Red pitaya juice supplementation ameliorates energy balance homeostasis by modulating obesity-related genes in high-carbohydrate, high-fat diet-induced metabolic syndrome rats

Background Red pitaya (Hylocereus polyrhizus) or known as buah naga merah in Malay belongs to the cactus family, Cactaceae. Red pitaya has been shown to give protection against liver damage and may reduce the stiffness of the heart. Besides, the beneficial effects of red pitaya against obesity have been reported; however, the mechanism of this protection is not clear. Therefore, in the present study, we have investigated the red pitaya-targeted genes in obesity using high-carbohydrate, high-fat diet-induced metabolic syndrome rat model. Methods A total of four groups were tested: corn-starch (CS), corn-starch + red pitaya juice (CRP), high-carbohydrate, high-fat (HCHF) and high-carbohydrate, high-fat + red pitaya juice (HRP). The intervention with 5 % red pitaya juice was continued for 8 weeks after 8 weeks initiation of the diet. Retroperitoneal, epididymal and omental fat pads were collected and weighed. Plasma concentration of IL-6 and TNF-α were measured using commercial kits. Gene expression analysis was conducted using RNA extracted from liver samples. A total of eighty-four genes related to obesity were analyzed using PCR array. Results The rats fed HCHF-diet for 16 weeks increased body weight, developed excess abdominal fat deposition and down-regulated the expression level of IL-1α, IL-1r1, and Cntfr as compared to the control group. Supplementation of red pitaya juice for 8 weeks increased omental and epididymal fat but no change in retroperitoneal fat was observed. Red pitaya juice reversed the changes in energy balance homeostasis in liver tissues by regulation of the expression levels of Pomc and Insr. The increased protein expression levels of IL-6 and TNF-α in HCHF group and red pitaya treated rats confirmed the results of gene expression. Conclusion Collectively, this study revealed the usefulness of this diet-induced rat model and the beneficial effects of red pitaya on energy balance homeostasis by modulating the anorectic, orexigenic and energy expenditure related genes.


Background
The increasing incidence of obesity worldwide could result from complex interactions between environmental, genetic [1] and psychosocial factors [2]. The social-environmental influences such as reduced physical activity, increased sedentary behavior and increased accessibility of high-fat and energy-dense foods facilitate the obesity pandemic by disrupting the body's energy balance. Energy balance is defined as the difference between energy intake and energy expenditure including basal metabolism, physical activity and adaptive thermogenesis [2]. A state of positive energy balance will occur when energy intake exceeds energy expenditure, leading to increased storage of energy as adipose tissue. Genetic factors may also influence body weight by affecting one or more component (s) of energy balance [3]. The physiological control system for energy intake and body weight is complex and involves extensive changes of gene expression throughout the body. It is postulated that the central nervous system controls energy balance through several mechanisms. These include it influence on feeding and physical activity behavior, the regulation of the autonomic nervous system activities including metabolism, and changes in key hormones such as leptin, ghrelin, insulin, growth hormone, sex steroids, cortisol and thyroid hormones [1,[4][5][6]. Therefore, it is critical to understand the gut-brain interaction underlying the appetite and feeding regulation of energy balance to develop new pharmacogenetical strategy for obesity studies.
Red pitaya is a cactus fruit originating from Mexico. In Malaysia, this fruit is known as red dragon fruit or 'buah naga merah' , possibly due to the scaly structure of the peel. The beneficial effects of red pitaya reported in laboratory animal studies could be due to its ability to increase antioxidant capacity and suppress oxidative stress damage [7][8][9]. Our previous study indicated that red pitaya supplementation ameliorated liver and cardiovascular damage induced by high-carbohydrate, high-fat feeding [10]. However, the effects of red pitaya supplementation on obesity along with its related mechanism not clear. Red pitaya supplementation increases energy intake without increasing body weight [10], so it is hypothesized that red pitaya may stimulate anorectic genes or down-regulate orexigenic genes to increase energy expenditure. Therefore, the present work determined the changes in anorectic and orexigenic genes as well as the inflammatory pathway in a rat model of diet-induced obesity following supplementation with red pitaya.

Preparation of diet
Red pitaya was obtained from Queensland Australia. The identification of the fruit has been done by a botanist from Biodiversity Unit, Institute of Biosciences, Universiti Putra Malaysia. The voucher number is SK-2440/14. The fruits were then cleaned, and the fruit pulp was squeezed using juice maker. Sample preparation was conducted in reduced light condition in order to minimize the pigment loss [10].

Animals and diet
The experimental protocols have been described in our previous publication [10]. In brief, the experimental groups consisted of 48 male Wistar rats (aged 8-9 weeks; weight 337 ± 5 g) supplied by and individually housed at The University of Southern Queensland animal house. All experimental groups were housed in a temperaturecontrolled, 12 hour light-dark cycle environment with ad libitum access to water and the group specific diet. Daily body weight, feed and water measurements were taken to monitor the day-to-day health of the rats and the results have been reported in our previous publication [10]. The rats were randomly divided into four groups based on their diet: corn starch (CS; n = 12); corn starch + red pitaya juice (CRP; 5 % in the diet; n = 12); high-carbohydrate, high-fat (HCHF; n = 12); High-carbohydrate, high-fat + red pitaya juice (HRP; n = 12). Fructose (25 %) was added as drinking water for all high-carbohydrate, high-fat fed rats, while corn starch group was given normal water. The detailed macroand micro-nutrient composition of the CS and HCHF diets are reported in previous publications [11,12]. Red pitaya juice was administered for 8 weeks starting from 8 weeks after the initiation of the CS or HCHF diet. All the experimental protocols were approved by the Animal Experimentation Ethics Committee of The University of Southern Queensland under the guidelines of the National Health and Medical Research Council of Australia.

Organ weights
Terminal anaesthesia was induced via intraperitoneal injection of pentobarbitone sodium (Lethabarb, 100 mg/kg). Heparin (Sigma-Aldrich Australia) was administered (100 IU) through the right femoral vein and blood (about 5 ml) was taken from the abdominal aorta. Immediately after the rats were killed, the abdominal fat mass as the retroperitoneal, epididymal and omental fat pads were collected. The organ weights were normalized to the tibial length at the time of their removal (in mg/mm).

Inflammatory markers
Plasma concentrations of interleukin 6 (IL-6) and TNFalpha were quantified based on manufacturer's guidelines using commercially available ELISA kits. Plasma Creactive protein (CRP) was estimated using a commercial kit according to the manufacturer-provided standards and protocol using a Roche/Hitachi cobas c system.

Isolation of total RNA
During terminal experiments, liver samples were collected and immediately stored at −80°C freezer. Total RNA from corn starch (CS), corn starch + red pitaya (CRP), highcarbohydrate, high-fat (HCHF) and high-carbohydrate, high-fat + red pitaya (HRP) groups was isolated from liver samples using RNeasy® plus mini kit (Qiagen, Valencia, CA) according to manufacturer's protocol. Three replicates were analyzed for each group (CS, CRP, HCHF and HRP). Basically, 25 mg liver samples were excised and immediately homogenized in Buffer RLT plus. The genomic DNA was removed and purified from the lysate liver sample. Finally, the RNeasy spin column was placed into a new 2 mL collection tube and centrifuged at 12000 rpm for 1 minute. The 2 mL collection tube was then discarded. The RNA bound at the RNeasy spin column was eluted in a new 1.5 mL collection tube with 40 μL RNase-free water and centrifuged at 12000 rpm for 1 minute. The concentration of extracted RNA from the samples was determined by measuring at 260 nm using a Nano-Drop 1000 Spectrophotometer (Thermo Scientific, Waltham, MA). In brief, 1 μL of RNase-free water was added to pedestal for blank sample. Then, 1 μL of RNA sample was added. The integrity of extracted RNA sample was measured using Bioanalyzer (Agilent's RNA 6000 Nano kit).
cDNA synthesis for real-time Reverse Transcription-(RT) PCR cDNA was prepared using RT 2 profiler PCR array first strand kit based on manufacturer's instructions. Briefly, genomic DNA elimination mix was prepared by mixing RNA (volume varies based on the concentration obtained), 2 μL of gDNA elimination buffer and RNasefree water to form a total volume of 10 μL. The mixture was mixed by gently pipetting and the genomic DNA elimination mix was then incubated for 5 minutes at 42°C. It was immediately placed on ice for at least 1 minute. The reverse-transcription mix was prepared according to manufacturer's guidelines. Ten μL of reversetranscription mix was added to tube containing 10 μL genomic DNA elimination mix, and incubated at 42°C for exactly 15 minutes. The reaction was stopped by incubating at 95°C for 5 minutes to inactivate the reverse transcriptase. The 20 μL of cDNA synthesis reaction mixture was mixed with 91 μL of RNase-free water making a total 111 μL of reaction mixture. The reaction mixture was placed on ice until further use.

Array-based SYBR® Green RT-PCR
The constitutive gene expression profiling was conducted using to RT 2 profiler PCR array related to obesity signal transduction according to manufacturer's protocols. The gene array profiled the expression of 84 genes including orexigenic genes, anorectic genes, and related to energy expenditure (Table 1, PARN-017Z-12, RT 2 Profiler™ PCR Array Rat Obesity). The array included the controls for human genomic DNA contamination, reverse-transcription, positive PCR control, and 5 housekeeping genes to normalize the relative gene expression for analysis of data. The five housekeeping genes were ribosomal protein large P1 (Rp1p1), hypoxanthine phosphoribosyltransferase 1 (Hprt1), ribosomal protein L13A (Rp113a), lactate dehydrogenase A (Ldha), and β-actin (Actb). The PCR components mix were prepared with 1150 μL of 2 x RT 2 SYBR Green ROX FAST mastermix, 102 μL of cDNA synthesis reaction, and 1048 μL of RNase-free water combined making a total volume of 2300 μL. Then, the RT 2 profiler PCR array was removed from its sealed bag, and the array was slid into the Rotor-Disc 100 Loading Block using the tab position A1 and the tube guide holes. Twenty μL of PCR component mix was pipetted into each well of the RT 2 profiler PCR array. For each sample, the RT 2 profiler PCR array was conducted in triplicate.
Real time PCR was performed using a two-step cycling program on rotor gene real time PCR machine (Qiagen, Valencia, CA): 10 min at 95°C (cycle 1) followed by 40 cycles of 15 s at 95°C and 1 min at 60°C. SYBR green fluorescence was detected and recorded. The threshold cycle (CT) above the background for each reaction was then calculated.

Statistical analysis
Data for organ weights, and inflammatory markers were analyzed using GraphPad Prism version 5.00 for Windows (San Diego, CA, USA). All data were presented as mean ± SEM. All group data were tested for variance using Bartlett's test. Variables that were not normally distributed were transformed (using log 10 function) prior to statistical analysis. The effects of diet, treatment and their interactions were tested by two-way analysis of variance. When interaction and/or the main effects were significant, means were compared using Newman-Keuls multiple-comparison post hoc test. A nonparametric test, the Kruskal-Wallis test, was performed when transformations did not result in normality or constant variance. The gene expression data was analyzed using RT 2 Profiler PCR Array Data analysis version 3.5 from SABiosciences website. All data were normalized by one housekeeping gene (endogenous control). The fold change among the groups were obtained from ΔΔCT. ΔCT was defined as the value of subtracting the CT value of endogenous control from the CT value of the target messenger RNA (mRNA). Student's ttest was used to determine the differences in gene expression. The significant values were considered at the level of p < 0.05.

Abdominal fats and visceral adiposity index
An increase in retroperitoneal adipose tissue was observed in CRP but not in HRP compared to their respective nontreated controls (CS and HCHF groups) ( Table 2). On the other hand, HRP group showed significant increase (p < 0.05) in epididymal and omental fat deposition compared with CS and HCHF diet-fed rats ( Table 2). No changes in visceral adiposity index were seen for red pitaya supplementation in high-carbohydrate, high-fat diet-fed rats ( Table 2). On the other hand, CRP rats showed a trend of increasing visceral adiposity index but the differences were not significant (p > 0.05) ( Table 2).

Plasma inflammatory markers
Results indicated that red pitaya supplementation significantly increased (p < 0.05) plasma concentration of TNFα and IL-6 ( Table 2). There was no difference for CRP group for IL-6 concentration in comparison with CS-fed rats ( Table 2). C-reactive protein, an acute phase reactant was increased with high-carbohydrate, high-fat feeding compared with corn starch fed control rats (Table 2). Red pitaya juice supplementation decreased C-reactive protein concentration in HRP rats as compared to controls rats ( Table 2).

Gene expressions alterations
The triplicate samples from each group produced reproducible results. Data analysis revealed that 14 genes (as measured by CT value) were detected while the remaining 71 genes were not detected and/or the genes were detected but the variation between the test sample and control is too great and thus the fold change results has to be validated further. Figure 1 shows the fold regulation  expression data for all the genes between CS and HCHF groups, CS and CRP groups and HCHF and HRP groups. Table 3 presents the fold regulation of the selected genes from HCHF, HRP, CS and CRP groups that gave reproducible results. In HCHF-fed rats, four genes were downregulated namely complement component 3 (C3), ciliary neurotrophic factor receptor (Cntfr), glucagon receptor (Gcgr) and interleukin 1 alpha (IL-1α) while three genes were up-regulated which are insulin receptor (Insr), neuropeptides Y (Npy) and Sigma non-opioid intracellular receptor 1 (Sigmar1) and four genes namely Apolipoprotein A-IV (Apoa4), Interleukin 1 receptor, type 1 (IL-1r1), Proopiomelanocortin (Pomc), and Thyroid hormone receptor beta (Thrb) showed no changed as compared to CS group (Table 3). Nevertheless, only the expression level Cntfr, IL-1α and IL-1r1 were significantly different (p < 0.05) compared to CS group (Fig. 2). Corn starch-fed supplemented rats (CRP group) significantly decreased (p <0.05) the expression of Cntfr and IL-1α (Fig. 3). Supplementation of red pitaya juice for eight weeks resulted in up-regulation of Insr and Pomc in HRP group compared to HCHF group (Table 3). However, the difference showed no statistical significance (p > 0.05) (Fig. 4).

Discussion
The present study evaluated the red pitaya-targeted genes in high-carbohydrate, high-fat diet-induced metabolic syndrome rats using polymerase chain reaction  Fig. 1 The changes in obesity-related genes among the controls, CRP and HRP groups. The heat map demonstrating fold regulation expression data between CS group and HCHF group (a), CS group and CRP group (b) and HCHF group and HRP group (c). d shows representative PCR array gene table (PCR) array in order to elucidate the molecular mechanism underlying the physiological responses. PCR array combines real-time PCR sensitivity and the ability of microarrays to detect the expression of many genes simultaneously [13]. This new technique of evaluating gene expression allows the expression levels of disease-or pathway-focused genes and provides reliable method, easy-to-use and is highly sensitive [14]. Up to now, quantitative RT-PCR arrays have had limited use to study nutrient-related interventions [15], maternal perinatal under-nutrition [16,17] and brown adipose tissue metabolism [18], probably because of high cost despite the improved sensitivity. Analysis from the present study revealed that several obesity-related genes change in HCHF groups, which further confirmed the development of this metabolic syndrome rat model. In obese rats, four genes were down-regulated (C3, Cntfr, Gcgr, IL-1α) while three genes were up-regulated (Insr, Npy and Sigmar1) and five genes (Adipor1, Apoa4, IL-r1, Pomc, Thrb) showed no change compared to CS group. However, among these, only Cntfr, IL-1α and IL-1r1 showed significant changes which might be due to large variations of the expression levels of other genes within the groups. IL-1α and IL-1r1 are classified as anorectic genes. The appetite suppressing effects of these two genes (IL-1α and IL-1r1) might be associated with fever, increased thermogenesis and reduced food intake due to inflammation and injury [19]. Interleukin-1α is cytokine of the interleukin-1 family (IL-1) which is a key mediator of inflammation, located on the long arm of human chromosome 2 [20]. IL-1 family also composed of IL-1β that exerts almost identical biological activities with IL-1α by binding to IL-1 type 1 receptor (IL-1r1). However, IL-1β does not occur in healthy subjects. In contrast, IL-1α is present at constitutive levels in primary cells such as hepatocytes and epithelial cells [21]. IL-1α forms heterodimeric complexes that induce inflammation when bound to the IL-1r1. The negative regulator of inflammation is IL-1 receptor antagonist (IL-1Ra) [18].
García et al. [19] showed that knockout of the gene coding for IL-1r1 resulted in mature-onset obesity due to reduced fat utilization, decreased locomotor activity and reduced leptin sensitivity. Data from our previous study indicated that body weight of HCHF-fed rats were significantly higher than CS-fed rats [10] despite the same expression level of IL-1r1. Possibly, other genes play role in increasing body weight of H-fed rats but not IL-1r1. Furthermore, serum amyloid A protein, an inflammatory marker in atherosclerosis, was reduced despite higher total cholesterol concentrations in IL-1α deficient mice fed with high-fat diet [22]. Similarly, the present study showed the expression level of IL-1α was significantly reduced in line with the decreased inflammatory markers of interleukin-6 in HCHF-fed rats compared to CS group. The relationship between inflammation and obesity is well established [23,24,25]. It is interesting to point out that the contradictory results obtained from the present study might be due to increased inflammation in control rats without high-fat diet probably due to enhance glucose intake after meal as reported by Gregerson et al. [26]. Importantly, these results explained the condition whereby the appetite suppressing effect of IL-1α was altered resulting in increased food intake, and hence increased body weight of H-fed rats. Recently, Dinarello and Netea [27] postulated that IL-1α deficient mice had reduced aortic lesion size due to the transferred of hematopoietic cells from the bone marrow. However, the aortic lesion was not measured in the present study, so the effect of IL-1α downregulation on the aortic lesion size was not known.

IL-1r1 IL-1α
Up-regulated genes Down-regulated genes Fig. 2 The changes of obesity-related genes from HCHF and CS groups. a The volcano plot demonstrating statistical significance versus fold regulation expression data between the HCHF group and the CS group. The blue line indicates p-value, set at 0.05. b Genes with significant differences (p < 0.05) between the two groups are presented in the histogram. CS, corn starch diet; HCHF, high-carbohydrate, high-fat diet; Cntfr, ciliary neurotrophic factor receptor; IL-1 α, Interleukin 1 alpha; IL-1r1, Interleukin 1 receptor, type I Cntfr, Gcgr and Insr are anorectic genes, Sigmar1 and Npy are orexigenic genes and C3 is the gene that relate to energy expenditure. Although there were no statistically significant for these genes between the HCHF group and CS group except for Cntfr, it is important to understand the changes of obesity-related genes as a first critical step towards evaluating this metabolic syndrome rat model at the molecular level. The role of C3 in obesity-related metabolic diseases has been recognized as it stimulates the accumulation of triglyceride, promotes the uptake of glucose and reduces the release of free fatty acids [28]. Besides, the up-regulation of C3 impaired energy expenditure and food intake via its action on the central nervous system [29]. It is fascinating that the C3 expression decreases with obesity in the present study given that C3 mRNA expression levels typically positively correlated with obesity. Nevertheless, this finding is in agreement with recent discovery by Gupta et al. [30] whereby they found down-regulation of complement C3 in subcutaneous tissue of obese women.
Cntfr reduces food intake and increase energy expenditure by directly induces the transcription of Pomc genes

IL-1α
Down-regulated genes Up-regulated genes a Fig. 3 The changes of obesity-related genes from CS and CRP groups. a The volcano plot demonstrating statistical significance versus fold regulation expression data between the CS group and the CRP group. The blue line indicates p-value, set at 0.05. b Genes with significant differences (p < 0.05) between the two groups are presented in the histogram. CS, corn starch diet; CRP, corn starch + red pitaya juice; Cntfr, ciliary neurotrophic factor receptor; IL-1 α, Interleukin 1 alpha [31,32]. Therefore, the absence of this protective factor against high-carbohydrate, high-fat diet resulted in weight gain and obesity. Gcgr is a receptor for glucagon which mediated the process of glycogenolysis, lipolysis, ketogenesis and gluconeogenesis. Following carbohydrate-rich meal, the expression level of Gcgr is significantly decreased [5], which is in agreement with the present study. Furthermore, the increased in Npy and Sigmar1 gene expression further supported this obesity rat model. Npy is part of the hypothalamic melanocortin pathway that regulates the central energy metabolism [33]. Previous study has demonstrated the increased in Npy expression stimulated food intake and hence resulted in weight gain [34]. Similarly, obese individual with non-alcoholic fatty liver diseases was found to have up-regulation of opioid signalling including Sigmar1 [35]. On the other hand, the expression level of Insr was up-regulated. Insr, the heterodimer genes coding for insulin signalling members, have been reported to be significantly reduced in obesity [36]. Thus, the up-regulation of Insr in the present study may lead to feedback down-regulation to reduce food intake due to increasing body weight of H-fed rats. The exact mechanism, however, is unknown. There was report that bioactive compounds and nutrients in food can interact with the genome by highly complex forms [37]. For instance, administration of oleic acid through intracerebroventricular was found to decrease body weight by reduction of Npy and increase Pomc neuron [38]. Additionally, Lu et al. [15] found that supplementation of green tea polyphenols in high-fat induced obese rats reduced body weight through mediating obesity-related genes. In the present study, the expression of Insr and Pomc increased in obese rats supplemented with red pitaya while no changes were observed in seven genes (Adipor1, Apoa4, C3, Gcgr, IL-1r1, Insr, Npy, Pomc, Sigmar1 and Thrb). There was no statistical significance between the HRP group and H group that warrant future research with bigger sample size. Insulin plays critical roles in energy functions particularly in carbohydrate and lipid metabolism. Obese individual usually has marked declined in insulin levels [5]. Thus, the increased gene expression level of Insr in HRP group indicating that red pitaya ameliorates the deficiency or resistant of insulin in obese rats. Meanwhile, Pomc is important for central regulation of energy balance whereby it reduce food intake and increase energy expenditure via the release of α melanocyte-stimulating hormone (MSH) and activation of melanocortin receptors [39].
In addition, CRP group revealed the decreased expression of anorectic genes, Apoa4, Cntfr, Ghr and IL-1α.

HCHF vs.HRP a
Down-regulated genes Up-regulated genes Fig. 4 The changes of obesity-related genes from HCHF and HRP groups. a The volcano plot demonstrating statistical significance versus fold regulation expression data between the HCHF group and the HRP group. The blue line indicates p-value, set at 0.05. None of the genes for this group is significantly different at p < 0.05. HCHF, high-carbohydrate, high-fat diet; HRP, high-carbohydrate, high-fat diet + red pitaya juice Among these, only IL-1α and Cntfr provides a significant reduction. As mentioned before, the increase expression of IL-1α and Cntfr induce satiety which in turn reduce the food intake and promote weight loss. Ghr plays a critical role in lipolysis, adipogenesis and lipogenesis and lower Ghr expression in the adipocyte is associated with obesity [40]. Likewise, Apoa4 triggered the satiety signal through dietary fat [41] and the decreased gene expression level of Apoa4 contributed to weight gain [15]. These findings confirmed that the lean control rats in the present study were more sensitive to the palatability of red pitaya juice as compared to high-carbohydrate, high-fat diet-induced obese rats. Red pitaya juice is an example of palatable foods that inhibit the satiety signals and hence increasing the food intake of CRP rats. Red pitaya juice contains higher amount of energy-supplying macronutrients causing progressive increment in body weight, and total body fat throughout the intervention period. This could explained the increased in abdominal fat deposition in CRP rats in the absence of a high carbohydrate, high fat diet. As discussed earlier, the analysis on the obesity-related genes after supplementation with red pitaya juice resulted in the detection of 14 genes. The remaining 70 genes were not detected in the liver samples of all the three groups. The likely reasons for this condition are those undetected genes were weakly expressed or not expressed at all in the liver tissues. For instance, Calcr is typically expressed in the kidney and brain, Cartpt is normally expressed in the spinal cord, testis, prostate and brain while Brs3 is normally detected in the testis, kidney and brain [42]. Moreover, Lu et al. [15] suggested that the gene array may have limitation in the sensitivity of the detection. The authors also proposed that the changes in gene expression might not be detected in the mixture of cell types in tissue samples.
Interestingly, data showed that supplementation of red pitaya juice decreased the circulating C-reactive protein (CRP), thus reducing the diet-induced low grade inflammation in-vivo. A possible explanation for this might be that red pitaya supplementation reduced CRP concentration by reducing its production rate without altering the liver genes. Mauger et al. [43] reported that the main determinant for CRP concentration is its production rate which showed significant association with metabolic syndrome characteristics. In contrast, a recent study on the mechanism of CRP reduction by statins, a drug used for the treatment of hypercholesterolemia, indicated that its production was not reduced but the fractional catabolic rate was enhanced [44]. It is speculated that the increased in pro-inflammatory cytokines (IL-6 and TNFalpha) was due to increase in liver fat as supplementation of red pitaya added to total energy content in high carbohydrate, high fat diet. Although the secretion of IL-6 regulates the induction of CRP in hepatocytes [45], the present study found contradicts results. As red pitaya supplementation increased IL-6, plasma concentration of CRP was reduced. In agreement with the present study, Malavazos et al. [46] found no positive association between IL-6 and CRP. In fact, a recent study reported that the intake of statin lowered CRP concentration but the study found no association between statin use and other inflammatory cytokines especially IL-6, TNF-α, and IL-1β [47].

Conclusions
In summary, the present results demonstrated that genetic mechanisms play a major role in determining body weight by controlling energy balance homeostasis. The present study provides striking results in that the changes in anorectic, orexigenic and energy expenditure related genes not only to advance the understanding of beneficial effects of red pitaya, but the usefulness of this diet-induced rat model. However, the overall effects of red pitaya supplementation are still controversial as inconsistent results were obtained. Longer duration of study may be required. Furthermore, it is important to highlight the complexity of energy balance homeostasis that may be time-specific, and/or tissue-and species specific [48]. Nevertheless, the changes in anorectic, orexigenic and energy expenditure related genes observed in the present study provide the salient and novel findings for obesity research. This work can be further extended to investigate possible therapeutic effects of red pitaya supplementation on metabolic syndrome patient which will in turn help to fight against the obesity epidemic.