RAT STRAIN DIFFERENCES IN BINGE EATING: IMPLICATIONS FOR
GENETIC DIFFERENCES
By
Britny Hildebrandt

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
PSYCHOLOGY- MASTER OF ARTS

2013

ABSTRACT
RAT STRAIN DIFFERENCES IN BINGE EATING: IMPLICATIONS FOR
GENETIC DIFFERENCES
By
Britny Hildebrandt
Binge eating is a significantly heritable phenotype, but efforts to identify specific risk
genes have fallen short. Identification of animal strain differences in risk for binge eating could
highlight genetic differences across animals that can be exploited in future animal and molecular
genetic research. The current study aimed to explore strain differences in risk for binge eating in
Sprague-Dawley versus Wistar female rats using the Binge Eating Resistant/Binge Eating Prone
model. A sample of male Sprague Dawley rats, a known low-risk group for binge eating, was
included as a comparison group. A total of 83 rats (23 Wistar female, 30 Sprague-Dawley
female, 30 Sprague-Dawley male) completed a protocol of intermittently administered, palatable
food. Binge eating prone (BEP) and binge eating resistant (BER) rats were identified using a
tertile approach. Sprague-Dawley female rats consumed the highest amount of palatable food
and were more likely to be classified as BEP compared to Wistar female and Sprague-Dawley
male rats. Wistar female rats were not significantly different from Sprague-Dawley male rats in
their palatable food intake and tendency to be classified as BER rather than BEP. SpragueDawley female rats appear to be a particularly vulnerable strain for binge eating. Comparisons
between this strain and others could help identify specific genetic/biological factors that
differentiate this strain from lower risk strains. The opioid and dopaminergic systems, linked to
binge eating in humans, are possible candidates to explore. Strain differences in these reward
processes and their genetic/biological underpinnings could help increase understanding of
individual differences in risk for binge eating in humans.

TABLE OF CONTENTS

LIST OF TABLES......................................................................................................................... iv
Introduction......................................................................................................................................1
Method.............................................................................................................................................6
Animals.......................................................................................................................................6
Experimental Design..................................................................................................................7
Data Analyses.............................................................................................................................8
Data Preparation...................................................................................................................8
Strain Differences in Palatable Food Intake.........................................................................8
Strain Differences in Proportion of Binge Eating Resistant and Binge Eating Prone
Groups...................................................................................................................................8
Exploratory Analyses - Strain Differences in the Nature of Binge Eating Resistant and
Binge Eating Prone Groups................................................................................................10
Results...........................................................................................................................................10
Strain Differences in Palatable Food Intake............................................................................11
Strain Differences in Proportion of Binge Eating Resistant and Binge Eating Prone
Groups......................................................................................................................................11
Exploratory Analyses - Strain Differences in the Nature of Binge Eating Resistant and Binge
Eating Prone Groups................................................................................................................11
Discussion......................................................................................................................................12
APPENDIX....................................................................................................................................17
REFERENCES..............................................................................................................................25

iii

LIST OF TABLES

Table 1: Means, Standard Deviations, and Effect Sizes for 1-hour, 4-hour, and 24-hour
Palatable Food Intake....................................................................................................................18
Table 2: Sample Size of Rat Strains by Gender and Cohort..........................................................19
Table 3: Means, and Standard Deviations, Effect Sizes, and ANOVA for 1-hour, 4-hour, and
24-hour Palatable Food Intake......................................................................................................20
Table 4: Differences in the Proportion of Categorical BER and BEP Phenotypes Using
Combined Group Tertiles (N = 83 rats, 30 Sprague-Dawley Female, 30 Sprague-Dawley
Male, 23 Wistar Female)...............................................................................................................21
Table 5: Means, and Standard Deviations, and ANOVA for BER/BEP Counts Based on 4-hr
Combined Group Tertiles..............................................................................................................22
Table 6: Results from Multiple Regressions Examining the Influence of BER/BEP Count and
Strain Group on Chow Intake, Body Weight, and Palatable Food Intake....................................23

iv

Introduction
Eating disorders are significant psychological disorders that are affected by both
biological and genetic factors. Of the primary eating disorders, Anorexia Nervosa (AN), Bulimia
Nervosa (BN) and Binge Eating Disorder (BED), binge eating is a core feature of each disorder
(Keel, Heatherton, Dorer, Joiner, & Zalta, 2006; Wade, Bergin, Tiggemann, Bulik, & Fairburn,
2006). Binge eating involves consumption of extremely large amounts of food in a short period
of time while experiencing a loss of control during the episode (American Psychiatric
Association [APA], 2000). In addition, binge eating is associated with elevated rates of obesity
(Spitzer et al., 1993; Stice, Cameron, Killen, Hayward, & Taylor, 1999; Stice, Presnell, &
Spangler, 2002) and psychopathology such as major depression (Telch & Stice, 1998).
Previous research has suggested that genetic factors may contribute to binge eating. Twin
studies have shown that eating disorders are significantly heritable at around 50-83% (Klump,
Bulik, Kaye, Treasure, & Tyson, 2008). Importantly, binge eating itself is also heritable with
estimates ranging from 50-82% (Bulik, Sullivan, & Kendler, 1998). While these data have
indicated the importance of genetic factors, molecular genetic research has been relatively
inconclusive in identifying specific risk genes for binge eating. Interestingly, specific genes
within the opioid/dopaminergic systems (e.g., mu-opioid receptor, dopamine D2 receptor) that
contribute to reward processes have been associated with binge eating and the types of palatable
food consumed during a binge episode (Davis et al., 2009). Further understanding of underlying
genetic factors within these systems may help to gain increased insight into binge eating.
While findings from human studies have been helpful in further understanding binge
eating, animal studies offer a unique perspective for understanding binge eating risk by providing
the opportunity to study behaviors in the absence of psychosocial influences found in human
1

research (Klump et al., 2011). For example, body dissatisfaction and peer influences are
psychosocial influences that have been shown to contribute to the development of binge eating in
humans (Stice et al., 2002; Vincent & McCabe, 2000). Animals likely do not experience these
same risk factors for development of binge eating, and therefore, animal models can more easily
isolate biological and genetic factors that influence binge eating.
To more clearly study biological and genetic differences, animal models, particularly
rodent models, frequently use genetically diverse outbred strains. Outbred rat strains start as
“inbred” strains, in that the strain begins by inbreeding rats from the same strain line. However,
over time, the strain multiples by breeding unrelated rats from the same strain, which serves to
increase genetic heterogeneity and produce an “outbred” (rather than purely “inbred”) strain.
This heterogeneity within the outbred strain allows for discovery of differential phenotypes
within the strain, which is similar to the diversity found in a human population. Importantly,
even though an outbred strain is heterogeneous, animals within the same strain are still more
similar to each other than animals of different outbred strains, since rats from the same outbred
strain were bred from the same initial parental animals. Taken together, these genetic features of
outbred strains allow for identification of extreme phenotypes within a strain, but also the
examination of strain differences in these phenotypes.
In relation to binge eating, identifying specific outbred strains with more or less binge
eating behaviors would make it possible to narrow the search for potential genetic and biological
risk factors. Follow-up research could then use information on phenotypic and genetic strain
differences to identify potential risk genes for binge eating. To date, however, no studies have
examined strain differences in binge eating in animal models, although, several animal models of
binge eating exist that would be appropriate for such investigations. One in particular is the
2

Binge Eating Resistant/Binge Eating Prone (BER/BEP) model, a well-established model of binge
eating that has been successfully utilized in animal research (Boggiano et al., 2007; Klump et al.,
2011; Oswald, Murdaugh, King, & Boggiano, 2011). The BER/BEP model does an excellent job
of representing binge eating behaviors as they appear in humans, and there are several features of
the model that make it ideal for studying binge eating. The BER/BEP model identifies binge
eating prone and binge eating resistant rats based on intermittent, palatable food intake by
examining four-hour intakes of palatable food during a 24-hour feeding test. Four-hour intakes
have been shown to be a reliable time frame in which to observe binge eating behaviors in rats
(Boggiano et al., 2007), and is similar to binge eating patterns observed in humans which occurs
over a short period of time. The feeding tests are administered every few days which models the
intermittent pattern typically seen in binge eating patterns in humans. Similar to humans, food
consumed during binge episodes in binge eating prone rats is typically palatable and high fat
foods (rather than chow) that lack nutritional value (Boggiano et al., 2007; Boggiano, Dorsey,
Thomas, & Murdaugh, 2009; Klump et al., 2011; Oswald et al., 2011). While cognitive
symptoms of binge eating are more difficult to model, research has also provided evidence that
binge eating prone rats will endure painful foot-shock for the opportunity to consume palatable
food (Oswald et al., 2011) showing that rats may experience a loss of control over binge eating,
much like is present in humans (Klump et al., 2011). Furthermore, binge eating prone animals do
not differ in their body weight when compared to binge eating resistant animals (Boggiano et al.,
2007). This is similar to data showing that women with BN typically are not overweight but are
of average weight. In sum, while it is difficult to model all aspects of human binge eating in an
animal model, overall, the BER/BEP model has strong face validity as it is able to model many
key features of binge eating behaviors as it appears in humans.
3

To date, all studies of the BER/BEP model have used rats from the outbred SpragueDawley strain, though other outbred strains have the possibility to show more or less
vulnerability for binge eating. Although no studies have specifically examined strain differences
in binge eating, one study examined strain differences in taste preferences of 17 liquid taste
compounds (Tordoff, Alarcon, & Lawler, 2008). This study used 14 different rat strains, three of
which were outbred strains (including Sprague-Dawley and Wistar), and found that the SpragueDawley strain had a stronger preference for the sweet compounds, and fell in the highest
preference category. This provides indirect evidence that the Sprague-Dawley strain may indeed
be particularly vulnerable to consumption of highly palatable food. Notably, another strain
showed a lack of preference for sweet solutions; the Wistar rat strain never fell in the highest
preference for the sweet compounds, suggesting that it may represent a particularly low risk
strain for palatable food consumption. As previously mentioned, preference for sweetness is
present in binge eating, as binge foods tend to be highly palatable food that are high-fat, highsweet foods. This suggests that the Sprague-Dawley strain may be a particularly high-risk strain
for binge eating, while the Wistar strain may be a low-risk strain that exhibits a lack of
preference for sweetness and binge eating.
The aim of the current study was to examine this possibility by directly comparing binge
eating proneness between 30 Sprague-Dawley rats and 23 Wistar rats phenotyped as binge eating
resistant versus binge eating prone. The objective of the first aim was to examine differences in
palatable food intake between Sprague-Dawley female rats and Wistar female rats during feeding
tests. Like the research showing Sprague-Dawley rats having a greater preference for sweet
solutions, it was predicted that the Sprague-Dawley female rats would consume a larger amount
of palatable food than Wistar female rats across testing days. The second aim directly determined
4

if strain differences in binge eating proneness are present. Much like hypotheses about palatable
food intake, it was predicted that the Sprague-Dawley female rats would exhibit higher rates of
binge eating proneness compared to the Wistar female rats.
Importantly, in both analyses, female rats were examined, but a group of SpragueDawley male rats was also included as a comparison group. Previous research has shown that
Sprague-Dawley male rats exhibit lower rates of palatable food intake and binge eating prone
phenotypes than the Sprague-Dawley female rats (Klump, Racine, Hildebrandt, & Sisk,
submitted). The inclusion of a male comparison group also helped determine whether the level of
binge eating proneness observed in Wistar female rats fell closer to a known low-risk group,
(i.e., the Sprague-Dawley male rats), or a more high-risk binge eating prone group (i.e., the
Sprague-Dawley female rats).
As an exploratory aim of the study, the feeding behavior (i.e., palatable food intake and
chow intake) and body weights of animals in both the binge eating prone and binge eating
resistant groups from both strains were examined for potential strain differences in the nature of
the binge eating prone phenotype. Previous research has shown that binge eating prone SpragueDawley female rats do not differ in body weight or daily chow intake from the binge eating
resistant Sprague-Dawley female rats on feeding test days or non-feeding test days (Boggiano et
al., 2007). By contrast, the feeding behaviors of Sprague-Dawley male rats are more
complicated. While the male Sprague-Dawley BER/BEP animals do not differ in their body
weight, there is a difference in their chow intake on feeding test days. Binge eating prone male
rats consume less chow than the binge eating resistant male rats, suggesting that they may be
compensating for over eating palatable on food by decreasing their chow intake (i.e., they
replace part of their chow intake with the palatable food) (Klump et al., submitted). These
5

findings provide more evidence of strain differences in the nature of binge eating prone
phenotype across groups.
Method
Animals
A convenience sample of 83 animals of both the Sprague-Dawley and Wistar strains were
obtained for the study. Sixty Sprague-Dawley rats (females, n = 30, males, n=30) were obtained
on approximately postnatal day 60 from Harlan (Madison, Wisconsin). Twenty-three female
Wistar rats were also obtained from Dr. James Galligan (Department of Pharmacology and
Toxicology, MSU) on approximately postnatal day 46-53. Importantly, this Wistar rat group was
comprised of both wild-type animals (n=12) and serotonin transporter knock-out animals (n=11).
All animals were exposed to the BER/BEP model as part of a larger study aiming to examine
differences in binge eating proneness between wild-type and serotonin knock-out Wistar female
rats. Analyses showed no significant differences in palatable food intake between the wild-type
and knock-out Wistar rat groups suggesting that removal of this serotonin the serotonin
transporter was not important for binge eating behaviors (see Table 1). Therefore, all Wistar rats
were combined into one group to allow for examination of strain differences in binge eating.
The animals were individually housed in clear plexiglass cages (45 x 23 x 21 cm) that
were outfitted with a wire cage lid. The animals were given ad lib access to both chow (Rodent
diet 8640; Harlan Teklad Global Diets, Madison, WI) and water. Temperature was held at 21 ±
2ºC, and the room was on a light cycle allowing for 12 hours of light, and 12 hours of dark (on at
2400h, off at 1200h). Animals were treated in accordance with the NIH Guide for the Care and
Use of Laboratory Animals, and all protocols were approved by the Michigan State University
Institutional Animal Care and Use Committee.
6

Experimental Design
The study design followed the well-established Binge Eating Resistant/Binge Eating
Prone (BER/BEP) model of binge eating (Boggiano et al., 2007; Oswald et al., 2011), with
modifications to feeding test frequency. Instead of the tests being administered 1-2 times per
week for two weeks (Boggiano et al., 2007), the current study administered feeding tests three
times per week (Monday, Wednesday, and Friday) over the course of two weeks. This is a
modification that has been used previously in our lab (Klump et al., 2011; Klump et al.,
submitted) and has been useful for generating more feeding test data for determining binge
eating status. Administering feeding tests three times per week showed highly similar results to
those observed in other studies of the BER/BEP model (Boggiano et al., 2007).
Animals were run in three different cohorts to accommodate the collection of palatable
food, chow, and body weight measurements under the time constraints (see Table 2 for a
breakdown of sample sizes in each cohort). A total of seven identical feeding tests were done,
though only six will be included in data analyses. During testing of the first cohort, there was a
malfunction with the air conditioning resulting in high temperatures that may have affected the
data from feeding test 3. I therefore excluded data from this feeding test for Cohort 1. To account
for the uneven number of tests across cohorts, I subsequently removed feeding test 3 data from
Cohorts 2 and Cohort 3 as well.
Each day prior to dark onset (1200h), daily body weight and chow measurements were
recorded. Chow was in pellet form to make locating spillage in the bedding easier. Any spillage
detected after searching the bedding was added to the chow measurement. All body weight and
chow measurements were taken to the nearest tenth of a gram using an electronic scale.

7

On feeding test days, the rats had ad lib access to chow, water, and palatable food for the
entirety of the feeding test (24 hours). Feeding tests began at 1200h when 50-80 grams of new
chow were added to the cage, and 15-25 grams of palatable food (i.e., Betty Crocker Creamy
Vanilla Frosting, General Mills Inc., Minneapolis, MN) were placed in small Petri dishes. The
palatable food dish was hung inside the cage via wire hook and was left in position for the full 24
hours of the feeding tests. Both chow and palatable food were weighed at the 1, 4, and 24-hour
time points using the same weighing and rounding cutoffs described above. The feeding tests
were administered identically each time following the same procedures described.
Data Analyses
Data Preparation. In order to account for differences in body weight/size across sex and
possibly strain, palatable food and chow intakes were standardized (intake(g)/body weight(g)2/3)
by body weight prior to analyses.
Strain Differences in Palatable Food Intake. A one-way analysis of variance (ANOVA)
with Tukey’s post hoc t-tests was used to examine mean differences in palatable food intake
across the three study groups (e.g., Wistar females, Sprague-Dawley females, and SpragueDawley male rats) at the 1-hour, 4-hour, and 24-hour time points.
Strain Differences in Proportion of Binge Eating Resistant and Binge Eating Prone
Groups. Using methods established by Boggiano et al. (2007) and Klump et al. (2011), animals
were identified as binge eating resistant (BER) or binge eating prone (BEP) by examining the 4hour time point of palatable food across the six feeding tests. The 4-hour food intake
measurement in particular has been used in previous studies using the BER/BEP model and has
proven to be an accurate measurement of binge eating (Boggiano et al., 2007; Klump et al.,
2011; Oswald et al., 2011). Animals scoring in the top tertile a majority of the time, and never
8

falling in the bottom tertile, were classified as binge eating prone. Conversely, animals scoring in
the bottom tertile the majority of the time, and never falling in the top tertile, were classified as
binge eating resistant. It is important to note that by using this categorical method, some animals
may not meet these classification requirements and therefore, are excluded from categorical
analyses. A classification for what is used as the majority of feeding tests has varied across
previous studies. Some have required three out of six, four out of six, and five out of six feeding
tests of scoring in the top or bottom tertile to determine BEP/BER status respectively (Klump et
al., 2011, Klump et al., submitted). All three classifications were used to explore whether strain
differences were present across all classifications or just some classifications. The tertiles for
determining BER/BEP groups were calculated using a combined total group (all animals from
both strains) in order to assign BER/BEP status.
Previous studies have compared the proportion of binge eating resistant and binge eating
prone categorical phenotypes using chi-squares analyses. These differences were explored here
using 2 (BER/BEP) x 3 (strain group) chi-square tests followed by two-proportion z-tests to
compare the proportion of binge eating resistant versus binge eating prone phenotypes across
Wistar female, Sprague-Dawley female, and Sprague-Dawley male rats. However, given the
small sample size in the Wistar group, and due to the BER/BEP categorical approach, which
excludes some animals from analyses, a continuous BER/BEP variable approach was also used.
This approach uses a binge eating prone “count” variable that counts the number of times each
rat scored in the highest tertile (i.e., highest PF intake) across the six feeding tests (score range 06). Likewise, a binge eating resistant count variable was calculated using the number of times
each rat scored in the lowest tertile (i.e., lowest PF intake) during the six feeding tests (score
range 0-6). Importantly, this allows for the inclusion all animals in analyses. A one-way
9

ANOVA with Tukey’s post hoc t-tests will be used to compare the average count variable across
both strain and sex.
Exploratory Analyses - Strain Differences in the Nature of Binge Eating Resistant and
Binge Eating Prone Groups. The feeding behavior and body weights of animals in the binge
eating prone and binge eating resistant categories from the Sprague-Dawley and Wistar strains
were examined for potential strain differences in the nature of the binge eating prone phenotype.
Specifically, I used multiple regression to examine whether strain type (Wistar versus SpragueDawley) interacts with the binge eating prone or binge eating resistant phenotypes to predict
palatable food intake, chow intake, and body weights. Analyses were conducted separately for
feeding test versus non-feeding test days.
Results
Strain Differences in Palatable Food Intake. Results from ANVOAs examining mean
differences in palatable food intake across study groups (e.g., Wistar female rats, Sprague1

Dawley female rats, and Sprague-Dawley male rats) are summarized in Table 3 . Findings
suggested that there were significant differences across groups, with Sprague-Dawley female rats
consuming significantly higher amounts of palatable food across all time points (i.e., 1-hour, 4hour, and 24-hours). Although Wistar female and Sprague-Dawley male rats consistently
consumed lower amounts of palatable food than the Sprague-Dawley females, differences
between the two groups varied depending upon the time frame examined (e.g., Wistar females
consumed significantly less palatable food at the 1-hour time point than Sprague Dawley males,
whereas Sprague Dawley males consumed significantly less at 24 hours). Nonetheless, overall,
1

Aim 1 and aim 2 analyses were also run excluding serotonin knock-out Wistar rats. Results
from these analyses showed the same patterns indicating no specific influences of the knock-out
animals on study results (data not shown).
10

results were remarkably consistent in suggesting medium-to-large differences (see effect sizes –
Table 3) in palatable food intake between the Sprague Dawley female and both the Wistar
female and Sprague-Dawley male groups.
Strain Differences in the Proportion of Binge Eating Resistant and Binge Eating Prone
Groups. Using a categorical approach, animals were classified as binge eating prone or binge
eating resistant using the 3/6, 4/6, and 5/6 feeding test classifications. Across all classifications,
chi-square tests indicated significant differences in the proportion of binge eating prone versus
2

binge eating resistant rats across rat strain (3/6 classification: χ [2, N = 34] = 26.41, p < .001;
2

2

4/6 classification: χ [2, N = 25] = 25.00, p < .001; 5/6 classification: χ [2, N = 14] = 14.00, p <
.001). Follow-up z-tests showed that there was a significantly higher proportion of binge eating
prone animals, and a lower proportion of binge eating resistant animals, in the Sprague-Dawley
female rats as compared to all other groups (see Table 4). By contrast, the Sprague-Dawley male
rats and Wistar female rats produced the opposite results, with more binge eating resistant, and
fewer binge eating prone, phenotypes compared to the Sprague-Dawley female rats. Very similar
results were obtained in the ANOVAs examining the binge eating proneness/resistance count
variable (see Table 5). Binge eating prone tertile counts were significantly higher in the SpragueDawley female rats, while binge eating resistant counts were significantly higher in the SpragueDawley male and Wistar female rats (all p’s < 001).
Exploratory Analyses - Strain Differences in the Nature of Binge Eating Resistant and
Binge Eating Prone Groups. Results from the regression testing whether strain group (i.e.,
Sprague-Dawley female rats, Sprague-Dawley male rats, and Wistar female rats) and binge
group (binge eating resistant vs. binge eating prone) affected differences in chow intake,

11

palatable food intake, or body weight are presented in Table 6. Because no Wistar female rats
were categorized as binge eating prone (see above), here, the focus was on the BER/BEP tertile
count variable instead of the BER/BEP categorical groupings. Notably, the binge eating resistant
tertile count x strain group and binge eating prone tertile count x strain group interactions were
of most interest in these analyses, since the interactions directly test whether differences in the
nature of the BER/BEP phenotypes (e.g., chow intake, body weight, or palatable food intake) are
present across strains.
As expected, the pattern of results for main effects was largely consistent with prior
ANOVA findings in the full sample of rats. There were significant main effects of strain group
for chow intake and body weight (p < .001) (see Table 6), with the male Sprague-Dawley rats
consuming more chow and weighing more than the other two groups (p < .001). Additionally,
animals with greater binge eating prone counts showed higher consumption of palatable food
(p’s < .01) and animals with more binge eating resistant counts showed lower palatable food
consumption (p’s < .01). There were no significant main effects of BER/BEP counts on chow
intake, with the exception that binge eating prone counts were positively associated with 4-hour
chow intake. Importantly, there were no significant tertile count x strain group interactions for
palatable food, chow intake, or body weight suggesting that levels of chow intake and palatable
food intake did not differ significantly between binge eating prone counts or the binge eating
resistant counts from each group.
Discussion
This was the first study to examine rat strain differences in binge eating proneness.
Results supported hypotheses that the Sprague-Dawley female strain is a particularly vulnerable
strain to binge eating behaviors while the Wistar female rat strain is particularly resistant to
12

binge eating. Furthermore, the Wistar female strain is similar to a previously determined low-risk
strain for binge eating, the Sprague-Dawley males. Importantly, there were no differences in the
nature of the binge eating prone phenotype in regards to palatable food or chow intake. These
significant results provide evidence that differences in binge eating expand beyond sex
differences, such that animals differing in genetic backgrounds (rather than just sex) differ in
their propensity to binge eat.
Previous research has been relatively inconclusive in identifying specific genes for binge
eating in humans. Exploration of possible genetic differences between the Wistar and SpragueDawley rat strains may provide insight into underlying genetic mechanisms that could be
fruitfully exploited in future human work. Promising candidates include genes within the opioid
and dopaminergic systems that underlie reward processes. Intake of palatable food, much like the
ingestion of substances of abuse, has been linked to these reward processes in both animal and
human work (Berridge, 2009). Indeed, previous research has focused on how the opioid and
dopaminergic systems differentially promote increased “liking” (i.e., a hedonic response when
consuming palatable food associated with opioid system) versus “wanting” (i.e., the motivation
to seek a reward associated with the dopaminergic system) of rewarding stimuli, including
palatable foods (Berridge, 2009). Importantly, increased “liking” and “wanting” in response to
palatable food has been found in individuals who binge eat as compared to controls, and specific
opioid genes (e.g., the mu-opioid receptor gene) have been linked to hedonic “liking” in binge
eaters (Davis et al., 2009). Taken together, these data suggest that increased liking and wanting
of palatable food could lead to increased risk for binge eating in humans, binge eating prone rats,
and furthermore, Sprague-Dawley female rats in general. New pharmacological research
showing significant reductions in liking responses to sweet food in binge eaters following mu13

opioid receptor antagonist administration add further support for this hypothesis (Ziauddeen et
al., 2012).
Research has isolated the nucleus accumbens as a key brain area for these liking and
wanting signals in response to palatable food (Berridge, 2009; Stice, Spoor, Ng, & Zald, 2009).
Moreover, this increased activation of the nucleus accumbens in response to palatable food has
been shown in women who binge eat (Wang et al., 2012). Critically, preliminary research also
suggests increased activation of the nucleus accumbens in binge eating prone rats. Binge eating
prone Sprague-Dawley female rats showed increased activation in the nucleus accumbens core
and shell in response to palatable food as compared to binge eating resistant rats (Gradl, Klump,
& Sisk, in preparation). Translating this information to strain differences, it is possible that
Sprague-Dawley females’ heightened sensitivity to the rewarding properties of palatable food in
the nucleus accumbens contributes to their increased rates of binge eating prone phenotypes.
Moreover, strain differences in these phenotypes and brain activation patterns could result from
risk genes/variants within the opioid and dopamine systems that differentiate the SpragueDawley females from other rat strains (e.g., Wistars). Future research should examine if liking,
wanting, and their underlying neurobiological processes contribute to binge eating prone
phenotypes and the overall increased vulnerability of the Sprague-Dawley female rats.
Importantly, the current study also found Wistar female rats to be a particularly resistant
group to binge eating. Despite ad lib access to palatable food during feeding tests, a substance
generally preferred by all rats, the Wistar female rats consistently consumed small amounts
across the study. Further exploration of the Wistar female rat strain could increase understanding
of mechanisms underlying the resistance to binge eating observed in some eating disorders (e.g.,
anorexia nervosa (AN), restricting subtype). The AN restricting subtype is characterized by low
14

body weight and caloric restriction in the absence of binge eating (APA, 2000). Currently, there
is one predominant animal model of these restricting eating disorders which is the Activity Based
Anorexia (ABA) model. This model uses of a protocol of restricted food and ad lib running
wheel access to categorize animals as “prone” or “resistant” to activity based anorexia. Prone
and resistant classifications are based on body weight that is lost across the study period (Dixon,
Ackert, & Eckel, 2003). For example, those rats losing a large amount of body weight within
first few study days are classified as prone and vice versa for resistant. Animals who are more
prone to activity based anorexia have increased running wheel activity despite their severe
weight loss (Dixon et al., 2003). While a range of behavioral data (e.g., food intake, running
wheel activity) can be collected, ABA only models one aspect of the restricting AN disorder –
weight loss in the presence of food restriction and exercise. Several other key behaviors make up
this disorder, including a resistance to developing binge eating, even in the presence of access to
palatable food and extreme dietary restriction. By utilizing the Wistar female rat strain, the binge
eating resistance component of AN could be further elucidated. Moreover, it would be
interesting to combine the two models by incorporating access to palatable food into the ABA
model (i.e., Do ABA prone rats binge eat when exposed to palatable food?) and food
restriction/activity levels into the BER/BEP model (i.e., Do BER rats run excessively in response
to food intake?). Specifically, examination of activity levels in the BER/BEP model context
could further differentiate possible strain differences in a model of compensatory behaviors
between the prone Sprague-Dawley female and Wistar female rat strains.
The current study was not without its limitations. First, the sample size was small across
all strain groups. The use of both categorical and continuous variables partially addressed this
limitation. The categorical approach used more stringent criteria for phenotypic binge eating
15

prone and binge eating resistant classifications, while the continuous approach increased
statistical power by including all animals in analyses. Nonetheless, it would be important to
replicate these results with larger sample sizes.
Second, although the binge eating prone/binge eating resistant model powerfully
evaluates overconsumption of palatable food in binge eating, it is unable to model the cognitive
aspects of binge eating common in clinical eating disorders (e.g., weight and shape concerns,
loss of control over eating). Although data suggest that some of these cognitive features may be
present (e.g., foot shock experiments that are suggestive of a loss of control over eating in binge
eating prone rats; see Oswald et al., 2011), more data are needed to further examine this
possibility. Finally, this naturalistic study only describes strain differences in behavior;
underlying, neurobiological mechanisms of effects were not explored. Future research should
examine potentially influential mechanisms, such as opioid and dopaminergic modulation of
reward processes. These mechanisms might contribute to strain differences in the BER/BEP
model and, in turn, this knowledge could be used to significantly increase the understanding of
risk for binge eating in humans.

16

APPENDIX

17

Table 1
Means, Standard Deviations, and Effect Sizes for 1-hour, 4-hour, and 24-hour Palatable Food
Intake
Mean (Standard Deviation)
Time
Wistar WT
Wistar KO
p
Cohen’s d
1-hour
.08 (.02)
.08 (.02)
0.64
0
4-hour

.23 (.02)

.22 (.03)

0.12

0.39

24-hour

.39 (.04)

.36 (.03)

0.65

0.85

Note. WT = wild type; KO = knock out

18

Table 2
Sample Size of Rat Strains by Gender and Cohort
Cohort 1
Cohort 2
(N = 30)
(N = 42)
Sprague-Dawley Female
15 (50%)
13 (30%)

Cohort 3
(N = 11)
2 (18%)

Sprague-Dawley Male
Wistar Female

15 (50%)

12 (29%)

3 (27%)

0 (0%)

17 (41%)

6 (55%)

Note. Values are Ns for each group and the percent total of each cohort.

19

Table 3
Means, and Standard Deviations, Effect Sizes, and ANOVA for 1-hour, 4-hour, and 24-hour
Palatable Food Intake
Mean (Standard Deviation)
ANOVA
Sprague-Dawley Sprague-Dawley
Wistar
F(df,df,)
Time
Females
Males
Females
Cohen’s d
a

1-hour

.14 (.03)

4-hour

.30 (.05)

a
a

b

c

.12 (.03)

.08 (.02)

b

b

.22 (.04)

.23 (.03)

b

c

0.66-2.26

27.23 (2,82)***

1.80

34.69 (2,82)***

.30 (.05)
.38 (.04)
.42 (.04)
0.95-2.66 57.82 (2,82)***
24-hour
Note. Cohen’s d = medium to large effect sizes. Superscripts of different values denote
significant differences between groups at p < .001.
***p < .001

20

Table 4
Differences in the Proportion of Categorical BER and BEP Phenotypes Using Combined Group
Tertiles (N = 83 rats, 30 Sprague-Dawley Female, 30 Sprague-Dawley Male, 23 Wistar Female)
Status

Sprague-Dawley Female

Sprague-Dawley Male

3/6 tertile classification (50%)
b
8 (27%)

a

BER

1 (3%)

BEP

19 (63%)

a

b

1 (3%)

Wistar Female
b

5 (22%)

b

0 (0%)

4/6 tertile classification (67%)
a

b

BER

0 (0%)

BEP

15 (50%)

6 (20%)
a

b

0 (0%)

b

4 (17%)

b

0 (0%)

5/6 tertile classification (83%)
BER
BEP

a

0 (0%)

b

6 (20%)

a

b

a

1 (4%)

b

7 (23%)
0 (0%)
0 (0%)
Note. Superscripts of different values denote significant differences at p < .001.

21

Table 5
Means, and Standard Deviations, and ANOVA for BER/BEP Counts Based on 4-hr Combined
Group Tertiles
Mean (Standard Deviation)
ANOVA
Status
Sprague-Dawley Sprague-Dawley
Wistar
F(df,df)
Females
Males
Females
BER

a

0.57 (1.04)

a

b

b

3.07 (1.72)

2.43 (1.27)

b

b

26.19 (2,82)***

3.27 (1.76)
1.30 (1.21)
1.17 (.94)
BEP
20.73 (2,82)***
Note. Superscripts of different values denotes significant differences at p < .001.
***p < .001

22

Table 6
Results from Multiple Regressions Examining the Influence of BER/BEP Count and Strain Group on Chow Intake, Body Weight, and
Palatable Food Intake
BER Count Models
BEP Count Models
B (SE)
t
p
B (SE)
t
p
2

Chow 4-HR FT (R =.70)
BER Count
Wistar – SD (F)
Wistar – SD (M)
Wistar – SD (F) * BER Count
Wistar – SD (M) * BER Count

2

-.01 (.01)
.06 (.01)
.11 (.01)
-.01 (.01)
.01 (.01)

-2.49
4.49
10.35
-.92
1.83

Chow 4-HR FT (R = .78)
.02 BEP Count
<.001 Wistar – SD (F)
<.001 Wistar – SD (M)
.36 Wistar – SD (F) * BEP Count
.07 Wistar – SD (M) * BEP Count

.49
1.82
9.17
1.17
-.39

Chow NFT (R = .69)
.63 BEP Count
.07 Wistar – SD (F)
<.001 Wistar – SD (M)
.24 Wistar – SD (F) * BEP Count
.70 Wistar – SD (M) * BEP Count

-.68
3.85
27.85
.59
.43

BW FT (R = .94)
.50 BEP Count
<.001 Wistar – SD (F)
<.001 Wistar – SD (M)
.56 Wistar – SD (F) * BEP Count
.67 Wistar – SD (M) * BEP Count

-.85
3.75
27.96
.60
.68

BW NFT (R = .94)
.40 BEP Count
<.001 Wistar – SD (F)
<.001 Wistar – SD (M)
.55 Wistar – SD (F) * BEP Count
.50 Wistar – SD (M) * BEP Count

2

Chow NFT (R = .66)
BER Count
Wistar – SD (F)
Wistar – SD (M)
Wistar – SD (F) * BER Count
Wistar – SD (M) * BER Count

.003 (.01)
.02 (.01)
.09 (.01)
.01 (.01)
-.002 (.01)

<.001
<.001
<.001
.33
.05

.002 (.00)
.02 (.01)
.09 (.01)
-.01 (.01)
.002 (.01)

.26
1.53
8.57
-1.65
.28

.79
.13
<.001
.10
.78

-3.23 (3.24)
24.43 (5.10)
126.73 (4.94)
2.67 (3.57)
4.13 (3.90)

-1.00
4.79
25.64
.75
1.06

.32
<.001
<.001
.47
.29

-2.86 (3.20)
23.91 (5.04)
125.36 (4.89)
2.41 (3.53)
3.43 (3.86)

-.89
4.74
25.65
.68
.89

.38
<.001
<.001
.50
.38

2

-1.61 (2.39)
21.04 (5.47)
123.23 (4.23)
2.07 (3.50)
1.23 (2.85)

2

BW NFT (R =.94 )
BER Count
Wistar – SD (F)
Wistar – SD (M)
Wistar – SD (F) * BER Count
Wistar – SD (M) * BER Count

3.87
5.00
9.90
-.99
-1.98

2

2

BW FT (R =.94)
BER Count
Wistar – SD (F)
Wistar – SD (M)
Wistar – SD (F) * BER Count
Wistar – SD (M) * BER Count

.03 (.01)
.05 (.01)
.10 (.01)
-.01 (.01)
-.02 (.01)

2

-2.00 (2.56)
20.25 (5.40)
121.90 (4.36)
2.06 (3.45)
1.89 (2.80)

23

Table 6 (cont’d)
2

PF 4-hour (R = .76)
BER Count
Wistar – SD (F)
Wistar – SD (M)
Wistar – SD (F) * BER Count
Wistar – SD (M) * BER Count

2

-.02 (.00)
.02 (.01)
.01 (.01)
-.01 (.01)
-.002 (.01)

PF 4-hour (R = .84)
-4.03 <.001 BEP Count
2.37
.02 Wistar – SD (F)
.51 Wistar – SD (M)
.67
-1.24
.22 Wistar – SD (F) * BEP Count
.76 Wistar – SD (M) * BEP Count
-.31

2

2

.02 (.01)
.02 (.01)
-.01 (.01)
.002 (.01)
.002 (.01)

4.64
2.67
-1.26
.38
.32

<.001
<.01
.21
.71
.75

PF 24-hour (R = .77)
PF 24-hour (R = .77)
BER Count
-.02 (.01)
-3.72 .002 BEP Count
.02 (.01)
2.64
.01
Wistar – SD (F)
.01 (.01)
.39
.70 Wistar – SD (F)
-.002 (.01)
-2.00
.85
Wistar – SD (M)
-.06 (.01)
-6.12 <.001 Wistar – SD (M)
-.08 (.01)
-6.92 <.001
Wistar – SD (F) * BER Count
-.001 (.01)
-.09
.93 Wistar – SD (F) * BEP Count
.001 (.01)
.07
.94
Wistar – SD (M) * BER Count
-.004 (.01)
-.66
.51 Wistar – SD (M) * BEP Count
.003 (.01)
.37
.71
Note: Wistar – SD (F) = dummy coded strain group variable for comparison of Sprague-Dawley female rats to Wistar female rats;
Wistar – SD (M) = dummy coded strain group variable for comparison of Sprague-Dawley male rats to Wistar female rats; HR = hour,
FT = feeding test days, NFT = non-feeding test days, BW = body weight, PF = palatable food

24

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