Close
About
FAQ
Home
Collections
Login
USC Login
Register
0
Selected
Invert selection
Deselect all
Deselect all
Click here to refresh results
Click here to refresh results
USC
/
Digital Library
/
University of Southern California Dissertations and Theses
/
Effects of sugar and non-nutritive sweetener consumption on neural processing, ingestive behavior, and appetite regulation
(USC Thesis Other)
Effects of sugar and non-nutritive sweetener consumption on neural processing, ingestive behavior, and appetite regulation
PDF
Download
Share
Open document
Flip pages
Contact Us
Contact Us
Copy asset link
Request this asset
Transcript (if available)
Content
Effects of Sugar and Non-nutritive Sweetener
Consumption on Neural Processing, Ingestive Behavior,
and Appetite Regulation
Hilary Michelle Kast Dorton
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
in Partial Fulfillment of the
Requirements for the Degree of
DOCTOR OF PHILOSOPHY
(NEUROSCIENCE)
December 2019
II
This dissertation is dedicated to anyone who’s still trying to figure it
out. May your doubts never stop you from finding the place in this
world that is beautifully and uniquely yours.
III
Acknowledgements
To my advisor and mentor, Dr. Katie Page: Thank you for taking a chance on me, for
your patience as I learned an entirely new technique in an entirely new field, for your
mentorship in writing, science, and life in general, and for seeing in me the potential that
I often overlook. You have provided the constant inspiration to persist through difficulty,
but also permission to take care of myself on this journey. You have allowed me to
grow in my independence, and also trusted me with numerous opportunities to lead
others. I consider it a gift to and have found a mentor, colleague, and friend in you, and
I couldn’t imagine taking this ride with anyone else!
To the past and current members of my committee, Dr. Judy Pa, Dr. John Monterosso,
Dr. Pat Levitt, Dr. Scott Kanoski: Your advice, guidance, and enthusiastic support have
helped me become the scientist I hoped to be. I’m blessed to have found a group of
people who genuinely care about and advocate for the success of their students.
To the faculty, administration, and fellow students of the Neuroscience Graduate
Program: Thank you for providing a safe, engaging, and supportive space to develop as
a scientist. To Dawn Burke and Deanna Solórzano: I see your tireless hard work and
appreciate everything you’ve done to ensure that NGP stays functional.
To the members of the Page Lab, past and present, who taught me, who showed up to
my talks and posters, who talked me through frustrations, and who cheered me on
through successes: Dr. Shan Luo, Jasmin Alves, Dr. James Melrose, Xochitl Cordova,
Esther Jahng, and Brendan Angelo. And to all my undergraduate RAs, especially Nate
Overholtzer, Jada Hislop, and Mary Ann Cabrales: thank you for the chance to mentor
such talented, brilliant students, and thank you for all of your hard work in our lab. I
recognize that great research can only be put into practice with commitment,
organization, and heart, and I want to extend a special thanks to Ana Romero and
Alexandra Yunker, who have consistently brought all three.
To my friends and family, notably my siblings, Kiley, Mari Kate, and Calleigh, as well as
my parents, Wendie Freedman, Jim Dorton, and Kathy Dorton: Your loving support at
every single stage of this ride has carried me through to this moment. I hope I have
made you proud!
To my stepfather, Fran, and my committee member, Dr. Bosco Tjan, the tragic losses of
whom served as incredibly important reminders that maintaining my mental health is
always a worthy effort. And to my therapist, Jeff, without whom I quite literally would not
have made it to this moment: thank you for doing the hard work with me through a very
difficult season of life.
IV
To my sweet kitty, Scissors, who has been the best little pal I could ask for.
And finally, to my forever co-author, labmate, best friend, and partner, Ryan: Words
can’t describe how much your support has meant to me. I look up to you as a scientist
and as a human—your kindness, wisdom, and love have helped me achieve so much
more than I ever thought possible. You have never asked me to be anything other than
exactly who I am, which has empowered me to make this journey my own. I am so
proud to be part of our relationship, and to have shared this experience with you. I love
you so much, my one!
V
Table of Contents
Dedication II
Acknowledgements III
Part I: Introduction 1
Part II: Influences of dietary added sugar consumption on
striatal food-cue reactivity and postprandial GLP-1 response 5
Part III: Effects of acute sucrose and glucose ingestion on
cerebral blood flow, appetite hormones, and ingestive
behavior in obese and lean individuals 30
Part IV: Differential brain responses to sucralose and glucose
in obese compared to lean individuals: Preliminary results
from the Brain Response to Sugar Study 55
Part V: Reflection and Final Remarks 81
References 83
1
Part I
Introduction
Recommendations from both the American Heart Association and the World Health
Organization suggest limiting daily added sugar consumption to about 6-9 teaspoons,
amounting to fewer than 200 kilocalories from sugar per day (Johnson et al., 2009).
However, recent analysis from the National Health and Nutrition Examination Survey
(NHANES) concluded that, on average, adults in the United States consume close to 22
teaspoons of added sugar per day (~350 kilocalories from sugar) (Ervin & Ogden,
2012). Dietary sugar intake has been linked with costly health outcomes such as
diabetes, cardiovascular disease, and obesity (Bray & Popkin, 2014; Malik et al., 2006;
Vartanian et al., 2007; Olsen & Heitmann, 2009). Importantly, the added sugar intake
statistics reported by NHANES include adults who range across the body mass index
(BMI), suggesting that, while excessive dietary added sugar may precede weight gain or
exacerbate the effects of obesity, a high-sugar diet may also impact health beyond its
effect on body size. Researchers, physicians, and public health experts have
emphasized the role of an “obesogenic environment,” which promotes an imbalance
between energy intake and physical activity, in hedonic overeating. A hallmark feature
of the obesogenic environment is the constant availability of highly palatable, energy-
dense foods (Lake & Townshend, 2006) combined with an abundance of food cues in
the environment that prime eating beyond homeostatic need. The overall goal of my
work has been to explore mechanistic links between eating behavior and neural
2
responses to both food cues and acute sugar intake in humans in order to explain a
tendency towards overeating sugar and the associated health outcomes.
Use of Functional Neuroimaging in Obesity and Eating Behavior Studies
Functional neuroimaging (fMRI) has been instrumental in allowing us to explore the
myriad ways the brain helps humans relate to our food environment (Smeets et al.,
2019). Neuroimaging paradigms have examined brain responses to numerous
elements of the anticipation of food, consummatory behaviors, and satiety including the
olfactory properties, taste sensations of food, size and type of meal, behavioral tasks
(e.g. willingness to pay), and responses to visual and other sensory cues for food. The
work described in this dissertation employed two non-invasive fMRI techniques to
understand how (both long-term and acute) sugar intake impacts brain function: blood-
oxygen level-dependent (BOLD) fMRI and arterial spin labeling (ASL). Measurements
from these methods are correlated with the hemodynamic response to neural activity
(Huppert et al., 2006), suggesting that they serve as markers for neural activity, albeit
indirectly. It cannot be overstated that, because the interpretation of this activity is often
ultimately at the discretion of the researcher or research team, a thoughtful and
intentional approach to both experimental design and analysis is crucial to maintain
rigor in every application of neuroimaging. I hope that the work presented in this
dissertation is a testament to my sincere efforts to propel the field forward with care and
integrity.
3
In Part II, we measured food-cue reactivity using BOLD fMRI. Both inhibitory and
excitatory neural activity result in metabolic changes that alterations in the rate of
oxygen consumption. BOLD fMRI generates a signal that relies on the amount of
deoxygenated hemoglobin within the brain at a given time, which is posited to act as a
spatiotemporal proxy for changes in brain activity (Detre & Wang, 2002). The resulting
data are typically presented as BOLD signal measured at a particular time (e.g. during
the presentation of a stimulus) relative to a given baseline condition, or “percent signal
change.” In the study presented in Chapter 2, we used a task-based BOLD fMRI scan
to measure striatal activation in response to images of food as a representation of how
the brain might respond in an environment where a participant is deciding whether and
what to eat in a sugar-replete state.
In the latter two parts, we shifted focus to acute ingestion of sucrose (Part III) and
sucralose (Part IV) and used ASL to measure slow, steady perfusion of blood
throughout the brain. Recent work from our lab has demonstrated a relationship
between BOLD food-cue reactivity and CBF response to glucose ingestion (Luo et al.,
2017). While BOLD signal is represented as a relative percentage, ASL is able to
measure blood flow to areas of the brain in discrete units (mg/100g/min). This method
involves “tagging” arterial blood by exploiting the spin of water molecules therein, and
detecting the transit time for the tagged blood to reach specific tissues within the brain.
PASL is sensitive to slow, regional metabolic changes that are task-independent, which
helps, making it a preferable method for capturing indicators of hunger, satiety, or
4
related metabolic responses that occur over the course of several minutes. This allows
a specificity that is especially useful when focusing on smaller brain regions, such as
the hypothalamus. In Part III, we used ASL imaging in obese and lean participants to
compare brain responses to acute ingestion of glucose with sucrose, a sugar which is
more commonly consumed (“real-world”) and which differs from glucose in chemical
composition. In Part IV, we used a similar paradigm to explore neural responses to the
non-nutritive sweetener, sucralose with glucose.
Though the results from fMRI experiments provide interesting insight on their own, my
goal was to combine them with two other measurable outcomes to aid interpretation:
appetite hormones and eating behavior. Using the additional methods helps
contextualize and interpret the results from neuroimaging studies while contributing
substantially to the field’s understanding of neural regulation of appetite and food intake.
5
Part II
Influences of dietary added sugar consumption on striatal food-cue
reactivity and postprandial GLP-1 response
Hilary M. Dorton, Shan Luo, John R. Monterosso, Kathleen A. Page
Published in Frontiers in Psychiatry, January 2018
Abstract
Sugar consumption in the United States exceeds recommendations from the American
Heart Association. Overconsumption of sugar is linked to risk for obesity and metabolic
disease. Animal studies suggest that high sugar diets alter functions in brain regions
associated with reward processing, including the dorsal and ventral striatum. Human
neuroimaging studies have shown that these regions are responsive to food cues, and
that the gut-derived satiety hormones, glucagon-like polypeptide-1 (GLP-1), and Peptide
YY (PYY), suppress striatal food-cue responsivity. We aimed to determine the
associations between dietary added sugar intake, striatal responsivity to food cues, and
post-prandial GLP-1 and PYY levels. Twenty-two lean volunteers underwent functional
magnetic resonance imaging (fMRI) scans during which they viewed pictures of food
and nonfood items on two separate occasions after a 12-hour fast. Prior to each scan,
participants consumed drinks containing either 75g glucose or water (as a control).
Blood was sampled for GLP-1 and PYY levels and hunger ratings were assessed at
baseline and ~75 minutes after glucose or water consumption. In-person 24-hour
dietary recalls were collected from each participant on 3 to 6 separate occasions over a
two-month period. Average percent calories from added sugar was calculated using
6
information from 24-hour dietary recalls. A region-of-interest analysis was performed to
compare the blood oxygen level dependent (BOLD) response to food vs. nonfood cues
in the bilateral dorsal striatum (caudate/putamen) and ventral striatum (nucleus
accumbens). The relationships between added sugar, striatal responses, and hormone
changes after drink consumption were assessed using Spearman’s correlations. We
observed a positive correlation between added sugar intake and BOLD response to
food cues in the dorsal striatum and a similar trend in the nucleus accumbens after
glucose, but not water, consumption. Added sugar intake was negatively associated
with GLP-1 response to glucose. Post-hoc analysis revealed a negative correlation
between GLP-1 response to glucose and BOLD response to food cues in the dorsal
striatum. Our findings suggest that habitual added sugar intake is related to increased
striatal response to food cues and decreased GLP-1 release following glucose intake,
which could contribute to susceptibility to overeating.
Introduction
The average intake of added sugars in the United States has increased by 19% over
the last three decades. Increases in added sugar consumption are linked to obesity,
diabetes and cardiovascular disease (Bray & Popkin, 2014; Malik et al., 2006; Vartanian
et al., 2007; Olsen & Heitmann, 2009; Johnson et al., 2009). Excessive sugar
consumption is due at least in part to the wide availability of convenient, high-sugar
foods coupled with an abundance of environmental food cues that prime eating
behavior (Lake & Townshend, 2006; Hill & Peters, 1998). In response to food cues, the
7
brain recruits regions important for reward anticipation and processing, including striatal
areas involved in dopaminergic signaling that motivate feeding behavior (Volkow et al.,
2003). Neuroimaging studies have consistently shown that striatal areas are activated in
response to pictures of palatable food cues (Tang et al., 2012; Schur et al., 2009;
Pelchat et al., 2004) and this response is exaggerated in obese individuals (Stoeckel et
al., 2008; Martin et al., 2010; Rothemund et al., 2007). Independent from suggested
effects of obesity, chronic exposure to specific nutrients, such as sugar, may affect
striatal responses to food cues. A number of studies in animal models have shown that
high sugar diets alter the striatal dopamine system (Hajnal & Norgren, 2002; Colantuoni
et al., 2002; Bello et al., 2002), including a recent study suggesting that seven months
of high sugar feeding increased basal glucose metabolism in mesolimbic reward regions
independent of insulin sensitivity or weight gain in Yucatan mini pigs (Ochoa et al.,
2016). However, it is currently unknown whether habitual dietary added sugar
consumption is related to striatal food cue reactivity in humans.
The incretin hormones glucagon-like peptide-1 (GLP-1) and peptide YY (PYY) are
released in response to food intake. These hormones are known to produce
anorexigenic effects through receptors concentrated in the arcuate nucleus of the
hypothalamus (Göke et al., 1995; Batterham et al., 2002). Aside from effects on
hypothalamic appetite circuits, GLP-1 and PYY also regulate food intake through their
action on regions associated with food reward and learning (Stadlbauer et al., 2015; van
Bloemendaal et al., 2014). Neuroimaging studies in humans have shown that the
8
infusions of GLP-1 and/or PYY reduce brain responses to food cues within
corticostriatal areas involved in the regulation of eating and these reductions in neural
food cue reactivity were associated with a decrease in ratings of appetite (De Silva et
al., 2011; Batterham et al., 2007; Weise et al., 2012). These findings suggest a
relationship between increases in circulating levels of incretin hormones, reductions in
food-cue reactivity, and discernable feelings of satiety.
GLP-1, in particular, has emerged as a possible mediator of processing food reward
and other rewarding stimuli through its action in the mesolimbic circuit (Dickson et al.,
2012; Richard et al., 2015). Interestingly, studies indicate that energy-dense diets may
reduce GLP-1 signaling in the brain (Lindqvist et al., 2008; Richards et al., 2016).
Recently, Richards et al. reported that a high-fat diet resulted in decreased numbers of
intestinal L-cells (the cells that secrete GLP-1) and a smaller GLP-1 response to nutrient
exposure (Richards et al., 2016). However, whether chronic dietary sugar consumption
affects endogenous GLP-1 secretion in humans is not yet known.
The aim of this study was to examine associations between dietary added sugar intake
and 1) striatal responsivity to food cues as well as 2) the rise in circulating hormones,
GLP-1 and PYY, in response to oral glucose in healthy-weight volunteers. Due to
specific interest in the dorsal striatum and nucleus accumbens, we used a region of
interest (ROI)-based analysis focusing on these regions. We used a standardized 75
gram oral glucose load that has been previously shown to stimulate gut hormone
secretion and to diminish food cue reactivity (Kroemer et al., 2013; Page et al., 2013) to
9
test the hypothesis that increases in dietary added sugar intake would be associated
with greater striatal responses to palatable food cues as well as decreased systemic
GLP-1 and PYY responses to glucose ingestion.
Methods
Participants
Twenty-two lean, young adult volunteers (12 females; 10 males) participated in the
study. Participants were lean (BMI 22.6 ± 1.9 kg/m²), right-handed, nonsmokers,
weight-stable for 3 months, non-dieters, not on any medication (except oral
contraceptives), with normal or corrected-to-normal vision and no history of diabetes,
eating disorders, or other medical diagnoses. During the course of the study,
participants were asked to adhere to their usual diet and physical activity levels.
Participants provided written informed consent compliant with the University of Southern
California Institutional Review Board.
Experiment overview
Each participant attended an initial screening visit to assess eligibility for participation in
the study. During the screening visit, we collected demographic information, and
anthropometric measurements including height (cm), weight (kg), waist and hip
circumferences (cm), and total body fat percentage using bioelectrical impedance
analysis (Model no. SC-331S, TANITA Corporation of America, Inc.). 24-hour dietary
intake and physical activity recalls were obtained at the screening visit. In addition, over
10
the course of two months, an additional 3 to 6 dietary recalls were obtained via in-
person interviews.
Magnetic Resonance Imaging (MRI) scans were performed at the Dornsife Cognitive
Neuroimaging Center of University of Southern California. Participants arrived at
approximately 8:00AM after a 12-hour overnight fast. A baseline (fasting) blood draw
was performed at approximately 8:45AM. After performing a T1 structural scan,
participants received a standardized drink containing 75g of glucose and 0.45g of non-
sweetened cherry flavoring dissolved in 300mL of water. The 75g oral glucose load has
been used extensively to examine changes in appetite hormones and brain regions
involved in hunger, reward, and food intake (Zanchi et al., 2017). Though the main aim
of the study was to examine the impact of sugar intake on food-cue reactivity and
satiety hormone release in response to a glucose load, a subset of 19 participants
(M=9; F=10) also underwent a second imaging session in which they consumed 300 mL
of water with flavoring as a control condition. The order of the drink sessions was
randomized, and the time interval between the two sessions was between 2 and 30
days. Participants were instructed to consume the drink within two minutes. After
consuming the drink, participants entered the scanner and underwent a food cue task
(described below). Another blood draw was performed after the scan at, on average, 75
minutes after the drink was consumed. Immediately following each blood draw,
participants were asked to rate their hunger from 1-10 on a visual analog scale.
Females underwent MRI scans during the follicular phase of the menstrual cycle. These
11
data were collected as part of a larger study aimed at determining brain responses to
sugar.
Assessments of dietary intake
To assess dietary intake, we used the multi-pass 24-hour dietary recall, a validated
method that probes quantities of food and drink consumed during the previous 24 hours
(Biro et al., 2002; Johnson et al., 1996). A trained staff member administered each
dietary recall interview, which spanned between 30 to 60 minutes. During the dietary
recall interview, participants were asked to report all food and beverage items (including
meals and snacks) they consumed during the prior 24 hours. Participants were also
asked to provide the amount of each item she or he consumed, approximate time of
consumption, a description of the preparation method, and additional details such as
brand name. Dietary recalls captured dietary intake on both weekend days and
weekdays to account for individual variations in dietary intake.
Physical activity assessments
We also collected information on habitual physical activity levels to control for the
potential confounding effects of physical activity on brain and endocrine responses
(Cornier et al., 2012; Evero et al., 2012). Physical activity data were recorded during
an interview with a trained staff member using a 24-hour physical activity recall (PAR).
Participants were asked to report what activities they did, in thirty-minute increments
between the hours of 7:00AM and 11:59PM on the previous day. Using data from each
12
participant’s PAR, we calculated daily physical activity by summing the metabolic
equivalence (MET) of each activity at each interval. We used the mean daily METs for
each participant to reflect the overall level of physical activity.
Food-cue task
Participants completed the food cue task in the MRI scanner. In a randomized block
design, participants were presented with a total of 12 visual food cue and nonfood cue
blocks using Matlab (MathWorks, Inc., Natick, Massachusetts, United States) and
Psychtoolbox on an Apple laptop. Four cue images per block were presented in random
order, each separated by 1s of a blank screen. Within a block, each image was
presented for 4s. Food cue stimuli were images of high-calorie, palatable food items
such as cookies and pizza. The control stimuli were images of neutral, nonfood items
such as buses and staircases. The set of food and nonfood cue images was matched
for visual appeal for use in prior published work (Page et al., 2011; Luo et al., 2013; Luo
et al., 2015)
MRI imaging parameters
Food-cue and structural MRI data were collected using 3T Siemens MAGNETOM
Tim/Trio scanner (N=12) and MAGNETOM Prisma fit MRI scanner (N=10) due to a
scanner upgrade in the middle of our study. Participants laid supine on the scanner bed,
viewing stimuli through a mirror mounted over the head coil. Functional BOLD signals
were acquired with a single-shot gradient echo planar imaging (EPI) sequence. Thirty-
13
two 4mm-thick slices covering the whole brain were acquired using the following
parameters: repetition time (TR)=2,000 ms, echo time (TE)=25 ms, bandwidth=2520
Hz/pixel, flip angle=85°, field of view (FOV) = 220x220 mm, matrix=64x64. A high-
resolution 3D Magnetization Prepared Rapid Gradient Echo (MPRAGE) sequence
(TR=2530 ms; TE=2.62 ms; bandwidth=240 Hz/pixel; flip angle= 9°; slice
thickness=1mm; FOV=256x256 mm; matrix=256 x 256) was used to acquire structural
images for multi-subject registration.
fMRI data analysis
To analyze fMRI data, we used several tools from the Oxford University Centre for
Functional MRI of the Brain Software Library (FMRIB) (Smith et al., 2004; Jenkinson, et
al., 2012; Woolrich et al., 2009). fMRI data were processed using the fMRI Expert
Analysis Tool (FEAT) version 6.00. Four functional volumes (4 TRs) acquired at the
beginning of each MRI session were discarded in order to account for magnetic
saturation effects. fMRI files were preprocessed using motion correction, high-pass
filtering (100s), and spatial smoothing with a Gaussian kernel of full-width at half-
maximum=5mm. Functional data were first mapped to each participant’s anatomical
image, and then registered into standard space [Montreal Neurological Institute (MNI)]
using affine transformation with FMRIB’s Linear Image Registration Tool (FLIRT) to the
avg152 T1 MNI template. Food and nonfood events were added to the General Linear
Model (GLM) after convolution with a canonical hemodynamic response function.
Temporal derivatives and temporal filtering were added to increase statistical sensitivity.
14
For each participant, food cues vs. nonfood cues contrast maps were created on the
first-level analysis. An additional explanatory variable was added in the group-level
analysis to account for variability due to the upgrade. Because of evidence suggesting
that the striatum may be particularly affected by dietary sugar (Colantuoni et al., 2002),
we used a ROI-based approach. Anatomical, bilateral ROIs of the dorsal striatum
(caudate/putamen) and the nucleus accumbens were created using the Harvard-Oxford
subcortical atlas, which provides probabilistic mapping of 21 subcortical brain structures
(Figure 2.1A,B). Percent signal change was extracted from each ROI for food vs.
nonfood contrast for each participant using FSL’s FEATquery, a tool within FEAT that
allows the mean signal to be extracted from a given ROI mask.
Figure 2.1 | Bilateral region-of-interest masks: (A) Dorsal striatum (caudate/putamen)
and (B) nucleus accumbens.
15
Dietary data analysis
Data from dietary recalls were manually checked for quality. To determine outliers, we
performed linear regression analysis, using bodyweight to predict caloric intake.
Residuals were standardized and examined for any values that were >3 standard
deviations from the mean. Using this method, 102 dietary recalls were included in this
analysis (an average of 4.7 recalls per participant) and none were excluded. Dietary
intake data were collected and analyzed using Nutrition Data System for Research
(NDSR) software version 2015, developed by the Nutrition Coordinating Center (NCC),
University of Minnesota, Minneapolis, MN (Feskanich et al., 1989). Using the output
from this software, each participant’s dietary recall was probed for intake of overall
calories, macronutrients, total sugar, and added sugar. We chose to use percent
calories from added sugar as our measure of sugar intake to account for total energy
intake. We calculated percent calories from added sugar by available carbohydrate
from each participant’s dietary recalls and used the participant’s mean values across all
recalls to represent average dietary added sugar consumption.
Hormone analysis
GLP-1
(7-36)
(active) and PYY (total) were measured using Luminex multiplex technology
(EMD Millipore, St. Charles, MO). Change from baseline GLP-1 (pg/mL) and PYY
(pg/mL) were calculated as a difference between hormone levels measured at ~75
minutes post drink ingestion and levels measures at the fasting blood draw.
16
Statistical analysis
All analyses were performed using R Statistical Software Version 3.1.2 (http://www.R-
project.org/). Descriptive statistics were derived using the ‘psych’ package. Spearman’s
correlations were performed between average percent calories from added sugar,
percent signal change in both ROIs, and GLP-1 and PYY change from baseline using
the ‘ppcor’ package. We performed Spearman’s correlations due to small sample size,
which is highly sensitive to outliers. Results with p< .05 were considered significant, and
imaging data were corrected for multiple comparisons (i.e. 2 regions of interest). All data
are reported in mean ± SD.
Results
Participants (Table 2.1)
Mean age, body mass index, and body fat percentage for 22 participants are described
in Table 2.1. Males and females did not differ in age (M=21.5 ± 1.8; F=21 ± 2.4), p= .5)
or body mass index (M=23.1 ± 1.5; F=22.1 ± 2.1; p= .2), but females had higher body
fat percentage than males (M=15.9 ± 3.1; F=24.4 ± 5.6; p< .001). Physical activity
levels did not differ between males and females (M= 61.9 ± 8.6 METs; F= 61.44 ± 3.8
METs; p= .87).
17
Table 2.1 | Participant characteristics (n=22).
CHARACTERISTIC MEAN ± SD
Sex Female: n=12, Male: n=10
Age (years) 21.2 ± 2.1
BMI (kg/m
2
) 22.6 ± 1.9
Total Body Fat (%) 20.6 ± 6.3
Dietary intake (Table 2.2)
Participants consumed an average of 1719.1 ± 470.4 kcal/day. Males consumed
significantly more total calories per day than females (M=1967.8 ± 429.4; F=1511.78 ±
410.4 kcal/day; p= .02). However, average calories consumed from added sugar did not
differ between males and females (M=252.2 ± 127.7; F=164.2 ± 120.8 kcal/day; p= .12),
and neither did percent calories consumed from added sugar (M=12.6 ± 6.4; F=10.8 ±
6.7 percent; p= .5). In general, the percent calories from carbohydrate, fat, and protein
consumed by our participants resembles the national averages for individuals in this
demographic range (Ervin & Ogden, 2013).
18
Table 2.2 | Results from 24H dietary recalls: average energy, fat, carbohydrate,
protein, total sugar, and added sugar intake (n=22).
NUTRIENT UNIT MEAN ± SD
Energy kcal/day 1719.1 ± 470.4
Fat g/day 67.4 ± 24.6
kcal/day 606.9 ± 221.5
% kcal 34.8 ± 7
Carbohydrate g/day 206.7 ± 71.8
kcal/day 826.6 ± 287.1
% kcal 48.1 ± 10.3
Protein g/day 72.1 ± 20.8
kcal/day 288.5 ± 83.3
% calories 17.2 ± 4
Total Sugar g/day 79.2 ± 34.7
kcal/day 316.6 ± 138.8
% calories 18.6 ± 6.9
Added Sugar g/day 51.1 ± 32.3
kcal/day 204.2 ± 129
% calories 11.6 ± 6.5
19
Added sugar intake and post-glucose brain response to food cues
ROI analysis revealed a positive correlation between percent calories consumed from
added sugar and dorsal striatum response to food versus nonfood cues (r
s
= .55, p= .02,
Figure 2.2), and the relationship remained significant after controlling for sex, percent
body fat, and average daily physical activity levels (r
s
= .62, p= .001). We observed a
trend towards a positive correlation between percent calories from added sugar and
nucleus accumbens response to food versus nonfood cues (r
s
= .41, p= .07), and
controlling for sex, percent body fat, and average daily physical activity levels
strengthened this relationship (r
s
= .47, p= .03). These findings suggest that,
independent of sex, adiposity or physical activity levels, dietary added sugar
consumption is associated with striatal reactivity to food cues. We did not observe a
significant correlation between added sugar intake and response to food cues in the
dorsal striatum (r
s
= -.22, p= .37) or nucleus accumbens (r
s
= .29, p= .21) after ingestion
of water.
20
Figure 2.2 | Positive correlation between BOLD response to food cues in the dorsal striatum
and percent calories consumed from added sugar (r
s
= .55, p= .02). One male participant’s fMRI
data from the glucose session was excluded due to an image acquisition error.
GLP-1 and PYY responses to oral glucose
Circulating GLP-1 levels were significantly greater ~75 minutes after glucose
consumption than at baseline (Baseline= 18.8 ± 18.9 pg/ml; 75min= 31.8 ± 24.5 pg/ml;
p< .001; Figure 2.3A). We observed a moderate rise in PYY levels ~75 minutes after
glucose ingestion compared to baseline (Baseline= 78 ± 25.6 pg/ml; 75min= 89 ± 36
pg/ml; p= .07). During the water session, there was not a significant increase in GLP-1
at the 75-minute timepoint relative to baseline (Baseline= 18.7 ± 18.4 pg/ml;
75min=18.6 ± 19.3 pg/ml, p= .78). Additionally, we observed a non-significant trend
toward a decrease in PYY levels at 75 min relative to baseline after water
(Baseline=79.3 ± 44.7 pg/ml; 75min= 71.7 ± 38.2 pg/ml, p= .07), likely attributable to
21
prolonged fasted state. Hunger ratings were significantly higher 75 minutes after the
water drink compared to the glucose drink (p<.001).
Added sugar intake and GLP-1 and PYY responses to oral glucose
Dietary added sugar intake was negatively correlated with the GLP-1 response to
glucose ingestion (r
s
= -.50, p= .04; Figure 2.3B). There was no significant correlation
between PYY response to glucose ingestion and dietary added sugar intake (r
s
= -.09,
p= .69). Additionally, we found no correlations between added sugar intake and GLP-1
or PYY response to water consumption (GLP-1: r
s
= .12, p= .6; PYY: r
s
= .13, p= .6).
Figure 2.3 | (A) Systemic GLP-1 increased significantly after consumption of oral glucose
(Baseline= 18.8 ± 18.9 pg/ml; 75min= 31.8 ± 24.5 pg/ml; p< .001).
(B) Negative correlation between GLP-1 response to oral glucose and percent calories
consumed from added sugar (r
s
= -.50, p= .04).
22
GLP-1 and PYY responses to oral glucose and striatal food-cue reactivity
A post-hoc analysis revealed that GLP-1 response to glucose was negatively correlated
with the dorsal striatal response to food cues (r
s
= -.46, p= .04; Figure 2.4). There was
no significant correlation between GLP-1 response to glucose and nucleus accumbens
food-cue reactivity (r
s
= -.16, p= .5). PYY response to glucose was not correlated with
either dorsal striatal or nucleus accumbens response to food cues. Neither GLP-1
response to glucose nor dorsal striatum response to food cues correlated with change
in hunger ratings after glucose (GLP-1: r
s
= .09, p= .7; dorsal striatum: r
s
= -.37, p= .1).
Figure 2.4 | Negative correlation between BOLD response to food cues in the dorsal striatum
and systemic GLP-1 response to oral glucose (r
s
= -.46, p= .04)
23
Discussion
We found that increased dietary added sugar intake, independent of sex, adiposity, and
physical activity, correlates with increased striatal reactivity to food cues following
glucose consumption. A number of neuroimaging studies have shown that obesity is
associated with increased striatal food-cue reactivity (Stoeckel et al., 2008; Martin et al.,
2010; Rothemund et al., 2007). Our findings add to the literature by demonstrating that,
among healthy-weight young adults, and after controlling for sex, physical activity, total
energy intake, and adiposity, those who consumed greater habitual dietary added sugar
had greater striatal responses to food cues in the postprandial state. It is possible that
habitual consumption of added sugars could sustain the salience of external food cues
and thus, the reward value of food.
Both the dorsal striatum and nucleus accumbens are associated with reward
anticipation, but the dorsal striatum is often specifically associated with motivation to
engage in rewarding behavior and anticipation of food and drugs (Volkow et al., 2006;
Volkow et al., 2002; McClernon et al., 2009). Studies in humans and animals indicate
that there are hunger state-dependent changes in striatal response to both food cues
and food receipt (Goldstone et al., 2009; Small et al., 2003). Participants in our study
reported higher hunger ratings following consumption of water than after consumption of
glucose. We found that higher added sugar intake was associated with reduced GLP-1
response to glucose. GLP-1 receptors are widely expressed in subcortical structures of
the brain that relate to both homeostatic and hedonic food intake including the
24
hypothalamus, hippocampus, amygdala, ventral tegmental area, and the nucleus of the
solitary tract (NTS) (Merchenthaler et al., 1999). Notably, GLP-1 receptors are
expressed in the substantia nigra pars compacta, which targets the dorsal striatum
through dopaminergic signaling and regulates feeding motivation (Skibicka, 2013; Sotak
et al., 2005; Palmiter, 2008).
Recently, the gut-brain axis has received attention as a crucial regulator of satiety,
metabolism, and food reward (Zanchi et al., 2017). The vagus nerve serves as the
direct connection between the gut and the brain (Berthoud, 2008). Vagal afferent
neurons express GLP-1 receptors and are thought to deliver short-term satiety signals
to the brain, regulating food intake, though possibly not long-term regulation of body
weight (Monteiro & Batterham, 2017). Animal studies suggest that impaired
communication between the gut and brain leads to interrupted satiety signaling. In
vagotomized rodents, peripheral administration of GLP-1 fails to reduce food intake
(Abbott et al., 2005) . Reduced expression of GLP-1 receptor in vagal afferents leads to
increases in food intake and post-feeding blood glucose levels in rats (Krieger et al.,
2016). Beyond homeostatic mechanisms, evidence suggests that GLP-1 may promote
satiety by modulating the rewarding properties of food (Hayes & Schmidt, 2016). In rats,
administration of Exendin-4, a GLP-1 analog, reduced sucrose intake, diminished
conditioned place preference for sweet reward, and reduced the motivation for feeding
behavior (Dickson et al., 2012; Pritchett & Hajnal, 2012), effects that are mediated by
the mesolimbic dopamine system. In humans, GLP-1 receptor blockade was shown to
25
reduce deactivation to food cues following a meal (ten Kulve et al., 2016). Thus, if
satiety signals are impaired after consuming calories, the salience of food cues may
remain high and lead to overeating and susceptibility for weight gain. A recent study
reported that the dorsal striatum processes the caloric value of sugar (Tellez et al.,
2016). In the context of the results of our study, this process may be interrupted by
reduced GLP-1 mediated satiety signaling. Post-hoc analysis revealed that, while
systemic PYY levels were not significantly correlated with striatal food-cue reactivity,
participants with smaller postprandial increases in GLP-1 had greater food-cue reactivity
in the dorsal striatum.
While examining the effects of obesity on the neural processing of food cues has
contributed to our understanding of obesity related changes in brain reward pathways, it
is important to consider underlying factors, such as dietary intake, that may contribute to
increased susceptibility to overeating and obesity. To that end, Burger & Stice recently
reported that total energy intake, independent of adiposity, is related to a greater
anticipatory response to food within areas in the brain involved in attention and reward
processing (Burger & Stice, 2013). Our results are in line with these findings and further
suggest that habitual consumption of added sugar, accounting for total energy intake,
may drive greater striatal reactivity to food cues. These findings, the context of the
dynamic vulnerability model of obesity, which suggests that a heightened brain reward
response to food cues is associated with greater susceptibility to food cue induced
26
overeating (Stice et al., 2011; Carnell et al., 2012), raise the possibility that habitually
high added sugar intake may increase vulnerability to cue-related overeating behavior.
Our study design is correlational and does not allow us to determine the directionality of
the relationship between added sugar intake and striatal food-cue reactivity (or indeed,
whether any causal association exists). Our findings are, however, consistent with
experimental animal models that do establish a causal link between excessive sugar
intake and greater striatal and behavioral responses to cues for sugar (Avena et al.,
2008). These data could suggest that habitually consuming added sugar affects the
brain’s regulation of food reward in a postprandial state, which may lead to overeating.
Alternatively, it is also possible that individuals who have greater striatal food-cue
reactivity may be motivated to consume more high sugar foods. Future studies should
directly assess this relationship through interventions that experimentally reduce or
increase dietary sugar intake in humans. The aim of our study was to examine
correlations between habitual added sugar intake, food-cue reactivity in brain reward
regions, and endocrine responses by focusing on a fed state induced by a standardized
glucose dose. While our approach was to observe such correlations in the glucose and
water control conditions separately, a larger sample size may allow hierarchical
modeling to directly compare the impact of added sugar intake on hormone and brain
responses in the fasted and fed state. Though our data suggest that the relationship
between added sugar intake and food-cue reactivity exist only after ingestion of a
27
caloric sugar preload, future analytical approaches directly comparing drink session
days would allow testing for interactions, where sample size is not a limitation.
By design, only lean young adults participated in this study since the purpose of this
study was to investigate the effects of dietary added sugar in a lean, healthy population.
It is possible that obesity status has an additive effect on the relationship between sugar
intake, food-cue reactivity, and satiety hormones. Thus, future studies should
investigate this relationship in obese individuals, as well as those who are obesity-prone
due to factors such as genetic predisposition, abnormal eating behaviors, or metabolic
dysfunction. We used a ROI-based approach to interrogate the effects of dietary added
sugar on responses to food cues in the dorsal striatum and nucleus accumbens based
on compelling evidence from animal studies that diets high in sugar and/or fat alter the
function of these regions. Future studies should profile the relationship of dietary added
sugar intake and food-cue related activity in other relevant regions important for appetite
regulation and reward.
The current study focused specifically on acute glucose ingestion, but it is notable that
“added sugar” typically consists of varying combinations of glucose and fructose, each
of which are metabolized differently (Lê & Tappy, 2006). Recent neuroimaging studies
have demonstrated that fructose and glucose differentially affect hormone release, food-
cue reactivity in brain reward regions, and resting-state functional connectivity between
limbic areas (Luo et al., 2015; Page et al., 2013; Page & Melrose, 2016; Wölnerhanssen
28
et al., 2015). These differences could be investigated in future experiments that seek to
define a relationship between acute and habitual sugar intake, perhaps by
administration of a disaccharide preload, such as sucrose. In our study, dietary intake
data were based on self-reported food intake. The 24-hour dietary recall is a widely
used method of collecting information about dietary intake, and our data were carefully
inspected to exclude over- or underreporting, but we acknowledge that it is an indirect
method that may not completely reflect an individual’s dietary habits. Participants in this
study completed an average of five 24-hour dietary recalls, which allowed us to capture
variability in intake and estimate habitual intake of added sugars.
In conclusion, to our knowledge, ours is the first study to show that dietary intake of
added sugar is positively correlated with striatal food-cue reactivity, independent of total
energy intake, sex, adiposity or physical activity. Our findings suggest that added sugar
intake is related to increased striatal response to food cues, but decreased GLP-1
release following glucose intake. Given that the striatum plays a critical role in
mediating reward processing and incentive salience for food cues, these findings
suggest that habitual consumption of added sugars may sustain salience of external
food cues, even in a postprandial state, which could contribute to susceptibility to
overeating and weight gain.
29
Funding
This work was supported in part by the following grants: NIH/NIDDK R01DK102794
(principal investigator [PI]: KAP), Doris Duke Charitable Foundation Clinical Scientist
Development Award 2012068 (PI: KAP), American Heart Association Beginning Grant
in Aid (PI: KAP).
Acknowledgements
We would like to thank the volunteers who participated in these studies. We would also
like to thank Ana Romero for assistance with study coordination; The Dana and David
Dornsife Cognitive Neuroimaging Center at USC, especially Bosco Tjan and J.C.
Zhuang for assistance with MRI protocols; the USC Diabetes and Obesity Research
Unit, especially Lilit Baronikian and Natasha Soares, for running the hormone assays.
The neuroimaging computation for the work described in this paper was
supported by the USC Center for High-Performance Computing. A Research
Electronic Data Capture, REDCap, database was used for this study, which is
supported by the Southern California Clinical and Translational Science Institute (SC
CTSI) through NIH UL1TR001855.
30
Part III
Effects of acute sucrose and glucose ingestion on cerebral blood
flow, appetite hormones, and ingestive behavior in obese and lean
individuals
Introduction
Americans consume sugar in amount well over what is recommended by the American
Heart Association and the World Health Organization alike. Studies over the last decade
have examined the effects of acute intake of the common dietary sugars, glucose and
fructose and have dissociated the effects of each on metabolic function, brain activity,
and various behavioral outcomes (Page et al., 2013; Sclafani et al., 2014; Luo et al.,
2015; Ochoa et al., 2015; Jastrebroff et al., 2016). While these studies lay important
groundwork for understanding how dietary sugars impact physiology and eating
behavior, the next logical step is to study sucrose (table sugar), a disaccharide
composed of one fructose and one glucose molecule. That glucose and fructose are
metabolized differently is well-established. In a healthy, non-diabetic individual, oral
glucose stimulates pancreatic beta cell production of insulin, which regulates the
metabolism of glucose throughout the body (Wilcox et al., 2005). Fructose, however, is
metabolized in the liver and, thus, results in a different insulin-response profile than
glucose (Kong et al., 1999). Several studies have linked consumption of fructose, or,
more commonly, a solution larger ratio of fructose to glucose monosaccharides (e.g.
high-fructose corn syrup), with a host of physical and mental health concerns including
increased adiposity (Stanhope et al., 2009), insulin resistance (Basciano et al., 2005),
31
non-alcoholic fatty liver disease (Ouyang et al., 2008), and cognitive impairments
(Lakhan & Kirchgessner, 2013; Hsu et al., 2015).
Insulin is widely studied in obesity research because of its important role in glucose
metabolism, both peripherally and within several sites of the central nervous system
(Marks et al., 1990). Insulin resistance is highly correlated with adiposity, BMI, and
leptin expression in mammals (DeFronzo & Ferrannini, 1991; Carey et al., 1996).
However, an insulin resistant phenotype can be separable from obesity. For instance,
in wild-type drosophila melanogaster (fruit fly), a high-sugar diet alone is sufficient to
cause insulin resistance (Musselman et al., 2011). In rats, different iterations of a high-
energy diet (high-fat and high-sugar, high-fat alone, or high-sugar alone) produced
differing effects on insulin response and glucose metabolism, but the changes in insulin
sensitivity could not be explained by the degree of obesity of the rat (la Fleur et al.,
2011). These data suggest the possibility that, in lean individuals, the contents and/or
caloric breakdown of the diet may first contribute to insulin resistance and that obesity
may follow this effect. Glucagon-like peptide-1 (GLP-1) is an incretin hormone that
regulates hunger and impacts learning and reward. GLP-1 is produced both
peripherally (in the L-cells of the ileum) and in the brain (in the nucleus tractus-
solitarius). The GLP-1 receptor is also found abundantly in the brain (Merchenthaler et
al., 1999). GLP-1 acts in the hypothalamus to promote insulin secretion, but high
volume of the GLP-1 receptor is also expressed in the ventral hippocampus (Turton et
al., 1996; Hsu et al., 2015).
32
Using a region of interest (ROI)-based approach allowed us to probe brain activity in
several regions that are specific to processing food receipt. The hypothalamus,
amygdala, insula, dorsal striatum and hippocampus have been previously identified as
regions that respond to food cues and/or food receipt (Page et al., 2013). The
hypothalamus regulates food intake and metabolism (Elmquist et al., 1999). The
striatum is a crucial region for motivation and reward processing, having been
implicated in various aspects of sugar ingestion, including binge behaviors, sugar-
seeking, craving, sweet taste, withdrawal, and dopamine and release in response to
intake (Avena et al., 2008; Tellez et al., 2016; Hoebel et al., 2009). The hippocampus is
thought to integrate interoceptive and external hunger cues—a feature that is
interrupted by Western (high-fat, high-sugar) diet (Sample et al., 2015). Damage to the
hippocampus is associated with altered eating behavior (Davidson & Jarrard, 1993;
Forloni et al., 1983). Thus, a diet high in sugar may affect the ability to properly
integrate reward and satiety signaling by inducing aberrant communication between the
hippocampus and other regions. In this study, we used a “composite” ROI, consisting of
the average CBF activation across each of the five aforementioned regions, to
represent an overall network of glucose-responsive regions.
The goal of this study was to understand the acute effects of sucrose vs glucose
ingestion on 1) circulating levels of plasma glucose (PG), insulin, and glucagon-like
peptide-1
(7-36)
(active) (GLP-1), 2) cerebrometabolic activity, and 3) short-term effects
on energy intake in non-diabetic lean and obese individuals. We hypothesized that
33
cerebral blood flow, hormone release, and caloric intake would differ in response to
acute ingestion of sucrose compared to glucose, and also that obesity would
significantly affect these responses. As a secondary outcome, we investigated whether
insulin sensitivity as a potential mechanism for differences between responses to
sucrose and glucose.
Methods
Participants
Thirty-seven volunteers (20 lean (BMI 22.06 +- 1.74) and 17 obese (33.21 +- 3.34))
participated in this study (Table 3.2). Inclusion criteria were: right-handedness, normal
or corrected-to-normal vision, no current dieting behaviors, no smoking or use of illicit
substances, weight stability for at least 3 months prior to and during the study, no
history of eating disorders, diabetes, or other medical diagnoses no use of taking any
medications except oral contraceptives. Participants were asked to maintain their typical
diets and levels of physical activity for the duration of the study. Female participants
underwent MRI visits during the follicular phase of the menstrual cycle to reduce
variability in hunger (Dye & Blundell, 1997; Krishnan et al., 2016). Participants provided
written informed consent compliant with the University of Southern California
Institutional Review Board (IRB # HS-09-00395).
34
Overview of Study Design and Visits
The study included an initial screening visit and three scan visits performed at the
Dornsife Cognitive Neuroimaging Center of University of Southern California. During the
screening visit, height was measured to the nearest 0.1 cm using a stadiometer and
weight to the nearest 0.1 kg using a calibrated digital scale. Body mass index (BMI) was
calculated as weight in kg divided by height in meters
2
. Waist and hip circumferences
were measured in triplicate to the nearest 0.1 cm. Waist circumference was measured
at the midpoint between the iliac crest and lower costal margin in the midaxillary line.
Hip circumference was measured around the maximum circumference of the buttocks.
Body fat percentage was measured using bioelectrical impedance (Tanita Body
Composition Analyzer, Tanita Corp. of America, Inc.).
The Magnetic Resonance Imaging (MRI) visits were performed in double-blinded,
random order on separate days between 2 and 30 days apart with ingestion of 300 mL
drinks containing 75g of either glucose (Tate & Lyle) or sucrose (C & H). Drinks were
flavored with 0.25 tsp (1.07g) of non-sweetened cherry flavoring (Kraft Foods Kool-Aid®
Unsweetened Cherry Drink Mix) to improve palatability.
For each visit, participants arrived at approximately 8:00AM after a 12-hour overnight
fast. Topical numbing cream (Lidocaine) was applied for 10-15 minutes before insertion
of an MR-safe catheter, from which blood draws were performed. A baseline (fasting)
blood draw was performed prior to entering the scanner. The MRI scan (Figure 3.1)
35
included a T1 structural scan (4min) followed by a baseline ASL scan lasting
approximately 8-minutes. Participants were then asked to consume the test drink
outside the scanner within two minutes, after which they rated the sweetness and
pleasantness of the test drink using a visual analog scale ranging from 1 to 10 (where 1
was “not at all” and 10 was “very sweet” or “very pleasant,” respectively). After
consuming the test drink, participants re-entered the scanner and underwent two
additional 8-minute ASL scans beginning ~10 and ~25 minutes relative to drink
ingestion. Blood draws were performed after each ASL acquisition at approximately 20-
minutes (TP20) and 35 minutes (TP35), as well as a final draw at 120 minutes after the
test drink was consumed (TP120). Though all 37 participants underwent ASL scans,
blood sampling was not possible under glucose and/or sucrose conditions in 4 (2 obese,
2 lean) participants.
Figure 3.1 | Schematic of study design, scaled for timing and length of scans.
Directly following the 120-minute blood draw, participants were presented with an ad-
libitum buffet of 32 pre-measured food and drink items in white bowls or on white plates
in identical order and standardized amounts (Figure 3.2). Total energy available from
36
the buffet meal was approximately 4650 kcal. Caloric value per gram or fluid ounce of
each item was previously calculated using the Nutritional Data System for Research
(NDSR) developed by the University of Minnesota (Table 3.1). Buffet meal items were
intended to be recognizable without their packages and widely available foods. Where
appropriate, items were removed from packaging and placed in the bowls (i.e. potato
chips, cookies). Remaining items were left in their packages, unopened (i.e. yogurt,
string cheese, beverages). Cups, napkins, and cutlery were provided. Participants
were given 20 minutes to eat any quantity they desired and were instructed not to leave
the room with any items. The buffet was presented in a private setting with no
researchers present. After the subjects exited the area, each buffet item was re-
weighed by a study team member. Calorie and nutrient intake during the buffet meal
were calculated using the difference between the pre-meal and post-meal weight for
each buffet item. One (obese male) participant did not complete the buffet meal portion
of the visit due to time constraints.
Figure 3.2 | Layout of ad-libitum buffet meal.
37
Table 3.1 | Nutrition information per unit for each item from the ad-libitum
buffet meal.
38
MRI Acquisition Parameters
Imaging data were collected using MAGNETOM Prisma
fit
MRI scanner. Participants laid
supine on the scanner bed during scanning and were asked to look a neutral white mark
in the middle of a gray screen during scanning to stabilize head position and
standardize stimulus attention across days and participants.
Magnetization Prepared Rapid Gradient Echo (MPRAGE)
On each scan day, we used a high-resolution 3D MPRAGE sequence (TR=2530 ms;
TE=2.62 ms; bandwidth=240 Hz/pixel; flip angle= 9°; slice thickness=1mm;
FOV=256x256
mm; matrix=256 x 256) to acquire a structural brain image for first-level
and multi-subject registration.
Pulsed Arterial Spin Labeling (pASL)
ASL MRI is a non-invasive method for the quantification of perfusion. pASL provides a
direct measure of cerebral blood flow by magnetically tagging arterial blood directly
before it enters the brain and measuring the transit time for the tagged blood to reach
specific tissues (Aguirre et al., 2005; Detre et al., 2009). The pASL acquisition
parameters used in this study were: FOV= 192 mm, matrix =64x64; bandwidth= 2232
Hz/Pixel; slice thickness=5 mm; interslice spacing= 0 mm; TR= 4000 ms, TE= 30 ms,
and flip angle=90°.
39
pASL analysis
We used the Bayesian Inference for Arterial Spin Labeling (BASIL) toolbox, part of the
Oxford FMRIB Software Library (FSL), to determine mean cerebral blood flow (mCBF)
across the entire brain, as well as regional CBF to five a priori regions of interest (ROIs)
that have previously been identified as responsive to glucose ingestion: the
hypothalamus, amygdala, hippocampus, insula, and dorsal striatum (Page et al., 2013;
Smeets et al., 2011; Smeets et al., 2007; Rudenga & Small, 2012). All ROIs were
bilateral and anatomically defined using the Harvard-Oxford atlas found in FSL, except
the hypothalamus which is not included in the axis and was defined bilaterally as a 2-
mm spherical ROI surrounding peak glucose-responsive voxels identified by Page et al.,
2011. pASL data were first motion corrected, then tagged and untagged images were
subtracted to obtain perfusion-weighted images. Regional CBF was calculated at
baseline (~5 minutes prior to drink ingestion), and again for the acquisitions beginning at
10 and 25 minutes after each drink.
To represent overall response to ingestion of each drink, we chose to calculate the area
under the curve (AUC) using the trapezoid method (Tai, 1994) across baseline, 10 min
post-drink, and 25 min post-drink timepoints. Difference scores were calculated
between the composite ROI response sucrose and to glucose. ANOVA using the
sucrose-glucose difference score as the outcome variable and the following predictors:
BMI status (obese: BMI >=29.99 kg/m^2/lean: BMI > 18.5, <24.9 kg/m^2), sex
(male/female), drink order, and global mCBF. Based on the results of the initial model,
40
we then tested differences in composite ROI in each BMI group separately. Finally, we
performed post-hoc t-tests to explore response patterns in each ROI between and
within obese and lean groups.
We also calculated the change in pASL response between timepoints as variables (i.e.
25-min TP minus baseline, 10-min TP minus baseline, 25-min TP minus 10-min TP) to
determine peak responses between and within groups and to use as covariates in post-
hoc correlations with energy intake and metabolic outcomes.
Metabolite and Hormone Analysis
Plasma glucose, insulin, and glucagon-like peptide 1
(7-36)
(active) were measured using
Luminex multiplex technology (EMD Millipore, St. Charles, MO). AUC was calculated for
plasma glucose and hormones using the trapezoid method (Tai, 1994). Insulin
sensitivity was calculated using 0, 35, and 120-minute timepoints and assigned
Matsuda Index value.
Separate repeated-measures ANOVAs were used to assess the effects of drink
condition on plasma glucose (PG), insulin, ghrelin, and glucagon-like peptide-1 (GLP-1)
measurements at 15, 35, and 120-minute timepoints (relative to baseline), using BMI
and sex as covariates, and including subject as a random effect. In each model, we
also probed for interactions between drink condition and BMI, as well as drink condition
and timepoint.
41
Behavioral Ratings and Energy Intake Analysis
Subjective ratings of sweetness and pleasantness, from 1 to 10, were examined directly
after each drink to assess whether perceived sweetness and pleasantness differed
between glucose, sucralose, and water control) drinks. Ratings of hunger (“How hungry
do you feel from 1-10?”) were measured after each blood draw (baseline, 15, 35, and
120 minutes). Separate repeated-measures ANOVAs were performed for sweetness,
pleasantness, and hunger ratings at each timepoint to assess whether participants’
ratings differed between drink conditions. Unpaired t-tests were run to examine group
differences in ratings.
During the screening visit, participants were asked to choose whether they like each
buffet meal item (i.e., “Do you like Oreos? Yes/No”). Percent of total “liked” items out of
all possible items was calculated and used to aid interpretation of the results. Total
energy intake (in kilocalories) consumed during the buffet meal was modeled as a
function of and interaction between drink, obesity, and sex in a repeated-measures
ANOVA. We then performed post-hoc Welch’s paired t-tests within and unpaired t-tests
between groups to determine food intake patterns under each drink condition.
All analyses were performed using R Statistical Software Version 3.1.2 (http://www.R-
project.org/). Statistical results were derived using the ‘psych’, ‘r-allfun-v33’, ‘gdata’,
packages. Results yielding p-values less than 0.05 were considered significant. Data
are reported in mean ± standard deviation (SD).
42
Results
Participants (Table 3.2)
Thirty-seven volunteers (14M, 23F) participated in the study. Mean age and body mass
index of the entire sample, as well subgroups separated by BMI status, are described in
Table 3.2.
Table 3.2 | Participant Characteristics
Plasma Glucose and Appetite Hormone Response to Glucose and Sucrose
Plasma Glucose (Figure 3.3A)
Repeated-measures ANOVA with PG as the outcome variable revealed a main effect of
timepoint (f(2, 184)=36.25, p<0.0001), a main effect of sex (f(1, 184)=4.17, p=0.043),
and a drink condition by timepoint interaction effect (f(2, 184)=3.62, p=0.03). There
were no main effects of drink condition (f(1, 184)=2.65, p=0.11), BMI status (f(1,
184)=1.33, p=0.025), or drink by obesity interaction (f(1, 184)=0.13, p=0.72). Post-hoc
paired-t tests indicate no significant difference in PG measurements at baseline
43
(t(31)=1.24, p=0.23) or at the 15-minute timepoint (t(31)=-1.12, p=0.27). We did
observe differences in PG between drinks at the 35-minute (t(32)=2.32, p=0.03) and
120-minute timepoints (t(32)=3.39, p<0.01), though these did not survive Bonferroni
correction for multiple comparisons.
Plasma Insulin (Figure 3.3B)
Repeated-measures ANOVA with plasma insulin as the outcome variable revealed main
effects of timepoint (f(2,184)=10.51, p<0.0001), drink condition (f(1,184)=17.43,
p<0.0001), BMI status (f(1,184)=14.9, p<0.001), sex (f(1,184)=6.77, p=0.01). We also
observed an interaction effect of drink by timepoint (f(2,184)=4.3, p=0.02), but not of
drink by obesity (f(1,184)=0.78, p=0.38). Post-hoc paired-t tests indicate no significant
difference in plasma insulin measurements at baseline (t(31)=1.58, p=0.12) or at the 15-
minute timepoint (t(31)=0.55, p=0.58). However, we did observe significant differences
in insulin measurements between drinks at the 35-minute (t(32)=4.05, p<0.001) and
120-minute timepoints (t(32)=4.1, p<0.001).
Plasma Glucagon-like Peptide-1 (Figure 3.3C)
Repeated-measures ANOVA using plasma GLP-1 (active) as the outcome variable
revealed main effects of timepoint (f(2,184)=10.53, p<0.0001) and BMI status
(f(1,184)=4.69, p=0.032), as well as drink by timepoint interaction effect (f(2,184)=4.3,
p=0.02). There were no significant main effects of drink (f(1,184)=0.46, p=0.5), sex
(f(1,184)=0.3, p=0.58) or drink by obesity interaction effects (f(1,184)=2.64, p=0.12).
44
Post-hoc paired-t tests indicate no significant difference in plasma GLP-1 (active)
measurements at baseline (t(31)=0.85, p=0.4) or at the 15-minute timepoint (t(31)=-
0.91, p=0.37). However, we did observe significant differences in insulin measurements
between drinks at the 35-minute timepoint (t(32)=2.89, p<0.01) and a trend towards
drink differences at the 120-minute timepoint (t(32)=1.81, p=0.08).
Figure 3.3 | Plasma glucose (A), insulin (B), and GLP-1 (C) responses to glucose (magenta)
and sucrose (green) in obese (dashed line) and lean (solid line) participants.
Pulsed Arterial Spin Labeling fMRI
ANOVA results revealed that differences between pASL response to the sucrose versus
glucose drink were predicted strongly by mCBF (f(1,32)=51.81, p<0.001), as
anticipated, as well as BMI status (f(1,32)=16.62, p<0.001). We did not observe
significant effects of sex (f(1,32)=0.64, p=0.43) or drink order (f(1,32)=0.03, p=0.87).
45
When we separated the sample into obese and lean groups, we observed that the
composite ROI pASL responses to sucrose and glucose drinks were similar (t(16)=-
1.59, p=0.13) in obese participants. In lean participants, overall pASL response to
sucrose was larger than to glucose (S: 916.31 ± 216.63, G: 855.18 ± 203.29
ml/100g/min, t(19)=2.14, p=0.045) (Figure 3.4). Post-hoc tests for within-group
differences in each ROI indicate that the difference between overall response profiles to
glucose and sucrose were driven by significant differences in amygdala response (S:
838.33 ± 243.77 G: 754.27 ± 209.7 ml/100g/min, t(19)=-2.31, p=0.03), insula response
(S: 1103.98 ± 294.5, G: 1000.66 ± 255.92 ml/100g/min, t(19)=-2.26, p=0.04), as well as
a marginally significant difference in hippocampus response (S: 1211.48 ± 320.31, G:
1142.42 ± 315.49 ml/100g/min, t(19)=-1.74, p=0.098) in the same direction, though it
should be noted that these differences did not remain after controlling for comparisons
of multiple regions. Additional results of post-hoc t-tests for within group differences are
found in Table 3.3.
46
Figure 3.4 | Overall CBF responses to glucose and sucrose in the composite ROI in lean and
obese participants. Obese participants showed similar responses to each drink (t(16)=-1.59,
p=0.13), while lean participants, showed a larger response to sucrose than to glucose (S:
916.31 ± 216.63, G: 855.18 ± 203.29 ml/100g/min, t(19)=2.14, p=0.045).
47
Table 3.3 | Within-group differences in CBF AUCs for each region of interest in
obese and lean participants.
Hunger, Sweetness, and Pleasantness Ratings
Hunger ratings were assessed at baseline, 10-minutes post-drink, 35-minutes post-
drink, 120-minutes post-drink. Repeated measures ANOVA revealed no main effects of
drink condition (f(1,287)=1.27, p=0.26) or BMI status (f(1, 287)=1.6, p=0.21), and no
drink by obesity interaction effects (f(1, 287)=0.09, p=0.76). We observed a main effect
of sex on hunger ratings (f(1, 287)=30.23, p<0.0001), whereby female participants
consistently rated lower levels of hunger than males. However, we did not find a sex by
48
drink interaction effect (f(1,287)=0.08, p=0.78), indicating that these sex effects
persisted under each drink condition. Sucrose and glucose drinks were rated similarly
pleasant (Glucose: 5.95 ± 2.38, Sucrose: 5.89 ± 2.25, t(37)=0.18, p=0.86) and similarly
sweet (Glucose: 7.43 ± 1.52, Sucrose: 7.59 ± 1.38, t(37)=-0.61, p=0.54).
Ad-libitum Buffet Meal
Overall, participants reported liking 61.2% ± 17.57% of the buffet meal items (rated p.
There was no difference in buffet meal food preference between BMI groups (Obese:
59.14 ± 17.31, Lean: 62.74 ± 18.05, t=0.6, p=0.55). Males indicated higher preference
for buffet foods than females (M: 69.98 ± 15.66, F: 56.01± 16.85, t=-2.48, p=0.02).
A paired t-test including the entire sample did not indicate differences in buffet meal
energy intake between drink conditions in the entire sample (G: 881.94 ± 469.67, S:
937.75 ± 588.05 kcal, t(35)=-1.04, p=0.3). However, we observed that while energy
intake after glucose and sucrose was similar within the obese group (G: 866.52 ±
418.93, S: 857.79 ± 444.98 kcal, t(15)=0.09, p=0.93), lean participants tended to
consume more energy under the sucrose condition than the glucose (G: 894.28 ±
517.13, S: 1001.72 ± 686.28 kcal, t(19)=-1.96, p=0.06) (Figure 3.5).
49
Figure 3.5 | Energy intake following the buffet meal 120-140 minutes after glucose or sucrose
consumption. Lean participants tended to consume more energy under the sucrose condition
than glucose (G: 894.28 ± 517.13, S: 1001.72 ± 686.28 kcal, t(19)=-1.96, p=0.06), while energy
intake within the obese group was similar across conditions (G: 866.52 ± 418.93, S: 857.79 ±
444.98 kcal, t(15)=0.09, p=0.93).
Relating pASL, hormones, and ingestive behavior
By design, the first three blood sampling timepoints were directly adjacent to the three
neuroimaging acquisitions. In an exploratory analysis, we tested for relationships
between pASL and hormone responses to sucrose and glucose to aid interpretation of
each. We chose to focus on hormone measurements 35-minute timepoint (relative to
baseline) due to proximity to the pASL scans, and also because we measured peak
hormone response to glucose at 35-min, as well as significant differences in hormone
dynamics between drinks.
50
Insulin Sensitivity and Peripheral Insulin Response
We measured insulin sensitivity based on insulin and PG measurements across the 2-
hour glucose drink session. We observed that, when controlling for BMI, sex, and
mCBF in all participants, pASL activity in the composite ROI was positively correlated
with insulin sensitivity after glucose ingestion (r=0.44, p=0.013), but not after sucrose
ingestion (r=0.21, p=0.28). In lean, but not obese, participants, the increase in CBF
relative to baseline in the composite ROI was positively correlated with the rise in insulin
over the same period (35-minute timepoint) after glucose, but not after sucrose (r=0.11,
p=0.67), possibly due to lower plasma insulin at this timepoint on the sucrose day
(Figure 3.3B).
Buffet Meal Energy Intake and Appetite Hormones
Because we observed marginally significant differences in energy intake between drinks
in lean participants, we examined whether appetite hormones at the 35-minute
timepoint related to differences in food intake. Controlling for drink order and sex, we
observed that the discrepancy in GLP-1 levels between drinks was positively correlated
with increased energy intake (r=0.7, p<0.001) (Figure 3.6). When the same model was
applied to insulin, we also observed a positive correlation between drink-driven
differences insulin at 35-minutes and differences in energy intake (r=0.48, p=0.048), but
this did not cross the significance threshold after controlling for multiple comparisons for
3 timepoints. In other words, a larger difference between measurements of these
51
satiety hormones 35 minutes post-ingestion of glucose and sucrose predicted increased
energy lean participants consumed after sucrose than glucose drinks.
Figure 3.6 | The difference between GLP-1 measurements at 35 minutes after Sucrose and
Glucose predicts increased food intake after sucrose relative to glucose in the ad-libitum
buffet meal paradigm (r=0.7, p<0.001).
Discussion
The goals of the current study were to understand the differences between responses to
acute consumption of glucose and sucrose across three domains: brain activity
(cerebral blood flow), appetite hormones, and eating behavior. We also examined
whether these differences were related to obesity and/or insulin sensitivity. We
observed that in all participants, the levels of circulating plasma glucose, insulin, and
52
GLP-1 30 and 120 minutes following sucrose ingestion compared to glucose ingestion.
Analysis of pASL imaging data revealed that lean, but not obese, participants showed
differential cerebrometabolic responses to sucrose and glucose in brain regions that are
stereotypically responsive to glucose ingestion. Furthermore, lean participants tended to
consume more energy from the ad-libitum buffet meal after ingestion of sucrose than
after glucose. We also observed that, when controlling for drink order, BMI, sex, and
total CBF, insulin sensitivity was positively correlated with CBF response to ingestion of
oral glucose, but not sucrose. Taken together, our findings suggest that the neural,
endocrine, and behavioral elements of a homeostatic system which is highly sensitive to
carbohydrate in the form of glucose may be less so in response to a “real-world” sugar,
sucrose, and in turn may result in susceptibility to overeating.
Sucrose, a typically consumed dietary added sugar, resulted in different response
profiles of insulin and GLP-1, two hormones related to satiety, and to a lesser extent,
plasma glucose. Our findings are in line with those from the limited literature comparing
appetite hormone responses to sucrose and glucose (Yau et al., 2017), though the
current study is the first of our knowledge to relate these peripheral appetite hormones
with neuroimaging and eating behavior. The use of pASL neuroimaging allowed us to
probe neural effects of acute ingestion of sucrose and glucose in a task-independent
manner. Given results from previous reports suggesting that obese and lean individuals
show differential brain responses to glucose ingestion (Matsuda et al., 1999), we
explored patterns of CBF response to sucrose by separating our sample by BMI
53
category. We observed that lean participants in the study exhibited an exaggerated
CBF response to sucrose compared to glucose, though the modest effect size should
be noted. Interestingly, we observed that insulin sensitivity (independent of BMI and
sex) was positively correlated with CBF response to ingestion of glucose, but not
sucrose.
Though the goal of the current study was to profile CBF response to each test drink
using a composite region-of-interest (ROI), future studies should further assess the
contribution of individual regions to the global CBF response either using a focused ROI
or network-based approach. To represent the overall magnitude of CBF response, we
chose to calculate the area under the curve (AUC), a method that may somewhat
ambiguate the nuanced patterns of activity over the time course of the imaging
acquisitions. Along with a larger sample size, and possibly more pASL acquisitions,
future work can meaningfully build on the analyses and results of the current study by
describing pASL patterns of activity in more detail.
The effects of sucrose (vs. glucose) on appetite hormones were especially pronounced
in lean participants, who also tended to consume more energy in an ad-libitum buffet
meal two hours after sucrose relative to glucose ingestion. Obese participants
consumed similar amounts after both sugar drinks. Furthermore, in lean participants,
the discrepancy between GLP-1 responses to sucrose and glucose was positively
correlated to increases in food intake in the buffet meal; that is, the larger the difference
54
in peripheral GLP-1 release between sucrose relative to glucose, the more calories
participants consumed after sucrose relative to glucose. The ad-libitum buffet meal
allowed us to model a food environment with which participants could freely interact.
While there reported effects of sucrose on satiety and food intake in prior work have
varied depending on the study design (Anderson & Woodend, 2003), the results from
the current study suggest that, at least for lean individuals, glucose is able to suppress
energy intake longer than sucrose, even though the two drinks had the same caloric
value (300 kcal). Post-hoc analyses of our dataset suggest a relationship between
circulating levels of insulin and GLP-1 and increased food intake under the sucrose
versus glucose conditions. Though this observation is reasonable given that these
hormones are each implicated in satiety signaling, it was nevertheless striking that
individual differences in insulin and GLP-1 release following sucrose vs. glucose were
correlated with feeding behavior. Along with a larger sample, future work should include
a non-caloric control condition.
Overall, the current study aids the understanding the effects of ingestion of a “real-
world” sugar, sucrose, on human neural activity, metabolic response, and eating
behavior as the medical, public health, and policy fields contend with high rates of
dietary sugar intake and the associated health outcomes.
55
Part IV
Differential brain responses to sucralose and glucose in obese
compared to lean individuals:
Findings from the Brain Response to Sugar Study
Hilary M.K. Dorton, Shan Luo, John R. Monterosso, Kathleen A. Page
Manuscript in Preparation
Abstract
Non-nutritive sweeteners (NNS) are increasingly used as an alternative to caloric
sweeteners to reduce caloric intake. However, epidemiological evidence and animal
studies suggest that NNSs may paradoxically stimulate food intake and contribute to
obesity. A proposed mechanism behind these findings is that NNSs uncouple sweet
taste from calorie intake, which may interfere with neurophysiological responses that
regulate feeding behavior. Functional magnetic resonance imaging (fMRI) studies in
humans have shown that NNSs and caloric sweeteners have differential effects on brain
reward circuitry, which may consequently impact feeding behavior. However, no studies
have compared the neurophysiological and feeding responses to NNSs and caloric
sweeteners in obese and lean individuals. Thus, we aimed to determine the effects of
acute consumption of the NNS, sucralose, compared to glucose, on the appetite
regulating hormones, ghrelin, insulin, peptide YY (PYY), and glucagon-like peptide-1
(GLP-1), and brain activity in appetite and reward regions in obese vs. lean individuals.
Thirty participants (14 M, 16 F; 15 Lean, 15 Obese) between 19 to 24 years old
underwent 3 visits with the ingestion of 300 mL drinks containing 75g glucose,
56
sucralose (2mmol/l), or water (drink order randomized) after an overnight fast. Visits
included blood sampling, fMRI scan and ad libitum buffet meal. Blood draws were
obtained at baseline, 40-min and 60-min after drink consumption. Arterial spin labeling
(ASL) acquisitions (~8 min in duration) were performed before drink, 10-20 minutes, and
30-40 minutes after drink to measure cerebral blood flow (CBF) (an indirect marker of
neural activity) responses to each drink. A priori regions-of-interests (ROI) included the
hypothalamus, amygdala, dorsal striatum, insula, and anterior cingulate cortex, brain
regions previously shown to regulate food intake and to respond to glucose ingestion
and/or sweet taste. Oral glucose resulted in significant increases in circulating insulin,
PYY, GLP-1, and reductions in ghrelin among all participants, whereas sucralose and
water had no effect on these hormones with no differences in obese and lean
participants. Obese vs. lean individuals had greater peak CBF responses to sucralose
across all the ROIs (2.10 ± 0.70 vs. -0.79 ± 0.44 ml/100g/min, respectively, p=0.002)
and tended to have attenuated CBF reductions to glucose (-0.24 ± 0.73 vs. -2.04 ± 1.05
ml/100g/min, p=0.17). In summary, acute ingestion of sucralose did not affect appetite
regulating hormones, but it resulted in greater neural signal change in appetite and
reward regions and greater food intake in obese compared to lean individuals.
Introduction
Obesity rates have risen dramatically over the last three decades posing a significant
challenge to public health (Hales CM, Carroll MD, Fryar CD, Ogden CL., 2017). A
growing body of evidence has linked increases in sugar-sweetened beverage
57
consumption to weight gain and obesity (Hu et al., 2010; Malik et al., 2013; Bray &
Popkin, 2014). In an effort to reduce sugar intake, non-nutritive sweeteners (NNS) are
increasingly consumed as an alternative to caloric sweeteners as a way to satisfy the
desire for sweet taste without consuming extra calories. Currently, NNSs are now used
by over 40% of American adults (Sylvetsky et al., 2017). The existing literature shows
mixed results on the effects of NNS on appetite, metabolic function, and body weight
(Rogers et al., 2015; Peters & Beck, 2016; Burke & Small, 2016; Lohner et al., 2017;
Toews et al., 2019) with no clear consensus on whether NNS are beneficial or harmful
for health (Gardner et al., 2014). Epidemiological evidence and animal models suggest
that NNS may paradoxically stimulate food intake, contributing to weight gain and
obesity (Feijo et al., 2013; Pepino et al., 2015; Fowler, 2016; Murray et al., 2016;
Nettleton et al., 2016). A proposed mechanism for these findings is that NNSs uncouple
sweet taste from caloric intake, which may interfere with neurophysiological responses
that regulate feeding behavior (Veldhuizen et al., 2017). For obese and overweight
individuals, this is particularly important as these groups consume a higher percentage
of NNS and may be more susceptible to the potential adverse effects of NNS on brain
food-cue reactivity and subsequent feeding behavior (Sylvetsky et al., 2017; Rother et
al., 2018,).
Functional magnetic resonance imaging (fMRI) studies in humans have shown that
NNS and caloric sweeteners have differential effects on brain reward circuitry, which
may consequently impact feeding behavior (Smeets et al., 2005; Frank et al.,
58
Neuroimage 2008; Smeets et al., Neuroimage 2011; van Rijn et al., 2011; van Opstal
Nutrition 2019; van Rijn Behavioural Brain Research 2011). Prior fMRI studies have
largely focused on brain activation that is driven by the sweet taste of NNS (Frank et al.,
Neuroimage 2008; Smeets et al., Neuroimage 2011) using paradigms that expose
participants to small amounts of NNS (e.g. applied to the tongue). Less is known about
how the brain responds to the acute consumption of a relevant dose of NNS. Moreover,
the majority of work in this area has been limited to studies of normal-weight individuals
(van Opstal et al., 2019; Smeets et al., 2005; Frank et al., Neuroimage 2008; Smeets et
al., Neuroimage 2011; van Rijn Behavioural Brain Research 2011; van Rijn Behavioural
Brain Research 2011). To the best of our knowledge, no studies have compared the
neurophysiological and feeding responses to NNS and caloric sweeteners in obese and
lean individuals.
We aimed to determine the effects of an acute consumption of the NNS, sucralose
compared to glucose on functional brain activity and hormones involved in appetite
regulation. We hypothesized that acute sucralose consumption would lead to differential
activation (quantified by ml/100g/min of cerebral blood flow) of brain regions involved in
appetite and reward processing. Based on prior reports (Ma et al., 2009; Ford et al.,
2011; Meyer-Gerspach, 2016; Tucker & Tan, 2017; Grotz et al., 2017), we predicted
that acute consumption of sucralose would have no significant effect on hormones
involved in appetite regulation in either obese or lean individuals.
59
Methods
Participants
Thirty volunteers (15 lean (BMI: 22.43 ± 1.82 kg/m
2
) and 15 obese (BMI: 33.93 ± 4.14
kg/m
2
) participated in this study (Table 4.1). Participants were right-handed with normal
or corrected-to-normal vision, nonsmokers, weight-stable for 3 months, non-dieters, with
no history of eating disorders, diabetes, or other medical diagnoses and were not taking
any medications (except oral contraceptives). Participants provided written informed
consent compliant with the University of Southern California Institutional Review Board
(IRB # HS-09-00395).
Overview of Study Design
The study included an initial screening visit and three MRI visits performed at the
Dornsife Cognitive Neuroimaging Center of University of Southern California. During the
screening visit, height was measured to the nearest 0.1 cm using a stadiometer and
weight to the nearest 0.1 kg using a calibrated digital scale. Body mass index (BMI) was
calculated as weight in kg divided by height in meters
2
. Waist and hip circumferences
were measured in triplicate to the nearest 0.1 cm. Waist circumference was measured
at the midpoint between the iliac crest and lower costal margin in the midaxillary line.
Hip circumference was measured around the maximum circumference of the buttocks.
Percent body fat was measured by a trained staff member using bioelectrical
impedance (Tanita Body Composition Analyzer, Tanita Corp. of America, Inc). We
administered 24-hour dietary recalls at the screening and each subsequent scan visit to
60
determine the proportion of participants who consume NNS in their diet. Participants
were classified as NNS users if they consumed a daily average of > 5 milligrams,
(roughly the amount found in one stick of sugar-free gum), of sucralose, aspartame,
acesulfame potassium, or any combination thereof. Females underwent MRI visits
during the follicular phase of the menstrual cycle to reduce variability in hunger (Dye &
Blundell, 1997; Krishnan et al., 2016).
The Magnetic Resonance Imaging (MRI) visits were performed in double-blinded,
random order on separate days between 2 and 30 days apart with ingestion of 300 mL
drinks containing either glucose (75 g), sucralose (2mM concentration, consistent with
the concentration typically found in diet soda (Pepino et al., 2013)), or a water control (in
a subset of 25 volunteers). Drinks were flavored with 0.25 tsp (1.07g) of non-sweetened
cherry flavoring (Kraft Foods Kool-Aid® Unsweetened Cherry Drink Mix) to improve
palatability.
For each visit, participants arrived at approximately 8:00AM after a 12-hour overnight
fast (Figure 4.1). A baseline (fasting) blood draw was performed prior to entering the
scanner. The MRI scan included a T1 structural scan (4min) followed by a baseline ASL
scan (8min). Participants were then asked to consume the test drink within two
minutes, after which they rated the sweetness and pleasantness of the test drink using
a visual analog scale ranging from 1 to 10 (where 1 was “not at all” and 10 was “very
sweet” or “very pleasant,” respectively). After consuming the test drink, participants re-
61
entered the scanner and underwent two additional 8-minute ASL scans beginning 10
and 30 minutes post-drink ingestion. Blood draws were performed again 40 minutes
and 60 minutes after the test drink was consumed.
Figure 4.1 | Schematic of study visit timeline. Blocks are to scale. MPRAGE Anatomical T1
image was acquired for ~4 minutes and each ASL acquisition was ~8 minutes. The test drinks
were given after the baseline ASL measurement and were consumed within 2 minutes. The
second and third ASL acquisitions were performed at 10 minutes and 30 minutes after drink
consumption, respectively. Blood draws were performed at 0 minutes, 40 minutes, and 60
minutes relative to drink consumption. A subset of participants underwent a 20-minute buffet
meal paradigm directly following the third blood draw.
MRI Acquisition Parameters
Imaging data were collected using MAGNETOM Prisma
fit
MRI scanner. Participants laid
supine on the scanner bed during scanning.
Magnetization Prepared Rapid Gradient Echo (MPRAGE)
On each scan day, we used a high-resolution 3D MPRAGE sequence (TR=2530 ms;
TE=2.62 ms; bandwidth=240 Hz/pixel; flip angle= 9°; slice thickness=1mm;
FOV=256x256
mm; matrix=256 x 256) to acquire a structural brain image for first-level
and multi-subject registration.
Blood Draw 1
Baseline / 0 Min
Baseline
Pre-Drink
+10 Min
Post-Drink
+30 Min
Post-Drink
ASL 1
8 Min
Blood Draw 2
40 Min
Post-Drink
fMRI Task
6 Min
Drink
(2 Min)
MPRAGE
4 Min
ASL 2
8 Min
ASL 3
8 Min
Blood Draw 3
60 Min
Post-Drink
62
Pulsed Arterial Spin Labeling (pASL)
ASL MRI is a non-invasive method for the quantification of perfusion. pASL provides a
direct measure of cerebral blood flow by magnetically tagging arterial blood directly
before it enters the brain and measuring the transit time for the tagged blood to reach
specific tissues (Aguirre et al., 2005; Detre et al., 2009). The pASL acquisition
parameters used in this study were: FOV= 192 mm, matrix =64x64; bandwidth= 2232
Hz/Pixel; slice thickness=5 mm; interslice spacing= 0 mm; TR= 4000 ms, TE= 30 ms,
and flip angle=90°.
pASL analysis
We used the Bayesian Inference for Arterial Spin Labeling (BASIL) toolbox, part of the
Oxford FMRIB Software Library (FSL), to determine mean cerebral blood flow (CBF)
across the entire brain, as well as regional CBF to five a priori regions of interest (ROIs)
that have previously been identified as responsive to glucose ingestion and/or sweet
taste: the hypothalamus, amygdala, anterior cingulate cortex (ACC), insula, and dorsal
striatum (Page et al., 2013; Smeets et al., 2010; Rudenga & Small, 2012). All ROIs
were bilateral and anatomically defined using the Harvard-Oxford atlas found in FSL,
except the hypothalamus which is not included in the axis and was defined bilaterally as
a 2-mm spherical ROI surrounding peak glucose-responsive voxels identified by Page
et al., 2011. pASL data were first motion corrected, then tagged and untagged images
were subtracted to obtain perfusion-weighted images. Regional CBF was calculated in
63
the baseline prior to drink ingestion, and again at 10-minute and 30-minute after a drink.
Post vs. pre-drink differences of CBF in each region were also compared at each time
point using paired-t tests.
Repeated measures ANOVA was used to determine the extent to which different
predictors accounted for variance in outcome measures with subject modeled as a
random effect. The CBF response to drink ingestion measured as change in CBF after
drink minus before drink at 2 time points (10 and 30min) in a composite of region-of-
interest (ROI) that was modeled with the following predictors: an interaction between
drink condition (glucose and sucralose) and obesity, an interaction between drink
condition, time point, and sex in 30 participants. CBF across the whole brain at baseline
(pre-drink) was also modeled based on drink to account for differences due to chance,
and change in CBF was modeled based on an interaction between drink and time point
to assess whether effects were unique to the predetermined ROIs. Next, this model was
repeated to include the water control drinks in the 25 participants who completed all
three drink sessions. We then performed post-hoc t-tests to explore response patterns
in each ROI between obese and lean groups.
Metabolite and Hormone Analysis
Plasma glucose, insulin, ghrelin (active), glucagon-like peptide 1
(7-36)
(active), and
peptide-YY (total) were measured using Luminex multiplex technology (EMD Millipore,
St. Charles, MO). Area under the curve (AUC) was calculated for glucose and
64
hormones using the trapezoid method (Tai, 1994). Plasma glucose and hormone levels
were analyzed using repeated measures ANOVA where glucose, insulin, GLP-1, PYY,
and ghrelin AUCs were modeled separately with the following predictors: an interaction
between drink (glucose, sucralose, water) and time (40 minutes-baseline; 60 minutes-
baseline), sex, and BMI status for the 25 participants who completed all three drink
days.
Behavioral Ratings and Energy Intake Analysis
Subjective ratings of sweetness and pleasantness (on a scale from 1 to 10) were
examined directly after each drink to assess whether perceived sweetness and
pleasantness differed between glucose, sucralose, and water control) drinks. Ratings of
hunger (i.e. “How hungry do you feel from 1-10?”) and prospective food intake (i.e.
“How much could you eat right now from 1-10?”) were measured after each blood draw
(baseline, 40, and 60 minutes). Separate repeated-measures ANOVAs were
performed for sweetness, pleasantness, and each timepoint of the appetite ratings to
assess whether participants’ ratings differed between the three drink conditions. Paired
t-tests were run between drink days to determine differences in ratings where
appropriate, and unpaired t-tests were run to determine whether there were group
differences in ratings.
All analyses were performed using R Statistical Software Version 3.1.2 (http://www.R-
project.org/). Descriptive statistics were derived using the ‘psych’ package, and
65
repeated measures analysis of variance (rmANOVA) was completed using the ‘nlme’
and ‘lme4’ packages. Results with p < 0.05 were considered significant, and imaging
data were corrected for multiple comparisons (5 regions of interest) when appropriate.
All data are reported in mean ± standard deviation (SD).
Results
Participants (Table 4.1)
Fifteen lean (6M, 9F) and 15 Obese (8M, 7F) young adults (22.64 ± 3.48 years)
completed this study. Obese compared to lean participants had greater BMI (33.93 ±
4.14 kg/m
2
vs. 22.43 ± 1.82 kg/m
2
, respectively, p<0.001), percent body fat (35.95 ±
7.56% vs. 21.02 ± 6.64%, p<0.001), and waist:hip ratio (0.9 ± 0.08 vs. 0.84 ± 0.06,
p=0.03). There were similar numbers of users and non-users of NNS in each group
(NNS Users: Obese: n=5, Lean: n=6; Non-users Obese: n=10, Lean: n=9).
Table 4.1 | Participant Demographics
Lean (Mean ± SD) Obese (Mean ± SD) All (Mean ± SD)
Age (y) 22.29 ± 3.53 22.99 ± 3.53 22.64 ± 3.48
Sex M=6; F=9 M=8; F=7 M=14; F=16
NNS Use Y=6; N=9 Y=5; N=10 Y=11; N=19
BMI (kg/m^2) 22.43 ± 1.82 33.93 ± 4.14 28.18 ± 6.64
Waist (cm) 80.17 ± 6.37 101.87 ± 8.86 91.02 ± 13.39
Hip (cm) 96.17 ± 5.94 113.85 ± 6.86 105.01±10.98
Body fat (%) 21.02 ± 6.64 35.95 ± 7.56 28.48 ± 10.32
66
Effects on circulating glucose and hormone levels (Figure 4.2)
Repeated measures ANOVA to examine hormone response to glucose, sucralose, and
water drinks revealed a main effect of drink (plasma glucose: F(2, 95)=74.5, p<0.0001;
plasma insulin: F(2, 95)=68.34, p<0.0001; plasma GLP-1: F(2, 94)=36.81, p<000.1;
PYY: F(2, 95)=27.12, p<.0001; plasma Ghrelin: F(2, 95)= -3.65, p<0.001), driven by
differences between glucose and both non-caloric drinks in each case. We found no
significant differences between obese or lean participants (S. Table 4.1) or interactions
of drink condition with obesity on circulating glucose and hormone responses to drinks.
Within each group, there were no differences in fasting plasma glucose or hormone
levels between the three study visits (S. Table 4.1). As our primary outcome of interest
was whether acute ingestion of sucralose alone is sufficient to stimulate appetite
hormones, we directly compared circulating glucose and hormone responses after
sucralose and water ingestion and found no significant difference between the drinks in
either group. We performed post-hoc Welch’s t-tests to determine whether there were
differences between obese and lean participants in hormone response to any of the
drink conditions (S. Table 4.1). As we expected, fasting and AUC plasma insulin levels
following the glucose drink were higher in obese compared to lean participants, but
there were no significant group differences in baseline or AUC plasma glucose, GLP-1,
PYY, or Ghrelin levels in response to any of the drink conditions (S. Table 4.1). Within
each group, we found no differences between drink conditions in baseline plasma
glucose or hormone differences (S. Table 4.2).
67
FIGURE 4.2 | Circulating plasma glucose and hormone responses to glucose, sucralose, and
water ingestion. Repeated-measures ANOVA revealed a main effect of drink condition on
circulating levels of plasma glucose (F(2, 95)=74.5, p<0.0001), insulin (F(2, 95)=68.34,
p<0.0001), GLP-1 (F(2, 94)=36.81, p<000.1), PYY (F(2, 95)=27.12, p<.0001), and ghrelin (F(2,
95)= -3.65, p<0.001). Following consumption of the glucose drink (purple), obese participants
(dotted line) showed differential responses compared to lean participants (solid line) in plasma
glucose, insulin, GLP-1, ghrelin, and PYY. After sucralose (navy blue) or water (light blue),
there were no differences between groups, and endocrine response patterns were similar.
68
Cerebral Blood Flow Responses to Sucralose and Glucose (Figure 4.3)
For the neuroimaging data analysis, we calculated the “CBF response” as a subtraction
of the baseline measurement from the subsequent measurements (e.g. ASL
measurement at 10-minutes minus baseline measurement). Repeated-measures
ANOVA was performed to examine the CBF response in the composite ROI to glucose
and sucralose drinks and revealed main effects of drink (F(1, 85)=30.22, p<.0001),
obesity (F(1, 27), p<.001), and time point (F(1, 85)=6.38, p=0.01). In a subset of 25
participants, we included a water control session in order to examine responses to a
neutral, non-caloric drink. In this model, repeated-measures ANOVA revealed main
effects of drink (F(2, 117)=16.33, p<.0001), obesity (F(1, 22)=4.86, p=0.038), and time
point (F(1, 117)=5.83, p=0.017). Additionally, we found a marginally significant
interaction between obesity and drink (F(2, 117)=2.63, p=0.076).
69
FIGURE 4.3 | Peak cerebral blood flow (CBF at 10-minutes minus CBF at baseline) response to
glucose, sucralose, and water drinks in obese (purple) and lean (light blue) participants in the
composite ROI. Composite ROI was calculated using the mean CBF in the amygdala, anterior
cingulate cortex, dorsal striatum, hypothalamus, and insula.
70
After determining main effects and interactions in each model, we performed post-hoc
analyses including paired t-tests to examine within-group CBF responses to glucose vs.
sucralose and sucralose vs. water and independent t-tests for between-group
comparisons (i.e., obese vs lean responses to sucralose, glucose, water) as shown in
Figure 4.3. Mean global CBF at baseline was not different on any drink condition
(Glucose: n=30, 20.51 ± 5.57 ml/100g/min; Sucralose: n=30, 20.6 ± 6 ml/100g/min;
Water: n=25, 20.78 ± 6.37 ml/100g/min). In all three drink conditions, we observed peak
CBF responses at the second timepoint in both groups (Glucose: Obese=-0.24 ± 2.84,
Lean=-2.04 ± 4.06 ml/100g/min; Sucralose: Obese=2.1 ± 2.72, Lean=-0.79 ± 1.69
ml/100g/min; Water: Obese=0.92 ± 3.15, Lean=0.45 ± 3.47 ml/100g/min). As we
measured change from baseline, a negative value indicates a reduction in CBF signal
following drink ingestion, and a positive value indicates an increase in CBF. We also
performed post-hoc analyses (Bonferroni corrected for 5 comparisons) comparing
responses to each drink in each ROI. The pattern of CBF in response to sucralose was
similar across all ROIs, and the greatest effect was in the Amygdala (Obese=4.68 ± 4,
Lean= 0.7 ± 3.73, t(27.9)= 2.82, p=0.009) (Figure 4.4).
71
Figure 4.4 | Peak CBF responses to sucralose in obese and lean groups by ROI. Post-hoc t-
tests were performed between groups in each ROI: Amygdala (O=4.68 ± 4, L= 0.7 ± 3.73,
t(27.9)= 2.82, p=0.009), Hypothalamus (O=1.44 ± 4.22, L= -2.32 ± 3.88, t(27.8)=2.54, p=0.02),
Insula (O= 1.09 ± 5.06, L= -1.39 ± 3.47, t(24.8)= 1.57, p=0.13), Dorsal Striatum
(Caudate/Putamen; O= 1.62 ± 2.6, L= 0.1 ± 2.63, t(28)=1.59, p=0.12), ACC (O= 1.68 ± 4.17, L=
-1.02 ± 3.13, t(26)= 1.99, p=0.06).
Appetite Ratings (S. Table 4.3)
Baseline hunger and prospective food intake ratings were similar on each day (Hunger:
Glucose: 5.27 ± 2.1, Sucralose: 5.1 ± 2.51, Water: 5.76 ± 2.03; Prospective Intake:
Glucose: 6.33 ± 1.56, Sucralose: 6.5 ± 1.55, Water: 7 ± 1.41). We observed effects of
drink on post-drink ratings of hunger (40 min: (F(2,78)= 3.95, p=0.023); 60 min:
(F(2,78)= 3.74, p=0.028) and prospective food intake (40 min: F(2,78)= 4.79, p=0.011;
72
60 min: F(2,78)= 3.69, p=0.03), with lower ratings following glucose ingestion. There
were no significant group differences in post-drink hunger ratings (40 min: F(1,78)=
2.82, p=0.097; 60 min: F(1,78)= 0.25, p=0.62) or prospective food intake ratings (40
min: F(1,78 )= 0.005, p=0.94; 60 min: F(1,78)= 0.022, p=0.88). Post-hoc t-tests showed
that hunger was higher after the water drink than the sucralose drink (40 min: water: 6.8
± 2.02, sucralose: 5.83 ± 1.9, t(24)= 3.1299, p=0.005; 60 min: water: t(24)= 3.024,
p=0.006), but no statistically significant difference in prospective food intake after
sucralose and water (40 min: t(24)= 1.7685, p=0.09; 60 min: t(24)= 1.633, p=0.12).
Sweetness and Pleasantness Ratings (S. Table 4.3)
There were main effects of drink condition on sweetness ratings (F(2,50)= 31.064,
p<0.001) and pleasantness ratings (F(2,75)= 4.751, p=0.011), with generally lower
ratings on the water day. We also found a main effect of BMI status on sweetness
ratings (F(1,50)=4.425, p=0.041). However, sweetness and pleasantness ratings did
not differ between sucralose and glucose conditions (t(18)= 1.36, p=0.19 ), and we
found no between-group differences in sweetness and pleasantness ratings for any of
the drink conditions (Table 4.3).
Discussion
We performed a combination of functional MRI, hormone assessments, and food intake
studies to examine neurophysiological and feeding responses to an acute consumption
of the NNS, sucralose, compared to glucose and water (as a control) in obese and lean
73
young adults. Our studies reveal three important findings. First, obese compared to lean
individuals had greater CBF (a marker of neural activity) in response to sucralose in
brain regions involved in appetite and reward processing. Second, the acute
consumption of sucralose did not stimulate appetite hormones or raise plasma glucose
levels in obese or lean individuals; these data are consistent with prior reports (Steinert
et al., 2011, Meyer-Gerspach et al., 2016; Nichol et al., 2018; others).
While some reports suggest that NNS consumption is associated with paradoxical
increases in food intake and weight gain (Feijo et al., 2013; Pepino et al., 2015; Fowler,
2016; Murray et al., 2016; Nettleton et al., 2016), others have shown that acute NNS
consumption may have little to no direct psychobehavioral impacts on subsequent
energy intake in non-obese individuals (Creze et al., 2018; Fantino et al., 2018)). Our
data suggest that obesity may affect brain and behavioral responses to acute sucralose
consumption and highlight the importance of considering how individual variability may
impact neurophysiological and feeding behaviors in response to NNS consumption.
These findings are relevant to the current conversation surrounding the effects of NNS
use, as recently reported data from NHANES suggest that obese adults are the most
frequent consumers of NNS and low-calorie sweeteners (Sylvetsky et al., 2017).
Recent neuroimaging studies in humans have shown that taste-related activation in the
insula, amygdala, and striatum differ depending on the presence or absence of caloric
value in a sweetened drink (Smeets et al., 2010). Even when calories are present, the
74
human brain is sensitive to a perceived mismatch between sweet taste concentration
and caloric value (Veldhuizen et al., 2017). Likewise, studies in rodents have shown that
when sweet taste flavor is uncoupled form calories it degrades an important flavor-
nutrient association and leads to vulnerabilities to overeating, particularly in rats that are
prone to obesity (Wald & Myers, 2015).
Collectively, these studies suggest that a discrepancy between the intense sweet taste
of NNS and lack of post-ingestive feedback may lead to subsequent compensatory
caloric intake. Our findings are in line with these past reports and provide novel
information in humans suggesting that obese individuals may be particularly vulnerable
to the uncoupling of sweet taste from nutrient signaling that occurs with NNS
consumption.
Using various functional neuroimaging techniques, ours and other groups have
observed a reduction in resting brain activity in homeostatic and hedonic brain regions,
such as the hypothalamus and striatum, after tasting or ingesting glucose in normal-
weight adults but an attenuated response in obese adults (Matsuda et al., 1999; Liu et
al., 2009; Smeets et al., 2005; Page et al., 2013; Luo et al., 2017; van Opstal et al.,
2019). Consistent with these previous findings, following the ingestion of glucose, lean
participants in the present study exhibited a larger drop in CBF (relative to baseline) in
each of our 5 ROIs than obese participants. In contrast, obese compared to lean
individuals showed greater increases in CBF in all ROIs following the acute
consumption of sucralose. In the present study, we used ASL imaging to quantify blood
75
flow to our regions of interest. ASL has particular advantages for studying slower
metabolic responses that occur in response to nutrient ingestion. ASL does not require
task-based or event-related acquisitions, which provides a way to profile brain activity in
response to the ingestion of each drink over a slower time course than a method more
reliant immediate responses to a taste stimulus or a food cue. The temporal stability of
ASL compliments the longer curve of endocrine responses we measured with the blood
draws over the one hour time course. Notably, the amygdala had the largest CBF
response to sucralose in obese participants among all five ROIs, and was area showing
the most robust between-group difference (Figure 4.4). The amygdala has been shown
to activate during presentation of sweet taste (Small et al., 2003) and is consistently
reported to be responsive to NNS (Smeets et al., 2005, Smeets et al., 2010). The
amygdala is particularly important for integrating the internal state and the
environmental cue, which is a crucial piece of a system that biases toward or away from
a stimulus based on the current homeostatic needs of the organism (Burgess et al.,
2016; Janak & Tye, 2015; Baxter & Murray, 2002). We observed increased CBF in the
amygdala in both groups (Figure 4.4), but a significantly larger increase in the obese
group. In the context of our results, it is possible that the increase in CBF to the
amygdala in response to sucralose is due at least in part to its role in evaluating and
updating the reward value of sweet taste relative to the caloric load and post-ingestive
consequences (or lack thereof) that follow. It is possible that in individuals with higher
BMIs, this process is disrupted in the amygdala, which could relate to altered sensitivity
to flavor nutrient associations (Wald & Myers, 2015).
76
The goal of the present study was to examine the effects of acute consumption of
sucralose when consumed in isolation. Our findings are in line with other studies
showing that sucralose consumed in a fasted state and in isolation has no effect on
circulating hormones or plasma glucose levels (Ma et al., 2009; Ford et al., 2011;
Meyer-Gerspach, 2016; Tucker & Tan, 2017; Grotz et al., 2017). It is important to note
that our study design differs from studies in which sucralose is consumed either
alongside or prior to a caloric sugar. For example, Pepino and colleagues reported an
exaggerated plasma glucose and insulin response to an oral glucose load when
consumed after sucralose (compared to water) in obese participants (Pepino et al.,
2013). Diet drink ingestion prior to an oral glucose load yielded similar results to Pepino
et al., along with an enhanced release of GLP-1 (Brown et al., 2009). Pronounced
increases in GLP-1 were also observed when sucralose was consumed during a
standard oral-glucose tolerance test (Temizkan et al., 2015). Thus, the results
presented in this paper should be integrated with the findings from previous work
towards a larger understanding of how NNS may alter neural, behavioral, and endocrine
responses through various patterns of use.
The results presented in this section are preliminary findings with a modest sample size.
Possibly due to the sample size, we were unable to detect a drink by BMI status
interaction effect on CBF response. ASL neuroimaging, which allows a discrete
measurement of blood flow throughout the brain, is ultimately an indirect marker of
77
neural activity. Finally, as with any cross-sectional study, the observed relationship
between BMI status and alterations of CBF or energy intake in response to sucralose
ingestion cannot determine causality; it is possible that obesity precedes these effects,
but it is also possible that these differences contributed to the development of obesity.
In this study, we showed that sucralose, when ingested in isolation, was insufficient to
stimulate circulating appetite hormones or plasma glucose in either obese or lean
individuals. However, brain response to sucralose differed in obese compared to lean
participants. These findings suggest that obese compared to lean individuals have
differential brain activity following the acute consumption of the NNS, sucralose, which
may be of particular import when considering recommending NNS as tools for weight
control.
78
Supplemental Table 4.1 | Baseline and area under the curve (AUC) values of
circulating plasma glucose and hormone responses to glucose, sucralose, and
water ingestion in obese and lean participants
Glucose Drink
Obese (Mean ± SD) Lean (Mean ± SD) t p-value 95% CI
Baseline Plasma Glucose 89.57 ± 10.54 85.14 ± 6.24 1.35 0.19 -2.37, 11.23
AUC Plasma Glucose 7316.54 ± 1639.27 6572.25 ± 716.84 1.49 0.15 -311.05, 1799.62
Baseline Plasma Insulin 23.28 ± 17.00 9.42 ± 4.24 2.96 0.01** 3.85, 23.86
AUC Plasma Insulin 4900.21 ± 2343.31 2895.75 ± 1212.59 2.72 0.01** 455.36, 3553.56
Baseline Plasma GLP-1 12.15 ± 14.86 9.46 ± 18.75 0.42 0.68 -10.48, 15.87
AUC Plasma GLP-1 1434.92 ± 1138.55 1100.35 ± 1215.01 0.71 0.49 -642.92, 1312.06
Baseline Plasma PYY 86.91 ± 59.10 87.98 ± 18.21 -0.06 0.95 -36.21, 34.074
AUC Plasma PYY 5807.89 ± 3613.26 5881.37 ± 1199.07 -0.07 0.95 -2335.91, 2188.97
Baseline Plasma Ghrelin 94.64 ± 43.77 126.73 ± 62.36 -1.58 0.13 -74.18, 10.01
AUC Plasma Ghrelin 4070.35 ± 1891.67 5762.77 ± 3670.26 -1.43 0.17 -4196.71, 811.86
Sucralose Drink
Obese Lean t p-value 95% CI
Baseline Plasma Glucose 88.34 ± 12.72 82.85 ± 5.97 1.46 0.16 -2.389, 13.36
AUC Plasma Glucose 5306.54 ± 659.75 4942.08 ± 294.56 1.81 0.09 -61.47, 790.39
Baseline Plasma Insulin 24.10 ± 18.77 9.54 ± 4.12 2.83 0.01** 3.56, 25.56
AUC Plasma Insulin 1250.07 ± 898.00 448.72 ± 195.32 3.02 0.01** 223.54, 1379.17
Baseline Plasma GLP-1 17.19 ± 20.16 9.84 ± 17.75 1.02 0.32 -7.42, 22.18
AUC Plasma GLP-1 921.56 ± 1056.86 620.61 ± 1198.79 0.65 0.52 -656.69, 1258.59
Baseline Plasma PYY 90.00 ± 55.90 95.56 ± 39.78 -0.30 0.76 -43.45, 32.33
AUC Plasma PYY 5351.28 ± 3516.22 5067.07 ± 2034.08 0.24 0.81 -2183.24, 2751.66
Baseline Plasma Ghrelin 104.92 ± 45.70 121.15 ± 58.70 -0.82 0.42 -57.22, 24.76
AUC Plasma Ghrelin 6508.93 ± 2319.01 8254.75 ± 4094.28 -1.29 0.22 -4606.66, 1115.01
Water Drink
Obese Lean t p-value 95% CI
Baseline Plasma Glucose 91.25 ± 10.69 83.68 ± 4.97 2.13 0.05 -0.04, 15.19
AUC Plasma Glucose 5529.38 ± 516.10 5043.33 ± 278.74 2.37 0.04* 32.64, 939.44
Baseline Plasma Insulin 20.52 ± 16.31 9.68 ± 3.73 2.15 0.05 -0.25, 21.95
AUC Plasma Insulin 1320.94 ± 996.19 496.39 ± 209.07 2.43 0.04* 53, 1596.1
Baseline Plasma GLP-1 10.59 ± 12.83 9.85 ± 17.58 0.11 0.91 -13.04, 14.51
AUC Plasma GLP-1 762.8 ± 864.56 607.72 ± 1097.82 0.33 0.75 -862.32, 1172.47
Baseline Plasma PYY 79.62 ± 63.19 100.45 ± 28.47 -1.00 0.34 -65.68, 24.02
AUC Plasma PYY 5076.56 ± 4044.12 5326.57 1712.56 -0.17 0.87 -3480.14, 2980.12
Baseline Plasma Ghrelin 100.52 ± 39.99 114.86 ± 45.78 -0.78 0.44 -52.61, 23.94
AUC Plasma Ghrelin 6505.36 ± 2443.57 7632.44 ± 2679.38 -0.93 0.37 -3691.33, 1437.15
79
Supplemental Table 4.2 | Within-group comparison of baseline plasma glucose,
insulin, GLP-1, PYY, and ghrelin across the three drink days.
OBESE LEAN
Baseline Plasma Glucose F(2,36)=0.2, p=0.82 F(2,36)=0.56, p=0.58
Baseline Plasma Insulin F(2,36)=0.14, p=0.87 F(2,36)=0.01, p=0.99
Baseline Plasma GLP-1 F(2,36)=0.57, p=0.57 F(2,36)=0.002, p=0.99
Baseline Plasma PYY F(2,36)=0.1, p=0.91 F(2,36)=0.54, p=0.59
Baseline Plasma Ghrelin F(2,36)=0.2, p=0.82 F(2,36)=0.13, p=0.88
80
Supplemental Table 4.3 | Self-reported ratings of hunger and prospective food
intake (at 40 and 60 minutes); sweetness and pleasantness of each drink
(directly after consumption).
Timepoint Rating (1-10) Obese Lean t df p-value
Glucose
Sweetness 7.13 ±1.36 7.45 ±1.29 -0.53 14.80 0.60
Pleasantness 5.17 ±1.98 5.64 ±2.24 -0.61 26.02 0.55
Baseline (0 Min) Hunger 5.20 ±2.21 5.33 ±2.06 -0.17 27.86 0.87
40 Min Hunger 4.93 ±2.22 5.73 ±1.94 -1.05 27.53 0.30
60 Min Hunger 5.53 ±2.10 6.13 ±1.19 -0.96 22.12 0.35
Baseline (0 Min) Prospective Intake 6.33 ±1.45 6.33 ±1.72 0.00 27.22 1.00
40 Min Prospective Intake 5.80 ±2.01 6.27 ±1.39 -0.74 24.89 0.47
60 Min Prospective Intake 6.00 ±1.89 6.27 ±1.22 -0.46 23.97 0.65
Water
Sweetness 2.50 ±1.51 3.91 ±2.43 -1.55 16.72 0.14
Pleasantness 3.67 ±2.10 4.00 ±2.09 -0.39 22.00 0.70
Baseline (0 Min) Hunger 5.67 ±2.06 5.85 ±2.08 -0.22 22.87 0.83
40 Min Hunger 6.50 ±2.35 7.08 ±1.71 -0.70 19.95 0.49
60 Min Hunger 7.58 ±2.15 6.85 ±1.86 0.91 21.89 0.37
Baseline (0 Min) Prospective Intake 7.08 ±1.24 6.92 ±1.61 0.28 22.34 0.78
40 Min Prospective Intake 7.75 ±1.48 6.77 ±1.17 1.83 20.88 0.08
60 Min Prospective Intake 7.42 ±2.27 7.08 ±1.19 0.46 16.29 0.65
Sucralose
Sweetness 5.94 ±1.78 7.09 ±1.64 -1.44 14.45 0.17
Pleasantness 5.17 ±2.12 5.00 ±1.47 0.25 24.99 0.81
Baseline (0 Min) Hunger 4.87 ±2.53 5.33 ±2.55 -0.50 28.00 0.62
40 Min Hunger 5.53 ±2.10 6.13 ±1.68 -0.86 26.74 0.40
60 Min Hunger 6.00 ±2.14 6.53 ±1.68 -0.76 26.55 0.45
Baseline (0 Min) Prospective Intake 6.07 ±1.39 6.93 ±1.62 -1.57 27.33 0.13
40 Min Prospective Intake 6.47 ±1.25 6.73 ±1.10 -0.62 27.58 0.54
60 Min Prospective Intake 6.73 ±1.22 6.93 ±1.10 -0.47 27.69 0.64
81
Part V
Reflection and Final Remarks
Collectively, the work described in this dissertation expands the current knowledge
around the relationship between environmental, metabolic, and neural regulation of
sugar and non-nutritive sweeteners. In Parts II and III, I discuss our work studying
brain, hormone, and behavioral responses to the caloric sugars glucose and sucrose. In
Part II (Dorton et al., 2018), I summarized our findings that, following a glucose preload,
individuals who consumed higher amounts of added dietary sugar exhibited 1)
increased striatal reactivity to visual food cues and 2) smaller levels of circulating
glucagon-like peptide-1 (GLP-1), a hormone that regulates satiety. Additionally, we
observed that GLP-1 release was negatively correlated to neural food-cue reactivity.
The results of this work suggest that individuals who consume high amounts of added
sugar in their regular diet may be more susceptible to environmental cues to eat,
coupled with reduced satiety signaling after eating.
In Part III, we moved to studying a “real-world” sugar, sucrose, otherwise known as
table sugar. These challenging experiments employed arterial spin labeling (pASL) and
involved in-scanner blood sampling and an ad-libitum buffet meal. All participants
displayed differential plasma glucose, insulin, and GLP-1 after consuming sucrose
compared to glucose. However, we found that lean (compared to obese) individuals
may be particularly sensitive to the differences between the two sugars, as these
82
participants showed a larger overall cerebrometabolic response, a vastly different profile
of circulating satiety hormones over two hours, and increased food intake after sucrose
compared to glucose. Interestingly, insulin sensitivity, independent of BMI or sex, was
positively correlated with neural response to glucose, but not sucrose. Taken together,
these findings begin to relate the neural, endocrine, and behavioral responses to
different types of carbohydrates—namely sucrose, which is widely consumed and may
be detrimental to metabolic health.
Finally, in Part IV, I discuss findings that relate to the non-nutritive sweetener sucralose.
Once again shifting towards studying a “real-world” substance, we sought to determine
whether sucralose was sufficient to differentially stimulate appetite hormones or
cerebral blood flow compared to glucose in obese and lean individuals. We found that
while sucralose did not stimulate insulin, GLP-1, plasma glucose, or ghrelin in either
group, obese participants displayed increased cerebral blood flow after sucralose
compared to glucose. These results suggest that, independent of metabolic response,
sucralose may stimulate areas of the brain that may lead to alterations in eating
behavior, though future work in this direction is required.
The projects described in this dissertation have been equal parts challenging,
fascinating, and gratifying for me. I hope that the work I’ve done during my time at USC
and the work that continues in the Page Lab contributes positively towards effective
scientific, public health, and policy outcomes that ensure a healthier world for all.
83
References
Abbott, C. R., Monteiro, M., Small, C. J., Sajedi, A., Smith, K. L., Parkinson, J. R. C., … Bloom,
S. R. (2005). The inhibitory effects of peripheral administration of peptide YY3–36 and
glucagon-like peptide-1 on food intake are attenuated by ablation of the vagal–brainstem–
hypothalamic pathway. Brain Research, 1044(1), 127–131.
https://doi.org/10.1016/j.brainres.2005.03.011
Aitken, T. J., Greenfield, V. Y., & Wassum, K. M. (2016). Nucleus accumbens core dopamine
signaling tracks the need-based motivational value of food-paired cues. Journal of
Neurochemistry, 136(5), 1026–1036. https://doi.org/10.1111/jnc.13494
Anderson, G. H., & Woodend, D. (2003). Effect of Glycemic Carbohydrates on Short-term
Satiety and Food Intake. Nutrition Reviews, 61(suppl_5), S17–S26.
https://doi.org/10.1301/nr.2003.may.S17-S26
Anton, S. D., Martin, C. K., Han, H., Coulon, S., Cefalu, W. T., Geiselman, P., & Williamson, D.
A. (2010). Effects of stevia, aspartame, and sucrose on food intake, satiety, and
postprandial glucose and insulin levels. Appetite, 55(1), 37–43.
https://doi.org/10.1016/j.appet.2010.03.009
Avena, N. M., Rada, P., & Hoebel, B. G. (2008). Evidence for sugar addiction: Behavioral and
neurochemical effects of intermittent, excessive sugar intake. Neuroscience and
Biobehavioral Reviews, 32(1), 20–39. https://doi.org/10.1016/j.neubiorev.2007.04.019
Bantle, J. P., Laine, D. C., Castle, G. W., Thomas, J. W., Hoogwerf, B. J., & Goetz, F. C. (1983).
Postprandial Glucose and Insulin Responses to Meals Containing Different Carbohydrates
in Normal and Diabetic Subjects. New England Journal of Medicine, 309(1), 7–12.
https://doi.org/10.1056/NEJM198307073090102
84
Barrios-Correa, A. A., Estrada, J. A., Martel, C., Olivier, M., López-Santiago, R., & Contreras, I.
(2018). Chronic Intake of Commercial Sweeteners Induces Changes in Feeding Behavior
and Signaling Pathways Related to the Control of Appetite in BALB/c Mice [Research
article]. https://doi.org/10.1155/2018/3628121
Basciano, H., Federico, L., & Adeli, K. (2005). Fructose, insulin resistance, and metabolic
dyslipidemia. Nutrition & Metabolism, 2(1), 5. https://doi.org/10.1186/1743-7075-2-5
Bello, N. T., Sweigart, K. L., Lakoski, J. M., Norgren, R., & Hajnal, A. (2003). Restricted feeding
with scheduled sucrose access results in an upregulation of the rat dopamine transporter.
American Journal of Physiology - Regulatory, Integrative and Comparative Physiology,
284(5), R1260–R1268. https://doi.org/10.1152/ajpregu.00716.2002
Berthoud, H.-R. (2008). The vagus nerve, food intake and obesity. Regulatory Peptides, 149(1),
15–25. https://doi.org/10.1016/j.regpep.2007.08.024
Bessesen, D. H. (2001). The Role of Carbohydrates in Insulin Resistance. The Journal of
Nutrition, 131(10), 2782S-2786S. https://doi.org/10.1093/jn/131.10.2782S
Biro, G., Hulshof, K. F. A. M., Ovesen, L., & Amorim Cruz, J. A. (2002). Selection of
methodology to assess food intake. European Journal of Clinical Nutrition; London, 56(S2),
S25-32. http://dx.doi.org/10.1038/sj.ejcn.1601426
Bloemendaal, L. van, IJzerman, R. G., Kulve, J. S. ten, Barkhof, F., Konrad, R. J., Drent, M.
L., … Diamant, M. (2014). GLP-1 Receptor Activation Modulates Appetite- and Reward-
Related Brain Areas in Humans. Diabetes, 63(12), 4186–4196.
https://doi.org/10.2337/db14-0849
85
Bray, G. A., & Popkin, B. M. (2014). Dietary Sugar and Body Weight: Have We Reached a Crisis
in the Epidemic of Obesity and Diabetes? Diabetes Care, 37(4), 950–956.
https://doi.org/10.2337/dc13-2085
Brown, R. J., Walter, M., & Rother, K. I. (2009). Ingestion of Diet Soda Before a Glucose Load
Augments Glucagon-Like Peptide-1 Secretion. Diabetes Care, 32(12), 2184–2186.
https://doi.org/10.2337/dc09-1185
Burger, K. S., & Stice, E. (2013). Elevated energy intake is correlated with hyperresponsivity in
attentional, gustatory, and reward brain regions while anticipating palatable food receipt.
The American Journal of Clinical Nutrition, 97(6), 1188–1194.
https://doi.org/10.3945/ajcn.112.055285
Carey, D. G., Jenkins, A. B., Campbell, L. V., Freund, J., & Chisholm, D. J. (1996). Abdominal
Fat and Insulin Resistance in Normal and Overweight Women: Direct Measurements
Reveal a Strong Relationship in Subjects at Both Low and High Risk of NIDDM. Diabetes,
45(5), 633–638. https://doi.org/10.2337/diab.45.5.633
Carnell, S., Gibson, C., Benson, L., Ochner, C. N., & Geliebter, A. (2012). Neuroimaging and
obesity: Current knowledge and future directions. Obesity Reviews, 13(1), 43–56.
https://doi.org/10.1111/j.1467-789X.2011.00927.x
Chappell, M. A., Groves, A. R., Whitcher, B., & Woolrich, M. W. (2009). Variational Bayesian
Inference for a Nonlinear Forward Model. Trans. Sig. Proc., 57(1), 223–236.
https://doi.org/10.1109/TSP.2008.2005752
Colantuoni, C., Rada, P., McCarthy, J., Patten, C., Avena, N. M., Chadeayne, A., & Hoebel, B.
G. (2002). Evidence That Intermittent, Excessive Sugar Intake Causes Endogenous Opioid
Dependence. Obesity Research, 10(6), 478–488. https://doi.org/10.1038/oby.2002.66
86
Committee, D. G. A., & others. (2015). Scientific report of the 2015 dietary guidelines advisory
committee. Washington (DC): USDA and US Department of Health and Human Services.
Cornier, M.-A., Melanson, E. L., Salzberg, A. K., Bechtell, J. L., & Tregellas, J. R. (2012). The
effects of exercise on the neuronal response to food cues. Physiology & Behavior, 105(4),
1028–1034. https://doi.org/10.1016/j.physbeh.2011.11.023
Crézé, C., Candal, L., Cros, J., Knebel, J.-F., Seyssel, K., Stefanoni, N., … Toepel, U. (2018).
The Impact of Caloric and Non-Caloric Sweeteners on Food Intake and Brain Responses to
Food: A Randomized Crossover Controlled Trial in Healthy Humans. Nutrients, 10(5), 615.
https://doi.org/10.3390/nu10050615
Davidson, T. L., & Jarrard, L. E. (1993). A role for hippocampus in the utilization of hunger
signals. Behavioral and Neural Biology, 59(2), 167–171.
Davidson, Terry L., Martin, A. A., Clark, K., & Swithers, S. E. (2011). Intake of High-Intensity
Sweeteners Alters the Ability of Sweet Taste to Signal Caloric Consequences: Implications
for the Learned Control of Energy and Body Weight Regulation. Quarterly Journal of
Experimental Psychology, 64(7), 1430–1441.
https://doi.org/10.1080/17470218.2011.552729
DeFronzo, R. A., & Ferrannini, E. (1991). Insulin Resistance: A Multifaceted Syndrome
Responsible for NIDDM, Obesity, Hypertension, Dyslipidemia, and Atherosclerotic
Cardiovascular Disease. Diabetes Care, 14(3), 173–194.
https://doi.org/10.2337/diacare.14.3.173
87
De Silva, A., Salem, V., Long, C. J., Makwana, A., Newbould, R. D., Rabiner, E. A., … Dhillo, W.
S. (2011). The Gut Hormones PYY3-36 and GLP-17-36 amide Reduce Food Intake and
Modulate Brain Activity in Appetite Centers in Humans. Cell Metabolism, 14(5), 700–706.
https://doi.org/10.1016/j.cmet.2011.09.010
Detre, J. A., & Wang, J. (2002). Technical aspects and utility of fMRI using BOLD and ASL.
Clinical Neurophysiology, 113(5), 621–634. https://doi.org/10.1016/S1388-2457(02)00038-X
Dickson, S. L., Shirazi, R. H., Hansson, C., Bergquist, F., Nissbrandt, H., & Skibicka, K. P.
(2012). The Glucagon-Like Peptide 1 (GLP-1) Analogue, Exendin-4, Decreases the
Rewarding Value of Food: A New Role for Mesolimbic GLP-1 Receptors. Journal of
Neuroscience, 32(14), 4812–4820. https://doi.org/10.1523/JNEUROSCI.6326-11.2012
Dorton, H. M., Luo, S., Monterosso, J. R., & Page, K. A. (2018). Influences of Dietary Added
Sugar Consumption on Striatal Food-Cue Reactivity and Postprandial GLP-1 Response.
Frontiers in Psychiatry, 8. https://doi.org/10.3389/fpsyt.2017.00297
Dye, L., & Blundell, J. E. (1997). Menstrual cycle and appetite control: Implications for weight
regulation. Human Reproduction (Oxford, England), 12(6), 1142–1151.
https://doi.org/10.1093/humrep/12.6.1142
Elmquist, J. K., Elias, C. F., & Saper, C. B. (1999). From lesions to leptin: Hypothalamic control
of food intake and body weight. Neuron, 22(2), 221–232. https://doi.org/10.1016/s0896-
6273(00)81084-3
88
Erickson, J., Sadeghirad, B., Lytvyn, L., Slavin, J., & Johnston, B. C. (2017). The Scientific Basis
of Guideline Recommendations on Sugar Intake: A Systematic Review. Annals of Internal
Medicine, 166(4), 257. https://doi.org/10.7326/M16-2020
Ervin, R. B., & Ogden, C. L. (2013). Consumption of added sugars among U.S. adults, 2005-
2010. NCHS Data Brief, (122), 1–8.
Evero, N., Hackett, L. C., Clark, R. D., Phelan, S., & Hagobian, T. A. (2012). Aerobic exercise
reduces neuronal responses in food reward brain regions. Journal of Applied Physiology,
112(9), 1612–1619. https://doi.org/10.1152/japplphysiol.01365.2011
Fantino, M., Fantino, A., Matray, M., & Mistretta, F. (2018). Beverages containing low energy
sweeteners do not differ from water in their effects on appetite, energy intake and food
choices in healthy, non-obese French adults. Appetite, 125, 557–565.
https://doi.org/10.1016/j.appet.2018.03.007
Feijó, F. de M., Ballard, C. R., Foletto, K. C., Batista, B. A. M., Neves, A. M., Ribeiro, M. F. M., &
Bertoluci, M. C. (2013). Saccharin and aspartame, compared with sucrose, induce greater
weight gain in adult Wistar rats, at similar total caloric intake levels. Appetite, 60, 203–207.
https://doi.org/10.1016/j.appet.2012.10.009
Feskanich, D., Sielaff, B. H., Chong, K., & Buzzard, I. M. (1989). Computerized collection and
analysis of dietary intake information. Computer Methods and Programs in Biomedicine,
30(1), 47–57.
Ford, H. E., Peters, V., Martin, N. M., Sleeth, M. L., Ghatei, M. A., Frost, G. S., & Bloom, S. R.
(2011). Effects of oral ingestion of sucralose on gut hormone response and appetite in
healthy normal-weight subjects. European Journal of Clinical Nutrition, 65(4), 508–513.
https://doi.org/10.1038/ejcn.2010.291
89
Forloni, G. L., Consolo, S., Grombi, P., Wang, J. X., Mennini, T., & Ladinsky, H. (1983).
Modifications in recognition sites for neurotransmitters in rat hippocampus by kainic acid
lesion. Brain Research, 274(1), 165–170. https://doi.org/10.1016/0006-8993(83)90534-6
Fowler, S. P. G. (2016). Low-calorie sweetener use and energy balance: Results from
experimental studies in animals, and large-scale prospective studies in humans. Physiology
& Behavior, 164(Pt B), 517–523. https://doi.org/10.1016/j.physbeh.2016.04.047
Gardner, C. (2014). Non-nutritive sweeteners: Evidence for benefit vs. risk. Current Opinion in
Lipidology, 25(1), 80–84. https://doi.org/10.1097/MOL.0000000000000034
Gardner, C., Wylie-Rosett, J., Gidding, S. S., Steffen, L. M., Johnson, R. K., Reader, D., &
Lichtenstein, A. H. (2012, August 1). Nonnutritive sweeteners: Current use and health
perspectives: a scientific statement from the American Heart Association and the American
Diabetes Association. Retrieved January 8, 2019, from Diabetes Care website:
http://link.galegroup.com/apps/doc/A299885927/AONE?sid=googlescholar
Glendinning, J. I. (2016). Do low-calorie sweeteners promote weight gain in rodents? Physiology
& Behavior, 164, 509–513. https://doi.org/10.1016/j.physbeh.2016.01.043
Göke, R., Larsen, P. J., Mikkelsen, J. D., & Sheikh, S. P. (1995). Distribution of GLP-1 Binding
Sites in the Rat Brain: Evidence that Exendin-4 is a Ligand of Brain GLP-1 Binding Sites.
European Journal of Neuroscience, 7(11), 2294–2300. https://doi.org/10.1111/j.1460-
9568.1995.tb00650.x
Goldstone, A. P., Prechtl de Hernandez, C. G., Beaver, J. D., Muhammed, K., Croese, C., Bell,
G., … Bell, J. D. (2009). Fasting biases brain reward systems towards high-calorie foods.
European Journal of Neuroscience, 30(8), 1625–1635. https://doi.org/10.1111/j.1460-
9568.2009.06949.x
90
Grimm, J. W., Fyall, A. M., & Osincup, D. P. (2005). Incubation of sucrose craving: Effects of
reduced training and sucrose pre-loading. Physiology & Behavior, 84(1), 73–79.
https://doi.org/10.1016/j.physbeh.2004.10.011
Groves, A. R., Chappell, M. A., & Woolrich, M. W. (2009). Combined spatial and non-spatial
prior for inference on MRI time-series. NeuroImage, 45(3), 795–809.
https://doi.org/10.1016/j.neuroimage.2008.12.027
Hajnal, A. C., & Norgren, R. (2002). Repeated access to sucrose augments dopamine turnover
in the nucleus accumbens. [Miscellaneous Article]. Neuroreport, 13(17), 2213–2216.
Hales, C. M., Carroll, M. D., Fryar, C. D., & Ogden, C. L. (2017). Prevalence of obesity among
adults and youth: United States, 2015–2016. Retrieved from
https://stacks.cdc.gov/view/cdc/49223
Harnack, P. L. (2013). Nutrition Data System for Research (NDSR). In M. D. Gellman & J. R.
Turner (Eds.), Encyclopedia of Behavioral Medicine (pp. 1348–1350).
https://doi.org/10.1007/978-1-4419-1005-9_1683
Hayes, M. R., & Schmidt, H. D. (2016). GLP-1 influences food and drug reward. Current Opinion
in Behavioral Sciences, 9, 66–70. https://doi.org/10.1016/j.cobeha.2016.02.005
Hill, J. O., & Peters, J. C. (1998). Environmental Contributions to the Obesity Epidemic. Science,
280(5368), 1371–1374. https://doi.org/10.1126/science.280.5368.1371
Hill, S. E., Prokosch, M. L., Morin, A., & Rodeheffer, C. D. (2014). The effect of non-caloric
sweeteners on cognition, choice, and post-consumption satisfaction. Appetite, 83, 82–88.
https://doi.org/10.1016/j.appet.2014.08.003
91
Hoebel, B. G., Avena, N. M., Bocarsly, M. E., & Rada, P. (2009). A Behavioral and Circuit Model
Based on Sugar Addiction in Rats. Journal of Addiction Medicine, 3(1), 33–41.
https://doi.org/10.1097/ADM.0b013e31819aa621
Hsu, T. M., Hahn, J. D., Konanur, V. R., Lam, A., & Kanoski, S. E. (2015). Hippocampal GLP-1
Receptors Influence Food Intake, Meal Size, and Effort-Based Responding for Food
through Volume Transmission. Neuropsychopharmacology, 40(2), 327–337.
https://doi.org/10.1038/npp.2014.175
Hsu, T. M., Konanur, V. R., Taing, L., Usui, R., Kayser, B. D., Goran, M. I., & Kanoski, S. E.
(2015). Effects of sucrose and high fructose corn syrup consumption on spatial memory
function and hippocampal neuroinflammation in adolescent rats. Hippocampus, 25(2), 227–
239. https://doi.org/10.1002/hipo.22368
Hu, F. B., & Malik, V. S. (2010). Sugar-sweetened beverages and risk of obesity and type 2
diabetes: Epidemiologic evidence. Physiology & Behavior, 100(1), 47–54.
https://doi.org/10.1016/j.physbeh.2010.01.036
Huppert, T. J., Hoge, R. D., Diamond, S. G., Franceschini, M. A., & Boas, D. A. (2006). A
temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in
adult humans. NeuroImage, 29(2), 368–382.
https://doi.org/10.1016/j.neuroimage.2005.08.065
Jastreboff, A. M., Sinha, R., Arora, J., Giannini, C., Kubat, J., Malik, S., … Caprio, S. (2016).
Altered Brain Response to Drinking Glucose and Fructose in Obese Adolescents. Diabetes,
65(7), 1929–1939. https://doi.org/10.2337/db15-1216
92
Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012). FSL.
NeuroImage, 62(2), 782–790. https://doi.org/10.1016/j.neuroimage.2011.09.015
Johnson, R. K., Appel, L. J., Brands, M., Howard, B. V., Lefevre, M., Lustig, R. H., … Wylie-
Rosett, J. (2009). Dietary Sugars Intake and Cardiovascular Health. Circulation, 120(11),
1011–1020. https://doi.org/10.1161/CIRCULATIONAHA.109.192627
JOHNSON, R. K., DRISCOLL, P., & GORAN, M. I. (1996). Comparison of Multiple-Pass 24-
Hour Recall Estimates of Energy Intake With Total Energy Expenditure Determined By the
Doubly Labeled Water Method in Young Children. Journal of the American Dietetic
Association, 96(11), 1140–1144. https://doi.org/10.1016/S0002-8223(96)00293-3
Kong, M. F., Chapman, I., Goble, E., Wishart, J., Wittert, G., Morris, H., & Horowitz, M. (1999).
Effects of oral fructose and glucose on plasma GLP-1 and appetite in normal subjects.
Peptides, 20(5), 545–551. https://doi.org/10.1016/s0196-9781(99)00006-6
Krieger, J.-P., Arnold, M., Pettersen, K. G., Lossel, P., Langhans, W., & Lee, S. J. (2016).
Knockdown of GLP-1 Receptors in Vagal Afferents Affects Normal Food Intake and
Glycemia. Diabetes, 65(1), 34–43. https://doi.org/10.2337/db15-0973
Krishnan, S., Tryon, R. R., Horn, W. F., Welch, L., & Keim, N. L. (2016). Estradiol, SHBG and
leptin interplay with food craving and intake across the menstrual cycle. Physiology &
Behavior, 165, 304–312. https://doi.org/10.1016/j.physbeh.2016.08.010
Kroemer, N. B., Krebs, L., Kobiella, A., Grimm, O., Vollstädt-Klein, S., Wolfensteller, U., …
Smolka, M. N. (2013). (Still) longing for food: Insulin reactivity modulates response to food
pictures. Human Brain Mapping, 34(10), 2367–2380. https://doi.org/10.1002/hbm.22071
93
la Fleur, S. E., Luijendijk, M. C. M., van Rozen, A. J., Kalsbeek, A., & Adan, R. a. H. (2011). A
free-choice high-fat high-sugar diet induces glucose intolerance and insulin
unresponsiveness to a glucose load not explained by obesity. International Journal of
Obesity, 35(4), 595–604. https://doi.org/10.1038/ijo.2010.164
Lake, A., & Townshend, T. (2006). Obesogenic environments: Exploring the built and food
environments. The Journal of the Royal Society for the Promotion of Health, 126(6), 262–
267. https://doi.org/10.1177/1466424006070487
Lakhan, S. E., & Kirchgessner, A. (2013). The emerging role of dietary fructose in obesity and
cognitive decline. Nutrition Journal, 12(1), 114. https://doi.org/10.1186/1475-2891-12-114
Lê, K., & Tappy, L. (2006). Metabolic effects of fructose. Current Opinion in Clinical Nutrition and
Metabolic Care, 9(4), 469–475. https://doi.org/10.1097/01.mco.0000232910.61612.4d
Liu, Y., Gao, J. H., Liu, H. L., & Fox, P. T. (2000). The temporal response of the brain after
eating revealed by functional MRI. Nature, 405(6790), 1058–1062.
https://doi.org/10.1038/35016590
Lobach, A. R., Roberts, A., & Rowland, I. R. (2019). Assessing the in vivo data on low/no-calorie
sweeteners and the gut microbiota. Food and Chemical Toxicology, 124, 385–399.
https://doi.org/10.1016/j.fct.2018.12.005
Luo, S., Melrose, A. J., Dorton, H., Alves, J., Monterosso, J. R., & Page, K. A. (2017). Resting
state hypothalamic response to glucose predicts glucose-induced attenuation in the ventral
striatal response to food cues. Appetite, 116, 464–470.
https://doi.org/10.1016/j.appet.2017.05.038
94
Luo, S., Monterosso, J. R., Sarpelleh, K., & Page, K. A. (2015). Differential effects of fructose
versus glucose on brain and appetitive responses to food cues and decisions for food
rewards. Proceedings of the National Academy of Sciences, 112(20), 6509–6514.
https://doi.org/10.1073/pnas.1503358112
Luo, S., Romero, A., Adam, T. C., Hu, H. H., Monterosso, J., & Page, K. A. (2013). Abdominal
fat is associated with a greater brain reward response to high-calorie food cues in hispanic
women. Obesity, 21(10), 2029–2036. https://doi.org/10.1002/oby.20344
Ma, J., Bellon, M., Wishart, J. M., Young, R., Blackshaw, L. A., Jones, K. L., … Rayner, C. K.
(2009). Effect of the artificial sweetener, sucralose, on gastric emptying and incretin
hormone release in healthy subjects. American Journal of Physiology-Gastrointestinal and
Liver Physiology, 296(4), G735–G739. https://doi.org/10.1152/ajpgi.90708.2008
Magnuson, B. A., Roberts, A., & Nestmann, E. R. (2017). Critical review of the current literature
on the safety of sucralose. Food and Chemical Toxicology, 106, 324–355.
https://doi.org/10.1016/j.fct.2017.05.047
Malik, V. S., Schulze, M. B., & Hu, F. B. (2006). Intake of sugar-sweetened beverages and
weight gain: A systematic review. The American Journal of Clinical Nutrition, 84(2), 274–
288.
Malik, V. S., Willett, W. C., & Hu, F. B. (2013). Global obesity: Trends, risk factors and policy
implications. Nature Reviews Endocrinology, 9(1), 13–27.
https://doi.org/10.1038/nrendo.2012.199
Marks, J. L., Porte, D., Stahl, W. L., & Baskin, D. G. (1990). Localization of insulin receptor
mRNA in rat brain by in situ hybridization. Endocrinology, 127(6), 3234–3236.
https://doi.org/10.1210/endo-127-6-3234
95
Martin, L. E., Holsen, L. M., Chambers, R. J., Bruce, A. S., Brooks, W. M., Zarcone, J. R., …
Savage, C. R. (2010). Neural Mechanisms Associated With Food Motivation in Obese and
Healthy Weight Adults. Obesity, 18(2), 254–260. https://doi.org/10.1038/oby.2009.220
Matsuda, M., Liu, Y., Mahankali, S., Pu, Y., Mahankali, A., Wang, J., … Gao, J. H. (1999).
Altered hypothalamic function in response to glucose ingestion in obese humans. Diabetes,
48(9), 1801–1806. https://doi.org/10.2337/diabetes.48.9.1801
McClernon, F. J., Kozink, R. V., Lutz, A. M., & Rose, J. E. (2009). 24-h smoking abstinence
potentiates fMRI-BOLD activation to smoking cues in cerebral cortex and dorsal striatum.
Psychopharmacology, 204(1), 25–35. https://doi.org/10.1007/s00213-008-1436-9
Merchenthaler, I., Lane, M., & Shughrue, P. (1999). Distribution of pre-pro-glucagon and
glucagon-like peptide-1 receptor messenger RNAs in the rat central nervous system. The
Journal of Comparative Neurology, 403(2), 261–280. https://doi.org/10.1002/(SICI)1096-
9861(19990111)403:2<261::AID-CNE8>3.0.CO;2-5
Mitsutomi, K., Masaki, T., Shimasaki, T., Gotoh, K., Chiba, S., Kakuma, T., & Shibata, H. (2014).
Effects of a nonnutritive sweetener on body adiposity and energy metabolism in mice with
diet-induced obesity. Metabolism, 63(1), 69–78.
https://doi.org/10.1016/j.metabol.2013.09.002
Monteiro, M. P., & Batterham, R. L. (2017). The Importance of the Gastrointestinal Tract in
Controlling Food Intake and Regulating Energy Balance. Gastroenterology, 152(7), 1707-
1717.e2. https://doi.org/10.1053/j.gastro.2017.01.053
96
Musselman, L. P., Fink, J. L., Narzinski, K., Ramachandran, P. V., Hathiramani, S. S., Cagan, R.
L., & Baranski, T. J. (2011). A high-sugar diet produces obesity and insulin resistance in
wild-type Drosophila. Disease Models & Mechanisms, 4(6), 842–849.
https://doi.org/10.1242/dmm.007948
Myers, K. P., & Sclafani, A. (2006). Development of learned flavor preferences. Developmental
Psychobiology, 48(5), 380–388. https://doi.org/10.1002/dev.20147
Nettleton, J. E., Reimer, R. A., & Shearer, J. (2016). Reshaping the gut microbiota: Impact of low
calorie sweeteners and the link to insulin resistance? Physiology & Behavior, 164, 488–493.
https://doi.org/10.1016/j.physbeh.2016.04.029
Nichol, A. D., Holle, M. J., & An, R. (2018). Glycemic impact of non-nutritive sweeteners: A
systematic review and meta-analysis of randomized controlled trials. European Journal of
Clinical Nutrition, 72(6), 796. https://doi.org/10.1038/s41430-018-0170-6
Nummenmaa, L., Hirvonen, J., Hannukainen, J. C., Immonen, H., Lindroos, M. M., Salminen, P.,
& Nuutila, P. (2012). Dorsal Striatum and Its Limbic Connectivity Mediate Abnormal
Anticipatory Reward Processing in Obesity. PLOS ONE, 7(2), e31089.
https://doi.org/10.1371/journal.pone.0031089
Ochoa, M., Lallès, J.-P., Malbert, C.-H., & Val-Laillet, D. (2015). Dietary sugars: Their detection
by the gut–brain axis and their peripheral and central effects in health and diseases.
European Journal of Nutrition, 54, 1–24. https://doi.org/10.1007/s00394-014-0776-y
Ochoa, M., Malbert, C.-H., Meurice, P., & Val-Laillet, D. (2016). Effects of Chronic Consumption
of Sugar-Enriched Diets on Brain Metabolism and Insulin Sensitivity in Adult Yucatan
Minipigs. PLOS ONE, 11(9), e0161228. https://doi.org/10.1371/journal.pone.0161228
97
Olsen, N. J., & Heitmann, B. L. (2009). Intake of calorically sweetened beverages and obesity.
Obesity Reviews, 10(1), 68–75. https://doi.org/10.1111/j.1467-789X.2008.00523.x
Ouyang, X., Cirillo, P., Sautin, Y., McCall, S., Bruchette, J. L., Diehl, A. M., … Abdelmalek, M. F.
(2008). Fructose consumption as a risk factor for non-alcoholic fatty liver disease. Journal of
Hepatology, 48(6), 993–999. https://doi.org/10.1016/j.jhep.2008.02.011
Page, K. A., Chan, O., Arora, J., Belfort-DeAguiar, R., Dzuira, J., Roehmholdt, B., … Sherwin, R.
S. (2013). Effects of Fructose vs Glucose on Regional Cerebral Blood Flow in Brain
Regions Involved With Appetite and Reward Pathways. JAMA, 309(1), 63–70.
https://doi.org/10.1001/jama.2012.116975
Page, K. A., & Melrose, A. J. (2016). Brain, hormone and appetite responses to glucose versus
fructose. Current Opinion in Behavioral Sciences, 9(Supplement C), 111–117.
https://doi.org/10.1016/j.cobeha.2016.03.002
Page, K. A., Seo, D., Belfort-DeAguiar, R., Lacadie, C., Dzuira, J., Naik, S., … Sinha, R. (2011).
Circulating glucose levels modulate neural control of desire for high-calorie foods in
humans. The Journal of Clinical Investigation, 121(10), 4161–4169.
https://doi.org/10.1172/JCI57873
Palmiter, R. D. (2008). Dopamine signaling in the dorsal striatum is essential for motivated
behaviors: Lessons from dopamine-deficient mice. Annals of the New York Academy of
Sciences, 1129, 35–46. https://doi.org/10.1196/annals.1417.003
Pelchat, M. L., Johnson, A., Chan, R., Valdez, J., & Ragland, J. D. (2004). Images of desire:
Food-craving activation during fMRI. NeuroImage, 23(4), 1486–1493.
https://doi.org/10.1016/j.neuroimage.2004.08.023
98
Pepino, M. Y. (2015). Metabolic effects of non-nutritive sweeteners. Physiology & Behavior, 152,
450–455. https://doi.org/10.1016/j.physbeh.2015.06.024
Pepino, M. Y., Tiemann, C. D., Patterson, B. W., Wice, B. M., & Klein, S. (2013). Sucralose
Affects Glycemic and Hormonal Responses to an Oral Glucose Load. Diabetes Care,
DC_122221. https://doi.org/10.2337/dc12-2221
Peters, J. C., & Beck, J. (2016). Low Calorie Sweetener (LCS) use and energy balance.
Physiology & Behavior, 164, 524–528. https://doi.org/10.1016/j.physbeh.2016.03.024
Piech, R. M., Pastorino, M. T., & Zald, D. H. (2010). All I saw was the cake. Hunger effects on
attentional capture by visual food cues. Appetite, 54(3), 579–582.
https://doi.org/10.1016/j.appet.2009.11.003
Popkin, B. M., & Nielsen, S. J. (2003). The Sweetening of the World’s Diet. Obesity Research,
11(11), 1325–1332. https://doi.org/10.1038/oby.2003.179
Pritchett, C. E., & Hajnal, A. (2012). Glucagon-Like Peptide-1 Regulation of Carbohydrate Intake
Is Differentially Affected by Obesogenic Diets. Obesity, 20(2), 313–317.
https://doi.org/10.1038/oby.2011.342
Richard, J. E., Anderberg, R. H., Göteson, A., Gribble, F. M., Reimann, F., & Skibicka, K. P.
(2015). Activation of the GLP-1 Receptors in the Nucleus of the Solitary Tract Reduces
Food Reward Behavior and Targets the Mesolimbic System. PLOS ONE, 10(3), e0119034.
https://doi.org/10.1371/journal.pone.0119034
Richards, P., Pais, R., Habib, A. M., Brighton, C. A., Yeo, G. S. H., Reimann, F., & Gribble, F. M.
(2016). High fat diet impairs the function of glucagon-like peptide-1 producing L-cells.
Peptides, 77, 21–27. https://doi.org/10.1016/j.peptides.2015.06.006
99
Rogers, P. J., Hogenkamp, P. S., de Graaf, C., Higgs, S., Lluch, A., Ness, A. R., … Mela, D. J.
(2016). Does low-energy sweetener consumption affect energy intake and body weight? A
systematic review, including meta-analyses, of the evidence from human and animal
studies. International Journal of Obesity, 40(3), 381–394.
https://doi.org/10.1038/ijo.2015.177
Rothemund, Y., Preuschhof, C., Bohner, G., Bauknecht, H.-C., Klingebiel, R., Flor, H., & Klapp,
B. F. (2007). Differential activation of the dorsal striatum by high-calorie visual food stimuli
in obese individuals. NeuroImage, 37(2), 410–421.
https://doi.org/10.1016/j.neuroimage.2007.05.008
Rother, K. I., Conway, E. M., & Sylvetsky, A. C. (2018). How Non-nutritive Sweeteners Influence
Hormones and Health. Trends in Endocrinology & Metabolism, 29(7), 455–467.
https://doi.org/10.1016/j.tem.2018.04.010
Rudenga, K. J., & Small, D. M. (2012). Amygdala response to sucrose consumption is inversely
related to artificial sweetener use. Appetite, 58(2), 504–507.
https://doi.org/10.1016/j.appet.2011.12.001
Sample, C. H., Martin, A. A., Jones, S., Hargrave, S. L., & Davidson, T. L. (2015). Western-style
diet impairs stimulus control by food deprivation state cues: Implications for obesogenic
environments. Appetite, 93, 13–23. https://doi.org/10.1016/j.appet.2015.05.018
Schur, E., Kleinhans, N., Goldberg, J., Buchwald, D., Schwartz, M., & Maravilla, K. (2009).
Activation in brain energy regulation and reward centers by food cues varies with choice of
visual stimulus. International Journal of Obesity (2005), 33(6), 653–661.
https://doi.org/10.1038/ijo.2009.56
100
Sclafani, A., Zukerman, S., & Ackroff, K. (2014). Fructose- and glucose-conditioned preferences
in FVB mice: Strain differences in post-oral sugar appetition. American Journal of
Physiology-Regulatory, Integrative and Comparative Physiology, 307(12), R1448–R1457.
https://doi.org/10.1152/ajpregu.00312.2014
Skibicka, K. P. (2013). The central GLP-1: Implications for food and drug reward. Frontiers in
Neuroscience, 7, 181. https://doi.org/10.3389/fnins.2013.00181
Sloth, B., Holst, J. J., Flint, A., Gregersen, N. T., & Astrup, A. (2007). Effects of PYY
1–36
and
PYY
3–36
on appetite, energy intake, energy expenditure, glucose and fat metabolism in
obese and lean subjects. American Journal of Physiology - Endocrinology and Metabolism,
292(4), E1062–E1068. https://doi.org/10.1152/ajpendo.00450.2006
Small, D. M., Jones-Gotman, M., & Dagher, A. (2003). Feeding-induced dopamine release in
dorsal striatum correlates with meal pleasantness ratings in healthy human volunteers.
NeuroImage, 19(4), 1709–1715. https://doi.org/10.1016/S1053-8119(03)00253-2
Smeets, P. A., de Graaf, C., Stafleu, A., van Osch, M. J., & van der Grond, J. (2005). Functional
magnetic resonance imaging of human hypothalamic responses to sweet taste and calories.
The American Journal of Clinical Nutrition, 82(5), 1011–1016.
https://doi.org/10.1093/ajcn/82.5.1011
Smeets, P. A. M., Dagher, A., Hare, T. A., Kullmann, S., van der Laan, L. N., Poldrack, R. A., …
Veldhuizen, M. G. (2019). Good practice in food-related neuroimaging. The American
Journal of Clinical Nutrition, 109(3), 491–503. https://doi.org/10.1093/ajcn/nqy344
101
Smeets, P. A. M., Vidarsdottir, S., de Graaf, C., Stafleu, A., van Osch, M. J. P., Viergever, M.
A., … van der Grond, J. (2007). Oral glucose intake inhibits hypothalamic neuronal activity
more effectively than glucose infusion. American Journal of Physiology-Endocrinology and
Metabolism, 293(3), E754–E758. https://doi.org/10.1152/ajpendo.00231.2007
Smeets, P. A. M., Weijzen, P., de Graaf, C., & Viergever, M. A. (2011). Consumption of caloric
and non-caloric versions of a soft drink differentially affects brain activation during tasting.
NeuroImage, 54(2), 1367–1374. https://doi.org/10.1016/j.neuroimage.2010.08.054
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-
Berg, H., … Matthews, P. M. (2004). Advances in functional and structural MR image
analysis and implementation as FSL. NeuroImage, 23 Suppl 1, S208-219.
https://doi.org/10.1016/j.neuroimage.2004.07.051
Sotak, B. N., Hnasko, T. S., Robinson, S., Kremer, E. J., & Palmiter, R. D. (2005). Dysregulation
of dopamine signaling in the dorsal striatum inhibits feeding. Brain Research, 1061(2), 88–
96. https://doi.org/10.1016/j.brainres.2005.08.053
Stadlbauer, U., Woods, S. C., Langhans, W., & Meyer, U. (2015). PYY3–36: Beyond food intake.
Frontiers in Neuroendocrinology, 38, 1–11. https://doi.org/10.1016/j.yfrne.2014.12.003
Stanhope, K. L., Schwarz, J. M., Keim, N. L., Griffen, S. C., Bremer, A. A., Graham, J. L., …
Havel, P. J. (2009). Consuming fructose-sweetened, not glucose-sweetened, beverages
increases visceral adiposity and lipids and decreases insulin sensitivity in overweight/obese
humans. The Journal of Clinical Investigation, 119(5), 1322–1334.
https://doi.org/10.1172/JCI37385
102
Steinert, R. E., Frey, F., Toepfer, A., Drewe, J., & Beglinger, C. (2011). Effects of carbohydrate
sugars and artificial sweeteners on appetite and the secretion of gastrointestinal satiety
peptides. Br J Nutr, 24, 1–9. Retrieved from Scopus.
Stice, E., Yokum, S., Burger, K. S., Epstein, L. H., & Small, D. M. (2011). Youth at Risk for
Obesity Show Greater Activation of Striatal and Somatosensory Regions to Food. Journal of
Neuroscience, 31(12), 4360–4366. https://doi.org/10.1523/JNEUROSCI.6604-10.2011
Stoeckel, L. E., Weller, R. E., Cook, E. W., Twieg, D. B., Knowlton, R. C., & Cox, J. E. (2008).
Widespread reward-system activation in obese women in response to pictures of high-
calorie foods. NeuroImage, 41(2), 636–647.
https://doi.org/10.1016/j.neuroimage.2008.02.031
Suez, J., Korem, T., Zeevi, D., Zilberman-Schapira, G., Thaiss, C. A., Maza, O., … Elinav, E.
(2014). Artificial sweeteners induce glucose intolerance by altering the gut microbiota.
Nature, 514(7521), 181–186. https://doi.org/10.1038/nature13793
Swithers, S. E. (2015). Not so Sweet Revenge: Unanticipated Consequences of High-Intensity
Sweeteners. The Behavior Analyst, 38(1), 1–17. https://doi.org/10.1007/s40614-015-0028-3
Swithers, S. E., Martin, A. A., & Davidson, T. L. (2010). High-intensity sweeteners and energy
balance. Physiology & Behavior, 100(1), 55–62.
https://doi.org/10.1016/j.physbeh.2009.12.021
Swithers, S. E., Sample, C. H., & Davidson, T. L. (2013). Adverse effects of high-intensity
sweeteners on energy intake and weight control in male and obesity-prone female rats.
Behavioral Neuroscience, 127(2), 262–274. https://doi.org/10.1037/a0031717
103
Sylvetsky, A. C., Jin, Y., Clark, E. J., Welsh, J. A., Rother, K. I., & Talegawkar, S. A. (2017).
Consumption of Low-Calorie Sweeteners among Children and Adults in the United States.
Journal of the Academy of Nutrition and Dietetics, 117(3), 441-448.e2.
https://doi.org/10.1016/j.jand.2016.11.004
Sylvetsky, A. C., & Rother, K. I. (2016). Trends in the consumption of low-calorie sweeteners.
Physiology & Behavior, 164, 446–450. https://doi.org/10.1016/j.physbeh.2016.03.030
Tai, M. M. (1994). A mathematical model for the determination of total area under glucose
tolerance and other metabolic curves. Diabetes Care, 17(2), 152–154.
Tang, D. W., Fellows, L. K., Small, D. M., & Dagher, A. (2012). Food and drug cues activate
similar brain regions: A meta-analysis of functional MRI studies. Physiology & Behavior,
106(3), 317–324. https://doi.org/10.1016/j.physbeh.2012.03.009
Tellez, L. A., Han, W., Zhang, X., Ferreira, T. L., Perez, I. O., Shammah-Lagnado, S. J., … de
Araujo, I. E. (2016). Separate circuitries encode the hedonic and nutritional values of sugar.
Nature Neuroscience, 19(3), 465–470. https://doi.org/10.1038/nn.4224
Temizkan, S., Deyneli, O., Yasar, M., Arpa, M., Gunes, M., Yazici, D., … Yavuz, D. G. (2015).
Sucralose enhances GLP-1 release and lowers blood glucose in the presence of
carbohydrate in healthy subjects but not in patients with type 2 diabetes. European Journal
of Clinical Nutrition, 69(2), 162–166. https://doi.org/10.1038/ejcn.2014.208
Ten Kulve, J. S., Veltman, D. J., van Bloemendaal, L., Groot, P. F. C., Ruhé, H. G., Barkhof,
F., … Ijzerman, R. G. (2016). Endogenous GLP1 and GLP1 analogue alter CNS responses
to palatable food consumption. The Journal of Endocrinology, 229(1), 1–12.
https://doi.org/10.1530/JOE-15-0461
104
Tucker, R. M., & Tan, S.-Y. (2017). Do non-nutritive sweeteners influence acute glucose
homeostasis in humans? A systematic review. Physiology & Behavior, 182, 17–26.
https://doi.org/10.1016/j.physbeh.2017.09.016
Turton, M. D., O’Shea, D., Gunn, I., Beak, S. A., Edwards, C. M. B., Meeran, K., … Bloom, S. R.
(1996). A role for glucagon-like peptide-1 in the central regulation of feeding. Nature,
379(6560), 69–72. https://doi.org/10.1038/379069a0
van Opstal, A. M., Kaal, I., van den Berg-Huysmans, A. A., Hoeksma, M., Blonk, C., Pijl, H., …
van der Grond, J. (2019). Dietary sugars and non-caloric sweeteners elicit different
homeostatic and hedonic responses in the brain. Nutrition, 60, 80–86.
https://doi.org/10.1016/j.nut.2018.09.004
Vartanian, L. R., Schwartz, M. B., & Brownell, K. D. (2007). Effects of Soft Drink Consumption on
Nutrition and Health: A Systematic Review and Meta-Analysis. American Journal of Public
Health, 97(4), 667–675. https://doi.org/10.2105/AJPH.2005.083782
Veldhuizen, M. G., Babbs, R. K., Patel, B., Fobbs, W., Kroemer, N. B., Garcia, E., … Small, D.
M. (2017). Integration of Sweet Taste and Metabolism Determines Carbohydrate Reward.
Current Biology, 27(16), 2476-2485.e6. https://doi.org/10.1016/j.cub.2017.07.018
Volkow, N. D., Wang, G.-J., Fowler, J. S., Logan, J., Jayne, M., Franceschi, D., … Pappas, N.
(2002). “Nonhedonic” food motivation in humans involves dopamine in the dorsal striatum
and methylphenidate amplifies this effect. Synapse (New York, N.Y.), 44(3), 175–180.
https://doi.org/10.1002/syn.10075
Volkow, N. D., Wang, G.-J., Maynard, L., Jayne, M., Fowler, J. S., Zhu, W., … Pappas, N.
(2003). Brain dopamine is associated with eating behaviors in humans. International Journal
of Eating Disorders, 33(2), 136–142. https://doi.org/10.1002/eat.10118
105
Volkow, N. D., Wang, G.-J., Telang, F., Fowler, J. S., Logan, J., Childress, A.-R., … Wong, C.
(2006). Cocaine Cues and Dopamine in Dorsal Striatum: Mechanism of Craving in Cocaine
Addiction. Journal of Neuroscience, 26(24), 6583–6588.
https://doi.org/10.1523/JNEUROSCI.1544-06.2006
Wald, H. S., & Myers, K. P. (2015). Enhanced flavor–nutrient conditioning in obese rats on a
high-fat, high-carbohydrate choice diet. Physiology & Behavior, 151, 102–110.
https://doi.org/10.1016/j.physbeh.2015.07.002
Weise, C. M., Thiyyagura, P., Reiman, E. M., Chen, K., & Krakoff, J. (2012). Postprandial
plasma PYY concentrations are associated with increased regional gray matter volume and
rCBF declines in caudate nuclei—A combined MRI and H215O PET study. NeuroImage,
60(1), 592–600. https://doi.org/10.1016/j.neuroimage.2011.12.023
Wilcox, G. (2005). Insulin and Insulin Resistance. Clinical Biochemist Reviews, 26(2), 19–39.
Wölnerhanssen, B. K., Meyer-Gerspach, A. C., Schmidt, A., Zimak, N., Peterli, R., Beglinger, C.,
& Borgwardt, S. (2015a). Dissociable Behavioral, Physiological and Neural Effects of Acute
Glucose and Fructose Ingestion: A Pilot Study. PLOS ONE, 10(6), e0130280.
https://doi.org/10.1371/journal.pone.0130280
Wölnerhanssen, B. K., Meyer-Gerspach, A. C., Schmidt, A., Zimak, N., Peterli, R., Beglinger, C.,
& Borgwardt, S. (2015b). Dissociable Behavioral, Physiological and Neural Effects of Acute
Glucose and Fructose Ingestion: A Pilot Study. PLOS ONE, 10(6), e0130280.
https://doi.org/10.1371/journal.pone.0130280
Woolrich, M. W., Jbabdi, S., Patenaude, B., Chappell, M., Makni, S., Behrens, T., … Smith, S.
M. (2009). Bayesian analysis of neuroimaging data in FSL. NeuroImage, 45(1 Suppl), S173-
186. https://doi.org/10.1016/j.neuroimage.2008.10.055
106
Wyvell, C. L., & Berridge, K. C. (2000). Intra-Accumbens Amphetamine Increases the
Conditioned Incentive Salience of Sucrose Reward: Enhancement of Reward “Wanting”
without Enhanced “Liking” or Response Reinforcement. Journal of Neuroscience, 20(21),
8122–8130.
Yau, A. M. W., McLaughlin, J., Gilmore, W., Maughan, R. J., & Evans, G. H. (2017). The Acute
Effects of Simple Sugar Ingestion on Appetite, Gut-Derived Hormone Response, and
Metabolic Markers in Men. Nutrients, 9(2). https://doi.org/10.3390/nu9020135
Yeomans, M. R. (2012). Flavour–nutrient learning in humans: An elusive phenomenon?
Physiology & Behavior, 106(3), 345–355. https://doi.org/10.1016/j.physbeh.2012.03.013
Zanchi, D., Depoorter, A., Egloff, L., Haller, S., Mählmann, L., Lang, U. E., … Borgwardt, S.
(2017). The impact of gut hormones on the neural circuit of appetite and satiety: A
systematic review. Neuroscience & Biobehavioral Reviews, 80(Supplement C), 457–475.
https://doi.org/10.1016/j.neubiorev.2017.06.013
Zandstra, E. H., & El-Deredy, W. (2011). Effects of energy conditioning on food preferences and
choice. Appetite, 57(1), 45–49. https://doi.org/10.1016/j.appet.2011.03.007
Abstract (if available)
Abstract
Research spanning the last two decades has linked excessive sugar intake with overall poor dietary habits and costly health outcomes such as Type 2 Diabetes and cardiovascular disease. Dietary sugar intake by Americans far exceeds guidelines set forth by the World Health Organization, which may be partly explained by the wide accessibility of processed, high-sugar foods paired with ever-present environmental cues (e.g. advertisements) to consume these foods. As healthcare providers and public health experts continue to grapple with addressing the effects of diets high in added sugar, researchers move towards unraveling the nuanced psychophysiological precedents and results of consuming sugar beyond homeostatic needs. The work presented in this dissertation explores mechanistic links between eating behavior, environmental food-cue reactivity, satiety signaling, and neural response to consumption of caloric sugars (glucose and sucrose) or non-nutritive sweeteners (sucralose) in humans. The results and conclusions described here will add to the scientific conversation about the effects of dietary sugar consumption, which will ultimately contribute to the goal of effective and well-informed public health and policy outcomes.
Linked assets
University of Southern California Dissertations and Theses
Conceptually similar
PDF
Effects of western dietary factors during early life on glucose metabolism, the gut microbiome, and neurocognition
PDF
The acute impact of glucose and sucralose on food decisions and brain responses to visual food cues
PDF
Effects of sugar and fiber consumption in minority adolescents and self-tracking as a potential dietary intervention tool
Asset Metadata
Creator
Dorton, Hilary Michelle Kast
(author)
Core Title
Effects of sugar and non-nutritive sweetener consumption on neural processing, ingestive behavior, and appetite regulation
School
Keck School of Medicine
Degree
Doctor of Philosophy
Degree Program
Neuroscience
Publication Date
12/18/2019
Defense Date
06/25/2019
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
ad libitum buffet,added sugars,amygdala, dorsal striatum,appetite regulation,dietary sugar,eating behavior,food-cue reactivity,GLP-1,glucose,glucose tolerance,gut-brain axis,Hypothalamus,insula,insulin,insulin resistance,insulin response,neuroimaging,Neuroscience,non-nutritive sweeteners,nucleus accumbens,OAI-PMH Harvest,satiety signaling,sucralose,sucrose
Language
English
Contributor
Electronically uploaded by the author
(provenance)
Advisor
Monterosso, John (
committee chair
), Kanoski, Scott (
committee member
), Levitt, Pat (
committee member
), Page, Kathleen (
committee member
)
Creator Email
hdorton@usc.edu,hilary.dorton@gmail.com
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-c89-257440
Unique identifier
UC11674111
Identifier
etd-DortonHila-8095.pdf (filename),usctheses-c89-257440 (legacy record id)
Legacy Identifier
etd-DortonHila-8095.pdf
Dmrecord
257440
Document Type
Dissertation
Rights
Dorton, Hilary Michelle Kast
Type
texts
Source
University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
(collection)
Access Conditions
The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...
Repository Name
University of Southern California Digital Library
Repository Location
USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA
Tags
ad libitum buffet
added sugars
amygdala, dorsal striatum
appetite regulation
dietary sugar
eating behavior
food-cue reactivity
GLP-1
glucose
glucose tolerance
gut-brain axis
insula
insulin
insulin resistance
insulin response
neuroimaging
non-nutritive sweeteners
nucleus accumbens
satiety signaling
sucralose
sucrose