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Relationship between hedonic hunger and serum levels of insulin, leptin and BDNF in the Iranian population
Physiology & Behavior 199 (2019) 84–87
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Physiology & Behavior journal homepage: www.elsevier.com/locate/physbeh
Relationship between hedonic hunger and serum levels of insulin, leptin and BDNF in the Iranian population Fereshteh Aliasgharia, Neda Lotfi Yaghina, Reza Mahdavib, a b
T
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Nutrition Research Center, Student Research Committee, School of Nutrition and Food Sciences, Tabriz University of Medical Sciences, Tabriz, Iran Nutrition Research Center, School of Nutrition and Food Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
A R T I C LE I N FO
A B S T R A C T
Keywords: Insulin Leptin BDNF Iranian women Hedonic hunger Power of food scale
Background: The prevalence of obesity has led the scientific community to investigate the cause of this multifactorial metabolic disorder. Highly palatable foods can stimulate hedonic hunger and could be a cause of obesity. In the present study, for the first time, the relationships between insulin, leptin and BDNF levels and hedonic hunger were investigated. Ninety overweight and obese women were studied. The demographic characteristics and anthropometric indices were measured and the power of food scale (PFS) questionnaire was used to assess hedonic hunger. In addition, the serum levels of fasting blood sugar (FBS), insulin, leptin and brainderived neurotrophic factor (BDNF) were determined. Regression analysis was used to predict hedonic hunger using age, body mass index (BMI) and body fat percentage (BFP) as covariates. The levels of insulin and leptin were found to be significantly correlated with the PFS total score and the scores of PFS-FA (food available), PFSFP (food present), and PFS-FT (food taste). The BDNF level showed a significant negative correlation only with PFS-FT. Multiple regression analysis showed statistically significant associations between hedonic hunger and levels of insulin [β coefficient: 1.29 (SE: 0.32), p < .001], leptin [β coefficient: 0.2 (SE: 0.09), p = .023] and BDNF [β coefficient: −6.29 (SE: 2.81), p = .028]. These three values were found to be predictors of hedonic hunger. The findings provide further evidence in favor of the role of these hormones in hedonic hunger.
1. Introduction Obesity is an important health problem in both developing and developed countries [1]. The global prevalence of overweight/obesity and concern over its increase are challenges for health authorities [2]. Obesity is a multifactorial metabolic disorder for which the main causes have not been well identified; however, excessive consumption of energy sources because of abnormal eating habits might be one of the major risk factors for obesity [3]. In general, food intake is regulated by two complementary drives: the homeostatic and hedonic pathways [4]. The former increases the motivation to eat following depletion of energy stores and thus controls the energy balance [4]. Hedonic hunger is the desire to consume food exclusively because of the gustatory rewards provided through the intermediacy of the mesolimbic dopamine system [5]. In this way, overconsumption in obesity may be due to either some defects in homeostatic signaling that fail to inhibit the motivation to eat or the excessive or inappropriate response to the hedonic aspects of food. However, it is assumed that reward eating or hedonic hunger is an important factor in the rapid development of obesity worldwide [6].
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Some appetite regulatory substances affect both homeostatic and hedonic control of eating [7]. Insulin, leptin and brain-derived neurotrophic factor (BDNF) are endogenous factors which are particularly involved in the modulation of both homeostatic and hedonic hunger [8,9]. Insulin is a hypothalamic energy regulatory signal which is released following food intake. In addition, insulin acts within the central nervous system (CNS) to decrease the food reward, possibly by direct alteration of dopaminergic signaling [10–13]. It has been shown that insulin administration decreases the intake of palatable food [14,15]. Obesity-induced insulin resistance causes changes in the homeostatic set point and may impair the suppressive effects of insulin on brain reward processing regions. Insulin action in the prefrontal cortex and hypothalamus may result in hedonic hunger and overeating [16–18]. Leptin is another appetite regulatory substance that takes part in homeostatic and reward regulating region activities [19]. A strong and positive correlation between leptin and body fat mass has been shown among weight-stable normal-weight adults and obese adults [20,21]. By acting as a body weight regulatory signal which reflects adipose tissue stores in the CNS, leptin affects energy homeostasis, eating behavior, appetite regulation and energy expenditure [22–25]. Leptin
Corresponding author. E-mail address: [email protected] (R. Mahdavi).
https://doi.org/10.1016/j.physbeh.2018.11.013 Received 18 March 2018; Received in revised form 3 November 2018; Accepted 12 November 2018 Available online 13 November 2018 0031-9384/ © 2018 Elsevier Inc. All rights reserved.
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2.4. Biochemical tests
resistance can lead to hyperleptinemia, which is associated with obesity [19,20,26,27]. It has been shown that the high levels of leptin might lead to exaggerated brain responses in the motivation-reward pathways, which in turn increases the risk of overconsumption and loss of eating control [19,26]. BDNF has also been identified as a key component of the hypothalamic pathway that controls body weight and energy homeostasis [28,29]. Previous studies suggested that BDNF synthesis in the brain reward regions is possibly involved in dopamine secretion and consequently in the regulation of hedonic hunger [30]. Disturbed BDNF signaling prevents the activity of the mesolimbic dopamine pathway, resulting in reward deficiency and compensatory overeating of palatable foods [8]. The present study investigated for the first time the relationships between hedonic hunger and the levels of biochemical modulators of appetite, including insulin, leptin and BDNF in adult Iranian women. We hypothesized that hedonic hunger may be associated with the levels of these endogenous factors that might affect the reward circuitry.
Blood samples were taken after a 12-h overnight fast. To separate the serum, the samples were centrifuged at 1000g at room temperature and the serum was kept at −80 °C until analysis. On the day of blood sampling, the fasting blood sugar (FBS) was determined enzymatically (commercial kits from Pars Azmun; Iran) using an auto-analyzer (Hitachi 717; Boehringer-Mannheim; Japan). The serum levels of insulin, leptin and BDNF were determined by human enzyme-linked immunosorbent assay (ELISA) kits (Monobind, USA; Mediagnost, Germany and Bioassay Technology Laboratory, China, respectively) based on manufacturer protocols. 2.5. Hedonic hunger The PFS scores were used to assess hedonic motivation for food in the participants [35]. PFS measures the appeal for highly palatable foods when an individual is not physiologically hungry (hedonic hunger). It measures the frequency of a strong desire to consume such food, but not how often or how much food is actually consumed. The PFS has been shown to have adequate internal consistency, test-retest reliability and convergent validity [35–37]. The PFS questionnaire consists of 15 items. Questions are answered on a 5-point Likert scale ranging from 1 (don't agree at all) to 5 (strongly agree). It consists of the three factors of “food available” (PFSFA), “food present” (PFS-FP) and “food tasted” (PFS-FT). A total mean score obtained from the questionnaire represents participant responsiveness to the food environment. A score of 15 to 75 is possible for each participant and the score for each factor is calculated by the scores of all related items. The PFS is intended to be scored by adding all items and dividing by the number of items involved. However, through an oversight, we simply used the sum scores on the PFS.
2. Material and methods 2.1. Participants and study design This cross-sectional study was conducted in the city of Tabriz in northwestern Iran from December 2016 to August 2017. Ninety adult women aged 19 to 50 years were studied. Before the onset of the study, the aim of the study was described for the participants and they all signed informed consent forms. The protocol of the study was approved by the ethical committee of Tabriz University of Medical Sciences [IR.TBZMED.REC.1395.1013]. The subjects were recruited through posters placed in public places and health care facilities. A total of 200 women applied to participate. Of these, 90 met the inclusion criteria and were enrolled in the study. On the first visit, the inclusion criteria were assessed and demographic information was collected and the power of food scale (PFS) questionnaire was administered. The women were required to be pre-menopausal and having had lived in Tabriz for at least five consecutive years prior to their enrolment in the study. Women with a body mass index (BMI) of < 18.5 kg/m2 or > 40 kg/m2, pregnant or lactating women, those with recent weight loss or participation in weight loss programs, current smokers, women with a recent history of supplement intake (within the previous 3 months), women with psychotic disorders, substance abuse, alcoholism and/or a serious medical illness such as cancer, heart disease or diabetes were excluded from the study.
2.6. Statistical analysis Data were analyzed using version 23.0 of SPSS software (SPSS; USA). Descriptive results are presented as mean ± standard deviation (SD). The Kolmogorov-Smirnov goodness-of-fit test was used to assess the normality of the distribution of the variables. The relationships between the PFS scores and other variables were assessed using Pearson's correlation coefficient. To investigate the relationship between the levels of the biochemical factors and the PFS scores, multiple linear regression analysis was used. We started with a backward strategy including age, BMI, BFP, insulin, leptin and BDNF in the model. The variables of age, BMI and BFP were removed and the levels of insulin, leptin and BDNF remained in the final model. In all statistical tests, a p-value < .05 was considered significant.
2.2. Anthropometric and body composition measurements 3. Results The body weight and height of the participants wearing light clothes without shoes were measured after a 12-h fast. Weight was measured with a calibrated electronic scale (Model 770; Seca; USA) with an accuracy of 0.1 kg. A fixed stadiometer was used to measure height to the nearest 0.1 cm. BMI was calculated by dividing the participant weight by the square of their height (kg/m2). The body fat percentage (BFP) and total body water (TBW) was evaluated by bioelectrical impedance analysis (BC-418MA; Tanita; Japan) [31–33].
Table 1 provides the demographic, anthropometric and biochemical characteristics of the participants. The mean age and BMI of participants were 34.64 ± 6.79 years and 32.16 ± 4.25 kg/m2, respectively. The PFS total score was 48.35 ± 15.17. The levels of FBS, insulin, leptin and BDNF were 84.97 ± 10.95 IU/ml, 11.30 ± 4.51 mg/dl, 34.37 ± 16.63 ng/ml and 2.07 ± 0.48 ng/ml, respectively. The majority (86.7%) of participants were inactive, about 10% were moderately active and 3.3% were highly active. Table 2 shows the correlations between PFS scores and BMI with BFP, physical activity, and biochemical factors. Significant associations were observed between the PFS total score and the PFS-FA, PFS-FP and PFS-FT and insulin and leptin levels. The level of BDNF correlated negatively only with the PFS-FT. In addition, BMI significantly correlated with the levels of BFP and leptin. No significant relationships were observed between BFP and physical activity and the PFS total or the scores of its three factors.
2.3. Physical activity The validated short form of the Persian version of the International Physical Activity Questionnaire (IPAQ) [34] was used to estimate the level of physical activity of each participant. The participants were divided into low, moderate and high activity groups according to their scores on the questionnaire. 85
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and the levels of biochemical factors were found, even after controlling for BMI and BFM, indicating that PFS has little or no correlation with BMI [37]. A significant relationship was observed between the PFS total score and the serum level of insulin. Evidence has shown that the brain is sensitive to insulin [40] and is involved in brain activities such as regulation of body metabolism, memory, pleasure and non-homeostatic feeding by signaling within mesolimbic reward circuits [40]. Because of the interaction between insulin and dopamine (DA) neurons in the VTA, current evidence suggests that insulin may affect the activity of VTA DA neurons and their output [12]. Moreover, obesity is commonly associated with hyperinsulinemia and, consequently, peripheral insulin resistance might be related to cerebral insulin resistance [41–43]. Individuals who are brain insulin resistant have a low potential to lose weight through a lifestyle intervention program [44]. Studies have shown that the insulin modulating effect of reward eating circuits is impaired by obesity [45–48]. In the present study, a significant relationship between the levels of leptin and hedonic hunger was observed that may be related to leptin resistance in reward circuitry. Studies suggest that leptin suppresses dopamine release and the activity of VTA DA neurons and thus regulates hedonic hunger and reward eating [25,39,49]. Some authors have assumed that obese individuals with considerable levels of leptin become resistant to the catabolic effect of leptin, including satiety signaling [50]. Jastreboff et al. observed a significant association between higher leptin concentrations and hyper-responsiveness of brain motivation-reward areas to palatable food pictures. This implies that the dysfunction of leptin signaling may lead to overconsumption of foods [19]. Hopkins et al. showed a positive relationship between the serum levels of leptin and food palatability even after adjusting for BMI or fat mass [51]. Elevated leptin levels and the desire for foods with hedonic value such as high fat foods, mainly high-fat and high-carbohydrate combinations, have also been stated in obese subjects [52]. BDNF aneroxigenic effect was first revealed by Lapchak and Hefti's study in which weight gain in rats reduced as a result of chronic intracerebroventricular infusion of BDNF [53]. However, uncertainty surrounds the relation between BDNF levels and eating behavior. In the present study, multiple linear regression analysis showed that the BDNF level had a significant inverse correlation only with PFS-FT (Table 3). It has been suggested that BDNF is a natural modulator of hedonic food intake; therefore, deregulation of BDNF signaling in the reward circuitry increases eating motivation without homeostatic need [54]. In support of these findings, Cordeira et al. showed that rats with lower levels of BDNF had higher desire for foods with hedonic value but no change in their desire for standard chow [8]. In this regard, human studies are limited; however, some studies indicate that BDNF insufficiency results in eating disorders, hyperphagia, and obesity [55,56].
Table 1 Demographic, anthropometric and biochemical characteristics in the studied participants. Variables
Mean ± SD
Age (year) BMI (kg/m2) BFP (%) TBW (kg) PFS scores Total PFS-FA PFS-FP PFS-FT FBS (mg/dl) Insulin (IU/ml) Leptin (ng/ml) BDNF (ng/ml) Physical activity levels Low Moderate High
34.64 32.16 39.14 35.65
± ± ± ±
6.79 4.25 4.92 3.33
48.35 ± 15.17 18.48 ± 7.27 14.60 ± 4.59 15.26 ± 4.94 84.97 ± 10.95 11.30 ± 4.51 34.37 ± 16.63 2.07 ± 0.48 n (%) 78 (86.7) 9 (10) 3 (3.3)
Table 2 The Pearson's correlations between PFS scores and BMI with physical activity, BFP, and the biochemical indices. Variables
PFS total score
PFS-FA score
PFS-FP score
PFS-FT score
BMI (kg/ m2)
BFP Physical activity Insulin(IU/ml) Leptin(ng/ml) BDNF(ng/ml)
0.161 0.124 0.475⁎⁎ 0.360⁎⁎ −0.183
0.203 0.080 0.487⁎⁎ 0.351⁎⁎ −0.127
0.210⁎ 0.107 0.403⁎⁎ 0.364⁎⁎ −0.166
0.001 0.164 0.368⁎⁎ 0.250⁎ −0.222⁎
0.775⁎⁎ 0.055 0.195 0.425⁎⁎ 0.139
PFS: Power of Food Scale; FA: Food available; FP: Food Present; FT: Food Taste; BMI: body mass index; BFP: body fat percentage; BDNF: brain derived neurotrophic factor. Pearson's correlation coefficient (*P < .05). Pearson's r correlation coefficient (**P < .01). Table 3 Multiple linear regression analysis of the relationship between PFS scores and the studied hormones. Independent variables
Regression coefficient (B)
SE
Standardized β coefficient
p-Value
Insulin (IU/ml) Leptin (ng/ml) BDNF (ng/ml)
1.29 0.20 −6.29
0.32 0.09 2.81
0.385 0.228 −0.202
< 0.001 0.023 0.028
Dependent Variable: Power of Food Scale aggregated score. Removed variables: Age, BMI, and BFP (P > .05). BDNF: brain derived neurotrophic factor.
5. Conclusions
Table 3 indicates the relation between hedonic hunger and the levels of the studied biochemical factors controlling for age, BMI and BFP. Linear regression analysis showed statistically significant associations between hedonic hunger and the levels of insulin (p < .001) and leptin (p = .023). In addition, a significant inverse association was observed between hedonic hunger and the level of BDNF (p = .028).
In the present study, the relationships between hedonic hunger and the levels of insulin, leptin, and BDNF were investigated. We did not investigate the mechanisms by which these hormones could act on hedonic hunger. Overall, this study provides evidence that leptin, insulin and BDNF may play a physiological role in modulating the reward pathway responses to food cues and hedonic hunger. It is highly recommended to carry out further studies with normal weight individuals as a control group to investigate the mechanisms by which these biochemical substances act.
4. Discussion Hedonic hunger is a major determinant of obesity [6]. It is controlled by dopaminergic neurons in the ventral tegmental area (VTA) and other regions in the brain that form a reward circuit [38]. There is a close relationship between hedonic hunger and endogenous regulators of appetite [39]. In the present study, we investigated the relationship between hedonic hunger and the levels of insulin, leptin, and BDNF in adult Iranian women. Significant associations between the PFS score
Acknowledgement The authors thank the participants for their time, patience, and cooperation. 86
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Funding
adult obesity, J. Clin. Endocrinol. Metabol. 92 (2007) 948–954. [28] S. Fanous, E.F. Terwilliger, R.P. Hammer Jr, E.M. Nikulina, Viral depletion of VTA BDNF in rats modulates social behavior, consequences of intermittent social defeat stress, and long-term weight regulation, Neurosci. Lett. 502 (2011) 192–196. [29] C.L. Roth, C. Elfers, U. Gebhardt, H.L. Müller, T. Reinehr, Brain-derived neurotrophic factor and its relation to leptin in obese children before and after weight loss, Metabolism 62 (2013) 226–234. [30] O. Berton, C.A. McClung, R.J. Dileone, V. Krishnan, W. Renthal, S.J. Russo, et al., Essential role of BDNF in the mesolimbic dopamine pathway in social defeat stress, Science 311 (2006) 864–868. [31] Z. Boneva-Asiova, M. Boyanov, Body composition analysis by leg-to-leg bioelectrical impedance and dual-energy X–ray absorptiometry in non-obese and obese individuals, Diabetes Obes. Metab. 10 (2008) 1012–1018. [32] M.Y. Jaffrin, R. Kieffer, M.-V. Moreno, Evaluation of a foot-to-foot impedance meter measuring extracellular fluid volume in addition to fat-free mass and fat tissue mass, Nutrition 21 (2005) 815–824. [33] D.C. Frankenfield, Bias and accuracy of resting metabolic rate equations in nonobese and obese adults, Clin. Nutr. 32 (2013) 976–982. [34] M.B. Moghaddam, F.B. Aghdam, M.A. Jafarabadi, H. Allahverdipour, S.D. Nikookheslat, S. Safarpour, The Iranian Version of International Physical Activity Questionnaire (IPAQ) in Iran: content and construct validity, factor structure, internal consistency and stability, World Appl. Sci. 18 (2012) 1073–1080. [35] J.C. Cappelleri, A.G. Bushmakin, R.A. Gerber, N.K. Leidy, C.C. Sexton, J. Karlsson, et al., Evaluating the Power of Food Scale in obese subjects and a general sample of individuals: development and measurement properties, Int. J. Obes. 33 (2009) 913. [36] M.R. Lowe, M.L. Butryn, E.R. Didie, R.A. Annunziato, J.G. Thomas, C.E. Crerand, et al., The Power of Food Scale. A new measure of the psychological influence of the food environment, Appetite 53 (2009) 114–118. [37] H. Espel-Huynh, A. Muratore, M. Lowe, A narrative review of the construct of hedonic hunger and its measurement by the Power of Food Scale, Obes. Sci. Pract. 4 (2018) 238–249. [38] C. Luck Gibson, Characterizing the Mesolimbic Dopamine Reward Pathway in a Magel2-null Mouse, a Model of Prader-Willi Syndrome, (2016). [39] E. Egecioglu, K.P. Skibicka, C. Hansson, M. Alvarez-Crespo, P.A. Friberg, E. Jerlhag, et al., Hedonic and incentive signals for body weight control, Rev. Endocr. Metab. Disord. 12 (2011) 141–151. [40] M. Heni, S. Kullmann, H. Preissl, A. Fritsche, H.-U. Häring, Impaired insulin action in the human brain: causes and metabolic consequences, Nat. Rev. Endocrinol. 11 (2015) 701. [41] C. Weyer, T. Funahashi, S. Tanaka, K. Hotta, Y. Matsuzawa, R.E. Pratley, et al., Hypoadiponectinemia in obesity and type 2 diabetes: close association with insulin resistance and hyperinsulinemia, J. Clin. Endocrinol. Metabol. 86 (2001) 1930–1935. [42] O. Tschritter, H. Preissl, A.M. Hennige, M. Stumvoll, K. Porubska, R. Frost, et al., The cerebrocortical response to hyperinsulinemia is reduced in overweight humans: a magnetoencephalographic study, Proc. Natl. Acad. Sci. 103 (2006) 12103–12108. [43] A.M. Jastreboff, R. Sinha, C. Lacadie, D.M. Small, R.S. Sherwin, M.N. Potenza, Neural correlates of stress-and food cue–induced food craving in obesity, Diabetes Care 36 (2013) 394–402. [44] O. Tschritter, H. Preissl, A. Hennige, T. Sartorius, K. Stingl, M. Heni, et al., High cerebral insulin sensitivity is associated with loss of body fat during lifestyle intervention, Diabetologia 55 (2012) 175–182. [45] K.T. Stingl, S. Kullmann, M. Guthoff, M. Heni, A. Fritsche, H. Preissl, Insulin modulation of magnetoencephalographic resting state dynamics in lean and obese subjects, Front. Syst. Neurosci. 4 (2010). [46] M. Guthoff, K.T. Stingl, O. Tschritter, M. Rogic, M. Heni, K. Stingl, et al., The insulin-mediated modulation of visually evoked magnetic fields is reduced in obese subjects, PLoS One 6 (2011) e19482. [47] N.B. Kroemer, L. Krebs, A. Kobiella, O. Grimm, S. Vollstädt-Klein, U. Wolfensteller, et al., (still) longing for food: insulin reactivity modulates response to food pictures, Hum. Brain Mapp. 34 (2013) 2367–2380. [48] L.J. Tiedemann, S.M. Schmid, J. Hettel, K. Giesen, P. Francke, C. Büchel, et al., Central insulin modulates food valuation via mesolimbic pathways, Nature Communications, vol. 8, 2017 p. ncomms16052. [49] J.D. Hommel, R. Trinko, R.M. Sears, D. Georgescu, Z.-W. Liu, X.-B. Gao, et al., Leptin receptor signaling in midbrain dopamine neurons regulates feeding, Neuron 51 (2006) 801–810. [50] M. Grosshans, C. Vollmert, S. Vollstädt-Klein, H. Tost, S. Leber, P. Bach, et al., Association of leptin with food cue–induced activation in human reward pathways, Arch. Gen. Psychiatry 69 (2012) 529–537. [51] M. Hopkins, C. Gibbons, P. Caudwell, D.-L. Webb, P.M. Hellström, E. Näslund, et al., Fasting leptin is a metabolic determinant of food reward in overweight and obese individuals during chronic aerobic exercise training, Int. J. Endocrinol. 2014 (2014). [52] L. Karhunen, R. Lappalainen, S. Haffner, R. Valve, H. Tuorila, H. Miettinen, et al., Serum leptin, food intake and preferences for sugar and fat in obese women, Int. J. Obes. Relat. Metab. Disord. 22 (1998). [53] P.A. Lapchak, F. Hefti, BDNF and NGF treatment in lesioned rats: effects on cholinergic function and weight gain, Neuroreport 3 (1992) 405–408. [54] M. Rios, New insights into the mechanisms underlying the effects of BDNF on eating behavior, Neuropsychopharmacology 36 (2011) 368. [55] H. Rosas-Vargas, J.D. Martínez-Ezquerro, T. Bienvenu, Brain-derived neurotrophic factor, food intake regulation, and obesity, Arch. Med. Res. 42 (2011) 482–494. [56] J.C. Han, Q.-R. Liu, M. Jones, R.L. Levinn, C.M. Menzie, K.S. Jefferson-George, et al., Brain-derived neurotrophic factor and obesity in the WAGR syndrome, N. Engl. J. Med. 359 (2008) 918–927.
This work was supported by the Tabriz University of Medical Sciences, Vice Chancellor for Research Affairs [grant number: 5/D/ 996710]. References [1] T. Bhurosy, R. Jeewon, Overweight and obesity epidemic in developing countries: a problem with diet, physical activity, or socioeconomic status? Sci. World J. 2014 (2014). [2] B. Nikooyeh, Z. Abdollahi, F. Salehi, S. Nourisaeidlou, M. Hajifaraji, M. Zahedirad, et al., Prevalence of obesity and overweight and its associated factors in urban adults from West Azerbaijan, Iran: the National Food and Nutritional Surveillance Program (NFNSP), Nutr. Food Sci. Res. 3 (2016) 21–26. [3] M.W. Schwartz, S.C. Woods, D. Porte Jr., R.J. Seeley, D.G. Baskin, Central nervous system control of food intake, Nature 404 (2000) 661. [4] M. Lutter, E.J. Nestler, Homeostatic and hedonic signals interact in the regulation of food intake, J. Nutr. 139 (2009) 629–632. [5] M. Nigro, A. Monteleone, V. Di Marzo, R. Dalle Grave, M. El Ghoch, M. Cimino, et al., Neuroendocrine responses to hedonic eating in obese patients with and without binge eating, Eur. Psychiatry 30 (2015) 1351. [6] J.F. Davis, D.L. Choi, S.C. Benoit, Insulin, leptin and reward, Trends Endocrinol. Metab. 21 (2010) 68–74. [7] D.P. Figlewicz, A.J. Sipols, Energy regulatory signals and food reward, Pharmacol. Biochem. Behav. 97 (2010) 15–24. [8] J.W. Cordeira, L. Frank, M. Sena-Esteves, E.N. Pothos, M. Rios, Brain-derived neurotrophic factor regulates hedonic feeding by acting on the mesolimbic dopamine system, J. Neurosci. 30 (2010) 2533–2541. [9] P. Monteleone, P. Scognamiglio, A.M. Monteleone, D. Perillo, B. Canestrelli, M. Maj, Gastroenteric hormone responses to hedonic eating in healthy humans, Psychoneuroendocrinology 38 (2013) 1435–1441. [10] D. Figlewicz, S. Evans, J. Murphy, M. Hoen, D. Baskin, Expression of receptors for insulin and leptin in the ventral tegmental area/substantia nigra (VTA/SN) of the rat, Brain Res. 964 (2003) 107–115. [11] S. Kullmann, M. Heni, R. Veit, K. Scheffler, J. Machann, H.-U. Häring, et al., Intranasal insulin enhances brain functional connectivity mediating the relationship between adiposity and subjective feeling of hunger, Sci. Rep. 7 (2017). [12] D. Mebel, J. Wong, Y. Dong, S. Borgland, Insulin in the ventral tegmental area reduces hedonic feeding and suppresses dopamine concentration via increased reuptake, Eur. J. Neurosci. 36 (2012) 2336–2346. [13] T.C. Adam, S. Tsao, K.A. Page, H. Hu, R.E. Hasson, M.I. Goran, Insulin sensitivity and brain reward activation in overweight Hispanic girls: a pilot study, Pediatr. Obes. 10 (2015) 30–36. [14] M. Hallschmid, S. Higgs, M. Thienel, V. Ott, H. Lehnert, Postprandial administration of intranasal insulin intensifies satiety and reduces intake of palatable snacks in women, Diabetes 61 (2012) 782–789. [15] K. Jauch-Chara, A. Friedrich, M. Rezmer, U.H. Melchert, H.G. Scholand-Engler, M. Hallschmid, et al., Intranasal insulin suppresses food intake via enhancement of brain energy levels in humans, Diabetes 61 (2012) 2261–2268. [16] S. Luo, K.A. Page, The roles of circulating peripheral appetite hormones and insulin resistance on brain reward responses to food cues, Obesity: Human Studies in Body Weight Regulation, Endocrine Society, 2015OR40-6-OR40-6. [17] S. Kullmann, M. Heni, R. Veit, K. Scheffler, J. Machann, H.-U. Häring, et al., Selective insulin resistance in homeostatic and cognitive control brain areas in overweight and obese adults, Diabetes Care 38 (2015) 1044–1050. [18] Y.S. Lee, O. Kim, R. Osborne, S. Sasik, A. Chen Schenk, et al., Increased adipocyte O2 consumption triggers HIF-1α, causing inflammation and insulin resistance in obesity, Cell 157 (2014) 1339–1352. [19] A.M. Jastreboff, C. Lacadie, D. Seo, J. Kubat, C. Giannini, M. Savoye, et al., Leptin is associated with exaggerated brain reward and emotion responses to food images in adolescent obesity, Diabetes Care 37 (2014) 3061–3068. [20] Z.A. Knight, K.S. Hannan, M.L. Greenberg, J.M. Friedman, Hyperleptinemia is required for the development of leptin resistance, PLoS One 5 (2010) e11376. [21] Y. Zhang, R. Proenca, M. Maffei, M. Barone, L. Leopold, J.M. Friedman, Positional cloning of the mouse obese gene and its human homologue, Nature 372 (1994) 425–432. [22] Q. Wang, C. Bing, K. Al-Barazanji, D.E. Mossakowaska, X.-M. Wang, D.L. McBay, et al., Interactions between leptin and hypothalamic neuropeptide Y neurons in the control of food intake and energy homeostasis in the rat, Diabetes 46 (1997) 335–341. [23] J.R. McDuffie, P.A. Riggs, K.A. Calis, R.J. Freedman, E.A. Oral, A.M. Depaoli, et al., Effects of exogenous leptin on satiety and satiation in patients with lipodystrophy and leptin insufficiency, J. Clin. Endocrinol. Metabol. 89 (2004) 4258–4263. [24] V.K. Yadav, F. Oury, N. Suda, Z.-W. Liu, X.-B. Gao, C. Confavreux, et al., A serotonin-dependent mechanism explains the leptin regulation of bone mass, appetite, and energy expenditure, Cell 138 (2009) 976–989. [25] D.M. Opland, G.M. Leinninger, M.G. Myers, Modulation of the mesolimbic dopamine system by leptin, Brain Res. 1350 (2010) 65–70. [26] R. Miller, M. Tanofsky-Kraff, L.B. Shomaker, S.E. Field, L. Hannallah, S.A. Reina, et al., Serum leptin and loss of control eating in children and adolescents, Int. J. Obes. 38 (2005) 397, 2014. [27] A.F. Fleisch, N. Agarwal, M.D. Roberts, J.C. Han, K.R. Theim, A. Vexler, et al., Influence of serum leptin on weight and body fat growth in children at high risk for
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Relationship between hedonic hunger and serum levels of insulin, leptin and BDNF in the Iranian population Fereshteh Aliasgharia, Neda Lotfi Yaghina, Reza Mahdavib, a b
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Nutrition Research Center, Student Research Committee, School of Nutrition and Food Sciences, Tabriz University of Medical Sciences, Tabriz, Iran Nutrition Research Center, School of Nutrition and Food Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
A R T I C LE I N FO
A B S T R A C T
Keywords: Insulin Leptin BDNF Iranian women Hedonic hunger Power of food scale
Background: The prevalence of obesity has led the scientific community to investigate the cause of this multifactorial metabolic disorder. Highly palatable foods can stimulate hedonic hunger and could be a cause of obesity. In the present study, for the first time, the relationships between insulin, leptin and BDNF levels and hedonic hunger were investigated. Ninety overweight and obese women were studied. The demographic characteristics and anthropometric indices were measured and the power of food scale (PFS) questionnaire was used to assess hedonic hunger. In addition, the serum levels of fasting blood sugar (FBS), insulin, leptin and brainderived neurotrophic factor (BDNF) were determined. Regression analysis was used to predict hedonic hunger using age, body mass index (BMI) and body fat percentage (BFP) as covariates. The levels of insulin and leptin were found to be significantly correlated with the PFS total score and the scores of PFS-FA (food available), PFSFP (food present), and PFS-FT (food taste). The BDNF level showed a significant negative correlation only with PFS-FT. Multiple regression analysis showed statistically significant associations between hedonic hunger and levels of insulin [β coefficient: 1.29 (SE: 0.32), p < .001], leptin [β coefficient: 0.2 (SE: 0.09), p = .023] and BDNF [β coefficient: −6.29 (SE: 2.81), p = .028]. These three values were found to be predictors of hedonic hunger. The findings provide further evidence in favor of the role of these hormones in hedonic hunger.
1. Introduction Obesity is an important health problem in both developing and developed countries [1]. The global prevalence of overweight/obesity and concern over its increase are challenges for health authorities [2]. Obesity is a multifactorial metabolic disorder for which the main causes have not been well identified; however, excessive consumption of energy sources because of abnormal eating habits might be one of the major risk factors for obesity [3]. In general, food intake is regulated by two complementary drives: the homeostatic and hedonic pathways [4]. The former increases the motivation to eat following depletion of energy stores and thus controls the energy balance [4]. Hedonic hunger is the desire to consume food exclusively because of the gustatory rewards provided through the intermediacy of the mesolimbic dopamine system [5]. In this way, overconsumption in obesity may be due to either some defects in homeostatic signaling that fail to inhibit the motivation to eat or the excessive or inappropriate response to the hedonic aspects of food. However, it is assumed that reward eating or hedonic hunger is an important factor in the rapid development of obesity worldwide [6].
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Some appetite regulatory substances affect both homeostatic and hedonic control of eating [7]. Insulin, leptin and brain-derived neurotrophic factor (BDNF) are endogenous factors which are particularly involved in the modulation of both homeostatic and hedonic hunger [8,9]. Insulin is a hypothalamic energy regulatory signal which is released following food intake. In addition, insulin acts within the central nervous system (CNS) to decrease the food reward, possibly by direct alteration of dopaminergic signaling [10–13]. It has been shown that insulin administration decreases the intake of palatable food [14,15]. Obesity-induced insulin resistance causes changes in the homeostatic set point and may impair the suppressive effects of insulin on brain reward processing regions. Insulin action in the prefrontal cortex and hypothalamus may result in hedonic hunger and overeating [16–18]. Leptin is another appetite regulatory substance that takes part in homeostatic and reward regulating region activities [19]. A strong and positive correlation between leptin and body fat mass has been shown among weight-stable normal-weight adults and obese adults [20,21]. By acting as a body weight regulatory signal which reflects adipose tissue stores in the CNS, leptin affects energy homeostasis, eating behavior, appetite regulation and energy expenditure [22–25]. Leptin
Corresponding author. E-mail address: [email protected] (R. Mahdavi).
https://doi.org/10.1016/j.physbeh.2018.11.013 Received 18 March 2018; Received in revised form 3 November 2018; Accepted 12 November 2018 Available online 13 November 2018 0031-9384/ © 2018 Elsevier Inc. All rights reserved.
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2.4. Biochemical tests
resistance can lead to hyperleptinemia, which is associated with obesity [19,20,26,27]. It has been shown that the high levels of leptin might lead to exaggerated brain responses in the motivation-reward pathways, which in turn increases the risk of overconsumption and loss of eating control [19,26]. BDNF has also been identified as a key component of the hypothalamic pathway that controls body weight and energy homeostasis [28,29]. Previous studies suggested that BDNF synthesis in the brain reward regions is possibly involved in dopamine secretion and consequently in the regulation of hedonic hunger [30]. Disturbed BDNF signaling prevents the activity of the mesolimbic dopamine pathway, resulting in reward deficiency and compensatory overeating of palatable foods [8]. The present study investigated for the first time the relationships between hedonic hunger and the levels of biochemical modulators of appetite, including insulin, leptin and BDNF in adult Iranian women. We hypothesized that hedonic hunger may be associated with the levels of these endogenous factors that might affect the reward circuitry.
Blood samples were taken after a 12-h overnight fast. To separate the serum, the samples were centrifuged at 1000g at room temperature and the serum was kept at −80 °C until analysis. On the day of blood sampling, the fasting blood sugar (FBS) was determined enzymatically (commercial kits from Pars Azmun; Iran) using an auto-analyzer (Hitachi 717; Boehringer-Mannheim; Japan). The serum levels of insulin, leptin and BDNF were determined by human enzyme-linked immunosorbent assay (ELISA) kits (Monobind, USA; Mediagnost, Germany and Bioassay Technology Laboratory, China, respectively) based on manufacturer protocols. 2.5. Hedonic hunger The PFS scores were used to assess hedonic motivation for food in the participants [35]. PFS measures the appeal for highly palatable foods when an individual is not physiologically hungry (hedonic hunger). It measures the frequency of a strong desire to consume such food, but not how often or how much food is actually consumed. The PFS has been shown to have adequate internal consistency, test-retest reliability and convergent validity [35–37]. The PFS questionnaire consists of 15 items. Questions are answered on a 5-point Likert scale ranging from 1 (don't agree at all) to 5 (strongly agree). It consists of the three factors of “food available” (PFSFA), “food present” (PFS-FP) and “food tasted” (PFS-FT). A total mean score obtained from the questionnaire represents participant responsiveness to the food environment. A score of 15 to 75 is possible for each participant and the score for each factor is calculated by the scores of all related items. The PFS is intended to be scored by adding all items and dividing by the number of items involved. However, through an oversight, we simply used the sum scores on the PFS.
2. Material and methods 2.1. Participants and study design This cross-sectional study was conducted in the city of Tabriz in northwestern Iran from December 2016 to August 2017. Ninety adult women aged 19 to 50 years were studied. Before the onset of the study, the aim of the study was described for the participants and they all signed informed consent forms. The protocol of the study was approved by the ethical committee of Tabriz University of Medical Sciences [IR.TBZMED.REC.1395.1013]. The subjects were recruited through posters placed in public places and health care facilities. A total of 200 women applied to participate. Of these, 90 met the inclusion criteria and were enrolled in the study. On the first visit, the inclusion criteria were assessed and demographic information was collected and the power of food scale (PFS) questionnaire was administered. The women were required to be pre-menopausal and having had lived in Tabriz for at least five consecutive years prior to their enrolment in the study. Women with a body mass index (BMI) of < 18.5 kg/m2 or > 40 kg/m2, pregnant or lactating women, those with recent weight loss or participation in weight loss programs, current smokers, women with a recent history of supplement intake (within the previous 3 months), women with psychotic disorders, substance abuse, alcoholism and/or a serious medical illness such as cancer, heart disease or diabetes were excluded from the study.
2.6. Statistical analysis Data were analyzed using version 23.0 of SPSS software (SPSS; USA). Descriptive results are presented as mean ± standard deviation (SD). The Kolmogorov-Smirnov goodness-of-fit test was used to assess the normality of the distribution of the variables. The relationships between the PFS scores and other variables were assessed using Pearson's correlation coefficient. To investigate the relationship between the levels of the biochemical factors and the PFS scores, multiple linear regression analysis was used. We started with a backward strategy including age, BMI, BFP, insulin, leptin and BDNF in the model. The variables of age, BMI and BFP were removed and the levels of insulin, leptin and BDNF remained in the final model. In all statistical tests, a p-value < .05 was considered significant.
2.2. Anthropometric and body composition measurements 3. Results The body weight and height of the participants wearing light clothes without shoes were measured after a 12-h fast. Weight was measured with a calibrated electronic scale (Model 770; Seca; USA) with an accuracy of 0.1 kg. A fixed stadiometer was used to measure height to the nearest 0.1 cm. BMI was calculated by dividing the participant weight by the square of their height (kg/m2). The body fat percentage (BFP) and total body water (TBW) was evaluated by bioelectrical impedance analysis (BC-418MA; Tanita; Japan) [31–33].
Table 1 provides the demographic, anthropometric and biochemical characteristics of the participants. The mean age and BMI of participants were 34.64 ± 6.79 years and 32.16 ± 4.25 kg/m2, respectively. The PFS total score was 48.35 ± 15.17. The levels of FBS, insulin, leptin and BDNF were 84.97 ± 10.95 IU/ml, 11.30 ± 4.51 mg/dl, 34.37 ± 16.63 ng/ml and 2.07 ± 0.48 ng/ml, respectively. The majority (86.7%) of participants were inactive, about 10% were moderately active and 3.3% were highly active. Table 2 shows the correlations between PFS scores and BMI with BFP, physical activity, and biochemical factors. Significant associations were observed between the PFS total score and the PFS-FA, PFS-FP and PFS-FT and insulin and leptin levels. The level of BDNF correlated negatively only with the PFS-FT. In addition, BMI significantly correlated with the levels of BFP and leptin. No significant relationships were observed between BFP and physical activity and the PFS total or the scores of its three factors.
2.3. Physical activity The validated short form of the Persian version of the International Physical Activity Questionnaire (IPAQ) [34] was used to estimate the level of physical activity of each participant. The participants were divided into low, moderate and high activity groups according to their scores on the questionnaire. 85
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and the levels of biochemical factors were found, even after controlling for BMI and BFM, indicating that PFS has little or no correlation with BMI [37]. A significant relationship was observed between the PFS total score and the serum level of insulin. Evidence has shown that the brain is sensitive to insulin [40] and is involved in brain activities such as regulation of body metabolism, memory, pleasure and non-homeostatic feeding by signaling within mesolimbic reward circuits [40]. Because of the interaction between insulin and dopamine (DA) neurons in the VTA, current evidence suggests that insulin may affect the activity of VTA DA neurons and their output [12]. Moreover, obesity is commonly associated with hyperinsulinemia and, consequently, peripheral insulin resistance might be related to cerebral insulin resistance [41–43]. Individuals who are brain insulin resistant have a low potential to lose weight through a lifestyle intervention program [44]. Studies have shown that the insulin modulating effect of reward eating circuits is impaired by obesity [45–48]. In the present study, a significant relationship between the levels of leptin and hedonic hunger was observed that may be related to leptin resistance in reward circuitry. Studies suggest that leptin suppresses dopamine release and the activity of VTA DA neurons and thus regulates hedonic hunger and reward eating [25,39,49]. Some authors have assumed that obese individuals with considerable levels of leptin become resistant to the catabolic effect of leptin, including satiety signaling [50]. Jastreboff et al. observed a significant association between higher leptin concentrations and hyper-responsiveness of brain motivation-reward areas to palatable food pictures. This implies that the dysfunction of leptin signaling may lead to overconsumption of foods [19]. Hopkins et al. showed a positive relationship between the serum levels of leptin and food palatability even after adjusting for BMI or fat mass [51]. Elevated leptin levels and the desire for foods with hedonic value such as high fat foods, mainly high-fat and high-carbohydrate combinations, have also been stated in obese subjects [52]. BDNF aneroxigenic effect was first revealed by Lapchak and Hefti's study in which weight gain in rats reduced as a result of chronic intracerebroventricular infusion of BDNF [53]. However, uncertainty surrounds the relation between BDNF levels and eating behavior. In the present study, multiple linear regression analysis showed that the BDNF level had a significant inverse correlation only with PFS-FT (Table 3). It has been suggested that BDNF is a natural modulator of hedonic food intake; therefore, deregulation of BDNF signaling in the reward circuitry increases eating motivation without homeostatic need [54]. In support of these findings, Cordeira et al. showed that rats with lower levels of BDNF had higher desire for foods with hedonic value but no change in their desire for standard chow [8]. In this regard, human studies are limited; however, some studies indicate that BDNF insufficiency results in eating disorders, hyperphagia, and obesity [55,56].
Table 1 Demographic, anthropometric and biochemical characteristics in the studied participants. Variables
Mean ± SD
Age (year) BMI (kg/m2) BFP (%) TBW (kg) PFS scores Total PFS-FA PFS-FP PFS-FT FBS (mg/dl) Insulin (IU/ml) Leptin (ng/ml) BDNF (ng/ml) Physical activity levels Low Moderate High
34.64 32.16 39.14 35.65
± ± ± ±
6.79 4.25 4.92 3.33
48.35 ± 15.17 18.48 ± 7.27 14.60 ± 4.59 15.26 ± 4.94 84.97 ± 10.95 11.30 ± 4.51 34.37 ± 16.63 2.07 ± 0.48 n (%) 78 (86.7) 9 (10) 3 (3.3)
Table 2 The Pearson's correlations between PFS scores and BMI with physical activity, BFP, and the biochemical indices. Variables
PFS total score
PFS-FA score
PFS-FP score
PFS-FT score
BMI (kg/ m2)
BFP Physical activity Insulin(IU/ml) Leptin(ng/ml) BDNF(ng/ml)
0.161 0.124 0.475⁎⁎ 0.360⁎⁎ −0.183
0.203 0.080 0.487⁎⁎ 0.351⁎⁎ −0.127
0.210⁎ 0.107 0.403⁎⁎ 0.364⁎⁎ −0.166
0.001 0.164 0.368⁎⁎ 0.250⁎ −0.222⁎
0.775⁎⁎ 0.055 0.195 0.425⁎⁎ 0.139
PFS: Power of Food Scale; FA: Food available; FP: Food Present; FT: Food Taste; BMI: body mass index; BFP: body fat percentage; BDNF: brain derived neurotrophic factor. Pearson's correlation coefficient (*P < .05). Pearson's r correlation coefficient (**P < .01). Table 3 Multiple linear regression analysis of the relationship between PFS scores and the studied hormones. Independent variables
Regression coefficient (B)
SE
Standardized β coefficient
p-Value
Insulin (IU/ml) Leptin (ng/ml) BDNF (ng/ml)
1.29 0.20 −6.29
0.32 0.09 2.81
0.385 0.228 −0.202
< 0.001 0.023 0.028
Dependent Variable: Power of Food Scale aggregated score. Removed variables: Age, BMI, and BFP (P > .05). BDNF: brain derived neurotrophic factor.
5. Conclusions
Table 3 indicates the relation between hedonic hunger and the levels of the studied biochemical factors controlling for age, BMI and BFP. Linear regression analysis showed statistically significant associations between hedonic hunger and the levels of insulin (p < .001) and leptin (p = .023). In addition, a significant inverse association was observed between hedonic hunger and the level of BDNF (p = .028).
In the present study, the relationships between hedonic hunger and the levels of insulin, leptin, and BDNF were investigated. We did not investigate the mechanisms by which these hormones could act on hedonic hunger. Overall, this study provides evidence that leptin, insulin and BDNF may play a physiological role in modulating the reward pathway responses to food cues and hedonic hunger. It is highly recommended to carry out further studies with normal weight individuals as a control group to investigate the mechanisms by which these biochemical substances act.
4. Discussion Hedonic hunger is a major determinant of obesity [6]. It is controlled by dopaminergic neurons in the ventral tegmental area (VTA) and other regions in the brain that form a reward circuit [38]. There is a close relationship between hedonic hunger and endogenous regulators of appetite [39]. In the present study, we investigated the relationship between hedonic hunger and the levels of insulin, leptin, and BDNF in adult Iranian women. Significant associations between the PFS score
Acknowledgement The authors thank the participants for their time, patience, and cooperation. 86
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Funding
adult obesity, J. Clin. Endocrinol. Metabol. 92 (2007) 948–954. [28] S. Fanous, E.F. Terwilliger, R.P. Hammer Jr, E.M. Nikulina, Viral depletion of VTA BDNF in rats modulates social behavior, consequences of intermittent social defeat stress, and long-term weight regulation, Neurosci. Lett. 502 (2011) 192–196. [29] C.L. Roth, C. Elfers, U. Gebhardt, H.L. Müller, T. Reinehr, Brain-derived neurotrophic factor and its relation to leptin in obese children before and after weight loss, Metabolism 62 (2013) 226–234. [30] O. Berton, C.A. McClung, R.J. Dileone, V. Krishnan, W. Renthal, S.J. Russo, et al., Essential role of BDNF in the mesolimbic dopamine pathway in social defeat stress, Science 311 (2006) 864–868. [31] Z. Boneva-Asiova, M. Boyanov, Body composition analysis by leg-to-leg bioelectrical impedance and dual-energy X–ray absorptiometry in non-obese and obese individuals, Diabetes Obes. Metab. 10 (2008) 1012–1018. [32] M.Y. Jaffrin, R. Kieffer, M.-V. Moreno, Evaluation of a foot-to-foot impedance meter measuring extracellular fluid volume in addition to fat-free mass and fat tissue mass, Nutrition 21 (2005) 815–824. [33] D.C. Frankenfield, Bias and accuracy of resting metabolic rate equations in nonobese and obese adults, Clin. Nutr. 32 (2013) 976–982. [34] M.B. Moghaddam, F.B. Aghdam, M.A. Jafarabadi, H. Allahverdipour, S.D. Nikookheslat, S. Safarpour, The Iranian Version of International Physical Activity Questionnaire (IPAQ) in Iran: content and construct validity, factor structure, internal consistency and stability, World Appl. Sci. 18 (2012) 1073–1080. [35] J.C. Cappelleri, A.G. Bushmakin, R.A. Gerber, N.K. Leidy, C.C. Sexton, J. Karlsson, et al., Evaluating the Power of Food Scale in obese subjects and a general sample of individuals: development and measurement properties, Int. J. Obes. 33 (2009) 913. [36] M.R. Lowe, M.L. Butryn, E.R. Didie, R.A. Annunziato, J.G. Thomas, C.E. Crerand, et al., The Power of Food Scale. A new measure of the psychological influence of the food environment, Appetite 53 (2009) 114–118. [37] H. Espel-Huynh, A. Muratore, M. Lowe, A narrative review of the construct of hedonic hunger and its measurement by the Power of Food Scale, Obes. Sci. Pract. 4 (2018) 238–249. [38] C. Luck Gibson, Characterizing the Mesolimbic Dopamine Reward Pathway in a Magel2-null Mouse, a Model of Prader-Willi Syndrome, (2016). [39] E. Egecioglu, K.P. Skibicka, C. Hansson, M. Alvarez-Crespo, P.A. Friberg, E. Jerlhag, et al., Hedonic and incentive signals for body weight control, Rev. Endocr. Metab. Disord. 12 (2011) 141–151. [40] M. Heni, S. Kullmann, H. Preissl, A. Fritsche, H.-U. Häring, Impaired insulin action in the human brain: causes and metabolic consequences, Nat. Rev. Endocrinol. 11 (2015) 701. [41] C. Weyer, T. Funahashi, S. Tanaka, K. Hotta, Y. Matsuzawa, R.E. Pratley, et al., Hypoadiponectinemia in obesity and type 2 diabetes: close association with insulin resistance and hyperinsulinemia, J. Clin. Endocrinol. Metabol. 86 (2001) 1930–1935. [42] O. Tschritter, H. Preissl, A.M. Hennige, M. Stumvoll, K. Porubska, R. Frost, et al., The cerebrocortical response to hyperinsulinemia is reduced in overweight humans: a magnetoencephalographic study, Proc. Natl. Acad. Sci. 103 (2006) 12103–12108. [43] A.M. Jastreboff, R. Sinha, C. Lacadie, D.M. Small, R.S. Sherwin, M.N. Potenza, Neural correlates of stress-and food cue–induced food craving in obesity, Diabetes Care 36 (2013) 394–402. [44] O. Tschritter, H. Preissl, A. Hennige, T. Sartorius, K. Stingl, M. Heni, et al., High cerebral insulin sensitivity is associated with loss of body fat during lifestyle intervention, Diabetologia 55 (2012) 175–182. [45] K.T. Stingl, S. Kullmann, M. Guthoff, M. Heni, A. Fritsche, H. Preissl, Insulin modulation of magnetoencephalographic resting state dynamics in lean and obese subjects, Front. Syst. Neurosci. 4 (2010). [46] M. Guthoff, K.T. Stingl, O. Tschritter, M. Rogic, M. Heni, K. Stingl, et al., The insulin-mediated modulation of visually evoked magnetic fields is reduced in obese subjects, PLoS One 6 (2011) e19482. [47] N.B. Kroemer, L. Krebs, A. Kobiella, O. Grimm, S. Vollstädt-Klein, U. Wolfensteller, et al., (still) longing for food: insulin reactivity modulates response to food pictures, Hum. Brain Mapp. 34 (2013) 2367–2380. [48] L.J. Tiedemann, S.M. Schmid, J. Hettel, K. Giesen, P. Francke, C. Büchel, et al., Central insulin modulates food valuation via mesolimbic pathways, Nature Communications, vol. 8, 2017 p. ncomms16052. [49] J.D. Hommel, R. Trinko, R.M. Sears, D. Georgescu, Z.-W. Liu, X.-B. Gao, et al., Leptin receptor signaling in midbrain dopamine neurons regulates feeding, Neuron 51 (2006) 801–810. [50] M. Grosshans, C. Vollmert, S. Vollstädt-Klein, H. Tost, S. Leber, P. Bach, et al., Association of leptin with food cue–induced activation in human reward pathways, Arch. Gen. Psychiatry 69 (2012) 529–537. [51] M. Hopkins, C. Gibbons, P. Caudwell, D.-L. Webb, P.M. Hellström, E. Näslund, et al., Fasting leptin is a metabolic determinant of food reward in overweight and obese individuals during chronic aerobic exercise training, Int. J. Endocrinol. 2014 (2014). [52] L. Karhunen, R. Lappalainen, S. Haffner, R. Valve, H. Tuorila, H. Miettinen, et al., Serum leptin, food intake and preferences for sugar and fat in obese women, Int. J. Obes. Relat. Metab. Disord. 22 (1998). [53] P.A. Lapchak, F. Hefti, BDNF and NGF treatment in lesioned rats: effects on cholinergic function and weight gain, Neuroreport 3 (1992) 405–408. [54] M. Rios, New insights into the mechanisms underlying the effects of BDNF on eating behavior, Neuropsychopharmacology 36 (2011) 368. [55] H. Rosas-Vargas, J.D. Martínez-Ezquerro, T. Bienvenu, Brain-derived neurotrophic factor, food intake regulation, and obesity, Arch. Med. Res. 42 (2011) 482–494. [56] J.C. Han, Q.-R. Liu, M. Jones, R.L. Levinn, C.M. Menzie, K.S. Jefferson-George, et al., Brain-derived neurotrophic factor and obesity in the WAGR syndrome, N. Engl. J. Med. 359 (2008) 918–927.
This work was supported by the Tabriz University of Medical Sciences, Vice Chancellor for Research Affairs [grant number: 5/D/ 996710]. References [1] T. Bhurosy, R. Jeewon, Overweight and obesity epidemic in developing countries: a problem with diet, physical activity, or socioeconomic status? Sci. World J. 2014 (2014). [2] B. Nikooyeh, Z. Abdollahi, F. Salehi, S. Nourisaeidlou, M. Hajifaraji, M. Zahedirad, et al., Prevalence of obesity and overweight and its associated factors in urban adults from West Azerbaijan, Iran: the National Food and Nutritional Surveillance Program (NFNSP), Nutr. Food Sci. Res. 3 (2016) 21–26. [3] M.W. Schwartz, S.C. Woods, D. Porte Jr., R.J. Seeley, D.G. Baskin, Central nervous system control of food intake, Nature 404 (2000) 661. [4] M. Lutter, E.J. Nestler, Homeostatic and hedonic signals interact in the regulation of food intake, J. Nutr. 139 (2009) 629–632. [5] M. Nigro, A. Monteleone, V. Di Marzo, R. Dalle Grave, M. El Ghoch, M. Cimino, et al., Neuroendocrine responses to hedonic eating in obese patients with and without binge eating, Eur. Psychiatry 30 (2015) 1351. [6] J.F. Davis, D.L. Choi, S.C. Benoit, Insulin, leptin and reward, Trends Endocrinol. Metab. 21 (2010) 68–74. [7] D.P. Figlewicz, A.J. Sipols, Energy regulatory signals and food reward, Pharmacol. Biochem. Behav. 97 (2010) 15–24. [8] J.W. Cordeira, L. Frank, M. Sena-Esteves, E.N. Pothos, M. Rios, Brain-derived neurotrophic factor regulates hedonic feeding by acting on the mesolimbic dopamine system, J. Neurosci. 30 (2010) 2533–2541. [9] P. Monteleone, P. Scognamiglio, A.M. Monteleone, D. Perillo, B. Canestrelli, M. Maj, Gastroenteric hormone responses to hedonic eating in healthy humans, Psychoneuroendocrinology 38 (2013) 1435–1441. [10] D. Figlewicz, S. Evans, J. Murphy, M. Hoen, D. Baskin, Expression of receptors for insulin and leptin in the ventral tegmental area/substantia nigra (VTA/SN) of the rat, Brain Res. 964 (2003) 107–115. [11] S. Kullmann, M. Heni, R. Veit, K. Scheffler, J. Machann, H.-U. Häring, et al., Intranasal insulin enhances brain functional connectivity mediating the relationship between adiposity and subjective feeling of hunger, Sci. Rep. 7 (2017). [12] D. Mebel, J. Wong, Y. Dong, S. Borgland, Insulin in the ventral tegmental area reduces hedonic feeding and suppresses dopamine concentration via increased reuptake, Eur. J. Neurosci. 36 (2012) 2336–2346. [13] T.C. Adam, S. Tsao, K.A. Page, H. Hu, R.E. Hasson, M.I. Goran, Insulin sensitivity and brain reward activation in overweight Hispanic girls: a pilot study, Pediatr. Obes. 10 (2015) 30–36. [14] M. Hallschmid, S. Higgs, M. Thienel, V. Ott, H. Lehnert, Postprandial administration of intranasal insulin intensifies satiety and reduces intake of palatable snacks in women, Diabetes 61 (2012) 782–789. [15] K. Jauch-Chara, A. Friedrich, M. Rezmer, U.H. Melchert, H.G. Scholand-Engler, M. Hallschmid, et al., Intranasal insulin suppresses food intake via enhancement of brain energy levels in humans, Diabetes 61 (2012) 2261–2268. [16] S. Luo, K.A. Page, The roles of circulating peripheral appetite hormones and insulin resistance on brain reward responses to food cues, Obesity: Human Studies in Body Weight Regulation, Endocrine Society, 2015OR40-6-OR40-6. [17] S. Kullmann, M. Heni, R. Veit, K. Scheffler, J. Machann, H.-U. Häring, et al., Selective insulin resistance in homeostatic and cognitive control brain areas in overweight and obese adults, Diabetes Care 38 (2015) 1044–1050. [18] Y.S. Lee, O. Kim, R. Osborne, S. Sasik, A. Chen Schenk, et al., Increased adipocyte O2 consumption triggers HIF-1α, causing inflammation and insulin resistance in obesity, Cell 157 (2014) 1339–1352. [19] A.M. Jastreboff, C. Lacadie, D. Seo, J. Kubat, C. Giannini, M. Savoye, et al., Leptin is associated with exaggerated brain reward and emotion responses to food images in adolescent obesity, Diabetes Care 37 (2014) 3061–3068. [20] Z.A. Knight, K.S. Hannan, M.L. Greenberg, J.M. Friedman, Hyperleptinemia is required for the development of leptin resistance, PLoS One 5 (2010) e11376. [21] Y. Zhang, R. Proenca, M. Maffei, M. Barone, L. Leopold, J.M. Friedman, Positional cloning of the mouse obese gene and its human homologue, Nature 372 (1994) 425–432. [22] Q. Wang, C. Bing, K. Al-Barazanji, D.E. Mossakowaska, X.-M. Wang, D.L. McBay, et al., Interactions between leptin and hypothalamic neuropeptide Y neurons in the control of food intake and energy homeostasis in the rat, Diabetes 46 (1997) 335–341. [23] J.R. McDuffie, P.A. Riggs, K.A. Calis, R.J. Freedman, E.A. Oral, A.M. Depaoli, et al., Effects of exogenous leptin on satiety and satiation in patients with lipodystrophy and leptin insufficiency, J. Clin. Endocrinol. Metabol. 89 (2004) 4258–4263. [24] V.K. Yadav, F. Oury, N. Suda, Z.-W. Liu, X.-B. Gao, C. Confavreux, et al., A serotonin-dependent mechanism explains the leptin regulation of bone mass, appetite, and energy expenditure, Cell 138 (2009) 976–989. [25] D.M. Opland, G.M. Leinninger, M.G. Myers, Modulation of the mesolimbic dopamine system by leptin, Brain Res. 1350 (2010) 65–70. [26] R. Miller, M. Tanofsky-Kraff, L.B. Shomaker, S.E. Field, L. Hannallah, S.A. Reina, et al., Serum leptin and loss of control eating in children and adolescents, Int. J. Obes. 38 (2005) 397, 2014. [27] A.F. Fleisch, N. Agarwal, M.D. Roberts, J.C. Han, K.R. Theim, A. Vexler, et al., Influence of serum leptin on weight and body fat growth in children at high risk for
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