Food Research International 53 (2013) 373–390

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Food Research International

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Metabolic profiling and biological mechanisms of body fat reduction inmice fed the ethanolic extract of black-colored rice

Hea-Won Kim a,b,1, A-Young Lee c,1, Siok Koon Yeo a,b, Hyun Chung c, Ji Hae Lee a,b, Minh-Hien Hoang a,b,Yaoyao Jia a,b, Sang-Ik Han d, Sea-Kwan Oh e, Sung-Joon Lee a,b,⁎, Young-Suk Kim c,⁎⁎a Department of Biotechnology, Graduate School of Life Sciences and Biotechnology, Korea University, Seoul 132-701, Republic of Koreab Division of Food Biosciences and Technology, College of Life Sciences and Biotechnology, Korea University, Seoul 132-701, Republic of Koreac Department of Food Science and Engineering, Ewha Womans University, 11-1 Daehyun-dong, Seodaemun-gu, Seoul 120-750, Republic of Koread National Institute of Crop Science, 20 Jeompiljaero, Milyang, Gyeongnam 627-803, Republic of Koreae Rice Research Division, National Institute of Crop Science, Rural Development Administration, Suwon 441-857, Republic of Korea

⁎ Corresponding author. Tel.: +82 2 3290 3029; fax:⁎⁎ Corresponding author. Tel.: +82 2 3277 3091; fax:

E-mail addresses: junelee@korea.ac.kr (S.-J. Lee), ysk1 Hea-Won Kim and A-Young Lee contributed equally

0963-9969/$ – see front matter © 2013 Elsevier Ltd. Allhttp://dx.doi.org/10.1016/j.foodres.2013.05.001

a b s t r a c t

a r t i c l e i n f o

Article history:Received 23 January 2013Accepted 1 May 2013Available online 9 May 2013

Keywords:Black colored riceAntiobesityMetabolic profilingGC–TOF-MS

We investigated the mechanisms underlying the antiobesogenic properties of black-colored rice (BCR; Oryzasativa L. cv. Heugkwangbyeo) with high levels of anthocyanins. Stimulation of cultured adipocytes with BCRextract caused a significant decrease in cellular lipid reduction, and C57BL/6J mice fed BCR extract for8 weeks exhibited significant reductions in body weight and body fat. Plasma concentrations of leptin and in-sulin decreased, while levels of adiponectin increased in BCR group. These metabolic alterations were medi-ated by the activation of peroxisome proliferator-activated receptors (PPARs), activating PPAR-α whileinhibiting PPAR-γ. These changes were accompanied by down-regulation and up-regulation of the genes in-volved in hepatic fatty acid synthesis and fatty acid oxidation, respectively. Evaluation of gene expression inadipocytes also suggested that those involved in lipid accumulation were reduced, with a concomitant induc-tion of the genes involved in lipid hydrolysis. The plasma metabolites in mice rendered obese by intake of ahigh-fat-diet were analyzed using GC-TOF-MS and multivariate analysis after being fed different diets for8 weeks: control, Garcinia cambogia, control rice (O. sativa L. cv. Ilpumbyeo), and BCR. The BCR group couldbe distinguished from the others after 4 weeks of the intervention using multivariate analysis. The majorcomponents related to this distinction were mannose, isoleucine, threonine, and phenylalanine. Also, bodyweight, glucose, and total cholesterol were positively correlated with the levels of propionic acid, butyricacid, cholesterol, alanine, ribitol, glucose, and galactose, whereas the high-density lipoprotein (HDL)/non-HDL ratio was negatively correlated with those of stearic acid, cholesterol, valine, leucine, glucose, andgalactose.

© 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Being overweight or obese, defined as abnormal or excessive fataccumulation that may impart a high risk on individual health(WHO, 2011), serves as a major risk factor for various chronic dis-eases including diabetes, heart diseases, and cancers. Biologically, ithas been shown that the process of adipocyte differentiation andlipid metabolism is controlled by a group of closely related transcrip-tion factors, the peroxisome proliferator activated receptors (PPARs)(Rosen et al., 2002). PPARs are well-characterized ligand-activatedtranscription factors that have been implicated in metabolic diseasessuch as obesity, diabetes, and atherosclerosis due to their role in theregulation of genes involved in lipid and glucose (Glc) homeostasis

+82 2 3290 3653.+82 2 3277 4213.im10@ewha.ac.kr (Y.-S. Kim).to this work.

rights reserved.

(Chinetti et al., 1998). PPARs form heterodimers with the retinoid Xreceptor and bind to specific PPAR response elements in the promoterregion of their target genes. The three subtypes of PPARs, namely, α,δ, and γ, exhibit a characteristic distribution in a variety of tissues:PPAR-α is predominantly present in the liver, heart, and kidney;PPAR-δ is ubiquitously expressed; and PPAR-γ is expressed mainlyin adipose tissue (Memon et al., 2000). PPAR-α and PPAR-γ playmore prominent roles in lipid homeostasis and they have been widelyinvestigated.

PPAR-γ is a dominant activator of adipocyte differentiation via thetransactivation of adipose-specific genes. Therefore, PPAR-γ agonistsmay cause the development of fat cells, while antagonists may exertan antiobesity therapeutic effect. Gong et al. (2009) found thatPPAR-γ antagonists significantly reduced the adipose mass, bodyweight (BW), and the ratio of low-density lipoprotein to high-densitylipoprotein (HDL) in high-fat-diet-induced obese mice. On the otherhand, PPAR-α plays a crucial role in lipid metabolism by regulatingthe expression of genes that encode for enzymes involved in the

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transport and β-oxidation of free fatty acids. Activation of PPAR-α byagonists such as fenofibrate has been reported to reduce weight gain,and plasma triglyceride (TG) and total cholesterol (TC) concentrationsin rats (Ferreira, Parreira, Green, & Botion, 2006). This has promptedthe development of various synthetic drugs targeting PPARs in attemptsto treat obesity. However, synthetic drugs are often costly and may im-part long-term side effects. In addition, most antiobesity drugs are onlyeffective when used appropriately, with additional weight-loss mea-sures such as diet modification (Sonnenberg, Matfin, & Reinhardt,2007). Thus, the development of sustainable dietary alternatives thatare easily accessible to all is a better and more rational approach toachieve efficient weight loss without undesirable adverse effects.

Rice (Oryza sativa) is a staple food for nearly one-half of theworld's population and it provides an excellent source of energy forhumans. However, rice has a high glycemic index (GI) (Miller, Pang,& Bramall, 1992) and so may contribute to weight gain and subse-quently lead to obesity. An in vivo study has demonstrated that theconsumption of a high-GI diet by normal and diabetic rats led to anincrease in adipocyte size and in plasma lipid levels (triacylglyceridesand phospholipids) compared to groups fed with a lower-GI diet(Lerer-Metzger et al., 1996). In addition, high-GI diets also promotethe expression and activity of fatty acid synthase (FAS) in the whiteadipose tissue of rats, leading to increased lipogenesis and weightgain (Kabir et al., 1998).

Pigmented rice, such as black-colored rice (BCR), is consumed inKorea, China, and Japan, and is recognized as a healthy and functionalfood (Han, Ryu, & Kang, 2004). BCR has two major anthocyanins(cyanidin-3-glucoside and peonidin-3-glucoside) that exhibit remark-able antioxidant and anti-inflammatory activities (Hu, Zawistowski,Ling, & Kitts, 2003). Anthocyanins exert a proven antiobesity effectand improve adipocyte function both in vitro and in vivo systems;they are significantly involved in the prevention ofmetabolic syndrome(Tsuda, Ueno, Yoshikawa, Kojo, & Osawa, 2006). Hence, BCR consump-tion could ameliorate the lipid profile (Ling, Wang, & Ma, 2002;Zawistowski, Kopec, & Kitts, 2009) and protect against oxidative stress,resulting in a decrease in atherosclerotic plaque formation (Ling et al.,2002).

Metabolomics is a technology used to identify and quantify a widevariety of small-molecular-weight compounds in cells, tissues, or bio-logical fluids including urine and blood (Kim, Kim, et al., 2010; Kim,Park, et al., 2010; Pan et al., 2010). Diverse small-molecule metabolites,which may play roles as disease biomarkers, include amino acids, or-ganic acids, lipids, carbohydrates, peptides, nucleic acids, and vitamins(A et al., 2005; Zhang, Sun, Wang, Han, & Wang, 2012). In particular,metabolite profiling which can interpret the interactions and the rela-tionships that originally occur through the regulation at the metaboliclevel (Fiehn et al., 2000), is mainly focused on a class of metabolites(e.g., amino acids, carbohydrates, or those related to a specific biologicalpathway) (Fiehn, 2001).

Metabolic profiling is a powerful approach for defining the rela-tionships between endogenous metabolic patterns and diseases, in-cluding obesity, diabetes, hypertension, and cardiovascular diseasethrough the integrated analysis of biological specimens using animalmodel systems (Kim, Kim, et al., 2010; Kim, Park, et al., 2010; Wanget al., 2009; Zeng et al., 2010).

The metabolic profiling of plasma and/or liver frommice fed low-and high-fat diets has recently been investigated using gas chroma-tography (GC)–mass spectrometry (MS) (Spagou et al., 2011), nucle-ar magnetic resonance (NMR) (Serkova et al., 2006) imaging, andultraperformance liquid chromatography (UPLC) quadrupole time-of-flight (TOF) MS (Kim, Kim, et al., 2010; Kim, Park, et al., 2010).Among these useful analytical methods, GC coupled to TOF-MS(GC–TOF-MS) has been used to characterize differences in metabo-lite profiles between biological specimens (Liu et al., 2010; Zhao etal., 2012). GC–TOF-MS has some advantages over other analyticalmethods due to its high resolution, sensitivity, and reproducibility

(Lee, Choi, Cho, & Kim, 2010). In addition, TOF-MS provides bothrapid analyte detection and the convenience of peak deconvolution,resulting in narrow peaks identified by rapid separations (Peterson,Bowerbank, Collins, Graves, & Lee, 2003).

The objective of this study was to identify the molecular mecha-nisms underlying the potential body-fat-reducing properties of BCR,both in vitro and in vivo. The metabolic profiling of plasma fromobese mice on a high-fat diet using GC–TOF-MS, and analysis of thecorrelation between metabolites and obesity using multivariate sta-tistical analysis were further investigated to provide in-depth infor-mation regarding the BCR-extract-induced metabolite alterations.

2. Materials and methods

2.1. Chemicals and materials

Reagents used for the biological mechanism, including Dulbecco'smodifiedEagle'smedium, fetal bovine serum, andpenicillin/streptomycinfrom Hyclone (Logan, UT, USA); troglitazone, GW9662, Oil Red O, fattyacid free bovine serum albumin, hematoxylin & eosin, and sodiumdeoxycholate from Sigma-Aldrich (Saint Louis, MO, USA); Vybrant DiIcell labeling solution from Molecular Probes, PBS, and adiponectinfrom Invitrogen (Carlsbad, CA, USA); isopropanol, hexane, and ethanolfrom Merck (Darmstadt, Germany); [1-14C] palmitate from PerkinElmer (Foster, CA, USA); plasma insulin from Alpco (Salem, NH, USA);leptin from Millipore (Bedford, MA, USA); formaldehyde from DuksanPure Chemical (Seoul, Korea); formalin from Daejung Chemical(Siheung, Korea); total RNA extraction reagent and SYBR Green (SYBRPremix Ex TaqII) from Takara Bio Inc. (Shiga, Japan); oligo(dT) primer,dNTP mixture, RNase-free water, and reverse transcriptase fromMbiotech (Seoul, Korea); Bradford reagent and Laemmli sample bufferfrom Bio-Rad (Hercules, CA, USA); Tris–HCl pH 7.5 from Acros (Geel,Belgium); NP-40, SDS, and protease inhibitor cocktail from Bio Basic(Markham Ontario, Canada); NaCl and EDTA from Junsei Chemical(Tokyo, Japan); and nitrocellulosemembrane from Schleicher & SchuellBioscience (Dassel, Germany) were purchased.

All internal and external standard compounds for phenolicacids, anthocyanins, and metabolic profiling, trifluoroacetic acid(TFA), and N-(tert-butyldimethylsilyl)-N-methyltrifluroacetamide(MTBSTFA) with 1% tert-butyldimethylchlorosilane (TBDMCS)were purchased from Sigma-Aldrich (St. Louis, MO, USA). N,O-bis(trimethylsilyl) trifluoroacetamide containing 1% trimethylchlorosilanewas obtained from Supelco (Belefonte, PA, USA). HPLC grade water, ac-etone, hexane, and methanol were purchased from J. T. Baker(Phillipsburg, NJ, USA).

2.2. Cell culture and treatments

HepG2 and 3T3-L1 cells were grown in Dulbecco's modifiedEagle's medium (DMEM) supplemented with 10% heat-inactivatedfetal bovine serum and 1% penicillin/streptomycin for 24 h at 37 °Cin 5% CO2. Cells were seeded into 100-mm culture dishes or 6-wellculture plates and subsequently treated with 10 M of troglitazone,10 M of GW9662, control rice (CR, 0.4, 2.0 and 10 M) or BCR (0.4,2.0 and 10 M).

2.3. DiI staining and Oil Red O staining

3T3-L1 cells were stained with the Vybrant DiI cell labeling solutionaccording to the manufacturer's instructions. 3T3-L1 cells were alsostained with Oil Red O. Briefly, cells were washed with cold PBS andfixed with 10% (v/v) formalin for 1 h at room temperature. The fixedcells were washed with distilled water and 60% (v/v) isopropanol. Sub-sequently, the cells were stained with Oil Red O (0.35%, v/v) for 1 h andfollowed by thorough rinsing. The stained cells were visualized usingEclipse Ti inverted microscope (Nikon, Tokyo, Japan).

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2.4. Cellular lipid measurements

The cellular lipids were extracted as described previously(Hozumi, Kawatno, & Jordan, 2000). Briefly, HepG2 and 3T3-L1 cellswere washed with cold PBS and cellular lipids were extracted atroom temperature with 2 mL of hexane:isopropanol mixture (2:1,v:v). The organic solvent was removed by vacuum centrifugationand the lipids were resuspended in 200 mL of 95% ethanol. Cellulartriglycerides were quantified enzymatically with an automatic ana-lyzer (Cobas C111, Roche, Basel, Switzerland) and the concentrationswere normalized with total protein concentrations. The total choles-terol levels and free fatty acids were measured with Amplex Red Cho-lesterol Assay Kit (Invitrogen, Carlsbad, CA, USA) and Free Fatty AcidQuantification Kit (Biovision, Mountain View, CA, USA), respectively,as per manufacturer's instructions.

2.5. Transfection and luciferase assays

Transfection and reporter gene assays were performed usingCHO-K1 cells as described previously (Jia et al., 2011).

2.6. Fatty acid oxidation

Fatty acid oxidation in HepG2 cells was measured with [1-14C]palmitate as previously described (Furth, Sprecher, Fisher, Fleishman,& Laposata, 1992). HepG2 cells were washed three times with PBSand warmed to 37 °C. Subsequently, 8 mL of DMEM containing1 mg mL−1 of fatty acid free bovine serum albumin and [1-14C]pamitate (1.75 mM, 57 mCi mmol−1) was added and incubatedfor 18 h. The liberated 14CO2 was trapped with NaOH and theevolved 14CO2 was counted by liquid scintillation. The values werenormalized with protein concentration.

2.7. Animals and diets

Animal care and handling were performed according to protocolsapproved by the Animal Experimentation and Ethics Committee ofKorea University (Protocol No. KUIACUC-20090420-4). C57BL/6Jmale mice (8 weeks old) were purchased from Samtako (Seoul,Korea) and maintained in a temperature-controlled (25 °C) specific-pathogen-free facility on a 12 h light/dark cycle. The mice were fedwith AIN-76A-based Western diet [40% of calories from fat and 0.15%(w/w) cholesterol] for 5 weeks to induce obesity. The mice werequarantined for 1 week prior to randomly assigned into four groups(n = 10–12 per group). The feeding experiments were performedwith C57BL/6J male mice. Mice were randomly divided into five groupsand fed AIN-76A diet. The tested materials including Garcinia cambogiaextract (CJ Food Co., Seoul Korea), CR extract (O. sativa L. cv.Ilpumbyeo), and BCR extract (O. sativa L. cv. Heugkwangbyeo) wereorally administered daily at 10 am for 8 weeks (900 mg/kg/day). Forthe preparation of the rice extracts, each finely ground rice flour(200 g) was extracted with 1 L of 80% ethanol (v/v) for 12 h at roomtemperature twice. Ethanolic extracts were then concentrated with arotary vacuum evaporator and freeze dried. Bodyweightwasmeasuredtwice per week. Food andwater were given ad libitum. Food intakewasmonitored once every 2 weeks. Blood samples were collectedretroorbitally after 8–10 h of fasting. After 8 weeks of feeding, micewere sacrificed and blood was collected by cardiac puncture and tissuesamples including liver, epididymal fat, perirenal fat, andmesenteric fatwere collected. The weight of the liver, epididymal fat, perirenal fat andmesenteric fat was determined and the anatomical views of the tissuesamples were captured. All samples were stored at −20 °C untilanalysis.

2.8. Phenolic compounds (phenolic acids and anthocyanins) analysis ofrice extracts

CR and BCR extracts prepared as explained above were subjected toanalyze phenolic acids and anthocyanins. For the targeted analysis ofphenolic acids including salicylic acid, 4-hydroxybenzoic acid, vanillicacid, syringic acid, p-coumaric acid, 3,4-dihydroxybenzoic acid, ferulicacid, sinapic acid, and caffeic acid, each rice extract sample, or phenolicacid standard solution prepared in a range of 0.01 to 50 μg/mLcontaining 50 μL of 3,4,5-trimethoxycinnamic acid (25 μg/mL) asan internal standard (IS) was hydrolyzed with 2 M NaOH for 4 h at30 °C and adjusted to a pH 1.5–2.0 with 2 M HCl. After extractedwith ethyl acetate three times and then dried in a Centri-Vap(Labconco Co., Kansas City, MO, USA) for 10 h, a mixture of 40 μL ofMTBSTFA with 1% TBDMCS and 40 μL of pyridine was transferred tothe dried samples for derivatization. Derivatized samples werethen heated for 30 min at 60 °C and allowed to cool for 10 minprior to GC–TOF-MS analysis. All analyses were performed on anAgilent 6890N GC (Agilent, Palo Alto, CA, USA) coupled to a PegasusIII time-of-flight (TOF) mass spectrometry (MS) (Leco, St. Joseph, MI,USA), in the electron ionization (EI) mode (70 eV). A DB-5MS col-umn (30 m length × 0.25 mm i.d. × 0.25 μm film thickness, J&WScientific, Folsom, CA, USA) was operated with helium as carriergas (flow rate, 1.0 mL min−1). The derivatized sample (1 μL) wasinjected in a split ratio of 1:10. The oven temperature was initiallyheld at 150 °C for 2 min, raised to 320 °C at a rate of 15 °C min−1,and held for 10 min. The data acquisition rate was set at20 scans s−1 in the mass scan range fromm/z 85 to 700. The injectorand detector transfer line temperatures were 230 °C and 250 °C, re-spectively. The identification of each phenolic acid was confirmed bycomparing their retention time and mass spectra with those of au-thentic reference standards. The levels of phenolic acids in the sam-ples were calculated by calibration curves constructed by linearregression of the peak area ratio of individual standards relative tothe peak area of the IS. The quantitative data were the mean valuesof replicates (n = 3).

For analysis of anthocyanins including cyanidin-3-glucoside (C3G)and peonidin-3-glucoside (P3G) in ethanolic rice extracts, the ex-tracts or anthocyanins such as C3G and P3G standard solutions pre-pared in a range of 1.56–200 μg/mL were filtered through a 0.2 μmmembrane syringe filter (Whatman) before injection. The quantita-tive analysis of anthocyanins was performed using ultra performanceliquid chromatography (UPLC)-photodiode array (PDA) (WatersAcquity, Milford, MA, USA) at 530 nm. UPLC was conducted on aUPLC BEH C18 column (2.1 × 100 mm i.d., 1.7 μm, Waters acquity)at a flow rate of 0.35 mL/min at 35 °C. Sample injection volume was1 μL. Anthocyanins were separated with a linear elution gradientwith solvent A (0.1% trifluoroacetic acid (TFA) in water) and solventB (0.1% TFA in methanol) from 0 min, 85% to 80% A in 0.4 min, 80%to 70% A in 8.5 min, returned to 85% A in 0.5 min, and held at 85% Afor 1 min. The levels of anthocyanins in the samples were calculatedby calibration curves constructed by linear regression of the peakarea with reference to respective standards. The quantitative datawere the mean values of replicates (n = 3).

2.9. Plasma lipid and glucose analysis

Plasma lipid and glucose analysis was performed once a monthduring the feeding period (0, 4 and 8 weeks). Blood samples werecollected retroorbitally after 8–10 h of fasting. Plasma TG, total cho-lesterol (TC), HDL-cholesterol, and non-HDL-cholesterol levels weredetermined using Cobas111. Plasma glucose concentrations weremeasured using a portable glucose meter. Plasma insulin, leptin, andadiponectin concentrations were measured with ELISA according tothe manufacturer's instructions.

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2.10. Liver triglyceride measurement

The liver triglyceride levels were determined enzymatically usingCobas111.

2.11. Histological analysis of adipose tissue

Epididymal adipose tissue was fixed in 4% formaldehyde andstained with hematoxylin and eosin. The size of the stained fatpads was determined using an upright microscope (Axio ImagerM1; Carl-Zeiss, Oberkochen, Germany).

2.12. Total RNA extraction

Total RNA was extracted using a total RNA extraction reagent(RNAiso Plus). One milliliter of RNA extraction reagent was addedto the cell/tissue samples. The samples were then homogenized andincubated for 5 min at room temperature and followed by centrifuga-tion (12,000 ×g; 10 min). The supernatant was added with 200 μL ofchloroform, hydrophilic layer was collected, and the RNA was precip-itated with isopropanol. The RNA pellets were washed with 75% eth-anol and dried at room temperature. The RNA concentration wasmeasured spectrophotometrically with Smart Spec Plus (Bio-Rad,Hercules, CA, USA).

2.13. Reverse transcription and quantitative PCR analysis

In order to synthesize cDNA, 2 μg of total RNA was initially incu-bated with 1 μL of oligo (dT) primer (50 pmol) and 4 μL of dNTP mix-ture (2.5 nM) in RNase-free water at 65 °C for 15 min. Uponincubation, the mixture was added with 1 μL of reverse transcriptase,5 × buffer, and 5 μL of RNase-free water, followed by incubation at42 °C for 60 min and 70 °C for 15 min. The synthesized cDNA wasamplified by real-time PCR (rt-PCR; iCycler iQ5l, Bio-Rad, Hercules,CA, USA) using rt-PCR premix solution containing SYBR Green(SYBR Premix Ex TaqII) and the appropriate primers. Amplificationwas performed with an initial denaturation step at 95 °C for 30 s,followed by 60 cycles of denaturation at 95 °C for 10 s, annealing at55–61 °C for 15 s, and extension at 68 °C for 20 s. The fluorescencesignal was automatically detected at the end of each PCR cycle. Primersequences of the investigated genes are presented in SupplementaryTable 1.

2.14. Protein isolation and immunoblot analysis

Tissue samples were lysed in buffer (10 mM Tris–HCl, pH 7.5, 1%NP-40, 0.1% sodium deoxycholate, 0.1% SDS, 150 mM NaCl, and1 mM EDTA) containing a protease inhibitor cocktail at 4 °C. Theprotein concentration was determined with Bradford reagent andbovine serum albumin was used as standard. Protein samples(50 μg) in Laemmli sample buffer were heated for 3 min and re-solved by 10% SDS-PAGE. The separated proteins were transferredonto a nitrocellulose membrane (Schleicher & Schuell Bioscience,Dassel, Germany) at 100 V for 1 h. Nonspecific binding was blockedwith 5% nonfat dry milk in TBS-T buffer for 1 h at room temperature.The membranes were incubated with primary antibody overnight at4 °C. Upon incubation, the membranes were washed with TBS-T for40 min and then incubated with secondary antibody for 1 h at 4 °C.Immunoreactive protein bandswere detected with enhanced chemi-luminescence system (Animal Genetics, Seoul, Korea), visualizedusing a ChemiDoc XRS+ System (Bio-Rad), and quantified withGel-Pro Analyzer software.

2.15. Pretreatment of plasma

Plasma was isolated after centrifugation of the blood samples at13,000 rpm and 4 °C for 15 min and kept in a deep freezer at −70 °Cuntil further analysis. Following a slightly modified method of Wanget al. (2009), blood plasma sample (30 μL) was extracted with 500 μLof cold methanol to precipitate the protein and stop enzymes activities.Then, each (10 μL) of the internal standard compounds containing2-deoxy-D-ribose [200 ppm (v/v) in water] for carbohydrates,heptadecanoic acid [100 ppm (v/v) in hexane] for lipids, tropic acid[50 ppm (v/v) in acetone] for organic acids, and norleucine [200 ppm(v/v) in water] for amino acids was added to the samples. Aftervortexing for 1 min, the extract was incubated at an ice temperaturefor 10 min and then centrifuged at 3000 rpm and 4 °C for 15 min. A400 μL aliquot of the supernatant was completely dried in aCentri-Vap (Labconco Co., Kansas City, MO, USA) for 10 h. For derivati-zation, a mixture of 50 μL of N,O-bis (trimethylsilyl) trifluoroacetamide(BSTFA) containing 1% trimethylchlorosilane (TMCS) and 50 μL of ace-tonitrile was transferred to the dried samples. The sampleswere heatedat 70 °C for 1 h and allowed to cool for 10 min prior to GC–TOF-MSanalysis.

2.16. GC–TOF-MS analysis of plasma extract

All analyses were performed on an Agilent 6890N GC (Agilent, PaloAlto, CA, USA) coupled to a Pegasus III time-of-flight (TOF) mass spec-trometry (MS) (Leco, St. Joseph, MI, USA), in the electron ionization(EI) mode (70 eV). A DB-5MS column (30 m length × 0.25 mmi.d. × 0.25 μm film thickness, J&W Scientific, Folsom, CA, USA) was op-erated with helium as carrier gas (flow rate, 1.0 mL min−1). Thederivatized sample (2 μL)was injected in splitlessmode. The oven tem-perature was initially held at 85 °C for 5 min, and raised from 85 to205 °C at a rate of 8 °C min−1. After maintaining at 205 °C for 5 min,the temperature was elevated to 300 °C at a rate of 8 °C min−1 andthen held for another 5 min. The data acquisition rate was set at20 scans s−1 in the mass scan range from m/z 45 to 550. The injectorand detector transfer line temperatures were 230 °C and 250 °C, re-spectively. The identification of eachmetabolitewas confirmed by com-paring their retention time and mass spectra with those of authenticreference standards. When the authentic compounds were not avail-able, each compound was tentatively identified on the base of theirmass spectral data using an on-line library such asWiley 7n mass spec-tral database (Hewlett-Packard, Palo Alto, CA, USA, 1995), NIST05 MSLibrary and MS search Program V.2.0d (NIST, 2005). The relative levelsof metabolites in the samples were calculated by comparing their peakareas to that of the internal standard compound. The quantitative datawere the mean values of replicates (n = 5).

2.17. Statistical analysis

In vitro and in vivo data were expressed as means ± SEM unlessexpressed otherwise. Student's t-test was performed for comparisonbetween two groups. In qPCR and immunoblotting, a between-groupanalysis was also performed with Student's t-test. Statistical signifi-cance level was preset at p b 0.05.

Formetabolic profiling, analysis of variance (ANOVA)was performedusing SPSS (Version 12.0, SPSS, Chicago, IL, USA) to evaluate statisticalsignificance among the metabolites including amino acid, organic acid,carbohydrate, and lipids in liver tissue and plasma of pigmentedrice-fed mice on high-fat diet. Duncan's multi-range test was usedwhen the samples presented significantly different peak areas of eachmetabolite with the level of significance at p b 0.05. Multivariate statis-tical analysis was employed by SIMCA-P+ 11.0 (Umetrics, Umeå,Sweden). Principal component analysis (PCA), an unsupervised cluster-ing technique that reduces a large data set to a few composite variables,is used for visualization and interpretation of the data matrix. Partial

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least squares regression (PLSR) analysis was also used to explorecorrelation betweenmetabolites and antiobesity activities of pigmentedrice. The metabolites with the largest variable importance in theprojection (VIP) values exceeding 1.0 were regarded as potentialbiomarkers.

3. Results

3.1. Evaluation of the antiobesity effect of BCR in vitro

3.1.1. Transactivation of PPAR-α and PPAR-γTreatment with BCR promoted the transactivation of PPAR-α in a

concentration-dependent manner. For all concentrations of BCR test-ed (0.4, 2, and 10 μg mL−1), the transactivation of PPAR-α was

Fig. 1. Luciferase assay result of PPARα and PPARγ (A) and expression of PPARγ

increased by more than 70% compared to the control rice extract(CR) condition (p b 0.05; Fig. 1A). Nevertheless, the transactivationof PPAR-α by BCR was also greater than that induced by fenofibrate,a known PPAR-α agonist. In contrast to PPAR-α, BCR significantly re-duced the transactivation of PPAR-γ compared to CR (p b 0.05;Fig. 1A). PPAR-β/δ transactivation was unaltered in CHO-K1 cellsstimulated with BCR.

3.1.2. mRNA expression of PPAR-γ target genes in hepatocytesThe expression of PPAR-γ is directly associated with hepatic

steatosis via the activation of sterol regulatory element-bindingprotein-1 (SREBP-1) and FAS. The gene expressions of PPAR-γ targetgenes, SREBP-1c and FAS, in HepG2 cells were evaluated using quanti-tative reverse-transcriptase polymerase chain reaction to determine

and its target genes (B). Gene expression was measured by real-time PCR.

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the effect of BCR on hepatic steatosis (Fig. 1B). The mRNA expressionsof both FAS and SREBP-1c were significantly down-regulated in hepa-tocytes treated with BCR, being respectively 82% and 34% lower thanthose in hepatocytes treated with CR (p b 0.05).

Fig. 2. Intracellular total cholesterol, triglyceride, free fatty acid content and fatty acid oxidatand DiI staining of differentiated 3T3-L1 (B). Troglitazone (Tro) is a PPARγ agonist and GW

3.1.3. Lipid profile in hepatocytes and adipocytesStimulation of hepatocytes (HepG2) with BCR extract significantly

reduced the synthesis of cholesterol (Cholest), TGs, and free fattyacids compared with cells treated with CR (Fig. 2A). The effect of

ion of HepG2 (A) as well as intracellular total cholesterol, triglyceride, Oil red O staining9662 is a PPARγ antagonist.

Table 2Initial weight, final weight, weight gain, diet intake and food efficiency ratio (FER) ofgroups treated with control, control rice, black-colored rice, and Garcinia cambogiafor 8 weeks.

Dietary group

Control Controlrice

Black-coloredrice

GCam

Food intake(g/day)

2.69 ± 0.09 2.58 ± 0.06 2.49 ± 0.05 2.49 ± 0.08

Initial bodyweight (g)

29.04 ± 0.49 29.79 ± 0.6 29.86 ± 0.84 30.22 ± 0.56

Final bodyweight (g)

46.34 ± 0.87 44.48 ± 1.1 40.83 ± 1.31 41.03 ± 0.91

Body weightgain (g/day)

0.31 ± 0.37 0.26 ± 0.27 0.19 ± 0.16 0.19 ± 0.16

Feed efficiencyratio (FER)

0.11 0.10 0.07 0.07

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BCR extract was comparable to that of PPAR-γ antagonist (GW9662).Fatty acid oxidation in hepatocytes was also positively regulated byBCR treatment (Fig. 2A) increasing it by 32% and 33% compared tothe CR and control groups, respectively (p b 0.05). In addition, BCRtreatment also markedly reduced the TC and TG contents of adipo-cytes (3T3-L1 cells) (p b 0.05; Fig. 2B).

3.1.4. Lipid accumulation in adipocytesThe presence of BCR severely impaired the extent of adipogenesis in

3T3-L1 cells compared to CR (Fig. 2B). In the presence of BCR, fewer cellsacquired the characteristic morphology of rounded and lipid-filled adi-pocytes, as assessed with the aid of light microscopy. This observationwas verified by labeling the cells with the lipid-specific dye VybrantCM-DiI. The adipocytes treated with CR were extensively labeled withVybrant CM-DiI (Fig. 2B). However, treatment with BCR substantiallyreduced the extent of labeling with Vybrant CM-DiI, indicating thatBCR treatment effectively decreased the lipid accumulation in adipo-cytes compared to CR treatment.

3.2. Phenolic compound (phenolic acids and anthocyanins) analysis

Total phenolic acids (free and esterified forms) including salicylicacid, 4-hydrocybenzoic acid, vanillic acid, syringic acid, p-coumaricacid, 3,4-dihydroxybenzoic acid, ferulic acid, sinapic acid, and caffeicacid from CR and BCR extracts were investigated using GC–TOF-MS(Table 1). In the current study, the contents of phenolic acids in BCRwere higher than those in CR extract. Among them, caffeic acid(352.06 and 33.69 μg/g of rice flour in CR and BCR, respectively)was predominant, followed by ferulic acid (2.13 μg/g of rice flour)in CR extract and 3,4-dihydroxybenzoic acid (160 μg/g of rice flour)in BCR extract, respectively.

In addition, anthocyanins from those rice extracts were also ana-lyzed using UPLC (Table 1). The main anthocyanins in BCR extractwere cyanidin-3-glucoside (C3G) and peonidin-3-glucoside (P3G)with 303.33 and 10.93 μg/g of rice flour, respectively.

3.3. Body fat reduction in vivo

3.3.1. BW gain as well as weight and lipid accumulation of liver andadipose tissue

The initial BW was the same across the groups (Table 2, Fig. 3A).Mice continuously gained weight upon feeding with a high-fat diet;the BW of the control mice fed solely a high-fat diet increased by17% after 8 weeks (Fig. 3A). The CR diet slightly reduced the weightgain induced by the previous high-fat diet, such that the BW in-creased by only 15% after 8 weeks. The BCR diet had a more markedeffect, such that a reduction in BW gain was clearly observed after

Table 1Phenolic compounds of control rice and black-colored rice (microgram per g of riceflour).

Control rice Black-colored rice

Phenolic acidsSalicylic acid 0.24 ± 0.01a 0.40 ± 0.044-Hydroxybenzoic acid 0.46 ± 0.01 0.98 ± 0.07Vanillic acid 0.19 ± 0.02 9.90 ± 0.42Syringic acid 0.06 ± 0.00 0.79 ± 0.07p-Coumaric acid 0.06 ± 0.00 1.42 ± 0.433,4-Dihydroxybenzoic acid 1.40 ± 0.59 160.37 ± 16.80Ferrulic acid 2.13 ± 0.12 17.63 ± 3.18Sinapic acid 0.53 ± 0.12 19.74 ± 10.30Caffeic acid 33.69 ± 0.96 352.06 ± 19.86

AnthocyaninsCyanidin-3-glucoside ndb 303.33 ± 2.89Peonidin-3-glucoside nd 10.93 ± 2.57

a Values are mean ± SD (n = 3).b Not detected.

only a short duration of 3 weeks (p b 0.05). The BW gain was signif-icantly lower in the BCR group (11% after 8 weeks) than in the CR andcontrol groups, even though the food intake did not differ significant-ly between them. The BW gain of the BCR group was also comparableto that of mice treated with G. cambogia (GCam), a plant extract withwell-documented antiobesity properties. These findings are clearlysupported by the images of representative mice from each group(Supplementary Fig. 1A).

The BCR diet also significantly reduced the liver weight of the mice(Fig. 3B). The liver weight was 29% and 42% lower in the BCR groupthan in the CR and control groups, respectively (p b 0.05). The effectof the rice diets on adipose tissue was also evaluated in this study.The weights of epididymal, perirenal, and mesenteric fat were 2.7,1.5, and 2.1 g, respectively, in the control mice (Fig. 3B). Feedingwith a CR diet had no significant effect on the weight of these adiposetissues. However, feeding with BCR significantly reduced the weightof adipose tissue, and in particular the perirenal and mesenteric fat,compared to the control and CR groups (p b 0.05). This finding wasalso clearly supported by anatomical views of the various adipose tis-sues (Supplementary Fig. 1B). There was clearly less white adiposetissue around the epididymis, kidney, and mesentery in the BCRgroup than in the CR and control groups, but appeared to be similarin the GCam group.

The light microscopy images and measurements of adipocyte sizealso revealed that feeding with BCR induced adipose delipidation, asindicated by the smaller adipocytes compared to the CR and controlgroups (p b 0.05; Fig. 3C). In addition to lipid accumulation in adiposetissue, we evaluated the lipid accumulation in the liver by hematoxy-lin and eosin staining. The control and CR groups exhibited severalmacrovesicular and microvesicular fatty infiltrations in the hepato-cytes (Fig. 3D); those observed in mice fed the BCR diet were smallerand fewer. The GCam group was similar to the BCR group in this re-gard. Thus, this histological examination revealed that feeding witheither BCR or GCam significantly reduced the lipid accumulation inthe liver. Feeding with BCR also effectively reduced the hepatic TGconcentration, with a reduction of 50% compared to the control con-dition (p b 0.05; Fig. 3E). The hepatic TG level was also lower in theBCR group than in the GCam group (p b 0.05).

3.3.2. Plasma Glc and the lipid profileOral administration of BCR for 8 weeks effectively increased the

concentration of plasma HDL Cholest, being 14–16% greater than inthe CR and control groups (p b 0.05; Table 3). Conversely, the plasmaconcentrations of non-HDL and Glc were 56% and 46% lower(p b 0.05), respectively, in the BCR group than in the control group.The concentrations of plasma HDL, non-HDL, and Glc did not differsignificantly between the BCR and GCam groups.

Fig. 3. Changes of bodyweight (A),weight of adipose tissue (epididymal fat, perirenal fat andmesenteric fat) and liver (B), histological staining ofwhite adipose tissue and liver (C), size ofadipocytes (D) and liver triglyceride level (E) in each group after treatment for 8 weeks. Thirty cells were chosen randomly and sizes were measured.

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3.3.3. Concentration of hormone in plasmaPlasma insulin and leptin concentrations were significantly re-

duced in mice fed with BCR compared to those fed with CR and con-trol diets (p b 0.05; Fig. 4A). The insulin and leptin concentrationswere 40–55% lower in the BCR group than in the CR and controlgroups. In contrast, the BCR diet positively regulated the concentra-tion of plasma adiponectin. The concentration of this hormone inmice fed with BCR was significantly elevated by 33–122% comparedto those fed with CR and control diets (p b 0.05).

3.3.4. mRNA and protein expressions in liver and adipose tissueThe expression levels of several genes involved in fatty acid and

lipid metabolism were quantified in the liver and white adipose

tissues of C57BL6/J mice fed with different diets. In mice fed withBCR, the mRNA and protein expression levels of PPAR-α were signif-icantly stimulated, by 28% and 65%, respectively, compared to thecontrol (p b 0.05; Fig. 4B, C). Similarly, PPAR-α expression in thewhite adipose tissue was also up-regulated in mice fed with BCR(30% greater than in the control).

On the other hand, the BCR diet was shown to down-regulate themRNA expression of PPAR-γ (65% lower) and its target genes in theliver, including SREBP-1c and ADRP (which encodes for adipose dif-ferentiation related protein; 55% and 71% lower than control-groupvalues, respectively) (p b 0.05; Fig. 5A). A similar trend was ob-served for the protein expression of PPAR-γ target genes in theliver (Fig. 5B). The BCR diet extensively down-regulated the protein

Table 3Concentration of plasma lipids for control-, control rice-, black-colored rice- andGarcinia cambogia-treated groups.

0 week 4 weeks 8 weeks

Total cholesterol (mg/dL)Control 125 ± 8 161 ± 6 abc 174 ± 8 aCR 135 ± 8 a 150 ± 4 a 198 ± 7 aBCR 116 ± 3 a 173 ± 8 b 176 ± 14 aGCam 115 ± 4 a 184 ± 5 bc 173 ± 10 a

Glucose (mg/dL)Control 56 ± 12 a 274 ± 21 a 310 ± 6 aCR 76 ± 11 a 160 ± 6 a 176 ± 11 bBCR 67 ± 10 a 178 ± 14 b 168 ± 12 bcGCam 72 ± 10 a 158 ± 8 b 130 ± 13 c

Triglycerides (mg/dL)Control 131 ± 8 a 77 ± 8 a 73 ± 2 abCR 124 ± 9 a 71 ± 6 a 100 ± 5 cBCR 122 ± 6 a 148 ± 5 b 84 ± 9 bcGCam 137 ± 3 a 122 ± 6 b 61 ± 3 a

HDL-C (mg/dL)Control 103 ± 9 a 126 ± 6 a 142 ± 7 aCR 107 ± 8 a 133 ± 3 ab 140 ± 5 abBCR 99 ± 3 a 141 ± 9 b 162 ± 8 bGCam 97 ± 6 a 152 ± 6 b 161 ± 8 ab

NonHDL-C (mg/dL)Control 19 ± 3 a 35 ± 2 b 32 ± 2 bCR 23 ± 1 a 18 ± 2 a 44 ± 3 cBCR 16 ± 2 a 23 ± 1 a 14 ± 2 aGCam 18 ± 2 a 31 ± 2 b 14 ± 3 a

HDL/nonHDL-CControl 6.33 ± 1.44 a 3.67 ± 0.35 a 4.50 ± 0.14 aCR 4.90 ± 0.53 a 7.75 ± 0.69 c 3.25 ± 0.16 aBCR 6.75 ± 1.01 a 6.16 ± 0.57 bc 12.42 ± 1.57 abGCam 6.99 ± 1.86 a 5.04 ± 0.45 ab 17.15 ± 3.94 b

HDL/total cholesterol (mg/dL)Control 082 ± 0.04 a 0.78 ± 0.04 a 0.82 ± 0.02 bCR 0.79 ± 0.01 a 0.89 ± 0.01 b 0.71 ± 0.00 aBCR 0.86 ± 0.03 a 0.81 ± 0.02 a 0.94 ± 0.04 cGCam 0.84 ± 0.02 a 0.82 ± 0.02 a 0.94 ± 0.02 c

Values are expressed as the mean ± SEM.Means within the same column followed by different lowercase letters are significantlydifferent (p b 0.05).

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expression of PPAR-γ-related genes in the liver, such asmature SREBP-1and FAS by 77% and 56%, respectively compared to control values(p b 0.05). Likewise, the protein expressions of mature SREBP-1 andFAS in white adipose tissue were also markedly reduced in the BCRgroup compared to the control (p b 0.05).

3.4. Plasma metabolic profiling by GC–TOF-MS

Twenty-five plasma metabolites were assessed in total (Table 4).These included 4 carbohydrates [mannose (Mnn), Glc, ribitol (Rbt),and galactose (Gal)], 6 lipids [propionic acid (PA), butyric acid (BA),palmitic acid (PA), linoleic acid (LA), stearic acid (SA), and Cholest],5 organic acids [acetic acid (AA), oxalic acid (OA), succinic acid(SA), malic acid (MA), and citric acid (CA)], and 10 amino acids [ala-nine (Ala), valine (Val), leucine (Leu), isoleucine (Ile), proline (Pro),glycine (Gly), serine (Ser), threonine (Thr), aspartic acid (Asp), andphenylalanine (Phe)]. The application of ANOVA to the GC–TOF-MSdata sets revealed differences among the mean values of the relativepeak areas of the metabolites between the biological specimens(p b 0.05).

The order of the plasma levels of Glc and Gal after 8 weeks of thediet interventions was as follows: control > CR > BCR = GCam. In-terestingly, plasma Glc levels did not differ significantly among thegroups after only 4 weeks of the diet interventions. For the lipids,there was no statistical difference in the levels of PA (3:0), PA

(16:0), LA (18:2), and SA (18:0) in plasma between the four groupsafter 4 weeks. However, after 8 weeks, the levels of PA (3:0), PA(16:0), SA (18:0), and Cholest were significantly higher in the CRgroup than in the BCR group, whereas that of LA (18:2) was signifi-cantly lower in the CR group than in the BCR group (p b 0.05). Withregard to the organic acids, the group of mice fed BCR had significant-ly lower levels of SA compared with those fed CR after 4 weeks, whilethere was no difference between them after 8 weeks. It was difficultto compare the levels of the other organic acids between the fourdiet groups because there were already significant differences be-tween the groups prior to the diet interventions (i.e., at 0 weeks).Among the amino acids, there were no changes of Ala, Val, Leu, Ser,and Asp levels between the BCR and CR groups except for the levelsof Ile, Gly, and Phe, which were elevated in the BCR group.

3.5. Multivariate statistical analysis of plasma metabolites

3.5.1. Principal component analysisGC–TOF-MS data of the biological specimens from the control, CR,

BCR, and GCam groups were subjected to principal component analy-sis (PCA) score plotting to enable visualization of the data matrix. Asshown in Fig. 6, 25 variables detected by Chroma TOF software dataprocessing were considered to be important metabolites in the sepa-ration of the respective clusters along the first two principal compo-nents (PC1 and PC2). The PCA score plot combining PC1 and PC2explained 45.7% of the total variance (29.9% and 15.8%, respectively).

All groups in the plot were shifted from left to right according todiet duration. Interestingly, the order of shifting the longest distanceamong the four groups when compared between week 0 and week8 was as follows: control > CR > GCam = BCR. The major metabo-lites contributing to the PC1 dimension were PA (3:0), Cholest, Ala,Mnn, Glc, and Gal (Fig. 7A). On the basis of PC2, BCR was discriminat-ed from the other diet groups from the 4-week time point. The majorcomponents associated with the separation were MA, Ile, Thr, andPhe along PC2 (Fig. 7B).

3.5.2. Partial least-squares regression analysisThe correlations between metabolites obtained by GC–TOF-MS

and antiobesity activities using parameters such as HDL, non-HDL,BW, Glc, TGs, TC, HDL/TC, and HDL/non-HDL (H/non-H) ratio wereassessed using partial least-squares (PLS) regression (PLSR) analysis.PLS is a multivariate technique that is used to evaluate the relation-ship between a descriptor matrix x and a response matrix y; GC–TOF-MS data and antiobesity effects were used as x variables and yvariables, respectively, in this study. In addition, we obtained thevariable importance in the projection (VIP) values to refine this anal-ysis (Eriksson, Johansson, & Kettaneh-Wold, 2006), which areknown to reflect the importance of terms in the model bothpertaining to x (the projection) and y (its correlation to all of the re-sponses). Variables with VIP values exceeding 1 are considered to berelevant for explaining the selection of y (Shearer et al., 2008; Wanget al., 2012).

PLS component 1 (PLS1) and PLS component 2 (PLS2) explained29.8% and 12.7%, respectively, of the total variance in the PLS loadingbiplot (Fig. 8). The variable importance of each metabolite in the PLSRscore plot is shown in Fig. 9 and detailed in Table 5. Fig. 8 shows thatPLS1 revealed mainly the differences according to the feeding dura-tion. For instance, all groups at 0 weeks (negative PLS1 values) wereclearly separated from those at 8 weeks (positive PLS2 values)along PLS1. The metabolites with VIP scores of more than 1 that con-tributed to the PLS1 dimension were Cholest, PA (3:0), Ala, Gal, Glc,BA (4:0), Mnn, AA, Rbt, Gly, and Ser (Fig. 9A). Furthermore, PLS2 rep-resented the alterations attributable to being fed a different diet for8 weeks. For example, two groups, GCam and BCR (positive PLS2values), were distinguishable from the other groups, except for8_1C, one out of five in control (n = 5; negative PLS2 values) along

Fig. 4. Plasma hormonal (insulin, leptin and adiponectin) concentrations (A), mRNA (B) and protein (C) expression of PPARα and its target genes in liver and WAT. X-axis is therelative expressions of gene or protein compared with controls. Measurements of mRNA were carried out by real-time PCR, and values shown were normalized to the respectivetranscript level of the constitutively expressed control gene GAPDH.

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Fig. 5. The mRNA (A) and protein (B) expression of PPARγ and its target genes in liver and WAT. X-axis is the relative expressions of gene or protein compared with controls. Mea-surements were carried out by real-time PCR, and values shown were normalized to the respective transcript level of the constitutively expressed control gene GAPDH.

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PLS2. Metabolites with VIP scores larger than 1—comprising Cholest,Ala, Glc, PA (3:0), Gal, BA (4:0), Mnn, Rbt, Ser, and AA—were consid-ered to be the potential contributors (Fig. 9B).

The specific metabolites that were markedly associated withantiobesity activities were analyzed on the basis of correlation coeffi-cients calculated using Statistica software. The major variables with

Table 4Identification of blood plasma metabolites from mice differing in diet and duration of diet (0, 4, and 8 weeks).

Metabolites RTa Week Relative peak area (mean ± SD)b IDc

Controld Control ricee Black-colored ricef Garcinia cambogiag

CarbohydratesMnn Mannose 20.44 0 0.819 ± 0.260 a 0.971 ± 0.460 a 0.739 ± 0.210 a 0.859 ± 0.333 a A

4 1.546 ± 0.262 bc 1.759 ± 0.464 c 0.715 ± 0.088 a 1.222 ± 0.486 b8 2.816 ± 0.631 a 2.672 ± 0.683 a 1.884 ± 0.680 a 1.965 ± 0.593 a

Glc Glucose 21.13 0 1.349 ± 0.319 a 1.212 ± 0.489 a 1.492 ± 0.706 a 1.613 ± 0.684 a A4 2.721 ± 0.597 b 2.628 ± 0.284 b 1.904 ± 0.325 a 2.046 ± 0.633 ab8 4.912 ± 0.801 c 3.842 ± 0.735 b 2.232 ± 0.361 a 2.508 ± 0.479 a

Rbt Ribitol 21.81 0 0.156 ± 0.031 ab 0.180 ± 0.039 b 0.146 ± 0.024 ab 0.129 ± 0.017 a B4 0.183 ± 0.025 a 0.198 ± 0.042 a 0.142 ± 0.108 a 0.200 ± 0.063 a8 0.232 ± 0.062 a 0.216 ± 0.055 a 0.246 ± 0.050 a 0.245 ± 0.053 a

Gal Galactose 22.68 0 0.830 ± 0.214 a 1.163 ± 0.288 a 1.055 ± 0.523 a 1.265 ± 0.662 a A4 1.918 ± 0.402 a 2.336 ± 0.665 a 1.813 ± 0.856 a 1.803 ± 0.310 a8 4.425 ± 0.762 c 3.450 ± 0.506 b 2.495 ± 0.664 a 2.009 ± 0.825 a

LipidsPA (3:0) Propionic acid 6.77 0 1.546 ± 0.132 a 1.349 ± 0.241 a 1.225 ± 0.329 a 1.563 ± 0.320 a A

4 2.184 ± 0.587 a 2.253 ± 0.599 a 2.129 ± 0.475 a 2.447 ± 0.444 a8 3.428 ± 0.690 b 3.456 ± 0.422 b 2.454 ± 0.391 a 2.337 ± 0.109 a

BA (4:0) Butyric acid 9.13 0 1.463 ± 0.681 a 1.989 ± 0.700 a 1.766 ± 0.650 a 1.587 ± 0.354 a B4 3.612 ± 0.838 b 2.414 ± 0.770 a 2.333 ± 0.861 a 2.404 ± 0.447 a8 5.174 ± 0.513 b 3.854 ± 1.951 ab 2.645 ± 0.760 a 5.239 ± 0.513 b

PA (16:0) Palmitic acid 24.29 0 3.410 ± 0.157 a 3.589 ± 0.645 a 3.286 ± 0.303 a 3.394 ± 0.408 a A4 3.470 ± 0.390 a 3.841 ± 1.076 a 3.047 ± 0.825 a 3.667 ± 0.668 a8 3.451 ± 0.071 b 3.922 ± 1.150 b 2.540 ± 0.361 a 3.843 ± 0.565 b

LA (18:2) Linoleic acid 27.97 0 0.427 ± 0.164 a 0.406 ± 0.161 a 0.382 ± 0.130 a 0.429 ± 0.084 a B4 0.293 ± 0.068 a 0.349 ± 0.032 a 0.365 ± 0.023 a 0.404 ± 0.211 a8 0.284 ± 0.046 a 0.294 ± 0.143 a 0.485 ± 0.208 b 0.376 ± 0.049 ab

SA (18:0) Stearic acid 28.61 0 2.870 ± 0.204 a 2.761 ± 0.383 a 2.589 ± 0.310 a 3.008 ± 0.296 a A4 3.029 ± 0.361 a 3.221 ± 0.788 a 2.921 ± 0.372 a 2.619 ± 0.501 a8 2.983 ± 0.568 ab 3.397 ± 0.586 b 2.450 ± 0.577 a 2.473 ± 0.276 a

Cholest Cholesterol 38.85 0 2.426 ± 0.480 a 2.487 ± 0.560 a 2.236 ± 0.366 a 2.631 ± 0.347 a A4 4.402 ± 0.370 b 3.856 ± 0.279 ab 3.331 ± 0.184 a 3.698 ± 0.802 a8 7.428 ± 0.810 c 5.692 ± 0.522 b 4.145 ± 0.667 a 4.202 ± 0.439 a

Organic acidsAA Acetic acid 7.06 0 0.615 ± 0.070 a 0.642 ± 0.072 a 0.751 ± 0.088 b 0.657 ± 0.069 ab B

4 0.969 ± 0.313 a 1.079 ± 0.691 a 0.797 ± 0.103 a 0.916 ± 0.377 a8 1.151 ± 0.151 ab 1.336 ± 0.131 b 0.969 ± 0.165 a 0.963 ± 0.202 a

OA Oxalic acid 8.47 0 1.140 ± 0.252 a 0.913 ± 0.200 a 1.061 ± 0.219 a 1.007 ± 0.158 a A4 1.062 ± 0.208 ab 0.863 ± 0.072 a 0.871 ± 0.061 a 1.390 ± 0.467 b8 1.015 ± 0.279 a 1.151 ± 0.086 a 1.033 ± 0.159 a 1.115 ± 0.924 a

SA Succinic acid 12.29 0 0.145 ± 0.068 a 0.131 ± 0.030 a 0.155 ± 0.023 a 0.149 ± 0.024 a A4 0.153 ± 0.061 a 0.283 ± 0.088 b 0.104 ± 0.056 a 0.289 ± 0.054 b8 0.046 ± 0.008 a 0.154 ± 0.070 c 0.123 ± 0.010 bc 0.085 ± 0.011 ab

MA Malic acid 15.29 0 0.734 ± 0.085 b 0.603 ± 0.097 a 0.543 ± 0.073 a 0.751 ± 0.026 b A4 0.653 ± 0.154 b 0.753 ± 0.079 bc 0.335 ± 0.060 a 0.887 ± 0.121 c8 0.335 ± 0.095 a 0.438 ± 0.071 b 0.298 ± 0.036 a 0.459 ± 0.055 b

CA Citric acid 20.09 0 4.016 ± 0.042 b 4.012 ± 0.368 b 3.186 ± 0.902 a 4.259 ± 0.506 b A4 3.664 ± 0.776 a 5.509 ± 0.885 b 2.966 ± 0.347 a 3.942 ± 0.930 a8 3.748 ± 0.391 a 3.809 ± 0.740 a 3.904 ± 0.309 a 3.693 ± 0.268 a

Amino acidsAla Alanine 7.75 0 0.483 ± 0.056 a 0.503 ± 0.041 a 0.480 ± 0.063 a 0.481 ± 0.081 a A

4 0.603 ± 0.066 b 0.722 ± 0.024 c 0.477 ± 0.046 a 0.805 ± 0.089 d8 1.099 ± 0.283 b 0.945 ± 0.094 ab 0.971 ± 0.080 ab 0.848 ± 0.092 a

Val Valine 10.26 0 0.333 ± 0.032 b 0.335 ± 0.075 b 0.322 ± 0.027 b 0.262 ± 0.019 a A4 0.299 ± 0.019 b 0.229 ± 0.010 a 0.354 ± 0.068 c 0.284 ± 0.005 b8 0.318 ± 0.090 ab 0.392 ± 0.065 b 0.343 ± 0.087 ab 0.262 ± 0.082 a

Leu Leucine 11.42 0 0.246 ± 0.037 a 0.220 ± 0.038 a 0.202 ± 0.025 a 0.221 ± 0.019 a A4 0.266 ± 0.033 b 0.184 ± 0.005 a 0.194 ± 0.019 a 0.250 ± 0.017 b8 0.256 ± 0.053 b 0.169 ± 0.031 a 0.136 ± 0.083 a 0.189 ± 0.039 ab

Ile Isoleucine 11.84 0 0.186 ± 0.045 a 0.158 ± 0.024 a 0.183 ± 0.024 a 0.191 ± 0.022 a A4 0.100 ± 0.052 a 0.103 ± 0.006 a 0.175 ± 0.037 b 0.131 ± 0.006 ab8 0.119 ± 0.034 a 0.102 ± 0.005 a 0.335 ± 0.066 b 0.091 ± 0.009 a

Pro Proline 11.9 0 0.155 ± 0.010 d 0.081 ± 0.004 a 0.097 ± 0.011 b 0.120 ± 0.007 c A4 0.132 ± 0.019 a 0.139 ± 0.006 a 0.116 ± 0.030 a 0.140 ± 0.006 a8 0.177 ± 0.036 b 0.139 ± 0.075 ab 0.088 ± 0.023 a 0.141 ± 0.023 ab

Gly Glycine 12.12 0 0.118 ± 0.022 a 0.107 ± 0.017 a 0.121 ± 0.039 a 0.133 ± 0.073 a A4 0.079 ± 0.038 a 0.112 ± 0.023 ab 0.143 ± 0.069 b 0.103 ± 0.013 ab8 0.293 ± 0.044 b 0.307 ± 0.075 b 0.415 ± 0.067 c 0.141 ± 0.022 a

Ser Serine 13.09 0 0.270 ± 0.030 a 0.269 ± 0.011 a 0.272 ± 0.025 a 0.287 ± 0.058 a A4 0.310 ± 0.038 a 0.266 ± 0.056 a 0.270 ± 0.075 a 0.385 ± 0.029 b8 0.391 ± 0.067 a 0.363 ± 0.096 a 0.350 ± 0.058 a 0.416 ± 0.081 a

Thr Threonine 13.53 0 0.298 ± 0.031 a 0.297 ± 0.007 a 0.295 ± 0.060 a 0.306 ± 0.045 a A4 0.351 ± 0.025 ab 0.347 ± 0.043 ab 0.311 ± 0.057 a 0.400 ± 0.015 b

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Table 4 (continued)

Metabolites RTa Week Relative peak area (mean ± SD)b IDc

Controld Control ricee Black-colored ricef Garcinia cambogiag

8 0.355 ± 0.076 b 0.322 ± 0.033 b 0.214 ± 0.051 a 0.322 ± 0.059 bAsp Aspartic acid 15.8 0 0.671 ± 0.056 a 0.677 ± 0.085 a 0.718 ± 0.053 a 0.693 ± 0.050 a A

4 0.447 ± 0.092 a 1.354 ± 0.482 b 1.847 ± 0.437 c 0.822 ± 0.058 a8 1.369 ± 0.317 b 1.025 ± 0.149 a 1.132 ± 0.148 ab 1.096 ± 0.123 ab

Phe Phenylalanine 17.42 0 0.121 ± 0.014 a 0.119 ± 0.014 a 0.108 ± 0.061 a 0.125 ± 0.020 a A4 0.124 ± 0.012 a 0.118 ± 0.040 a 0.232 ± 0.045 b 0.128 ± 0.032 a8 0.189 ± 0.066 ab 0.132 ± 0.056 a 0.235 ± 0.082 b 0.147 ± 0.022 a

There are significant difference (p b 0.05) among samples using Duncan's multiple comparison test between samples having different letters in a row (n = 5).a Retention time.b Values are means of five replicate relative peak areas to that of the internal standard ± standard deviation (SD).c Identification: A, mass spectrum and retention time were consistent with those of an authentic standard; B, mass spectrum was identical with that of Wiley 275 mass spectrum

database and NIST05 MS Library.d High fat diet 45%.e High fat diet 45% + control rice.f High fat diet 45% + black-colored rice.g High fat diet 45% + hydroxycitric acid (Garcinia cambogia extract).

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antiobesity effects with correlation coefficient scores above 0.1were selected. Some metabolites of the control group (8 weeks)—PA (3:0) (r = 0.104), Cholest (r = 0.110), Glc (r = 0.100), andGal (r = 0.105)—were strongly correlated with BW, whereas Glc(detected using a portable Glc meter) was closely related to Cholest(r = 0.108), as assessed using GC–TOF-MS analysis. Moreover, TC,found nearby CR (at 8 weeks), was correlated with metabolitessuch as BA (4:0) (r = 0.109), Al (r = 0.105), and Rbt (r =0.105). For the BCR group at 8 weeks, HDL/non-HDL was negative-ly associated with the lipids SA (18:0) (r = −0.178) and Cholest(r = −0.100), amino acids containing Val (r = −0.227) and Leu(r = −0.124), and carbohydrates such as Glc (r = −0.129) andGal (r = −0.106).

Fig. 6. The score plot of PCA for blood plasma specimens from m

4. Discussions

The prevalence of obesity is increasing at an alarming rate and hasquickly become a global epidemic in both industrialized and developingnations. In recent years, considerable attention has been focused on theimprovement of BW and prevention of obesity. There is accumulatingevidence that dietary modification of daily food intake can improvelipid metabolism so as to control BW gain and reduce fat deposition.To date, little attention has been paid to the potential antiobesity ef-fect of rice, a diet consumed almost daily by a wide global population.In the present study we investigated the potential antiobesity effectsof a new variety of rice, BCR (O. sativa L. cv. Heugkwangbyeo), ascompared to CR.

ice differing in diet and duration of diet (0, 4, and 8 weeks).

Fig. 7. The column loading plot of PCA for blood plasma specimens from mice differing in diet and duration of diet (0, 4, and 8 weeks).

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We found that BCR significantly reduced the BW gain and weightof the liver and adipose tissue. The reducedweight was accompaniedby a decreased lipid accumulation in the liver and adipose tissue ofmice fed with BCR. In fact, the effect of BCR was also comparable tothat of GCam, a supplement that is used widely to improve BW. Thereduced fat accumulation was most probably due to the effect ofBCR suppressing the biosynthesis and increasing the oxidation offatty acids. Decreased biosynthesis of fatty acids would lead to de-creases in production of non-HDL Cholests, such as very-low-density lipoprotein and low-density lipoprotein, and hence less fatdeposition in the body (Yang et al., 2001). There was also evidenceof this effect in the present study, in that we found that BCR signifi-cantly reduced the concentration of non-HDL Cholest, while increas-ing the concentration of HDL in the plasma of BCR-fed mice.

We also evaluated the level of obesity-related hormones in theplasma of BCR-fed mice. The BCR significantly induced adiponectinconcentration while decreasing that of both insulin and leptin. The in-creased concentration of adiponectin upon feeding with BCR was in-deed beneficial since this is a protein hormone that modulatesmetabolic processes such as Glc regulation and fatty acid oxidation.Increased concentrations of adiponectin hormone were also correlat-ed with a reduction of BW in obese subjects (Yang et al., 2001). In tan-dem with the increased concentration of adiponectin, our result alsoshowed that BCR potentially induced fatty acid oxidation and reducedplasma Glc levels. Leptin is a key hormone that regulates food intakeand BW. The serum leptin concentration is correlated with the massof body fat and size of the adipocytes (Considine et al., 1996;

Rönnemaa et al., 1997). Thus, the decreased leptin level was mostlikely due to the decrease in adipocyte size induced by the BCR diet.This finding was supported by our histological analysis, whichshowed a reduction in adipocyte size in mice fed with BCR. Thelower plasma leptin level also indicated that the BCR potentially re-duced the BW, as the concentration of this hormone was found tobe lower in normal-weight subjects compared to obese subjects(Considine et al., 1996; Sahu, 2002). Insulin is another hormonethat contributes to the development of obesity. Insulin is involvedin lipid metabolism and adiposity via various mechanisms includingfostering the differentiation of preadipocytes to adipocytes, stimulat-ing Glc transport and lipogenesis, and inhibiting lipolysis (Kahn &Flier, 2000). Thus, the reduced plasma insulin level probably contrib-uted to the reduction of BW and adiposity observed in the BCR-fedmice. Taken together, our findings indicate that the BCR diet mayserve as an alternative to improve BW.

The most frequently suggested antiobesity mechanism is the dis-ruption of lipid synthesis and oxidation by regulation of PPARs.Thus, in order to define the mechanism underlying the antiobesityproperties of BCR, we further elucidated the expression of PPARsand its target genes. PPARs, and especially PPAR-α and PPAR-γ,have been well-documented to play a critical physiological role inthe regulation of obesity. Up-regulation of PPAR-α is associatedwith the promotion of fatty acid oxidation and ketone body synthe-sis. In the current study, BCR up-regulated the expression of PPAR-αin both the liver and adipose tissue. This was most likely due to theincreased transactivation of PPAR-α upon treatment with BCR, as

Fig. 8. The correlation plot of relationship between plasma metabolites (▲) of GC–TOF-MS data and antiobesity effects using parameters ( ). The four group of mice such as Control,CR (Control rice), BCR (Black-colored rice), and GCam (Garcinia cambogia) analyzed by GC–TOF-MS is shown in abbreviation with green ( ); for example, 0 (week)_1 (# ofreplicate).

387H.-W. Kim et al. / Food Research International 53 (2013) 373–390

indicated in our in vitro model. This finding suggests that BCR con-tains compounds that act as PPAR-α agonists, which can amelioratelipid metabolism via a mechanism involving increases in the expres-sion and biosynthesis of liver fatty acid-binding protein (LFABP)(Linden et al., 2002). Increased expression of LFABP would in turnincrease the secretion of apolipoprotein B-100 and decrease TG bio-synthesis. In addition, LFABPs are involved in hepatic fatty aciduptake/oxidation, and should thereby reduce the concentration offatty acids available for storage in adipose tissue (Newberry,Kennedy, Xie, Luo, & Davidson, 2009). This mechanism may in partexplain the effect of BCR on fatty acid oxidation and reduced adiposefat in our current study.

In addition to PPAR-α, we evaluated the effect of BCR on PPAR-γ.PPAR-γ functions as a major regulator in the differentiation and prolif-eration of adipocytes (Larsen, Toubro, & Astrup, 2003). We found thatBCR decreased the transactivation and expression of PPAR-γ, whichmay lead to reduced adiposity, and hence reduced obesity. Obesity isgenerally associated with lipid accumulation and cellular dysfunctionin various organs including the liver. The degree of obesity is directlycorrelated with the prevalence and severity of nonalcoholic hepaticsteatosis disease. This indicates that transcription factors that involve

in adipogenesis (e.g., PPAR-γ) are also involved in hepatic steatosis. Ithas been suggested that PPAR-γ enhances lipid accumulation in hepa-tocytes via the activation of SREBP-1 (Schadinger, Bucher, Schreiber, &Farmer, 2005), which is a transcription factor that positively regulatesthe genes involved in lipid uptake and de novo fatty acid synthesis, in-cluding FAS. Considering the decreased transactivation and expressionof PPAR-γ observed herein, we further evaluated the potential of BCRto decrease the prevalence of hepatic steatosis. In this study, BCR signif-icantly down-regulated the expressions of both SREBP-1 and FAS inHepG2 cells and the mouse liver, indicating that BCR may reduce therisk of hepatic steatosis.

Metabolic profilingwith the aid of GC–TOF-MS combinedwithmul-tivariate statistical analysis (e.g., PCA and PLSR) was used to explore theplasma metabolic patterns and potential contributors to the distinctionbetween the BCR group and the other diet-intervention and controlgroups. Interestingly, the plasma levels of PA (3:0), PA (16:0), SA(18:0), Cholest, Glc, and Gal at 8 weeks were markedly higher in theCR group than the BCR group, whereas LA (18:0) was significantlylower in the CR group than in the BCR group (p b 0.05). These findingssuggest that anthocyanins, which are responsible for the color of BCR(Kim et al., 2008), are involved in the antiobesity effects of this rice

Fig. 9. The value of variable importance plot selected by partial least square regression analysis (PLSR). (A) VIP scores based on PLS1; (B) VIP scores along PLS2.

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(Guo et al., 2012; Savitha & Singh, 2011; Tsuda et al., 2006). In the cur-rent study, the levels of C3G in BCR analyzed by UPLC were predomi-nant and higher than those of Thailand and China black rice varieties(Sompong, Siebenhandl-Ehn, Linsberger-Martin, & Berghofer, 2011).The ratio of C3G and P3G was similar to the results of previous studies(Hou et al., 2013; Sompong et al., 2011). In details, the effects of C3G,one of the most abundant anthocyanins in pigmented rice (Frank,Reichardt, Shu, & Engel, 2012), were recently investigated for its poten-tials to lower fasting Glc levels in mice fed a high-fat diet (Guo et al.,2012) and improve adipocyte function in obesity by adjustingadipocytokine gene expression (Tsuda et al., 2006).

In this study, the levels of all phenolic acids determined in BCRwerehigher than those in CR extract. Among them, caffeic acid was predom-inant in both CR and BCR extracts followed by ferulic acid of CR and3,4-dihydroxybenzoic acid of BCR extracts, respectively. Specifically,3,4-dihydroxybenzoic acid, vanillic acid, p-coumaric acid, and sinapicacid in BCR revealed over 20 times higher than those in CR extract. Inaddition, 3,4-dihydroxybenzoic acid, which was considered as the

Table 5Major metabolites contributed to the PLS1 and 2 in plasma samples frommice differingin diet and duration of diet (0, 4, and 8 weeks) analyzed by GC–TOF-MS.

RT (min) Tentatively identification PLS1 scorea PLS2 scorea

PA (3:0) 6.77 Propionic acid (+) (−)AA 7.06 Acetic acid (+) (−)Ala 7.75 Alanine (+) (+)BA (4:0) 9.13 Butyric acid (+) (+)Gly 12.12 Glycine (+)Ser 13.09 Serine (+) (−)Mnn 20.44 Mannose (+) (−)Glc 21.13 Glucose (+) (−)Rbt 21.81 Ribitol (+) (+)Gal 22.68 Galactose (+) (−)Cholest 38.85 Cholesterol (+) (−)

Listed in order of retention time (RT) (VIP > 1).a Locations of metabolites in the PLS regression loading plot space related to figure

15 score plot: (−), on the left side of the PLS1 or 2 axis, scores have negative values;(+), on the right side of the PLS1 or 2 axis, scores have positive values.

major human metabolite of cyanidin-glucosides (Vitaglione et al.,2007), was abundant in BCR extract compared to that of CR extract.This result exhibited similar pattern compared to a previous study(Morimitsu et al., 2002). Furthermore, according to the study of Choet al. (2010), caffeic acid showed potential antiobesity activity inhigh-fat diet-induced-obesemicewith lowering bodyweight, triglycer-ide (in plasma, liver, and heart), and cholesterol (in plasma, adipose tis-sue and heart) levels. In an agreement of this study, abundant levels ofcaffeic acid in BCR might contribute to lower those levels in the plasmamice fed a BCR diet compared to other diet groups.

Additionally, Zawistowski et al. (2009) reported that LA (18:2), oleicacid, and oryzanol (a family of triterpene alcohol and ferulic acid esters(Akihisa et al., 2000) found in extract of BCR with anthocyanins) couldlower Cholest levels using plasma lipid parameters inWistar Kyoto rats.We also evaluated that the higher levels of LA (18:2) in the plasma ofmice fed a BCR diet compared to other diet groups concur with that ofa previous study showing that anthocyanin pigment could inhibit theoxidation of unsaturated fatty acids in urine and serum (Jankowski,Jankowska, & Niedworok, 2000), given that phenolic compounds in-cluding flavonoids (anthocyanin) may increase the antioxidant abilityof blood (Molan, Lila, & Mawson, 2008). The difference in plasma Glclevels between the BCR and CR groups was comparable to the resultsof a previous study (Savitha & Singh, 2011) showing higher levels ofwater-soluble fiber in those fed a BCR diet compared with a CR diet.Vaskonen, Mervaala, Krogerus, and Karppanen (2002) evaluated thatdietary soluble fiber could bind fatty acids, Cholest, and bile acids, andreduce their absorption in the intestine. Therefore, rice, a good sourceof dietary soluble fiber (Wan, Prudente, & Sathivel, 2012), is expectedto be helpful in lowering digestion of fatty acids, Cholest, and bileacids from the intestine.

Among the amino acids, levels of Ile and Gly were higher in the BCRgroup than in the CR group at 8 weeks (p b 0.05), whereas the levels ofAla, Val, Leu, Ser, and Asp did not differ significantly between these twogroups at the same timepoint. Zeng et al. (2010) found no statistical dif-ferences in Ala, Leu, Pro, Ile, Ser, Thr, and Phe between control, over-weight, and obese children. On the other hand, Newgard et al. (2009)showed that levels of Leu and Ile were increased in obese compared

389H.-W. Kim et al. / Food Research International 53 (2013) 373–390

to lean participants (p b 0.0007). Surprisingly, it has been shownthat gender impacts on outcome with respect to plasma aminoacids; an NMR study on the effects of gender on plasma metabolismfound that boys had markedly higher levels of amino acidscontaining Leu, Ile, and Val than girls (Bertram et al., 2009). An espe-cially noteworthy result was that the level of Phe, which is a biosyn-thetic precursor of anthocyanin (Edahiro, Nakamura, Seki, &Furusaki, 2005), was significantly higher in the BCR group than inthe CR group fromweek 4 to week 8. Phe is a known powerful releas-er of the satiety hormone, cholecystokinin, via central and peripheralmechanisms, resulting in reduced energy intake (Pohle-Krauza, Navia,Madore, Nyrop, & Pelkman, 2008); thus, BCRmight be beneficial in low-ering the energy intake related to obesity. The literature (Katsanos et al.,2008; Volpi, Kobayashi, Sheffield-Moore, Mittendorfer, & Wolfe, 2003)observed that the plasma availability of essential amino acids such asVal, Leu, Ile, tryptophan, lysine, Thr, Phe, andmethionine results inmus-cle protein synthesis, whereas nonessential amino acids alone are notresponsible for stimulating muscle protein anabolism.

The BCR group was markedly distinct from other groups from4 weeks, and the most important compounds related to this distinc-tion were plasma MA, Ile, Thr, and Phe. Finally, several potential pa-rameters for evaluating obesity (e.g., BW, Glc, and TC) were foundto be positively related to PA (3:0), BA (4:0), Cholest, Ala, Rbt, Glc,and Gal, whereas HDL/non-HDL was negatively correlated with SA(18:0), Cholest, Val, Leu, Glc, and Gal. Thus, it appears that BW, Glc,and TC, which are markers of obesity, were positively related to PA(3:0), BA (4:0), Cholest, Ala, Rbt, Glc, and Gal, while HDL/non-HDL,which can be used to evaluate antiobesity effects, was negatively cor-related with SA (18:0), Cholest, Val, Leu, Glc, and Gal.

5. Conclusion

We propose that the up-regulation of PPAR-α and down-regulationof PPAR-γ in the liver and adipose tissue are the key mechanisms un-derlying the antiobesogenic effect of BCR. The regulation of PPARs byBCR leads tomultiple effects including induction of fatty acid oxidation,an improved lipid profile, and a reduction of lipid accumulation in theliver and adipose tissue. Thus, our findings suggest that BCR couldserve as a useful dietary component for improving BW and reducingthe risk of hepatic steatosis.

Acknowledgment

This work was carried out with the support of “Cooperative Re-search Program for Agriculture Science & Technology Development”Rural Development Administration, Republic of Korea (Project Nos.PJ906950 and PJ00842203).

Appendix A. Supplementary data

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.foodres.2013.05.001.

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