GLP-1 Receptor Activation Modulates Appetite- and Reward-Related Brain Areas in Humans

Participants

After screening 79 individuals, we included 16 obese T2DM patients and 16 obese normoglycemic and 16 healthy lean individuals, matched for sex and age. Inclusion criteria were age range 40–70 years, Caucasian ethnicity, right handedness, and stable body weight (<5% reported change during the previous 3 months). Women had to be postmenopausal (as ascertained by serum follicle-stimulating hormone >40 units/L) in order to avoid variations related to the menstrual cycle. Other inclusion criteria included BMI >30 kg/m² for obese individuals and T2DM patients, BMI <25 kg/m² for lean controls, and normoglycemia for obese individuals and lean controls as defined by fasting plasma glucose <5.6 mmol/L and 2-h glucose <7.8 mmol/L after a 75-g oral glucose tolerance test. For T2DM patients, HbA_1c had to be 6.0–8.5% (42–69 mmol/mol) during treatment with metformin and/or sulfonylurea derivative. Exclusion criteria for all participants were a history of cardiovascular, renal, and liver disease; malignancies; neurological or psychiatric disorders including eating disorders (assessed by the Eating Disorder Inventory II) (18) and depression (assessed by Center for Epidemiologic Studies Depression scale) (19); inability to undergo MRI scanning; past exposure to incretin-based therapy; substance abuse; and the use of oral glucocorticoids or any centrally acting agent. Twelve T2DM patients and 3 obese participants used antihypertensive medication, and 13 T2DM patients and 1 obese participant used statins. In the T2DM group, eight participants were treated with metformin monotherapy and eight used metformin in combination with a sulfonylurea. The lean controls did not use any medication. Reasons for screen failure were as follows: impaired glucose tolerance (n = 9), HbA_1c not between 6.0 and 8.5% for patients with T2DM (n = 3), inability to undergo MRI scanning (n = 6), depressive symptoms (n = 1), incidental findings (n = 4), microalbuminuria (n = 5), no accessible veins (n = 2), and no stable body weight (n = 1).

The study (NCT01281228) was approved by the Medical Ethics Committee of the VU University Medical Center and was performed in accordance with the Helsinki Declaration. All participants provided written informed consent before participation.

Experimental Design

The study was a randomized, placebo-controlled, crossover study. Each participant underwent three fMRI sessions at separate visits (with at least 1 week between visits) in random order with intravenous infusion of 1) exenatide, 2) exenatide together with the GLP-1 receptor antagonist exendin 9-39, or 3) placebo (Fig. 1A). The participants were blinded to the type of infusions. All visits commenced at 8:30 a.m. after an overnight fast, and participants did not exercise or drink alcohol for 24 h before the sessions. Sulfonylureas were discontinued 3 days prior to examination, and metformin was discontinued on the day of examination. In order to study the effects of GLP-1 receptor activation per se, i.e., independent of hormonal or metabolic changes induced by GLP-1 receptor activation, all measurements were performed during a somatostatin pancreatic-pituitary clamp according to principles previously described (20). In short, somatostatin (Somatostatin; Eumedica) was infused at a rate of 60 ng/kg/min to suppress endogenous insulin, glucagon, growth hormone, and GLP-1 production. Human glucagon (0.6 ng/kg/min) (Glucagen; Novo Nordisk), growth hormone (2 ng/kg/min) (Genotropin; Pfizer), and insulin (0.6 mU/kg/min) (Actrapid; Novo Nordisk) were infused at constant rates to achieve stable levels. Glucose (200 g/L) was infused at a variable rate to clamp plasma glucose at 5.0 mmol/L. Intravenous exendin 9-39 (Bachem; Clinalfa Products, Bubendorf, Switzerland) or placebo was started 30 min after the start of the clamp at an infusion rate of 600 pmol/kg/min (21). Intravenous exenatide (Byetta; Eli Lilly and Company) or placebo infusion was started 60 min after the start of the clamp at an infusion rate of 50 ng/min for 30 min and was decreased to 25 ng/min for the remaining time of the clamp procedure (22). Each peptide was diluted in saline containing 0.5% human serum albumin and was infused using a separate MRI-compatible infusion pump (MRidium 3850 MRI IV pump; Iradimed, Winter Park, FL). Participants assumed the reclining position and a catheter was inserted into a cubital vein for infusion of glucose and clamp hormones, and for the infusion of exenatide and/or exendin 9-39 or saline. A second catheter was inserted into a contralateral cubital vein for blood sampling. This arm was kept in a heated box (50°C) throughout the experiment to arterialize the venous blood. During the MRI session, a MRI-compatible heated box was used. Plasma glucose was measured every 10 min. Blood for measuring insulin, glucagon, and exenatide levels was drawn every 30 min. Blood for measuring growth hormone, free fatty acids, and cortisol was drawn at t = 0 min (fasting) and t = 120 min (during the fMRI session).

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Figure 1

Study protocol. A: Study design. Obese T2DM patients (n = 16) and obese normoglycemic (n = 16) and lean (n = 16) subjects were studied in a randomized, placebo-controlled, crossover study. The acute effects of either intravenous exenatide or exenatide together with the GLP-1 receptor antagonist exendin 9-39 on CNS appetite and reward responses to visual food-related stimuli were measured, using fMRI, during a somatostatin pituitary-pancreatic clamp (glucose 5 mmol/L) after an overnight fast. After the fMRI, subjects were presented a lunch buffet to assess energy intake. Hunger and nausea scores were taken at t = 0, 50, 110, 150, and 180 min. B: fMRI paradigm. One of the three runs within one fMRI session. Each run consisted of six blocks of pictures, two each of high-calorie foods, low-calorie foods, and neutral pictures. The blocks were separated by 9 s of gray blank screen with a fixation cross. The order of blocks was randomized with the constraint that a given picture category was not followed by the same category. Cal, calorie; GH, growth hormone.

Questionnaires

At five time points (beginning of the experiment, before and after the MRI scan, and before and after the ad libitum lunch), participants were asked to rate their hunger, fullness, appetite, prospective food consumption, desire to eat, and feelings of nausea on a 10-point Likert scale (23). Participants also filled out the shortened version of the profile of mood state (POMS) at the beginning of the experiment and after the MRI scan (24).

fMRI Paradigm

The fMRI task consisted of 126 pictures within three categories: 1) high-calorie food, 2) low-calorie food, and 3) nonfood, adapted from previous studies (6,25). High-calorie food pictures consisted of sweet and savory foods including ice cream, chocolate chip cookies, cheesecake, french fries, hamburgers, and pizza. Low-calorie food pictures consisted of fruit and vegetables including fruit salads, apples, strawberries, garden salads, cucumber, and tomatoes. Nonfood pictures consisted of rocks, shrubs, bricks, trees, and flowers. All pictures were presented via Eprime 1.2 (Psychology Software Tools, Inc., Pittsburg, PA). Pictures were presented in a block design format, with a total of three runs. Each run consisted of six blocks of pictures: two blocks of high-calorie foods, two blocks of low-calorie foods, and two blocks of nonfood pictures (Fig. 1B). Within each block of 21 s, seven individual pictures were presented for 2.5 s each followed by a 0.5-s gap each. The blocks were separated by 9 s of gray blank screen with a fixation cross. The order of blocks was randomized from run to run with the constraint that a given picture category was not followed by the same category. Pictures were matched across blocks and sessions for shape and color. Since each participant was scanned three times, three versions with different pictures but otherwise identical design were created. The sequences of blocks were randomized across study visits. Participants were instructed to watch all pictures and to try to remember them for a recognition test. After the measurements, participants were given a recognition test consisting of 20 laminated color food and nonfood pictures (10 pictures of the MRI task randomly intermixed with 10 novel pictures).

Image Acquisition and Analysis

MRI data were acquired on a 3.0 Tesla GE Sigma HDxt scanner (General Electric, Milwaukee, WI). A 3-D structural MRI was obtained using a T1-weighted sequence. fMRI data were acquired using an echo planar imaging T2* blood oxygen level–dependent (BOLD) pulse sequence (repetition time = 2,160 ms, echo time = 30 ms, matrix 64 × 64, 211 mm² field of view, and flip angle = 80) with 40 ascending slices per volume (3 mm thickness, 0 mm gap), which gave whole-brain coverage.

Functional images were analyzed with SPM8 software (Wellcome Trust Centre for Neuroimaging, London, U.K.). The origin of each magnetic resonance volume was aligned to the anterior commissure. Series were corrected for differences in slice acquisition times and were realigned to the first volume. T1-coregistered volumes were normalized to Montreal Neurological Institute (MNI) space, resliced to 3 × 3 × 3 mm voxels and spatially smoothed using an 8-mm full width at half maximum Gaussian kernel. After high-pass filtering (cutoff 128 s) to remove low-frequency noise, functional scans were analyzed in the context of the general linear model. At the first level, each block of high-calorie food, low-calorie food, and nonfood was modeled using boxcar functions convolved with the canonical hemodynamic response function. For each subject and for each condition, contrast images were computed (all food pictures vs. nonfood pictures; high-calorie food pictures vs. nonfood pictures). These first-level contrast images were entered into two separate second-level, random-effects ANOVAs to assess between- and within-group differences. To determine if changes in neuronal response (placebo vs. exenatide) were related to changes in caloric intake (placebo vs. exenatide), we performed a regression analysis in SPM using the change in caloric intake as a covariate of interest. A priori regions of interest were determined based on previous studies (i.e., insula, striatum, amygdala, and OFC) (4–6). Only significant brain activations that survived family-wise error (FWE) correction for multiple comparisons on the voxel level within the regions of interest using a small volume correction, or across the entire brain for regions not a priori of interest, are reported. Small volume correction was performed using 5-mm (for amygdala) or 10-mm (for insula, putamen, and OFC) radius spheres (26,27).

Ad Libitum Lunch Buffet

After the MRI session, while all infusions continued, participants were presented a varied choice buffet to assess energy intake (28). Participants were advised to eat as much as they wanted. They were not aware that their choices and food intake were being monitored. After 30 min, the buffet was taken away and the total kilocalories consumed and the percentages of kilocalories derived from fat, carbohydrates, and protein were being calculated.

Assays

Blood glucose was measured immediately after sampling using the glucose dehydrogenase method (Glucose Analyzer; HemoCue, Ängelholm, Sweden). All other blood samples were stored at −80°C until assay. Insulin levels were determined using an immunometric assay (ADVIA Centaur; Siemens Medical Solutions Diagnostics). Growth hormone levels were determined using a chemiluminescence immunoassay (Liaison Diasorin S.p.A., Saluggia, Italy). Nonesterified fatty acid (NEFA) levels were measured with an enzymatic colorimetric method (Wako Chemicals GmbH, Neuss, Germany). Cortisol levels were determined using a competitive assay (ADVIA Centaur). Glucagon levels were measured using an immunoassay (Lilly Research Laboratories, Indianapolis, IN). Exenatide levels were determined by an immunoenzymatic assay (Tandem Laboratories, San Diego, CA), with a detection limit of 20 pg/mL.

Statistical Analyses

Clinical group data are expressed as mean ± SEM (unless otherwise stated) and were analyzed with the Statistical Package for the Social Sciences (SPSS) version 20. Between-group differences in the placebo condition were analyzed with ANOVA or, in case of more than one time point, with repeated measures ANOVA using time (minutes) as within-subject factor and group as between-subject factor. Within-group differences were analyzed using repeated measures ANOVA using treatment and time (minutes) as within-subject factor. In case of a significant result, a post hoc Bonferroni multiple comparisons correction was used. In case of skewed data, Kruskal-Wallis test was used for between-group differences and Friedman test for within-group differences. In case of a significant result, Mann-Whitney U test for between-group analysis or Wilcoxon signed rank for within-group analysis with post hoc Bonferroni correction was performed. P < 0.05 was considered statistically significant.