Nutrition Research and Practice (Nutr Res Pract) 2011;5(3):253-259
Survey of American food trends and the growing obesity epidemic
Qin Shao1 and Khew-Voon Chin2,§
1Department of Mathematics, College of Arts and Sciences, University of Toledo, Toledo, Ohio 43606, USA
2Department of Medicine, University of Toledo College of Medicine, 3000 Arlington Avenue, BHS 377, Toledo, Ohio 43614,
The rapid rise in the incidence of obesity has emerged as one of the most pressing global public health issues in recent years. The underlying
etiological causes of obesity, whether behavioral, environmental, genetic, or a combination of several of them, have not been completely elucidated.
The obesity epidemic has been attributed to the ready availability, abundance, and overconsumption of high-energy content food. We determined
here by Pearson’s correlation the relationship between food type consumption and rising obesity using the loss-adjusted food availability data from
the United States Department of Agriculture (USDA) Economic Research Services (ERS) as well as the obesity prevalence data from the Behavioral
Risk Factor Surveillance System (BRFSS) and the National Health and Nutrition Examination Survey (NHANES) at the Centers for Disease Control
and Prevention (CDC). Our analysis showed that total calorie intake and consumption of high fructose corn syrup (HFCS) did not correlate with
rising obesity trends. Intake of other major food types, including chicken, dairy fats, salad and cooking oils, and cheese also did not correlate with
obesity trends. However, our results surprisingly revealed that consumption of corn products correlated with rising obesity and was independent
of gender and race/ethnicity among population dynamics in the U.S. Therefore, we were able to demonstrate a novel link between the consumption
of corn products and rising obesity trends that has not been previously attributed to the obesity epidemic. This correlation coincides with the introduction
of bioengineered corns into the human food chain, thus raising a new hypothesis that should be tested in molecular and animal models of obesity.
Key Words: Obesity, food trend, corn product, genetically modified, bioengineered
It is estimated that, worldwide, approximately 937 million
adults are overweight and 396 million are obese . This rising
trend continues unabated both globally and in the United States,
which claims the largest population of overweight and obese
adults [2,3]. Various etiologic factors associated with obesity
have been reported, including a number of genes identified from
genome-wide scans and functional genomic studies as well as
some viruses and bacteria [4-7]. The current prevailing hypothesis
centers on the premise that the problem of obesity is one of
energy imbalance, wherein total energy intake far exceeds energy
output . In addition, the global epidemic of obesity has been
attributed to heuristic observations of an increase in the
consumption of high-energy/high-fat content foods coupled with
a sedentary lifestyle that expends little energy.
The notion that particular nutrients or food sources might
influence obesity is controversial . For example, the increased
consumption of some food types, including beverages and foods
that contain high-fructose corn syrup (HFCS), is speculated to
be associated with obesity [10,11]. Moreover, in a previous study,
mice given HFCS-sweetened water gained more weight and
showed increase adiposity . While the results of this animal
study seem to provide experimental evidence that supports the
hypothesis that consumption of HFCS causes obesity, the results
from epidemiological and clinical studies in human are
inconclusive [13,14], leaving the question of HFCS association
with obesity unanswered. Therefore, whether or not the intake
of certain food types predisposes an individual to increased risk
for obesity needs to be examined.
Quantifying the amount of food an individual consumes daily
is difficult, and determining the intake of specific food types
is intractable, thus posing significant challenges to the
investigation of food intake and the development of obesity. It
is known that the Loss-Adjusted Food Availability Data from
the Economic Research Services (ERS) of the United States
Department of Agriculture (USDA) constitute time series data
on the national food supply of several hundred food-types
targeted to the food marketing system. These data are represented
as per capita food availability and are useful for studying food
consumption trends, as they are an indirect measurement of actual
food intake .
This work was supported in part by a National Institutes of Health grant CA102204.
§Corresponding Author: Khew-Voon Chin, Tel. 1-419-383-3502, Fax. 1-419-383-4473, Email. email@example.com
Received: October 6, 2010, Revised: April 29, 2011, Accepted: May 3, 2011
ⓒ2011 The Korean Nutrition Society and the Korean Society of Community Nutrition
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/)
which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
American food trends and obesity
Fig. 1. Comparison of rising obesity trends of NHANES and BRFSS datasets.
Alignment of the NHANES and BRFSS obesity trend datasets was performed to
find the optimal correspondence. The left ordinate indicates the obesity prevalence
by median %; and the right ordinate shows the average daily per capita calories
consumed for each food type. The rising obesity trends are similar between the
NHANES and BRFSS datasets. Open circles (?) are BRFSS; and closed circles
(?) are NHANES obesity trends data with BMI ≥30 from their respective datasets.
To determine whether or not excess energy intake or the
consumption specific food types contribute to the development
of obesity, we surveyed Loss-Adjusted Food Availability and obesity
prevalence data to investigate the correlation between total
energy intake and consumption of certain food types with rising
trends in obesity. We confirmed a novel association of rising
obesity trends with increased corn product consumption that may
be linked to the growing and ubiquitous presence of genetically
modified (GM) or engineered (GE) corn in the human diet.
Subjects and Methods
We obtained the Loss-Adjusted Food Availability Data
(1970-2008) from the Food Availability (Per Capita) Data System
at the United States Department of Agriculture (USDA) Economic
Research Services (ERS). The per capita food availability data
was adjusted for food spoilage, waste, and other losses, as well
as converted to average daily per capita calories. The data are
downloadable at the USDA ERS data bank .
The obesity prevalence and trends data spanning from 1995
and 2008, comprised of health survey data on health risk
behaviors such as obesity, were obtained from the Behavioral
Risk Factor Surveillance System (BRFSS) at the Centers for
Disease Control and Prevention (CDC) . Trends data with
body mass index (BMI) ≥ 30, classified as obese, were used
in this study. The National Health and Nutrition Examination
Survey (NHANES) obesity data between 1960 and 2008 were
downloaded from the E-Stats data bank at CDC .
Data for genetically modified corn varieties adopted in the U.S.
were obtained from USDA ERS National Agricultural Statistics
We analyzed the relationship between the trends in obesity
prevalence and the average daily per capita calories consumed
for various food types using Pearson’s correlation. To validate
the positive correlations, we investigated the dependence of the
obesity trends on different food types by fitting a multiple linear
regression using both full and reduced model functions.
Since the BRFSS obesity prevalence and trends data were only
available between 1995 and 2008, we tested the correlation
between each food type from 1995 through 2008 by Pearson’s
correlation and multiple linear regression. The food types
analyzed were red meat, poultry, dairy products, grains, fats and
oils, sweeteners, and average total calories consumed daily per
capita. In addition, specific food types such as fish, eggs, nuts,
and some dairy products exhibiting either negative or no change
in the trends of consumption between 1995 and 2008 were not
subjected to Pearson’s analysis.
To determine the correlation between obesity and food type
intake, we first assessed the suitability of the BRFSS dataset for
our analysis. Use of the NHANES obesity prevalence data was
hampered by the availability of only five data points, NHANES
(1999-2000), (2001-2002), (2003-2004), (2005-2006), and (2007-
2008), in contrast to the 14 data points in the BRFSS dataset
between 1995 and 2008, which is statistically more favorable
for our study. However, the BRFSS dataset, which is based on
self-reported weights and heights, is generally considered inferior
to the NHANES obesity prevalence data [20,21]. Despite the
qualitative and quantitative differences between the NHANES
and BRFSS data, our results show that the obesity trends between
1995 and 2008 derived from the two datasets were remarkably
similar by regression analysis (Fig. 1). Therefore, we used the
BRFSS data for our correlation study with the food trends data
since it is statistically more robust than the NHANES dataset.
We analyzed the USDA ERS Loss-Adjusted Food Availability
Data, which include seven major aggregated food groups
including 1, meat, eggs, and nuts; 2, dairy; 3, fruit; 4, vegetables;
5, flour and cereal products; 6, added fats and oils, and dairy
fats; and 7, caloric sweeteners. These groups are further
comprised of more than 100 individual or specific food types
(commodities). Analysis of these food types revealed that a large
number of them including fresh vegetables, fresh fruit, beverage
milk, fish and shellfish, fruit juice, nuts, and others, showed either
negative trends or no change in trends of consumption and did
not coincide with rising trends in obesity (Fig. 2).
Since energy imbalance resulting from excess calorie intake
is thought to contribute to obesity, we first analyzed the trends
in calorie intake between 1995 and 2008. The food availability
data indicated that the average daily per capita total calorie intake
has plateaued since year 2000, whereas obesity exhibited a rising
trend (Fig. 3A), and Pearson’s analysis showed a correlation coefficient
of 0.79 (Table 1). In contrast, strong positive correlations with
Qin Shao and Khew-Voon Chin
Fig. 2. Food types showing no correlation with rising obesity trends. The left ordinate indicates the obesity prevalence by median %; and the right ordinate shows the
average daily per capita calories consumed for each indicated food type. Rising obesity did not overlap with the trends in consumption of the indicated food types. Open
squares (?), obesity trends data with BMI > 30; and closed circles (?), food type.
Fig. 3. Regression analysis of the relationship between food type consumption and obesity trends by Pearson’s correlation. Regression analysis by Pearson’s correlation
was performed to determine the relationship between obesity trends and the average daily per capita calories consumed for various food types. The left ordinate indicates
the obesity prevalence by median %; and the right ordinate shows the average daily per capita calories consumed for each food type. Open squares (?), obesity trends
data with BMI > 30; and closed circles (?), food type.
American food trends and obesity
Fig. 4. Trends in corn consumption and rising obesity. (A) Relationship of corn product consumption and obesity prevalence between 1970 and 1994. Obesity prevalence
from NHANES I (1971-1975), II (1976-1980), and III (1988-1994) were plotted against corn product consumption, revealing the lack of overlap in the trends of these data.
Open squares (?), obesity trends data with BMI > 30; and closed circles (?), corn products. (B) Relationship between rising obesity and rate of GM corn adoption in U.S.
between 2000 and 2008. Rate of adoption of GM corn by farmers represented as the acreage of farms planted with GM corn as a percent of total acreage of corn planted
in the U.S. was plotted against rising obesity trends. Open squares (?), obesity trends data with BMI > 30; and closed circles (▲), GM corn.
Chicken -0.045 (0.077)0.578
Corn products0.349 (0.107)0.011
Dairy fats -0.257 (0.226)0.289
Salad and cooking oils-0.000 (0.009)0.987
Total cheese 0.257 (0.120)0.065
Corn products0.302 (0.061)0.000
Total cheese 0.162 (0.071)0.044
Table 2. Multiple linear regression analysis of food types and obesity trends
Food type Correlation coefficient
Total calories intake 0.79
Red meat -0.40
Flour and cereal products 0.03
Added fats and oils and dairy fats0.86
Added fats and oils0.85
Salad and cooking oils0.97
High fructose corn syrup (HFCS)-0.38
Table 1. Correlation between trends in food type consumption and obesity
obesity were unexpectedly found for chicken and corn products
(Fig. 3B and C), with Pearson’s correlation coefficients of 0.96
and 0.99, respectively (Table 1).
We also observed a positive correlation between total cheese
intake and obesity (Fig. 3D). However, further analysis revealed
that, with the exception of cheddar and mozzarella cheese, most
other cheeses, such as provolone, parmesan, Swiss cheese, blue
cheese, and others, showed little or no changes in consumption
trends between 1995 and 2008, and Pearson’s analysis of either
cheddar (Fig. 3E) or mozzarella (Fig. 3F) did not show correlation
with rising obesity.
Even though correlation with obesity was not found for “Added
Fats and Oils, and Dairy Fats” (Fig. 3G), with a correlation
coefficient of 0.86 (Table 1), analysis of Salad and Cooking Oils
(Fig. 3H) and Dairy Fats (Fig. 3I) revealed correlation with
obesity, each with a correlation coefficient of 0.97 (Table 1).
These correlations subsequently did not cross-validate upon
further analysis by multiple linear regression (see below).
Additionally, either poor or negative correlations were found
for foods such as flour and cereal products, shortening, red meat,
caloric sweeteners, and HFCS, with correlation coefficients of
-0.03, -0.18, -0.40, -0.74, and -0.38, respectively (Fig. 3J-N, and
Table 1). The consumption of refined cane and beet sugar (Fig.
3O) as well as sweet corn as a fresh vegetable (Fig. 3P) also
did not correlate with obesity. The consumption of corn as a
fresh vegetable constituted only a small percentage (averaging
0.01%) of the total calorie intake between 1995 and 2008.
To further test these positive correlations with obesity trends,
we performed a fitting by multiple linear regression analysis with
food types that showed correlation coefficients > 0.95, which
included chicken, corn products, dairy fats, salad and cooking
oils, and total cheese, in a full model function. This analysis
showed that only corn products had p-values smaller than 0.05
(Table 2), suggesting that consumption of corn products had a
significant effect on rising obesity trends. In the reduced model,
we analyzed corn products and total cheese, which have p-values
closest to 0.05 from the full model analysis, and our results
confirmed a correlation between corn products, but not total
cheese, and obesity trends (Table 2).
The observed correlation between consumption of corn
products and rising obesity is surprising. It is noteworthy that
HFCS is classified separately as a caloric sweetener and not
aggregated with other corn products. Moreover, HFCS showed
a negative correlation with rising obesity (Table 1). We were
not able to fully analyze whether or not corn product consumption
correlated with obesity trends between 1970 and 1994 because
the National Health and Nutrition Examination Survey
(NHANES) datasets are only available in four cross-sectional,
Qin Shao and Khew-Voon Chin
Fig. 5. Correlation of corn products intake with NHANES obesity prevalence
data stratified by race/ethnicity and gender. Obesity prevalence data stratified by
race/ethnicity and gender from NHANES III (1988-1994), NHANES (1999-2000),
(2001-2002), (2003-2004), (2005-2006), and (2007-2008) were plotted against corn
product consumption between 1995 and 2008. Open squares (?), obesity trends
data with BMI ≥ 30; and closed circles (?), average daily per capita corn products
intake in calories. Alignment was performed for the relationship of rising obesity with
corn product consumption between 1995 and 2008 for non-Hispanic white men (A)
and women (B); non-Hispanic black men (C) and women (D); and Mexican-American
men (E) and women (F).
nationally representative surveys prior to 1995, including
NHANES I (1971-1975), II (1976-1980), and III (1988-1994)
, thus yielding only three data points for Pearson's correlation
analysis of corn-rich products. Nevertheless, we showed that the
trends in obesity prevalence and corn product consumption
between 1970 and 1994 did not align (Fig. 4A).
We were also aware that genetically modified (GM) corn has
been planted in the U.S. since 1996 . To further investigate
the relationship between bioengineered corn and rising obesity,
we obtained data on the adoption of GM corn from the USDA,
which covered the period between 2000 and 2008, for comparison
with rising obesity. These data did not take into account the use
of GM corn for other purposes besides as a food or animal feed.
Despite this limitation, our result shows that the trends of obesity
and adoption of GM corn were similar (Fig. 4B).
We further asked whether or not the consumption of corn
products might be associated with the demographic distribution
of the population. Using the NHANES stratified obesity
prevalence data between NHANES III (1988-1994), NHANES
(1999-2000), (2001-2002), (2003-2004), (2005-2006), and (2007-
2008), we examined the relationship between corn product
consumption and race/ethnicity of men and women between 1995
and 2008. Our results show that the trends of obesity and corn
product consumption rose in parallel irrespective of gender
among non-Hispanic white men and women (Fig. 5A and B),
non-Hispanic black men and women (Fig. 5C and D), and
Mexican-American men and women (Fig. 5E and F), thus
suggesting that the association of rising obesity trends with
increased corn product consumption is independent of race/
ethnicity and gender.
Our analysis of obesity and food type consumption trends data
in this report yielded three major findings. First, it has been long
accepted that overconsumption of food coupled with a sedentary
lifestyle results in a positive energy imbalance, which is a formula
for obesity development. Our analysis in this report, however,
indicates that even though total calorie intake in the U.S. has
plateaued in recent years, the incidence of obesity continues to
rise, thus suggesting that rising obesity trends do not correlate
with total energy intake. Alternatively, it is conceivable that the
total caloric intake has plateaued while the levels of physical
activity have also not increased, thus explaining the intransigent
Second, HFCS as a cause of obesity has been intensely debated.
It was shown recently that rats given HFCS along with a regular
chow diet gained more weight than control rats, even when they
consumed the same amount of calories . Further, consumption
of an HFCS-containing diet increased visceral fats and blood
triglycerides over time. However, our results show a negative
correlation of HFCS with rising obesity, as HFCS consumption
has been on the decline since 2000. However, this negative
correlation does not refute the underlying biological role of HFCS
in obesity. Instead, it suggests that HFCS consumption on the
whole may not contribute to rising obesity trends. Though we
initially also observed positive correlations between increased
consumption of chicken, salad and cooking oils, dairy fats, and
total cheese with obesity, subsequent multiple linear regression
analysis and cross-validation of these results revealed a lack of
significance in these correlations.
The above observations suggest that additional factors may be
involved in rising obesity trends. Therefore, our third finding
of a correlation between increased corn product intake and rising
obesity between 1995 and 2008 is intriguing, as these foods are
not generally considered unhealthy. What are the underlying
etiologic links between these foods and obesity?
In the ERS dataset, corn products are considered an aggregate
comprised of flour and meal, hominy and grits, cornstarch, and
other corn products, which are widely used in the manufacture
of a large variety of food products consumed by humans.
Recently, it was reported that approximately 85% of the corn
grown in the U.S. is transgenic . The increased ubiquity of
GM or genetically engineered corn products in human food
sources is noted, but their potential impact on human health has
American food trends and obesity
not been investigated despite recent reports of hepatorenal
toxicity in rats fed GM maize [26,27]. Moreover, the rising trends
in obesity coincide, in part, with the introduction of GM corn
in foods and animal feeds in the U.S. [28,29]. These observations
prompted us to hypothesize that consumption of GM corn
products may contribute to rising obesity trends. The implications
of our results and the new hypothesis raised here are provocative
but testable, as the effects of GM corn products can be assessed
in molecular and animal models of obesity. No data are currently
available on how much genetically engineered food is on the
market due to a lack of proper labeling and traceability.
We further speculate that the bacterial antigen derived from
the Bacillus thuringiensis (Bt) entomocidal crystalline protein
protoxin , which is genetically engineered into corn to confer
resistance to insect pests, may be the underlying culprit that
causes anomalous adipose tissue dysregulation and obesity
While our trends study has yielded novel insights into the
potential impact that some food types may have on the
development of obesity, there are some possible confounding
factors that should be discussed. It is noteworthy that the
Loss-Adjusted Data do not reflect actual consumption or the
quantities of food ingested. Moreover, it was difficult to collect
data on the actual amount of food or the specific food types
consumed, as most previous clinical studies have relied on
questionnaires and voluntary reporting or recollection of food
consumed by study subjects, which are well known to be prone
to psychosocial behavioral errors. In the absence of true food
consumption data, therefore, trends in food use obtained from
the USDA ERS Food Availability Data served as an alternative
indirect measure of whether Americans are consuming more or
less of various foods over time. Similarly, the weight and height
information collected from phone interviews for the BRFSS
obesity trends data is also highly susceptible to erroneous
self-reporting. In contrast, physical measurements for weight and
height were obtained from participants in the NHANES studies.
Despite such shortcomings, the BRFSS obesity trends were
remarkably similar to the NHANES dataset. In addition, the data
for the rate of GM corn adoption in the U.S. did not take into
consideration the different uses of these transgenic corns other
than as foods and feeds. Although it is clear that transgenic corn
has penetrated into human foods and animal feeds, and the
consumption of GM crops has been deemed safe , precise
data regarding the amounts and types of foods containing
transgenic corn products are unavailable, and the correlation with
increased emergence of common human diseases including
diabetes and obesity has not been investigated.
Taken together, our results reveal a novel association of corn
product consumption with rising trends of obesity, which may
be linked to the increased ubiquity of transgenic corn in the diet.
These trends data findings warrant further investigation and
confirmation through laboratory testing.
We thank Jennifer E.W. Hill, Ph.D. (Department of Physiology
and Pharmacology, University of Toledo, College of Medicine,
Toledo, Ohio) for helpful comments and discussions regarding
1. Kelly T, Yang W, Chen CS, Reynolds K, He J. Global burden
of obesity in 2005 and projections to 2030. Int J Obes (Lond)
2. Popkin BM. Recent dynamics suggest selected countries catching
up to US obesity. Am J Clin Nutr 2010;91:284S-288S.
3. Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and
trends in obesity among US adults, 1999-2008. JAMA 2010;
4. Pomp D, Mohlke KL. Obesity genes: so close and yet so far...
J Biol 2008;7:36.
5. Atkinson RL. Viruses as an etiology of obesity. Mayo Clin Proc
6. Tsai F, Coyle WJ. The microbiome and obesity: is obesity linked
to our gut flora? Curr Gastroenterol Rep 2009;11:307-13.
7. Vijay-Kumar M, Aitken JD, Carvalho FA, Cullender TC,
Mwangi S, Srinivasan S, Sitaraman SV, Knight R, Ley RE,
Gewirtz AT. Metabolic syndrome and altered gut microbiota in
mice lacking Toll-like receptor 5. Science 2010;328:228-31.
8. Swinburn BA, Sacks G, Lo SK, Westerterp KR, Rush EC,
Rosenbaum M, Luke A, Schoeller DA, DeLany JP, Butte NF,
Ravussin E. Estimating the changes in energy flux that characterize
the rise in obesity prevalence. Am J Clin Nutr 2009;89:1723-8.
9. Foreyt JP, Salas-Salvado J, Caballero B, Bulló M, Gifford KD,
Bautista I, Serra-Majem L. Weight-reducing diets: are there any
differences? Nutr Rev 2009;67 Suppl 1:S99-101.
10. Bray GA, Nielsen SJ, Popkin BM. Consumption of high-fructose
corn syrup in beverages may play a role in the epidemic of
obesity. Am J Clin Nutr 2004;79:537-43.
11. Johnson RJ, Segal MS, Sautin Y, Nakagawa T, Feig DI, Kang
DH, Gersch MS, Benner S, Sánchez-Lozada LG. Potential role
of sugar (fructose) in the epidemic of hypertension, obesity and
the metabolic syndrome, diabetes, kidney disease, and
cardiovascular disease. Am J Clin Nutr 2007;86:899-906.
12. Jürgens H, Haass W, Castañeda TR, Schürmann A, Koebnick C,
Dombrowski F, Otto B, Nawrocki AR, Scherer PE, Spranger J,
Ristow M, Joost HG, Havel PJ, Tschöp MH. Consuming
fructose-sweetened beverages increases body adiposity in mice.
Obes Res 2005;13:1146-56.
13. Teff KL, Elliott SS, Tschöp M, Kieffer TJ, Rader D, Heiman
M, Townsend RR, Keim NL, D'Alessio D, Havel PJ. Dietary
fructose reduces circulating insulin and leptin, attenuates postprandial
suppression of ghrelin, and increases triglycerides in women. J
Clin Endocrinol Metab 2004;89:2963-72.
14. Melanson KJ, Zukley L, Lowndes J, Nguyen V, Angelopoulos
TJ, Rippe JM. Effects of high-fructose corn syrup and sucrose
consumption on circulating glucose, insulin, leptin, and ghrelin
and on appetite in normal-weight women. Nutrition 2007;23:
15. Barnard ND. Trends in food availability, 1909-2007. Am J Clin
Qin Shao and Khew-Voon Chin
16. United States Department of Agriculture, Economic Research
Service [Internet]. Food Availability (Per Capita) Data System;
[cited 2010 September 29]. Available from: http://www.ers.usda.
17. Centers for Disease Control and Prevention, Office of
Surveillance, Epidemiology, and Laboratory Services, Behavioral
Risk Factor Surveillance System [Internet]. Prevalence and Trends
Data; [cited 2010 September 29]. Available from: http://apps.
18. Centers for Disease Control and Prevention, National Center for
Health Statistics [Internet]. National Health and Nutrition
Examination Survey; [cited 2010 September 29]. Available from:
19. United States Department of Agriculture, Economic Research
Service [Internet]. Adoption of Genetically Engineered Crops in
the U.S.; [cited 2010 September 29] Available from: http://www.
20. Mokdad AH, Serdula MK, Dietz WH, Bowman BA, Marks JS,
Koplan JP. The spread of the obesity epidemic in the United
States, 1991-1998. JAMA 1999;282:1519-22.
21. Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and
trends in obesity among US adults, 1999-2000. JAMA 2002;288:
22. Flegal KM, Carroll MD, Kuczmarski RJ, Johnson CL. Overweight
and obesity in the United States: prevalence and trends,
1960-1994. Int J Obes Relat Metab Disord 1998;22:39-47.
23. Estruch JJ, Carozzi NB, Desai N, Duck NB, Warren GW, Koziel
MG. Transgenic plants: an emerging approach to pest control.
Nat Biotechnol 1997;15:137-41.
24. Bocarsly ME, Powell ES, Avena NM, Hoebel BG. High-fructose
corn syrup causes characteristics of obesity in rats: Increased
body weight, body fat and triglyceride levels. Pharmacol
Biochem Behav 2010;97:101-6.
25. Marshall A. 2nd-generation GM traits progress. Nat Biotechnol
26. Magaña-Gómez JA, de la Barca AM. Risk assessment of genetically
modified crops for nutrition and health. Nutr Rev 2009;67:1-16.
27. de Vendômois JS, Roullier F, Cellier D, Séralini GE. A comparison
of the effects of three GM corn varieties on mammalian health.
Int J Biol Sci 2009;5:706-26.
28. Formanek R Jr. Proposed rules issued for bioengineered foods.
FDA Consum 2001;35:9-11.
29. Falk MC, Chassy BM, Harlander SK, Hoban TJ 4th, McGloughlin
MN, Akhlaghi AR. Food biotechnology: benefits and concerns.
J Nutr 2002;132:1384-90.
30. Pigott CR, Ellar DJ. Role of receptors in Bacillus thuringiensis
crystal toxin activity. Microbiol Mol Biol Rev 2007;71:255-81.
31. Key S, Ma JK, Drake PM. Genetically modified plants and
human health. J R Soc Med 2008;101:290-8.