Short communicationBlunted striatal responses to favorite-food cues in smokers
Introduction
A major barrier to quitting smoking (Chiolero et al., 2008) involves cessation-related weight gain (Filozof et al., 2004) and smoking-related leanness (Albanes et al., 1987, Chiolero et al., 2008). Concerns of weight gain may undermine smoking-cessation efforts and promote relapse (Meyers et al., 1997, Clark et al., 2004). Foods and drugs of abuse, including nicotine, activate brain circuits involved in motivation, learning, and behavioral reinforcement (Di Chiara, 2002, Volkow et al., 2008). Brain reward regions, such as the striatum, and increased dopaminergic transmission (Wang et al., 2001) therein are associated with exposure to palatable foods and food cues, with concomitant increases in food craving and eating motivations (Kelley et al., 2005, Small et al., 2001). Neural responses to food cues differ by body mass index (BMI; Martin et al., 2010, Stice et al., 2008, Stoeckel et al., 2008), with greater responsivity of brain-reward regions and subjective food craving associated with higher BMI (Pelchat et al., 2004, Simansky, 2005, Stice et al., 2008). Nicotine has been found to decrease appetite and neural differences between responses to food and control pictures in lean, never-smokers (Kroemer et al., 2013).
Given these data, we tested the hypothesis that smoking status would be associated with differences in neural responses in reward-motivation regions independent of BMI. We used an established fMRI task involving guided-imagery procedures employing individually generated personalized scripts. Using this procedure, we previously observed in obese and lean individuals differences in neural responses to favorite-food cue (Jastreboff et al., 2013). Here, we examined neural responses to favorite-food cues and stress in smokers and non-smokers of similar BMI. We hypothesized that smokers as compared to non-smokers would exhibit decreased neural response to favorite-food cues in corticostriatal-limbic regions.
Section snippets
Subjects
Men and women, between ages 19 and 50 years, who were current daily smokers (≥10 cigarettes/day) and non-smokers (never smokers), but otherwise healthy, were recruited from the community via advertisement. Smoking status was assessed by number of cigarettes/day, age at onset of smoking, years of smoking, carbon monoxide (CO) level, and Fagerstrom Test for Nicotine Dependence (FTND; Heatherton et al., 1991). Exclusion criteria included chronic medical conditions, psychiatric disorders (DSM-IV
Group demographics and fasting metabolic parameters
Twenty-three otherwise healthy smokers and 23 non-smokers were matched on age (mean ± SD, smokers: 26.8 ± 7.2 years, non-smokers: 27.4 ± 7.0 years), gender (both groups 39% female), and BMI (smokers: 28.0 ± 4.7 kg/m2, non-smokers: 26.9 ± 5.1 kg/m2) (Table 1). There were no between-group differences with regard to fasting insulin levels (mean ± SD, smokers: 14.1 ± 6.5 uU/mL; non-smokers: 13.9 ± 5.0 uU/mL, t(42) = 0.09, p = 0.93) or fasting plasma glucose levels (smokers: 93.3 ± 7.5 mg/dL, non-smokers: 93.5 ± 8.5 mg/dL; t(42) =
Discussion
In this study, we examined neural responses to favorite-food cues and stress in smokers and non-smokers. Smokers versus non-smokers exhibited relatively blunted corticostriatal-limbic activations to favorite-food cues. Consistent with findings in lean adolescents showing that light smokers relative to non-smokers exhibited diminished activation in the insula, putamen, inferior frontal cortex, and rolandic operculum in response to pictures of desirable foods (Rubinstein et al., 2011), we found
Role of funding source
This work was supported by NIDDK/NIH DRC P30DK045735, and R37-DK 20495, NIAAA RL1AA017539, NIDA PL1-DA024859, and the NIH Roadmap for Medical Research Common Fund Grants UL1-DE019586 and UL1-RR024139.
This data was presented in poster format as an abstract at the American Diabetes Association (ADA) 72th Scientific Sessions 2012. Philadelphia, PA.
Author contributions
M.N.P and R.S. were responsible for the study design and funding. M.N.P., I.B., and R.S. collected the data; A.M.J. and C.L. conducted data analysis; M.N.P, R.S., R.S.S., and A.M.J. contributed to the interpretation of the data; A.M.J, M.N.P., R.S.S., and R.S. wrote the manuscript.
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