Skip to main content
NCBI home page
The Journal of Neuroscience logoLink to The Journal of Neuroscience
. 2015 Mar 4;35(9):3959–3965. doi: 10.1523/JNEUROSCI.4744-14.2015

Obesity Is Associated with Decreased μ-Opioid But Unaltered Dopamine D2 Receptor Availability in the Brain

Henry K Karlsson 1,✉, Lauri Tuominen 1,2, Jetro J Tuulari 1, Jussi Hirvonen 1,3, Riitta Parkkola 3, Semi Helin 1, Paulina Salminen 4, Pirjo Nuutila 1,5, Lauri Nummenmaa 1,6,✉
PMCID: PMC6605573  PMID: 25740524

Abstract

Neurochemical pathways involved in pathological overeating and obesity are poorly understood. Although previous studies have shown increased μ-opioid receptor (MOR) and decreased dopamine D2 receptor (D2R) availability in addictive disorders, the role that these systems play in human obesity still remains unclear. We studied 13 morbidly obese women [mean body mass index (BMI), 42 kg/m2] and 14 nonobese age-matched women, and measured brain MOR and D2R availability using PET with selective radioligands [11C]carfentanil and [11C]raclopride, respectively. We also used quantitative meta-analytic techniques to pool previous evidence on the effects of obesity on altered D2R availability. Morbidly obese subjects had significantly lower MOR availability than control subjects in brain regions relevant for reward processing, including ventral striatum, insula, and thalamus. Moreover, in these areas, BMI correlated negatively with MOR availability. Striatal MOR availability was also negatively associated with self-reported food addiction and restrained eating patterns. There were no significant differences in D2R availability between obese and nonobese subjects in any brain region. Meta-analysis confirmed that current evidence for altered D2R availability in obesity is only modest. Obesity appears to have unique neurobiological underpinnings in the reward circuit, whereby it is more similar to opioid addiction than to other addictive disorders. The opioid system modulates motivation and reward processing, and low μ-opioid availability may promote overeating to compensate decreased hedonic responses in this system. Behavioral and pharmacological strategies for recovering opioidergic function might thus be critical to curb the obesity epidemic.

Keywords: dopamine, obesity, opioids, positron emission tomography, receptors, reward

Introduction

Obesity is a great challenge to human health worldwide because it is associated with serious medical conditions such as type 2 diabetes, coronary heart disease, and stroke. Food reward is driven by functionally distinct neurochemical mechanisms promoting incentive motivation (“wanting”) and hedonic impact (“liking”) when food is consumed (Berridge, 2009). Accumulating evidence suggests that obesity is related to altered neurochemistry of the reward circuitry of the brain, making obese individuals prone to overeating (Berridge et al., 2010; Kenny, 2011; Volkow et al., 2013). The dopamine system supports incentive motivation, and dopaminergic reward system dysfunctions are associated with addictive disorders. In the striatum, alcohol and drug dependence are associated with lower dopamine D2 receptor (D2R) availability (Volkow et al., 1996, 2001; Martinez et al., 2012). Obese animals with unhealthy eating habits also show downregulation of D2R (Johnson and Kenny, 2010). However, studies in obese human subjects have provided conflicting results, with some finding lower striatal D2R availability (Wang et al., 2001; Volkow et al., 2008; de Weijer et al., 2011), and others unaltered striatal D2R availability (Haltia et al., 2007, 2008; Steele et al., 2010).

Whereas the dopaminergic system is implicated in the desire for eating, the endogenous opioid system is involved in both incentive motivation and hedonic functions, also generating pleasurable sensations when palatable foods are consumed (Berridge et al., 2010). The μ-opioid receptors (MORs) function as a part of complex opioid system, and mediate the effects of endogenous opioids, such as β-endorphins and endomorphins, and various exogenous opioid agonists (Henriksen and Willoch, 2008). Alcohol dependence is associated with increased MOR availability in ventral striatum, possibly be due the upregulation of MORs or a reduction in endogenous opioids (Heinz et al., 2005; Weerts et al., 2011). Moreover, cocaine dependence is linked to increased MOR availability in more extensive neural areas, such as anterior cingulate and frontal cortex (Gorelick et al., 2005). However, long-term opiate drug use is associated with downregulation in MORs (Koch and Höllt, 2008; Whistler, 2012).

Animal studies suggest that endogenous opioid system has an important role in the control of appetite. MOR agonists increase and opioid antagonists decrease food intake and hedonic pleasures caused by palatable foods (Gosnell and Levine, 2009; Peciña and Smith, 2010). Opioid antagonists also prevent food seeking and binge-like eating (Giuliano et al., 2012; Cambridge et al., 2013). Moreover, stimulation of the MOR in the shell of nucleus accumbens increases the pleasure responses for foods and may also trigger eating behavior (Peciña and Berridge, 2005). The μ-opioid receptor gene OPRM1 also modulates the intake of fat and possibly the risk for gaining weight in humans (Haghighi et al., 2014). Accordingly, changes in MOR rather than D2R availability can maintain excessive energy uptake due to altered hedonic processing of food. Here we determined the association between of obesity on the availability of D2R and MOR using positron emission tomography (PET) in a cross-sectional design. We hypothesized that obesity would be associated with opioid and possibly dopamine neurotransmitter systems, which is reflected in decreased D2R and MOR availabilities.

Materials and Methods

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethical Committee of the Hospital District of South-Western Finland (SleevePET2, NCT01373892, http://www.clinicaltrials.gov). All participants signed ethics committee-approved informed consent forms before scans.

Subjects.

We recruited 13 morbidly obese women [mean body mass index (BMI), 41.9 kg/m2; mean age, 39.1 years] for the study (Table 1). The BMI range was 37.1–49.3 kg/m2. The obese subjects were compared with 14 healthy nonobese female subjects (mean BMI, 22.7 kg/m2; mean age, 44.9 years) that were age and height matched with the obese subjects (Table 1). Clinical screening included history, physical examination, anthropometric measurements, and laboratory tests. Exclusion criteria involved binge-eating disorders (BEDs); neurological or severe mental disorders; and any kind of opiate drug use, substance abuse, excessive alcohol consumption (>8 U/week), determined by clinical interview, medical history, and blood tests. Subjects also completed questionnaires that measured emotional and reward functioning [Beck Depression Inventory-II (BDI-II), State-Trait Anxiety Inventory (STAI), and behavioral inhibition system (BIS)/behavioral approach system (BAS) scales] as well as food craving and eating behavior [Trait and State Food Cravings Questionnaires (FCQ), Dutch Eating Behavior Questionnaire (DEBQ), Yale Food Addiction Scale (YFAS)]. None of the controls smoked tobacco, but five obese subjects were light smokers (smoking range, 3–15 cigarettes/d). None of the obese subjects had type 2 diabetes or used antidiabetic medications. Of the obese group, five subjects used oral medication for treatment of elevated blood pressure, three for treatment of hypothyreosis, and two for treatment of hypercholesterolemia. Use of antihypertensive and cholesterol-lowering drugs were discontinued before the experiments.

Table 1.

Characteristics of the participants

Obese subjects (n = 13)
 Nonobese subjects (n = 14)
 p value*
Mean SD Mean SD
Age (years) 39.08 10.74 44.86 12.88 0.22
Weight (kg) 116.82 16.60 61.39 7.19 <0.001
Height (cm) 166.27 6.94 164.86 6.32 0.59
BMI (kg/m2) 41.89 3.88 22.65 2.94 <0.001
Fat (%) 50.44 4.06 30.21 10.17 <0.001
Subcutaneous fat mass (kg) 17.50 3.53 4.16 1.28 <0.001
Visceral fat mass (kg) 3.60 2.18 0.78 0.44 <0.001
HbA1c (%) 5.52 0.39 5.60 0.30 0.50
Systolic blood pressure (mmHg) 129.64 12.72 129.54 13.75 0.99
Diastolic blood pressure (mmHg) 83.27 8.81 80.23 8.25 0.40
Amount of alcohol use (drinks/week) 1.50 1.35 2.75 2.09 0.10
Tobacco smokers/nonsmokers (N) 5/13 0/14 <0.001
Injected activity of [11C]raclopride (MBq) 243.84 27.83 257.78 18.51 0.14
Injected activity of [11C]carfentanil (MBq) 251.00 10.38 250.14 9.23 0.82
Open in a new tab

HbA1c, Glycated hemoglobin A1c.

*Between-groups differences; significant differences in two-sample t test are shown in bold.

Image acquisition and quantification of receptor availability.

We measured D2 receptor availability with the antagonist [11C]raclopride (Farde et al., 1986), and μ-opioid receptor availability with the high-affinity agonist [11C]carfentanil (Frost et al., 1985) using PET on two separate visits. [11C]Raclopride was synthesized using [11C]methyl triflate, where cyclotron-produced [11C]methane was halogenated by gas phase reaction into [11C]methyl iodide (Larsen et al., 1997) and converted on-line into [11C]methyl triflate (Jewett, 1992). The approach used was adapted from the published method (Langer et al., 1999) with the following modifications. The [11C]methane was produced with a CC18/9 cyclotron (Efremov Institute, St. Petersburg, Russia) using 17 MeV protons for 14N(p,α)11C nuclear reaction in a N2-H2 target gas (10% H2). [11C]methyl triflate was bubbled into a solution containing acetone (200 μl), O-desmethyl precursor (0.4 mg, 1.2 μmol), and NaOH (2.8 μl, 0.5 m) at 0°C. At the HPLC purification step, the mobile-phase composition was adjusted into (32:68) acetonitrile/0.01 m H3PO4, and [11C]raclopride peak was cut into a rotary evaporator already containing propylene glycol/ethanol (7:3, 0.4 ml). The evaporation residue was formulated in phosphate buffer (8 ml, 0.1 m) and sterile filtered. A analytical HPLC column (Kinetex XB-C18, Phenomenex; 2.6 μm, 3.00 × 50 mm), acetonitrile in 0.05 m H3PO4 (23:77) mobile phase, 1 ml/min flow rate, 3.5 min run time, and detectors in series for UV absorption (214 nm) and radioactivity were used for the determination of identity, radiochemical purity, and mass concentration. [11C]Carfentanil was produced as previously described (Hirvonen et al., 2009), except the mobile phase was changed into CH3OH/0.1 m NH4HCO2 (70:30).

Both radioligands had high radiochemical purity (>99%). Before scanning, a catheter was placed in the subject's left antecubital vein for tracer administration. Head was strapped to the scanner table to prevent head movement. Subjects fasted for 2 h before scanning. A computed tomography (CT) scan was performed to serve as an attenuation map and a reference anatomical image of the brain. The clinical well being of subjects was monitored during the scanning.

We injected 251 ± 24 MBq of [11C]raclopride and 251 ± 10 MBq of [11C]carfentanil in separate scans on separate days. After injection, radioactivity in brain was measured with the GE Healthcare Discovery 690 PET/CT scanner for 51 min, using 13 time frames. MRI was performed with a Philips Gyroscan Intera 1.5 T CV Nova Dual scanner to exclude structural abnormalities and to provide anatomical reference images for the PET scans. High-resolution anatomical images (1 mm3 voxel size) were acquired using a T1-weighted sequence (TR, 25 ms; TE, 4.6 ms; flip angle, 30°; scan time, 376 s).

All alignment and coregistration steps were performed using SPM8 software (www.fil.ion.ucl.ac.uk/spm/) running on Matlab R2012a (MathWorks). To correct for head motion, dynamic PET images were first realigned frame to frame. The individual T1-weighted MR images were coregistered to the summation images calculated from the realigned frames. Regions of interest (ROIs) for reference regions were drawn manually on MR images using PMOD version 3.4 software (PMOD Technologies). Occipital cortex was used as the reference region for [11C]carfentanil and cerebellum for [11C]raclopride. Receptor availability was expressed in terms of BPND, which is the ratio of specific to nondisplaceable binding in brain. BPND was calculated applying the basis function method for each voxel using the simplified reference tissue model with reference tissue time activity curves as input data (Gunn et al., 1997). This outcome measure is not confounded by differences in peripheral distribution or radiotracer metabolism.

The subject-wise parametric BPND images were normalized to the MNI space using the T1-weighted MR images, and smoothed with a Gaussian kernel of 8 mm FWHM. Subsequently, between-groups, voxelwise differences in D2R and MOR BPND were compared using independent samples t tests in SPM8. The statistical threshold was set at p < 0.05, false discovery rate (FDR) corrected at the cluster level. In a complementary approach, anatomic regions of interest were generated in ventral striatum, dorsal caudate nucleus, putamen, insula, amygdala, thalamus, orbitofrontal cortex, anterior cingulate cortex, medial cingulate cortex, and posterior cingulate cortex using the AAL (Tzourio-Mazoyer et al., 2002) and Anatomy (Eickhoff et al., 2005) toolboxes. These data were analyzed with a 2 (group) × 10 (ROI) mixed ANOVA. Associations among receptor availabilities (i.e., BPND values in each ROI), BMI, and questionnaire scores were assessed using Pearson correlations.

Finally, to weight the existing evidence on striatal D2R availability in obesity, we conducted a meta-analysis on human PET studies targeting obesity using [11C]raclopride. The meta-analysis includes peer-reviewed studies written in English and published through the end of April 2014. Several search methods were used. The Web of Science, PubMed, and Scopus databases were searched to retrieve documents containing the terms “dopamine,” “obesity,” “PET,” “raclopride,” and “receptor,” in article title, abstract, or keywords. Articles referred to in articles found by the preceding method were examined. Studies were accepted for the meta-analysis if they met the following criteria: (1) they had compared D2R availability in obese versus normal-weight subjects using PET; and (2) they used [11C]raclopride as a radiotracer. Effect sizes were estimated using the r statistic based on means and variances and the number of participants, or, alternatively, the F or t test values and degrees of freedom (Rosenthal, 1984; Rosenthal and DiMatteo, 2001). Effect sizes were consistently computed in such a way that positive values reflect lowered D2R availability in obese individuals. Subsequently, weighted effect sizes were computed and subjected to meta-analysis using unbiased estimates of correlation coefficients and a restricted maximum likelihood estimator, yielding mean and 95% confidence intervals (CIs) for the effect sizes. This model assumes that effect sizes are contingent on study parameters, thus allowing for an estimation of both within- and between-studies variances. Altogether with the present data, the meta-analysis included data from 105 subjects stemming from five independent studies.

Results

Full-volume analysis revealed that morbidly obese patients had significantly lower [11C]carfentanil BPND values (p < 0.05, FDR corrected in the SPM analysis), versus control subjects, throughout the reward circuit, including the ventral striatum, dorsal caudate, thalamus, insula, orbitofrontal cortex, and anterior cingulate cortex (Figs. 1, 2, 3). However, there were no significant differences in [11C]raclopride BPND values in any brain region. Furthermore, there were no regions with higher [11C]carfentanil or [11C]raclopride BPND values in obese versus normal-weight individuals. These effects were corroborated in the ROI analysis. For [11C]carfentanil, the ANOVA revealed that BPND values were lower for obese versus lean subjects (F(1,25) = 6.17, p = 0.02, ηp2 = 0.20) and differed across ROIs (F(1,25) = 400.42, p < 0.001, ηp2 = 0.89), yet no interaction between subject group and ROI was observed (F = 2.16, p > 0.05). For [11C]raclopride, there was a main effect of ROI (F(1,25) = 246.92, p < 0.001, ηp2 = 0.92), but there was neither a difference between groups (F = 1.04, p > 0.05) nor an interaction between group and ROI (F = 0.52, p > 0.05).

Figure 1.

Figure 1.

Open in a new tab

Averaged images of [11C]carfentanil and [11C]raclopride showing lowered MOR availability but unaltered D2R availability among obese patients.

Figure 2.

Figure 2.

Open in a new tab

Brain regions showing lowered MOR availability in morbidly obese patients versus healthy control subjects. The data are thresholded at p < 0.05, FDR corrected. The blue outline shows regions where the effect is significant at a more stringent statistical threshold (p < 0.01, FDR corrected).

Figure 3.

Figure 3.

Open in a new tab

Mean [11C]carfentanil and [11C]raclopride BPND in regions of interest. Note: MOR data show a main effect of group (obese < lean) with no interaction with ROI. *p < 0.05 in complementary contrast test.

Across the whole sample, BMI correlated negatively with [11C]carfentanil BPND in ventral striatum, dorsal caudate, putamen, insula, amygdala, thalamus, as well as orbitofrontal cortex (rs ≤ −0.42; p < 0.03; Fig. 4). No significant correlations between BMI and [11C]raclopride BPND were observed in any region.

Figure 4.

Figure 4.

Open in a new tab

Scatterplots illustrating the relationship between BMI and BPND in ventral striatum, dorsal caudate, and putamen.

To rule out the possible effect of smoking on receptor availability, we reanalyzed the data excluding the smokers. This analysis yielded results for MOR and D2R that were similar to those for the whole sample population, confirming that decreased MOR in obese subjects is not due to smoking. We also compared the BPND values between the obese smokers and obese nonsmokers, and found no significant differences.

Even though obesity was not associated with D2R availability per se, we next asked whether MOR and D2R availabilities would have a joint contribution to an individual's BMI. To this end, we conducted a regression analysis where we predicted BMIs with regional MOR and D2R availability, running a separate regression model for each striatal ROI. For all tested ROIs, MOR (p < 0.05) but neither D2R availability nor interaction between MOR and D2R availability (p > 0.05) predicted an individual's BMI.

Morbidly obese subjects scored significantly higher on the scales measuring pathological eating (DEBQ restrained and emotional eating) and food addiction (YFAS; Table 2). They also scored significantly lower in the BIS scale measuring behavioral inhibition, but the groups did not differ from each other in depression (BDI-II), trait anxiety (STAI), behavioral activation (BAS), or food craving (FCQ) scores. The STAI scores were negatively associated with MOR availability in anterior cingulate cortex, middle cingulate cortex, and posterior cingulate cortex (r ≤ −0.38, p < 0.05). The DEBQ restrained eating score correlated negatively with MOR availability in ventral striatum, amygdala, and thalamus (r ≤ −0.38, p < 0.05), and YFAS scores were negatively associated with MOR availability in dorsal caudate (r = −0.39, p < 0.05). However, these analyses were not significant after adjusting for multiple comparisons using the Bonferroni procedure. No significant associations between D2R availability and questionnaire scores were found. The full volume group differences in MOR availability were in general similar, albeit with slightly weaker effects when each of these factors was included as covariates in the SPM analyses. Including these covariates in the analysis of D2R availability did not alter the corresponding results.

Table 2.

Questionnaire scores

Obese subjects (n = 13)
 Nonobese subjects (n = 14)
 p value*
Mean SD Mean SD
BDI-II 5.92 5.77 4.36 4.11 0.43
STAI 38.77 9.14 34.07 5.47 0.12
BIS 13.46 3.18 16.64 2.79 0.01
BAS
    Drive 9.92 4.27 8.71 2.55 0.39
    Fun seeking 11.08 3.09 10.57 2.44 0.64
    Reward responsiveness 10.00 3.63 10.50 1.83 0.66
FCQ
    State 30.92 10.32 26.71 6.28 0.22
    Trait 104.31 35.34 86.21 25.61 0.14
DEBQ
    Restrained eating 33.54 5.66 26.00 6.18 0.003
    Emotional eating 31.00 11.31 22.07 6.33 0.02
    External eating 26.31 6.73 25.36 5.76 0.70
YFAS 18.00 11.00 7.86 5.95 0.009
Open in a new tab

*Between-groups differences; significant differences in two-sample t test are shown in bold.

Finally, the meta-analysis (Fig. 5) confirmed that, even though the overall effect size for BMI on D2R availability was positive (r = 0.14), its 95% CI overlapped with zero (−0.11 to 0.40), suggesting that there was no effect of BMI on D2R availability. However, moderator analysis using Wald-type test for model coefficients revealed that the effect size had a quadratic relationship between the BMI of the patients studied (QM(1) = 4.3439, p = 0.04), suggesting that only extreme obesity may lead to lowered D2R availability (p > 0.05).

Figure 5.

Figure 5.

Open in a new tab

Random-effects analysis for the effects of obesity on D2R availability in PET studies using [11C]raclopride. RE, Random effects.

Discussion

Cerebral MOR availability was lowered in morbidly obese patients in brain regions implicated in reward processing, including ventral striatum, orbitofrontal cortex, amygdala, putamen, insula, and anterior cingulate, while D2R availability remains unaltered. Altered MOR availability was also paralleled with alterations in affect-driven eating, as indicated by elevated self-reported food addiction and restrained eating behavior. Critically, food addiction and restrained eating scores were also associated with MOR availability, suggesting that the lowered MOR availability is directly linked with the tendency to compulsively eat regardless of internal state of hunger or satiety.

Prior work has established that the opioid system is involved in the pathophysiology of addictive disorders by causing altered sensations of pleasure, but it is also involved in hedonic and motivational processing of food (Peciña and Smith, 2010). Opioid receptor blockage with opioid antagonists decreases, whereas stimulation with agonists increases, the food intake in both rodents and humans (Glass et al., 1999; Yeomans and Gray, 2002; Giuliano et al., 2012; Ziauddeen et al., 2013). Inverse MOR agonists also reduce the hedonic properties of food and eating in humans (Nathan et al., 2012), and obese humans with BEDs show reduced responses to pictures of high-calorie food in fMRI when using a MOR antagonist, which suggest a strong link with altered MOR functioning and food-related behavior (Cambridge et al., 2013). Opioid peptides and receptors are abundantly expressed in the reinforcement–reward circuit of the human brain (Le Merrer et al., 2009), and in the present study obesity was associated with marked MOR downregulation within this system. Accordingly, altered MOR functioning could underlie obesity and maintain pathological eating behaviors due to altered hedonic processing of palatable foods.

Obesity has been proposed to share neural pathophysiology with addictive disorders (Volkow and Wise, 2005); however, our findings challenge this concept as overly simplistic. Even though the observed downregulation of the MOR system in obesity is in good accordance with that observed in opiate addictions, our results contrast with findings from previous human PET studies measuring MOR and D2R systems in subjects with other addictive disorders. For example, cocaine addiction is associated with increased rather than decreased MOR availability in frontal, lateral temporal, and anterior cingulate cortices, and the elevated levels are observed also in abstinence (Zubieta et al., 1996; Gorelick et al., 2005). Similarly, patients with alcohol dependency have increased MOR availability in the ventral striatum (Heinz et al., 2005). Thus, different neuromolecular changes appear to underlie obesity on the one hand, and addictive disorders and nonopiate substance abuse on the other.

Our outcome measure, BPND, does not distinguish between receptor density and affinity. Therefore, our finding of lower [11C]carfentanil BPND values may reflect either a decreased number of receptor proteins or a lowered affinity to bind this radioligand agonist. Downregulation of the receptor protein itself may be caused by long-term overstimulation by endogenous agonists, as a homeostatic mechanism common to various G-protein-coupled receptors. A lower number or a reduced affinity of receptors will result in diminished overall net stimulation of the MOR system after pleasurable stimuli, such as eating, for a given amount of endogenous opioids released. We thus propose that perpetual amplification of the sensory pleasure of eating in the MOR system (Berridge, 2009) may lead to subsequent downregulation of the MOR. Alternatively, lower MOR density could be a predisposing trait factor, making subjects vulnerable to becoming obese. Whatever the cause, this downregulation may render these individuals susceptible to overeating to gain the desired hedonic response and maintain pathological eating behaviors.

In contrast with our hypothesis, we did not observe lowered D2R availability in the obese subjects. Similarly, when predicting BMIs with regional D2R and MOR availabilities, only MOR availability was established as a significant predictor. Of the five existing studies on D2R availability in obese versus lean individuals using [11C]raclopride, only one (Wang et al., 2001) has unambiguously found lowered striatal D2R availability in obese patients. All the remaining studies (Haltia et al., 2007, 2008; Steele et al., 2010; and the present study) failed to observe any differences between obese and lean subjects in regional analyses in striatum. Consistent with this, our meta-analysis failed to establish a strong association between striatal D2R availability and BMI, resulting in a modest (r = 0.14) negative effect size whose 95% CI included zero. Nevertheless, the strongest effect of BMI on D2R availability was found in a study (Wang et al., 2001) where the mean BMI among obese subjects was >50 kg/m2 (compared with 42 kg/m2 in our study), which suggests extremely pathological eating habits and metabolically more severe disease. This was confirmed in a moderator analysis, which revealed that BMI had a quadratic relationship with lowered D2R availability. In sum, the PET data seem to suggest that lowered D2R availability in obesity may be an exception restricted to the most morbidly obese individuals, rather than a general pathophysiological feature.

However, it must be noted that previous human brain-imaging studies support the role of D2R function in food intake (Guo et al., 2014). Feeding is associated with elevated dopamine release, especially in dorsal striatum (Small et al., 2003; Volkow et al., 2011), and animal studies show that deficits in dopamine signaling and low availability of dopamine receptors in the striatum is associated with weight gain (Geiger et al., 2009; Michaelides et al., 2012). Animal studies also suggest that diet-induced obesity and the resulting blunted dopamine signaling may lead to compensatory eating to normalize dopamine activity (Tellez et al., 2013). Furthermore, The D2R TaqI A1 allele is also associated with obesity (Carpenter et al., 2013), and it moderates blunted striatal responses to food (Stice et al., 2008). The endogenous opioid system interacts with the dopaminergic system via GABAergic neurons. GABAergic neurons inhibit the dopaminergic system, but MORs prevent this inhibition and may cause elevated dopamine release (Yeomans and Gray, 2002; Volkow and Wise, 2005). This is in line with the fact that MOR agonists increase and MOR antagonists reduce food intake in rodents and humans (Glass et al., 1999; Yeomans and Gray, 2002; Giuliano et al., 2012; Ziauddeen et al., 2013). It is thus possible that interactions between opioid and dopamine systems could be a critical factor underlying the pathophysiology of obesity, even though mere changes in D2R availability in obesity cannot be consistently observed with PET.

Limitations

Because the present study involved only female subjects, we cannot rule out sex effects. Furthermore, we did not control for the cycle phase in the current study, but the phase of the menstrual cycle was distributed evenly (data not shown). Even though our sample was sizeable, it is possible that more pronounced differences associated with the obese phenotype could be established in larger studies. Finally, it must be borne in mind that the present cross-sectional study cannot reveal whether obesity causes MOR downregulation or vice versa.

Conclusions

Morbid obesity is associated with decreased MOR availability in the brain, while D2R availability remains unaltered. We propose that the endogenous opioid system is a key component underlying human obesity, whereas the function of the dopaminergic system is less profound. The neurochemical changes associated with obesity are partially distinct from those observed in patients with addictive disorders and substance abuse. Future longitudinal studies should examine whether decreased MOR function is a trait phenomenon reflecting a vulnerability to develop obesity by overeating, or a direct and possibly recoverable consequence of obesity on the brain.

Footnotes

The authors declare no competing financial interests.

The study was conducted within the Finnish Centre of Excellence in Cardiovascular and Metabolic Diseases, and was supported by the Academy of Finland (Grants 251125 and 121031); the Sigrid Juselius Foundation; the University of Turku; Turku University Hospital; and Åbo Akademi University. H.K.K. was supported by personal grants from The Finnish Diabetes Research Foundation and The National Graduate School of Clinical Investigation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank the staff of the Turku PET Centre for assistance in the PET imaging. We especially thank research nurse Mia Koutu. We also thank Dr. Robert M. Badeau, Aura Professional English Consulting Ltd., for the language content editing and proofreading the language of this manuscript.

References

  1. Berridge KC. “Liking” and “wanting” food rewards: brain substrates and roles in eating disorders. Physiol Behav. 2009;97:537–550. doi: 10.1016/j.physbeh.2009.02.044. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Berridge KC, Ho CY, Richard JM, DiFeliceantonio AG. The tempted brain eats: pleasure and desire circuits in obesity and eating disorders. Brain Res. 2010;1350:43–64. doi: 10.1016/j.brainres.2010.04.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Cambridge VC, Ziauddeen H, Nathan PJ, Subramaniam N, Dodds C, Chamberlain SR, Koch A, Maltby K, Skeggs AL, Napolitano A, Farooqi IS, Bullmore ET, Fletcher PC. Neural and behavioral effects of a novel mu opioid receptor antagonist in binge-eating obese people. Biol Psychiatry. 2013;73:887–894. doi: 10.1016/j.biopsych.2012.10.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Carpenter CL, Wong AM, Li Z, Noble EP, Heber D. Association of dopamine D2 receptor and leptin receptor genes with clinically severe obesity. Obesity (Silver Spring) 2013;21:E467–E473. doi: 10.1002/oby.20202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. de Weijer BA, van de Giessen E, van Amelsvoort TA, Boot E, Braak B, Janssen IM, van de Laar A, Fliers E, Serlie MJ, Booij J. Lower striatal dopamine D2/3 receptor availability in obese compared with non-obese subjects. EJNMMI Res. 2011;1:37. doi: 10.1186/2191-219X-1-37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Eickhoff SB, Stephan KE, Mohlberg H, Grefkes C, Fink GR, Amunts K, Zilles K. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage. 2005;25:1325–1335. doi: 10.1016/j.neuroimage.2004.12.034. [DOI] [PubMed] [Google Scholar]
  7. Farde L, Hall H, Ehrin E, Sedvall G. Quantitative analysis of D2 dopamine receptor binding in the living human brain by PET. Science. 1986;231:258–261. doi: 10.1126/science.2867601. [DOI] [PubMed] [Google Scholar]
  8. Frost JJ, Wagner HN, Jr, Dannals RF, Ravert HT, Links JM, Wilson AA, Burns HD, Wong DF, McPherson RW, Rosenbaum AE. Imaging opiate receptors in the human brain by positron tomography. J Comput Assist Tomogr. 1985;9:231–236. doi: 10.1097/00004728-198503000-00001. [DOI] [PubMed] [Google Scholar]
  9. Geiger BM, Haburcak M, Avena NM, Moyer MC, Hoebel BG, Pothos EN. Deficits of mesolimbic dopamine neurotransmission in rat dietary obesity. Neuroscience. 2009;159:1193–1199. doi: 10.1016/j.neuroscience.2009.02.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Giuliano C, Robbins TW, Nathan PJ, Bullmore ET, Everitt BJ. Inhibition of opioid transmission at the mu-opioid receptor prevents both food seeking and binge-like eating. Neuropsychopharmacology. 2012;37:2643–2652. doi: 10.1038/npp.2012.128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Glass MJ, Billington CJ, Levine AS. Opioids and food intake: distributed functional neural pathways? Neuropeptides. 1999;33:360–368. doi: 10.1054/npep.1999.0050. [DOI] [PubMed] [Google Scholar]
  12. Gorelick DA, Kim YK, Bencherif B, Boyd SJ, Nelson R, Copersino M, Endres CJ, Dannals RF, Frost JJ. Imaging brain mu-opioid receptors in abstinent cocaine users: time course and relation to cocaine craving. Biol Psychiatry. 2005;57:1573–1582. doi: 10.1016/j.biopsych.2005.02.026. [DOI] [PubMed] [Google Scholar]
  13. Gosnell BA, Levine AS. Reward systems and food intake: role of opioids. Int J Obes (Lond) 2009;33(Suppl 2):S54–S58. doi: 10.1038/ijo.2009.73. [DOI] [PubMed] [Google Scholar]
  14. Gunn RN, Lammertsma AA, Hume SP, Cunningham VJ. Parametric imaging of ligand-receptor binding in PET using a simplified reference region model. Neuroimage. 1997;6:279–287. doi: 10.1006/nimg.1997.0303. [DOI] [PubMed] [Google Scholar]
  15. Guo J, Simmons WK, Herscovitch P, Martin A, Hall KD. Striatal dopamine D2-like receptor correlation patterns with human obesity and opportunistic eating behavior. Mol Psychiatry. 2014;19:1078–1084. doi: 10.1038/mp.2014.102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Haghighi A, Melka MG, Bernard M, Abrahamowicz M, Leonard GT, Richer L, Perron M, Veillette S, Xu CJ, Greenwood CM, Dias A, El-Sohemy A, Gaudet D, Paus T, Pausova Z. Opioid receptor mu 1 gene, fat intake and obesity in adolescence. Mol Psychiatry. 2014;19:63–68. doi: 10.1038/mp.2012.179. [DOI] [PubMed] [Google Scholar]
  17. Haltia LT, Rinne JO, Merisaari H, Maguire RP, Savontaus E, Helin S, Nagren K, Kaasinen V. Effects of intravenous glucose on dopaminergic function in the human brain in vivo. Synapse. 2007;61:748–756. doi: 10.1002/syn.20418. [DOI] [PubMed] [Google Scholar]
  18. Haltia LT, Rinne JO, Helin S, Parkkola R, Nagren K, Kaasinen V. Effects of intravenous placebo with glucose expectation on human basal ganglia dopaminergic function. Synapse. 2008;62:682–688. doi: 10.1002/syn.20541. [DOI] [PubMed] [Google Scholar]
  19. Heinz A, Reimold M, Wrase J, Hermann D, Croissant B, Mundle G, Dohmen BM, Braus DH, Schumann G, Machulla HJ, Bares R, Mann K. Correlation of stable elevations in striatal mu-opioid receptor availability in detoxified alcoholic patients with alcohol craving: a positron emission tomography study using carbon 11-labeled carfentanil. Arch Gen Psychiatry. 2005;62:57–64. doi: 10.1001/archpsyc.62.1.57. [DOI] [PubMed] [Google Scholar]
  20. Henriksen G, Willoch F. Imaging of opioid receptors in the central nervous system. Brain. 2008;131:1171–1196. doi: 10.1093/brain/awm255. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Hirvonen J, Aalto S, Hagelberg N, Maksimow A, Ingman K, Oikonen V, Virkkala J, Någren K, Scheinin H. Measurement of central mu-opioid receptor binding in vivo with PET and [11C]carfentanil: a test-retest study in healthy subjects. Eur J Nucl Med Mol Imaging. 2009;36:275–286. doi: 10.1007/s00259-008-0935-6. [DOI] [PubMed] [Google Scholar]
  22. Jewett DM. A simple synthesis of [11C]methyl triflate. Int J Rad Appl Instrum A. 1992;43:1383–1385. doi: 10.1016/0883-2889(92)90012-4. [DOI] [PubMed] [Google Scholar]
  23. Johnson PM, Kenny PJ. Dopamine D2 receptors in addiction-like reward dysfunction and compulsive eating in obese rats. Nat Neurosci. 2010;13:635–641. doi: 10.1038/nn.2519. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Kenny PJ. Common cellular and molecular mechanisms in obesity and drug addiction. Nat Rev Neurosci. 2011;12:638–651. doi: 10.1038/nrn3105. [DOI] [PubMed] [Google Scholar]
  25. Koch T, Höllt V. Role of receptor internalization in opioid tolerance and dependence. Pharmacol Ther. 2008;117:199–206. doi: 10.1016/j.pharmthera.2007.10.003. [DOI] [PubMed] [Google Scholar]
  26. Langer O, Nagren K, Dolle F, Lundkvist C, Sandell J, Swahn CG, Vaufrey F, Crouzel C, Maziere B, Halldin C. Precursor synthesis and radiolabelling of the dopamine D-2 receptor ligand C-11 raclopride from C-11 methyl triflate. J Labelled Comp Radiopharm. 1999;42:1183–1193. [Google Scholar]
  27. Larsen P, Ulin J, Dahlstrom K, Jensen M. Synthesis of C-11 iodomethane by iodination of C-11 methane. Appl Radiat Isot. 1997;48:153–157. doi: 10.1016/S0969-8043(96)00177-7. [DOI] [Google Scholar]
  28. Le Merrer J, Becker JA, Befort K, Kieffer BL. Reward processing by the opioid system in the brain. Physiol Rev. 2009;89:1379–1412. doi: 10.1152/physrev.00005.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Martinez D, Saccone PA, Liu F, Slifstein M, Orlowska D, Grassetti A, Cook S, Broft A, Van Heertum R, Comer SD. Deficits in dopamine D(2) receptors and presynaptic dopamine in heroin dependence: commonalities and differences with other types of addiction. Biol Psychiatry. 2012;71:192–198. doi: 10.1016/j.biopsych.2011.08.024. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Michaelides M, Thanos PK, Kim R, Cho J, Ananth M, Wang GJ, Volkow ND. PET imaging predicts future body weight and cocaine preference. Neuroimage. 2012;59:1508–1513. doi: 10.1016/j.neuroimage.2011.08.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Nathan PJ, O'Neill BV, Bush MA, Koch A, Tao WX, Maltby K, Napolitano A, Brooke AC, Skeggs AL, Herman CS, Larkin AL, Ignar DM, Richards DB, Williams PM, Bullmore ET. Opioid receptor modulation of hedonic taste preference and food intake: a single-dose safety, pharmacokinetic, and pharmacodynamic investigation with GSK1521498, a novel mu-opioid receptor inverse agonist. J Clin Pharmacol. 2012;52:464–474. doi: 10.1177/0091270011399577. [DOI] [PubMed] [Google Scholar]
  32. Peciña S, Berridge KC. Hedonic hot spot in nucleus accumbens shell: where do mu-opioids cause increased hedonic impact of sweetness? J Neurosci. 2005;25:11777–11786. doi: 10.1523/JNEUROSCI.2329-05.2005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Peciña S, Smith KS. Hedonic and motivational roles of opioids in food reward: implications for overeating disorders. Pharmacol Biochem Behav. 2010;97:34–46. doi: 10.1016/j.pbb.2010.05.016. [DOI] [PubMed] [Google Scholar]
  34. Rosenthal R. Meta-analytic procedures for social research. Beverly Hills, CA: Sage; 1984. [Google Scholar]
  35. Rosenthal R, DiMatteo MR. Meta-analysis: recent developments in quantitative methods for literature reviews. Annu Rev Psychol. 2001;52:59–82. doi: 10.1146/annurev.psych.52.1.59. [DOI] [PubMed] [Google Scholar]
  36. Small DM, Jones-Gotman M, Dagher A. Feeding-induced dopamine release in dorsal striatum correlates with meal pleasantness ratings in healthy human volunteers. Neuroimage. 2003;19:1709–1715. doi: 10.1016/S1053-8119(03)00253-2. [DOI] [PubMed] [Google Scholar]
  37. Steele KE, Prokopowicz GP, Schweitzer MA, Magunsuon TH, Lidor AO, Kuwabawa H, Kumar A, Brasic J, Wong DF. Alterations of central dopamine receptors before and after gastric bypass surgery. Obes Surg. 2010;20:369–374. doi: 10.1007/s11695-009-0015-4. [DOI] [PubMed] [Google Scholar]
  38. Stice E, Spoor S, Bohon C, Small DM. Relation between obesity and blunted striatal response to food is moderated by TaqIA A1 allele. Science. 2008;322:449–452. doi: 10.1126/science.1161550. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Tellez LA, Medina S, Han W, Ferreira JG, Licona-Limón P, Ren X, Lam TT, Schwartz GJ, de Araujo IE. A gut lipid messenger links excess dietary fat to dopamine deficiency. Science. 2013;341:800–802. doi: 10.1126/science.1239275. [DOI] [PubMed] [Google Scholar]
  40. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15:273–289. doi: 10.1006/nimg.2001.0978. [DOI] [PubMed] [Google Scholar]
  41. Volkow ND, Wise RA. How can drug addiction help us understand obesity? Nat Neurosci. 2005;8:555–560. doi: 10.1038/nn1452. [DOI] [PubMed] [Google Scholar]
  42. Volkow ND, Wang GJ, Fowler JS, Logan J, Hitzemann R, Ding YS, Pappas N, Shea C, Piscani K. Decreases in dopamine receptors but not in dopamine transporters in alcoholics. Alcohol Clin Exp Res. 1996;20:1594–1598. doi: 10.1111/j.1530-0277.1996.tb05936.x. [DOI] [PubMed] [Google Scholar]
  43. Volkow ND, Chang L, Wang GJ, Fowler JS, Ding YS, Sedler M, Logan J, Franceschi D, Gatley J, Hitzemann R, Gifford A, Wong C, Pappas N. Low level of brain dopamine D2 receptors in methamphetamine abusers: association with metabolism in the orbitofrontal cortex. Am J Psychiatry. 2001;158:2015–2021. doi: 10.1176/appi.ajp.158.12.2015. [DOI] [PubMed] [Google Scholar]
  44. Volkow ND, Wang GJ, Telang F, Fowler JS, Thanos PK, Logan J, Alexoff D, Ding YS, Wong C, Ma Y, Pradhan K. Low dopamine striatal D2 receptors are associated with prefrontal metabolism in obese subjects: possible contributing factors. Neuroimage. 2008;42:1537–1543. doi: 10.1016/j.neuroimage.2008.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Volkow ND, Wang GJ, Baler RD. Reward, dopamine and the control of food intake: implications for obesity. Trends Cogn Sci. 2011;15:37–46. doi: 10.1016/j.tics.2010.11.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Volkow ND, Wang GJ, Tomasi D, Baler RD. Obesity and addiction: neurobiological overlaps. Obes Rev. 2013;14:2–18. doi: 10.1111/j.1467-789X.2012.01031.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Wang GJ, Volkow ND, Logan J, Pappas NR, Wong CT, Zhu W, Netusil N, Fowler JS. Brain dopamine and obesity. Lancet. 2001;357:354–357. doi: 10.1016/S0140-6736(00)03643-6. [DOI] [PubMed] [Google Scholar]
  48. Weerts EM, Wand GS, Kuwabara H, Munro CA, Dannals RF, Hilton J, Frost JJ, McCaul ME. Positron emission tomography imaging of mu- and delta-opioid receptor binding in alcohol-dependent and healthy control subjects. Alcohol Clin Exp Res. 2011;35:2162–2173. doi: 10.1111/j.1530-0277.2011.01565.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Whistler JL. Examining the role of mu opioid receptor endocytosis in the beneficial and side-effects of prolonged opioid use: from a symposium on new concepts in mu-opioid pharmacology. Drug Alcohol Depend. 2012;121:189–204. doi: 10.1016/j.drugalcdep.2011.10.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Yeomans MR, Gray RW. Opioid peptides and the control of human ingestive behaviour. Neurosci Biobehav Rev. 2002;26:713–728. doi: 10.1016/S0149-7634(02)00041-6. [DOI] [PubMed] [Google Scholar]
  51. Ziauddeen H, Chamberlain SR, Nathan PJ, Koch A, Maltby K, Bush M, Tao WX, Napolitano A, Skeggs AL, Brooke AC, Cheke L, Clayton NS, Sadaf Farooqi I, O'Rahilly S, Waterworth D, Song K, Hosking L, Richards DB, Fletcher PC, Bullmore ET. Effects of the mu-opioid receptor antagonist GSK1521498 on hedonic and consummatory eating behaviour: a proof of mechanism study in binge-eating obese subjects. Mol Psychiatry. 2013;18:1287–1293. doi: 10.1038/mp.2012.154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Zubieta JK, Gorelick DA, Stauffer R, Ravert HT, Dannals RF, Frost JJ. Increased mu opioid receptor binding detected by PET in cocaine-dependent men is associated with cocaine craving. Nat Med. 1996;2:1225–1229. doi: 10.1038/nm1196-1225. [DOI] [PubMed] [Google Scholar]

Articles from The Journal of Neuroscience are provided here courtesy of Society for Neuroscience

RESOURCES