the

proportion of energy derived from fat. In 1965, the percentage of total energy (caloric)

intake derived from fat was 38.7%. This proportion decreased steadily until 1996 when it

accounted for only 32.7% of total intake. Similarly, the consumption of saturated fats

followed similar trends falling from 15% (1965) to 11.6% (1996). [Fig 11] According to

their analysis, there was a 17% decrease of overall energy intake during this 30 year

period which they acknowledge seems “counter intuitive” given the rise in adolescent

overweight and obesity.


While this study suggests dietary fats are not positively correlated with obesity it

could be that, as with sugars, it is the type of dietary fat that is most influential. As with

the rise of HFCS, the use of hydrogenated oils (a.k.a. trans fatty acids) in the food

industry surged during the 1980’s and 1990’s. Approximately 40% of all processed

foods in the United States contain trans fatty acids.264 According to some estimates,

hydrogenated oils comprised 4-7% of U.S. caloric fat content by 1990.265 While attractive

to the food industry because of its long shelf life duration and stability during high

temperature deep-frying,266 trans fatty acids have been associated with cardiovascular

disease, high cholesterol, systemic inflammation, insulin resistance, visceral adiposity,

and type 2 diabetes.267 Additionally, partially hydrogenated soybean oils have been

shown to increase blood glucose levels, insulin and LDL levels.268 In fact, the adverse

health affects associated with trans fatty acids led to the FDA requiring manufacturers to

list trans fat content on all food labels as of January 2006.269 While the labeling is

required and can certainly be found on national fast food restaurant chain websites, most

Fig. 11 Percentage of Total Energy Intake among U.S. Adolescents 1965 to 1996. Data Source: Nationwide Food Consumption Surveys (NFCS) Cavadini et al. 2000. Graphic created by author.

0

10

20

30

40

50

60

1965 1977 1989-‐91 1994-‐96

Percentage of Total Intake

Percentage of Total Energy Intake Among U.S. Adolescents 1965-‐1996

Total Fat

Saturated

Carbohydrate


foods consumed in fast food restaurants do not have food labels printed on the wrapper or

cardboard container.

St.-Onge et al. (2003) reports a significant increase in fast food consumption among

children and adolescents over the last 20 to 30 years. Between 1977 and 1989, fast food

consumption among adolescents 12-18 years old increased from 6.5% to 16.7%, and had

reached 19.3% by 1994.270 While overall fat consumption had decreased, studies indicate

that individuals (children and adults) who consume fast foods on a regular basis consume

more calories, more fat, more sugar and less fruits and vegetables than individuals who

do not eat fast food.271 Fast food is laden with added sugars and fats and thus combined

increase the energy content (calories) significantly. In a three-year study, Duffey et al.

(2007) concluded that fast food consumption has a positive association with BMI. The

greater the fast food consumption the higher the corresponding BMI.272

SODA CONSUMPTION

Not surprisingly, there has likewise been a dramatic increase in the consumption of

carbonated beverages during emergence of HFCS into the food and beverage market. In

1986, approximately 28 gallons of non-diet, carbonated beverages were consumed

annually per person, by 1997 that number grew to 41 gallons per person. The 2009

YSSR Surveys found that 29.2% of students drank ≥ 1 soda per day. The average soda

contains 30-40 g. of sugar (primarily from HFCS-55) averaging approximately 150

calories. At the minimum rate of one soda per day that is an additional 1050 k/cal.

(calories) per week and 54,600 k/cal. per year. Studies have associated sugar-sweetened

beverages with obesity in children.273 However, in a review of children’s beverage


consumption from 1987-1998, researchers Park et al. (2002) discovered that carbonated

beverage consumption actually decreased from 1987-88 to 1997-98 from 84% to 72%

respectively.274 In 2000, soda consumption accounted for one third of all added sugar

intake in the U.S. diet.275 In an investigation of the effects of HFCS sweetened soda on

body weight and food intake, Todoff and Alleva (2001) found that after three weeks of

HFCS consumption both male and female subjects gained weight (females significantly

and males to a lesser, but still measureable, extent).276 Other studies have concluded that

consumption of sugar-sweetened beverages is associated with increased BMI and obesity

in children and adolescents.277 However, a long-term study investigating beverage

consumption and BMI in children (Blum et al. 2005) had results researchers were not

anticipating.

Consumption of regular soda, diet soda, milk, 100% juice, or other sweetened

beverages (sports drinks, kool-ade etc.) in 166 children (grades 3rd-6th) over a two-year

period was examined. Researchers discovered that children who were overweight and/or

who had gained weight during the two-year period had a significantly higher

consumption of diet soda than normal weight subjects.278 In fact, diet soda was the only

beverage associated with increased BMI. Blum et al. concluded that the mechanism

behind the increased BMI and diet soda consumption remained unclear [positing that

perhaps the overweight subjects increased their consumption of diet soda in an attempt to

lose weight] and suggested that further longitudinal studies were needed. A common

question that arises when discussing the role of soft drinks and obesity/adiposity is

whether it is the beverage itself that is responsible for the purported weight gain or

whether it is the increase of total caloric consumption that is the real culprit? Again,


there is evidence on both sides. Some studies show a clear correlation between soft drink

consumption and adiposity279 while others show no correlation at all.280 Others suggest

that increase in overall caloric consumption is responsible and sugar-sweetened

beverages, specifically soft drinks, should not be singled out.281

ROLE OF GENETICS

To paraphrase Dr. Dean Ornish when asked about the role of genetics and obesity,

indeed overweight children frequently have parents who are also overweight but so are

the family dog and cat.282 Some researchers investigating monozygotic twins have

implied that body weight and composition are influenced by genetic factors283 while

others contend that genetic factors appear to influence and affect the body’s response to

external factors (i.e. environmental factors)284and that expression of a specific genotype

is dependent upon the environment.285 As seen in the discussion on leptin, genes and

genetic coding certainly participate in human physiology and metabolic processes,

however a genetic predisposition to a condition does not definitively guarantee an

expression of that condition. The environmental conditions must support, if not

catalyze, the emergence of that condition. For example, dry wood has a predisposition

to burn when ignited, dry wood soaked in gasoline has a greater predisposition to burn

when ignited but without the catalyst of fire, neither scenario will result in burning

wood. The environmental factor of fire must be present for the ignition to occur.

“Biological” and “environmental” have often been theoretical rivals. In psychology,

there are those who contend behavior is determined by environmental factors and those

who contend it is determined by innate biological factors. Similarly, the debate

continues regarding the origins of overweight and obesity and the truth most likely lies


somewhere in the middle. Children learn from and imitate their parents’ behavior. Just

as healthy eating patterns are established by parents286 so are unhealthy eating patterns.

Most children are overweight and obese because they consume the same foods that their

parents eat and employ the same behaviors that their parents employ. For example,

researchers at Harvard’s School of Medicine (Gilman et al. 2009) have found a

significant association between parental smoking and smoking initiation among

adolescents 12-17 years old.287 The reality is that in today’s society it is much easier

(and sadly encouraged) to cast blame for being overweight/obese on “genetics” than to

take responsibility for one’s own inability to exercise restraint and self-control.


CONCLUSION

The obesity epidemic in the United States is reaching critical mass (no pun

intended) and a solution must be found. If nothing is done to arrest this epidemic or at

the very least, slow down the rate of acceleration, the impending health and economic

costs could be cataclysmic. Obesity has been directly associated with a cornucopia of

adverse health conditions including, but not limited to, CVD, hyperlipidemia, cancer,

breathing impairments, sleep disorders, high blood pressure, insulin resistance, NIDDM

(type 2 diabetes) and psychological problems.288

There are additional tangential health concerns potentially associated with mercury

byproducts that have been detected in HFCS. Laboratory tests have clearly detected

trace, yet significant, amounts of mercury in HFCS produced in the United States. Aside

from hepatic and renal toxicity, mercury has been the center of much debate regarding a

possible association (and some would contend causal relationship) with birth defects and

learning disabilities such as Autism, Autism Spectrum Disorder (ASD) and Attention

Deficit Hyperactivity Disorder (ADHD).289 Dufault et al. (2009) found a striking

similarity between HFCS consumption rates and annual growth rates of ASD in

California. While no direct causal relationship was determined, they rightly concluded

that mercury contamination could be a contributing factor and that significant research is

needed in this area of neurodevelopment. In an analysis of blood mercury levels and

diagnosis of autism, Drs. Catherine DeSoto and Robert Hitlan (2007) found a

“statistically significant” relationship between blood mercury levels and the diagnosis of

ASD.290 In another prospective study of mercury toxicity and autism, Geier and Geier


(2007) found that over 50% of individuals diagnosed with ASD had mercury toxicity

biomarkers (specifically coproporphy, pentacarboyxyprophyrin and precoproporphyrin)

that were more than two standard deviations above the mean than their non-ASD

siblings.291 While there may not yet be a definitive causal relationship between HFCS

and learning disabilities such as ASD and ADHD, there is little evidence vindicating it

either. Clearly, this is an area of research that needs further investigation, especially in

light of potential en utero fetal toxicity.

The goal of this research was to determine whether or not high fructose corn syrup

(HFCS) is responsible, either in whole or in part, for the current obesity epidemic

plaguing children in the United States. To claim that HFCS is the sole contributor and/or

cause of child overweight and obesity would be synonymous with claiming cigarette

smoking is the only cause of lung cancer or driving while intoxicated is the only cause of

automobile accidents. However, is it just as erroneous to claim that cigarette smoking

does not contribute to lung cancer or that driving while intoxicated does not result in

automobile accidents, and the same applies for HFCS and obesity. While there are

several aggregate contributors to the overweight and obesity epidemic, such as decrease

in physical activity, increased use and prevalence of television, computers and electronic

media etc., increase in portion sizes and prevalence of prepared, packaged and “fast”

foods, research shows that HFCS is clearly a contributing factor and, in the eyes of this

author, one of the most significant ones.

There have been many studies investigating metabolic differences and/or

similarities between various sweeteners: glucose, fructose, sucrose and HFCS. However,

the majority are short-term studies often 24-48 hours in duration. While short-term


metabolic profile results are valuable, they are not applicable for determining long-term

physiological responses and metabolic profiles. They are woefully insufficient in

predicting long term adiposity as confirmed by Light et al.’s research. Results from this

eight-week study revealed a significant difference in adiposity between subjects that

consumed HFCS and those that consumed sucrose, glucose and fructose. Not only did

the HFCS subjects have an overall higher weight gain and greater abdominal fat increase,

their livers weighed significantly more than all the other subjects… even more than the

sucrose “equivalent” group. A healthy liver is essential for proper metabolism, fat

emulsification and energy production/storage. Light et al.’s study clearly shows that long

term HFCS consumption has an adverse affect on the liver and similar studies with

human subjects are desperately needed. However, champions of HFCS and industry

supporters have altogether ignored studies like Light et al. choosing rather to focus on

short-term blood profile results in defense of their pro-HFCS stance. If the liver is

overloaded or worse, damaged, normal metabolic processes will be impeded. Studies

have shown that mercury and recombinant DNA splicing both adversely affect the liver

(as well as other organs). Mercury is known to cause hepatotoxicity292 and trace, yet

cumulatively significant, amounts have been detected in HFCS. Add to this equation the

fact that the majority of HFCS is produced from genetically modified corn and GMO/GE

corn has toxic effects on the liver.293 In addition, like Light et al.’s rats fed HFCS, rats

fed GMO/GE corn experienced liver weight gain (11% in 14 weeks).294 When these three

aspects of HFCS are looked at collectively, the case against HFCS becomes even more

damaging.

The real question that many would prefer to remain the proverbial “white elephant”


is what do we do about it? As with many policy decisions, this will most likely be a

function of economics. To ban the use of HFCS in foods and beverages would have a

significant financial impact to food and beverage manufacturers, as profit margins would

shrink. This, of course, would be passed along to the consumer (as seen in the case with

Capri Sun) with most likely higher prices and smaller portions, the days of “super size

me” would come to an abrupt end (which is not altogether an adverse side effect). There

could also be a potential impact on commercial farmers. Although a large portion of corn

is used for grain-feed, a significant portion is used to make HFCS. It is unknown whether

or not other industries (biofuels for example) would absorb the utilization. If not, there

could be a potential surplus of corn especially given the more stringent regulations other

nations have regarding use of GE/GMO corn imported from the U.S.

There is of course, the separate matter of the use of GE/GMO corn and the

unknown, yet potential, adverse health ramifications to humans. Again, this will most

likely be a decision of economics rather than consumer health. Powerful corporations

such as Monsanto, ConAgra and Calgene, Inc. have invested tens of billions of dollars

into R & D and are not likely to let the return on their “investment” dissipate, and

certainly not without a lengthy and costly fight. Add to this equation other special

interest groups (the Corn Growers Association for example) and federal bureaucracies

(FDA, EPA and UDSA) and the removal of HFCS from the U.S. food supply becomes all

the more unlikely. Nevertheless, unlikely not does mean unnecessary. For years,

radium was touted as containing beneficial properties and curative powers and was a

common additive in products such as toothpaste, hair creams, salves and even food

items.295 However, it has since been discovered that this wonder compound is poisonous


and exposure, much less ingestion, results in serious and often deadly effects.

There is of course the issue of current and future economic costs of obesity-related

health issues. The economic impact of the treatment of obesity-related diseases and

disorders is astounding and one that, given the current economic climate, cannot be

sustained indefinitely. At some point the money will run out.

At this juncture, the most viable solution appears to be grassroots education for

both children and adults. It is not the role of government to mandate what individuals eat

or do not eat, but it is their role to provide accurate information particularly regarding

dangerous and/or toxic substances in our food supply. In the United States there are

many examples of consumer education changing consumer trends. While corporations

(such as biotech companies, pharmaceutical companies, food and beverage manufacturers

etc.) protectively guard their profits they know that at the end of the day the customer is

always right. If consumers don’t buy the product they change the product. In the 1980’s

when Coca- Cola changed their formula sales decreased so significantly that they had to

revert to the original formula. The growing “organic” movement is another example of

consumer driven manufacturing. Consumers are demanding that stores carry organic

products and what was once only found at health food stores and local farmer’s markets

is now widely accessible in superstores such as Target, Costco, Kroger and Walmart.

Even manufactures have changed what they provide due to consumer pressure and desire

to maintain their profit margin.

In addition to consumer education, the FDA should require manufacturers to list

the amount of HFCS contained in the food/beverage just as the 2006 ruling requires trans

fats to be listed. Currently, if a food/beverage contains HFCS it must be listed as an


ingredient but that does not give the consumer information as to how much is contained

in the food/beverage. There should also be careful consideration given to developing a

national database for analysis of food composition with respect to types of sugars and

types of fats. This would enable researchers to accurately analyze caloric and nutrient

trends.

There clearly needs to be more specific, long-term studies regarding HFCS and

metabolic processes. As shown, the majority of the experiments to date are short term

(24 to 48 hours) and with small sample sizes. Additionally, large scale, independent

studies regarding GMO/GE foods and the effects of long-term ingestion are needed. The

current studies are scarce, insufficient and often industry driven. There is too little

information and too many unknowns (and what is known is extremely disturbing). With

respect to adiposity and obesity, more research should specifically examine the effect

GMO/GE foods have on the liver and hepatic enzymes as well as potential effects on

regulatory hormones such as insulin, leptin and ghrelin. The rats fed MON 863 who

exhibited a “pre-diabetic profile” are a clear example of why this needs further

examination as this could have significant implications for human health.

In short, there is no “smoking gun”, no magic bullet that the “blame” can be cast

upon. Multiple facets contribute to and fuel this epidemic. This means that each factor

must be carefully examined and dealt with accordingly. Those in the food and beverage

industry often shift the focus towards the lack of physical activity and we, as a society,

certainly need to look at ways to address this significant decline. Physical activity burns

calories. To maintain a healthy body weight, energy expenditure should mirror energy

consumption. However, that does not mean that we should ignore and fail to address


other contributing factors such as portion size, hydrogenated oils or, as this author

contends, the utilization of HFCS as the primary food and beverage sweetener.


APPENDIX Table 1. MMWR Youth Risk Behavior Surveillance Summaries-‐ United States 2003 and 2009

2003 2009

Physical Activity * Participated in rigorous physical activity -‐ 7 days/week n/a 18.4%

Participated in rigorous physical activity -‐ 5 days/week n/a 37.0% Participated in rigorous physical activity ≥ 3 days/week 62.6% n/a Participated in moderate physical activity ≥ 5 days/week 24.7% n/a Insufficient moderate physical activity -‐7 days/week 33.4% n/a No rigorous physical activity during the week 11.5% 23.1% P.E. Class Attendance

1 day a week or more 55.7% 56.4% 5 days a week or more 28.4% 33.3% Computer use ≥ 3 hours per day ** n/a 24.9% Television use ≥ 3 hours per day 38.2% 32.8% Dietary Behaviors

Soda Consumption-‐ drank ≥ 1 soda per day *** n/a 29.2% Milk Consumption-‐ drank ≥ 3 glasses of milk per day 17.0% 14.5% male 22.7% 19.8% female 11.2% 8.7% Ate vegetables ≥ 3 times a day n/a 13.8% Ate fruits and vegetables ≥ 5 times a day 22.0% 22.3% Obesity, Overweight and Weight Control

Obese

12.0% Overweight 13.5% 15.8% Described themselves as overweight 29.6% 27.7% Trying to lose weight 43.8% 44.4% Had exercised to lose weight and/or prevent weight gain 57.1% 61.5% Had restricted food consumption to lose weight and/or prevent weight gain 42.2% 39.5%

* The 2003 and 2009 Youth Risk Behavior Surveillance Surveys differ in data collected and reported for Physical Activity. In 2003, the three designated categories were: Sufficient Rigorous Physical Activity (e.g., running, swimming, soccer, cycling etc. for ≥20 minutes for ≥ 3 days/week), Sufficient Moderate Physical Activity (walking, skating, pushing lawnmower, mopping floors etc. for ≥30 minutes ≥5 days/week) and Insufficient Amount of Physical Activity. The 2009 survey designations: Physically active at least 60 minutes 7 days/week (activity that elevated heart rate and made them breath hard), Physically Active at least 60 minutes 5 days/week (activity that elevated heart rate and made them breath hard) and Did not participate in at least 60 minutes of activity on any day. ** Computer usage was not measured in the 2003 survey. *** Soda consumption was not measured in the 2003 survey.


End Notes 1 (World Health Organization (WHO), 2000) 2 (The Mayo Clinic, 2011) (The Mayo Clinic, 2011) 3 (UK Daily Mail, 2011) 4 (Dehghani, 2005) 5 (U.S. Department of Health & Human Services) 6 (Rodin, 1988) (Elliott, 2002) (Crapo, 1982) (Light, 2009) (Swarbrick, 2008) (Stanhope K. H., 2008) (Stanhope K. G., 2008) (Jurgens, 2005) 7 (Light, 2009) 8 (Soenen, 2007) (Monsivais, 2007) (Melanson K. A., 2008) 9 (Melanson K. Z., 2007) (Teff, 2004) 10 (Pusztai A. , 2001) (Pusztai D. A., 2000) (de Vendomois, 2009) (Ho M.-W. R., 2000) (Ho M.-W. R., 1999) 11 (Dufault R. L., 2009) (Dufault R. S., 2009) 12 (Wadaan, 2009) (Ung, 2010) (Donaldson, 1978) 13 (Krebs, 2007) 14 (World Health Organization (WHO), 2000) 15 (Dalton, 2004) 16 (World Health Organization, 2011) 17 (Whitney, 1998) 18 (Centers for Disease Control and Prevention, 2011) 19 (World Health Organization (WHO), 2000) 20 (Bedogni, 2003) (Reilly, 2000) (Lazarus, 1996) (Sarria, 2001) (Ellis, 1999) 21 (Conus, 2004) (Conus F. R.-L., 2007) 22 (Conus F. R.-L., 2007) (Conus F. A.-L.-O.-P.-L., 2004) 23 (Krebs, 2007) 24 (Ferreira, 2004) (Schouten, 2011) (Gosnell, 2007) (World Health Organization (WHO), 2000) 25 (Reilly, 2000) 26 (Savva SC, 2000) 27 (World Health Organization, 2011) 28 (Williams, 1997) (Goran M. G., 1999) (Sjostrom, 1992) (Goodpaster, 2000) (World Health Organization

(WHO), 2000) 29 (Maffeis, 2001) (Gower, 1999) (Larsson, 1984) (Freedman D. D., 2009) 30 (Krebs, 2007) 31 (Gower, 1999) (Addo, 2010) 32 (Krebs, 2007) 33 (Goran M. I., 1998) 34 (Goran M. I., 1998) 35 (Georgia State University Department of Kineseology and Health) 36 (Kuczmarski, 2000) (National Center for Health Statistics, 1977) 37 (Kuczmarski, 2000) 38 (National Center for Health Statistics, 1977) (Kuczmarski, 2000) 39 (Krebs, 2007) 40 (Himes J.H., 1994; Hedley A.A., 2004; Guo S.S., 2002; Gower, 1999; Maffeis, 2001; Savva SC, 2000;

St. Onge, 2003; Paeratakul S, 2003; Young, 2002; Bowman S. G., 2004) (Krebs, 2007) (Elliott, 2002) 41 (Krebs, 2007) 42 (Centers for Disease Control and Prevention, 2011) (Centers for Disease Control and Prevention, 2011) 43 (World Health Organization, 2005) 44 (Associated Press, 2011) 45 (UK Daily Mail, 2011) 46 (Wang Y. B., 2007) 47 (World Health Organization, 2005) 48 (World Health Organization, 2010) 49 (World Health Organization, 2010) 50 (Dalton, 2004)


51 (Dalton, 2004) 52 (Crowely, 2010) (Meyer, 2006) 53 (Chiolero, 2007) (Boyd, 2005) 54 (Gidding, 1995) 55 (U.S. National Institute of Health, 2006) 56 (Gidding, 1995) (Tulane University School of Medicine, 2011) 57 (U.S. National Institute of Health, 2006) 58 (Freedman D. K., 2005) 59 (Richards, 1985) 60 (Mustillo, 2003) (Woo, 2009) 61 (Dietz W. G., 1982) 62 (Carroll, 2007) (Goldsobel, 2008) (Belamarich, 2000) 63 (U.S. Department of Health & Human Services) 64 (Ogden, 2010) 65 (Ogden, 2010) 66 (Wang Y. B., 2007) 67 (Van Cleave, 2005) (U.S. Department of Health & Human Services) (U.S. National Library of Medicine

National Institutes of Health) (Wang G. D., 2002) (Miller, 2004) (Ogden C, 2010) (Krebs, 2007) (Langreth, 2009)

68 (Must A, 1992) 69 (Moss, 2011) 70 (Stettler N. S., 2005) 71 (Ekelund, 2006) (Stettler N. K., 2003) 72 (Whitaker R. P., 1998) 73 (Gordon-Larsen, 2010) 74 (World Health Organization (WHO), 2000) 75 (Centers for Disease Control and Prevention, 2011) (Colditz, 1992) 76 (Wolf A. C., 1994) 77 (Woo, 2009) 78 (Merriam-Webster, 2011) 79 (Tortora, 1993) 80 (Tortora, 1993) 81 (Guyton, 1991) 82 (Bray G. N., 2004) (Whitney, 1998) (Guyton, 1991) 83 (Bray G. N., 2004) 84 (Guyton, 1991) 85 (Tortora, 1993) (Whitney, 1998) (Parker, 2010) 86 (Tortora, 1993) (MedBio, 2011) (Heinz, 1968) 87 (Stanhope K. S., 2009) 88 (McDevitt, 2001) 89 (Schwartz, 1995) 90 (Stanhope K. S., 2009) 91 (Guyton, 1991) 92 (Saad, 1998) (Bray G. N., 2004) (Farooqi S. K., 2001) 93 (Tortora, 1993) 94 (Guyton, 1991) (Tortora, 1993) 95 (Gautron, 2011) 96 (Saad, 1998) 97 (Dickie, 1946) 98 (The Jackson Laboratory, 2011) 99 (The Jackson Laboratory, 2011) 100 (The Jackson Laboratory, 2011) 101 (The Jackson Laboratory, 2011) 102 (The Jackson Laboratory, 2011) 103 (Zhang, 1994)


104 (Farooqi I. J., 1999) (Zhang, 1994) (Maffei, 1995) (Frederich, 1995) (Pelleymounter, 1995) (Halaas,

1995) (Campfield, 1995) 105 (Campfield, 1995) 106 (Friedman, 2002) 107 (Howard Hughes Medical Institute, 2004) 108 (Millington, 2007) 109 (Perl, 2004) 110 (Liu L. K., 1998) 111 (Liu L. K., 1998) 112 (Medical Research Council, 2011) 113 (Farooqi I. J., 1999) 114 (Farooqui, 2002) 115 (Emilsson, 1997) 116 (Murray, 2003) (Tortora, 1993) 117 (Grundy, 1998) 118 (Elliott, 2002) 119 (Schwarzbein, 1999) 120 (Tortora, 1993) 121 (Murray, 2003) 122 (Bhosale, 1996) (Wikipedia, 2011) 123 (Bhosale, 1996) 124 (Bhosale, 1996) 125 (Marshall, 1957) 126 (Marshall, 1957) 127 (Takasaki, 1971) (Takasaki, 1972) (Takasaki, 1966) 128 (Takasaki, 1966) 129 (Takasaki, 1966) (Takasaki, Formation of Glucose Isomerase by Streptomyces sp., 1973) 130 (Takasaki, 1971) 131 (Lamot, 1983) (Bhosale, 1996) 132 (U.S. Department of Agriculture (USDA), 2011) 133 (Parker, 2010) 134 (Parker, 2010) 135 (Dufault R. L., 2009) 136 (Dufault R. L., 2009) 137 (Wadaan, 2009) 138 (Ung, 2010) 139 (Ung, 2010) 140 (Elmhurst College, 2011) (OUKosher, 2011) 141 (Bhosale, 1996) 142 (Parker, 2010) 143 (Schoonover, 2006) 144 (Haley S. R.-H., 2005) 145 (Bray G. N., 2004) 146 (Putnam, 1999) (Parker, 2010) 147 (U.S. Department of Agriculture (USDA), 2011) 148 (Schoonover, 2006) 149 (Whitney, 1998) 150 (Wells, 2008) 151 (Haley S. R.-H., 2005) 152 (Haley S. R.-H., 2005) 153 (U.S. Department of Agriculture (USDA), 2011) 154 (White J. , 2009) (White J. , 2010) 155 (Crapo, 1982) (Angelopoulos, 2009) (Swarbrick, 2008) (Stanhope K. G., 2008) (Stanhope K. H., 2008)

(Melanson K. A., 2008) (Petersen, 2001) (Jurgens, 2005) (Elliott, 2002) 156 (Stanhope K. G., 2008) (Stanhope K. H., 2008) (Light, 2009; Bowman S. G., 2004) (Rodin, 1988)


157 (Teff, 2004) 158 (Teff, 2004) 159 (WIkipedia, 2011) 160 (Wikipedia, 2011) 161 (Cummings, 2001) 162 (Lutter, 2008) 163 (Atcha, 2009) 164 (Cummings, 2001) 165 (Teff, 2004) 166 (Stanhope K. G., 2008) 167 (Melanson K. Z., 2007) 168 (Jurgens, 2005) (Elliott, 2002) (White J. 2008) (White J. F., 2010) 169 (White J., 2008) (White J., 2009) 170 (Swarbrick, 2008) 171 (Swarbrick, 2008) 172 (White J. F., 2010) 173 (White J. F., 2010) 174 (Council on Science and Public Health, 2008) 175 (Council on Science and Public Health, 2008) 176 (American Dietetic Association, 2004) 177 (American Dietetic Association, 2004) (Lineback, 2003) 178 (Lineback, 2003) 179 (Lineback, 2003) 180 (Kraft Brands, 2011) 181 (McDonalds, 2011) 182 (White J. , 2009) 183 (Melanson K. Z., 2007) 184 (Angelopoulos, 2009) 185 (White J. , 2009) 186 (White J. , 2010) 187 (Light, 2009) 188 (Light, 2009) 189 (American Diabetes Association, 2010) 190 (White J. F., 2010) 191 (U.S. Department of Agriculture (USDA), 2011) 192 (Bray G. P., 1998) (Lineback, 2003) 193 (Willett, Is dietray fat a major determinant of body fat?, 1998) (Willett, Dietary fat and obesity: and

unconvincing relation, 1998) 194 (Gazzinga, 1993) 195 (Putnam, 1999) 196 (Schoonover, 2006) 197 (Kraft Brands, 2011) 198 (Schoonover, 2006) 199 (Schoonover, 2006) 200 (Farrell, 1987) 201 (Pusztai A. , 2001) 202 (Cummins, 2000) 203 (Pusztai D. A., 2000) 204 (Cummins, 2000) (Pusztai D. A., 2000) 205 (Pusztai D. A., 2000) 206 (Pusztai D. A., 2000) 207 (Pusztai D. A., 2000) 208 (Ho M.-W. R., 2000) 209 (Ho M.-W. R., 1999) (Ho M.-W. R., 2000) 210 (Ho M.-W. R., 1999)


211 (Cummins, 2000) 212 (U.S. Food and Drug Administration (FDA), 1992) 213 (Food and Drug Administration (FDA), 1958) 214 (U.S. Food and Drug Administration (FDA), 1992) 215 (U.S. Food and Drug Administration (FDA), 1992) 216 (U.S. Food and Drug Administration (FDA), 1992) 217 (Alliance for Bio-Integrity, 2001) 218 (U.S. Department of Health & Human Services, 2001) 219 (University of Kentucky College of Agriculture, 2003) 220 (de Vendomois, 2009) 221 (de Vendomois, 2009) 222 (de Vendomois, 2009) 223 (U.S. Food and Drug Association (FDA), 2000) 224 (de Vendomois, 2009) 225 (Food and Agriculture Organization of the United Nations, 2000) 226 (Nielsen, 2003) (Young, 2002) 227 (Young, 2002) 228 (Fisher, 2003) 229 (McConahy, 2002) 230 (Rolls B. R., 2004) 231 (Rolls B. R., 2004) 232 (Linn, 2008) 233 (Linn, 2008) 234 (World Health Organization (WHO), 2003) 235 (Rolls B. M., 2002) (McConahy, 2002) (Rolls B. R., 2004) (Fisher, 2003) (Nielsen, 2003) (Young, 2002) 236 (Kral, 2004) (Rolls B. R., 2006) 237 (Krebs, 2007) 238 (Kral, 2004) 239 (Cavadini, 2000) (St. Onge, 2003) (Enns, 2003) (Enns, 2002) (Paeratakul S, 2003) 240 (Cavadini, 2000) 241 (Ornish, 2001) (Ruiz, 2006) (Ulrich, 1996) (McTiernan, 2003) (Michaud, 2001) (Rizzo, 2008) 242 (Rising, 1994) (Mo-suwan, 1998) (Jakicic, 2003) (Rizzo, 2008) 243 (Jakicic, 2003) 244 (Mo-suwan, 1998) 245 (Grunbaum, 2003) (Eaton, 2009) 246 (Chaput, 2011) 247 (Chaput, 2011) 248 (Temple, 2007) 249 (Biddiss, 2010) 250 (Maddison, 2011) 251 (Kaur, 2003) (Dietz W. G., 1985) (Andersen, 1998) (Dennison B. E., 2002) (Dennison B. E., 2002) (Matheson, 2004) (Matheson, 2004) 252 (Kaur, 2003) 253 (Dietz W. G., 1985) 254 (Andersen, 1998) 255 (Boyland, 2011) 256 (Temple, 2007) 257 (Coon, 2001) 258 (Sen, 2006) (Neumark-Sztainer, 2004) (Gillman M. R.-S., 2000) (Fulkerson, 2009) 259 (Videon, 2002) (Neumark-Sztainer, 2004) 260 (Fulkerson, 2009) (Taveras, 2005) 261 (Gillman M. R.-S., 2000) 262 (Gillman M. R.-S., 2000)


263 (Enns, 2003) (Enns, 2002) (Cavadini, 2000) (St. Onge, 2003) (Willett, Is dietray fat a major determinant of body fat?, 1998) 264 (Choi, 2008) 265 (Harvard School of Public Health, 2011) 266 (Mozaffarian, 2006) 267 (Mozaffiarian, 2010) 268 (Vega-Lopez, 2006) 269 (Choi, 2008) (U.S. Department of Health and Human Services, 2011) 270 (St. Onge, 2003) 271 (Paeratakul S, 2003) (Enns,, 2003) (Enns, 2002) 272 (Duffey K. G.-L., 2007) 273 (Ludwig, 2001) 274 (Park, 2002) 275 (Guthrie, 2000) 276 (Tordoff, 1990) 277 (Ludwig, 2001) (Gillis, 2003) 278 (Blum, 2005) 279 (Bray G. N., 2004) (Tordoff, 1990) (Ludwig, 2001) 280 (Gillis, 2003) (Forshee R. S., 2001) 281 (Forshee R. S., 2007) (White J. , 2010) (White J. , 2008) (Murray R. F., 2005) 282 (Ornish, 2001) 283 (Bouchard, 1990) (O'Rahilly, 2006) (Butte, 2006) 284 (Grundy, 1998) 285 (Butte, 2006) 286 (Sen, 2006) 287 (Gilman, 2009) 288 (Boyd, 2005) (Chiolero, 2007) (Meyer, 2006) (Freedman D. D., 2009) (Hannon, 2005) (Carroll, 2007) (Waring., 2008) (Mustillo, 2003) (Taylor, 2006) (Elliott, 2002) 289 (Geier, 2007) (Cheuk, 2006) (Bradstreed, 2003) (Dufault R. S., 2009) 290 (DeSoto, 2007) 291 (Geier, 2007) 292 (Ung, 2010) 293 (de Vendomois, 2009) 294 (de Vendomois, 2009) 295 (The National Museum of Nuclear Science & History, 2011)


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