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Ahn et al. BMC Public Health 2013, ESEARCH ARTICLEOpen AccessRice-eating pattern and the risk of metabolicsyndrome especially waist circumference inKorean Genome and Epidemiology Study (KoGES)Younjhin Ahn1, Seon-Joo Park1, Hye-kyoung Kwack1, Mi Kyung Kim2, Kwang-Pil Ko3 and Sung Soo Kim1*AbstractBackground: Metabolic syndrome poses a serious health threat in Asian countries. Rice is a staple food in Korea,and carbohydrate intake is associated with the risk of MetS. We hypothesized that various rice-eating patterns in acarbohydrate-based diet would have different effects on the risk of MetS.Methods: Participants were 26,006 subjects enrolled in the Korean Genome and Epidemiology Study between 2004and 2006. They were classified into four dietary patterns - white rice, rice with beans, rice with multi-grains, andmixed based on their food frequency questionnaire responses. We compared metabolic risk traits according to therice-eating patterns.Results: Nutrients consumption and the presence of MetS risk factors differed according to rice-eating patterns. Inmen odds ratio(OR) for central obesity was slightly elevated in mixed group(1.18). In women, the risk for centralobesity and abnormal fasting glucose were lower in the rice with beans group (adjusted OR 0.79, 0.83respectively) and central obesity in rice with multi-grains(adjusted OR 0.91) than the white rice group. Inpostmenopausal women, ORs for central obesity (0.78) and abnormal fasting glucose (0.75) in the rice with beansgroup and ORs for central obesity (0.83), abnormal HDL-cholesterol (0.87) and MetS(0.85) in the rice withmulti-grains group was lower than those in white rice group. In premenopausal women, the risk for central obesity(OR 0.77) was reduced in the rice with beans group.Conclusion: The risk for MetS was lower in the rice with beans and rice with multi-grains groups compared withthe white rice group, particularly in postmenopausal women.Keywords: Rice, Beans, Multi-grains, Metabolic syndrome (MetS), Postmenopausal womenBackgroundMetabolic syndrome (MetS) is characterized by a combination of disturbed glucose and insulin metabolism,central obesity, dyslipidemia, and hypertension [1]. MetSis a risk factor for type 2 diabetes and cardiovascular disease [1-4] and presents a serious health threat that is onthe rise in Western and Asian countries [5-7]. Theprevalence of MetS has been reported to be 34% amongadults aged 20 years and over in the United States [8],and 32.5% for men and 31.8% for women over 30 yearsof age in Korea [9]. The cause of MetS is unclear but it* Correspondence: ksungsoo@korea.kr1Division of Epidemiology and Health Index, Center for Genome Science,National Institute of Health, Center for Disease Control and Prevention,Chungcheongbuk-do, South KoreaFull list of author information is available at the end of the articleis thought to be associated with genetic, environmental,and dietary factors [10-14].The amount of carbohydrate intake affects bloodglucose and insulin. Furthermore, the quality of thecarbohydrate determined by the composition of diet isassociated with metabolic consequences. Several studieshave reported associations between dietary carbohydratequality/quantity and lipid profile [15], risk of diabetes[16], and insulin action [11].Koreans consume 306.58 g of carbohydrate, whichcontributes 63.4% of their total daily energy consumption in 2005 [9]. The main source of carbohydrate isrice. Koreans consume rice two or three times a day as astaple food and it supplies 37.9% of their total energy 2013 Ahn et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the CreativeCommons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited.

Ahn et al. BMC Public Health 2013, onsumption [9]. White rice is usually cooked alone ortogether with beans and multi-grains in Korea.In the present study, to determine whether variousrice-eating patterns in a carbohydrate-based diet wouldhave different effects on the risk of MetS, we comparedthe metabolic risk traits according to rice-eating patternsclassified using the food frequency questionnaire (FFQ)data collected in the Korean Genome and EpidemiologyStudy (KoGES), a large population-based cohort study ofKoreans.MethodsSubjectsThe Korean Genome and Epidemiology Study (KoGES)was launched in 2001 with a population based cohort of2 cities each in rural and urban area of central part ofKorea. The main objectives of this study were to establish national genomic cohort and to examine causal relationship between the genetic and environmental factorsassociated with major diseases such as diabetes mellitus,hypertension, obesity, and MetS in Korea. The size andscope of the project were gradually expanded and itencompasses 7 cohort studies mainly targeting 40 to69 years age group. KoGES has recruited more than240,000 participants by the end of 2011. The writteninformed consent was obtained from each participant.We analyzed data of participants who joined KoGESbetween November 2004 and December 2006 in thisstudy. Of those, 40,254 subjects had available dietarydata of food frequency questionnaire (FFQ). The recordswith extremely low or high energy intake ( 800 or 4,000 kcal for men and 500 or 3,500 kcal for women)were excluded (n 978).Subjects with previous diagnosed hypertension, hyperlipidemia, diabetes mellitus, or cancer (any type) wereexcluded from the study (n 11,842) because of the possibility of dietary habit change after diagnosis. Subjectswith no waist circumference, triglyceride, high-densitylipoprotein (HDL) cholesterol, blood pressure (BP), orfasting glucose data (n 1,428) were further excludedfrom our study. Finally, 26,006 subjects were includedfor the analysis of this study.Data collectionParticipants were recruited in 28 health examinationcenters across the country (Chonbuk National University Hospital, Chonnam National University Hospital,Chunchoen Sacred Heart Hospital, Daegu CatholicUniversity Medical Center, Dankook University Hospital,Dobong-Gu Health Center, Dong-A University MedicalCenter, Ehwa Womans University Mokdong Hospital,Gangdong Health Center, Gimje Public Health Center,Green Hospital, Gyeongju Health Center, HallymUniversity Sacred Heart Hospital, Inha UniversityPage 2 of 11Hospital, Inje University Sanggye Paik Hospital, JechoenPublic Health Center, Jungnang Health Center, JungwonGu Health Center, Kangbuk Samsung Hospital, KonkukUniversity Chungju Hospital, Korea University AnsanHospital,Kosin University Gospel Hospital, KyungpookNational University Hospital, Seongbuk Health Center,Sujeong-Gu Health Center, Ulsan University Hospital,Yeoncheon-Gun Health Center and County Hospital,Yonsei University Hospital Occupational Health Center).They originally visited for their health check-up and voluntarily agreed to participate in the KoGES. Questionnaires including age, gender, education level ( 6 years,7–12 years, 12 years ), smoking status (never/ex-/current), drinking status (non/ex-/current), dietary supplement use (yes/no), regular exercise (yes/no), and menopause status (yes/no) and FFQ were administered bytrained interviewers.Blood pressures (BPs) and anthropometric measurement were obtained by trained personnel. Waist circumference was measured midway between the inferiormargin of last rib and crest of the ilium in a horizontalplane in 0.1cm unit. Blood pressures were measured bya trained technician using a mercury sphygmomanometer. Subjects had 10 minutes resting before taking theBP measurements. Two readings were taken on the subject’s dominant arm while in the sitting position with a5-minute interval between readings. The BP valuesreported here are the mean of the two measurements.Participants were asked to fast more than 8 hours before examination and fast venous blood samples weredrawn at study site. Fasting glucose, triglycerides, andHDL cholesterol were assessed using a 7600–210 automatic analyzer (Hitachi, Tokyo, Japan).Dietary dataDietary intake was assessed using the 103-item FFQ(Additional file 1: Appendix 1). The development andvalidation of the questionnaire has been described inprevious reports [17,18]. Briefly, the FFQ was designedusing dietary data obtained from the Korea Health andNutrition Examination Survey (KHANES) 24-h recalldata in 1998. Food items were selected using a databased approach, and several seasonal foods were addedto the final food list.The frequency of servings was classified into nine categories: never or seldom, once a month, two or three timesa month, one or two times a week, three or four times aweek, five or six times a week, once a day, twice a day, orthree times or more a day. The portion size was categorizedas small, medium, or large. Participants were requested toanswer the length of time they used seasonal food items as3, 6, 9, or 12 months. The FFQ has been validated in 124subjects using a 12-day diet record.

Ahn et al. BMC Public Health 2013, he nutrient intake from each food item in the FFQwas determined by the weight derived from the consumption frequency and portion size of each food item.The daily nutrient intake of each individual was calculated as the sum of the nutrient intake from each fooditem. The nutrient and isoflavone compositions used inthe calculation were based on the Food CompositionTable [19,20].Glycemic load was calculated by multiplying carbohydrate content of each food item, which was based on thereported consumption frequency and portion size by itsglycemic index. The glycemic load value for one day wascalculated as the sum of all food items. The overall dietaryglycemic index was calculated by dividing the averagedaily glycemic load by the average daily carbohydrate intake. The glycemic index value for each food item wasobtained from the International Tables of GlycemicIndex [21], the online Glycemic Index Database maintained by the University of Sydney [22], and a previousreport that listed the glycemic index of Korean foods[23]. The reference of glycemic index values was glucose (glycemic index for glucose 100).Classification of subjectsTotal of 26,006 subjects were included for analysis andclassified into four groups according to their response onthe rice-eating pattern. Before asking consumption frequency and amount of cooked rice, we asked ‘What kindof cooked rice do you usually eat? (white rice only/ricewith other foods/mix two types)’ and then ‘What kind offoods do you mainly eat cooked rice with? (beans/multigrains)’ Rice-eating patterns were defined as ‘eating whiterice group’ (n 6,136), ‘eating rice with beans’ (n 2,589),‘eating rice with multi-grains’ (n 12,440), and ‘eating ricein mixed’ (n 4,841).MetS was defined by the presence of three or more ofthe following five components according the AdultTreatment Panel using waist circumference for Asians[24,25]: central obesity ( 90 cm for men or 80 cm forwomen); reduced HDL cholesterol ( 40 mg/dL for menor 50 mg/dL for women); hypertriglyceridemia asdefined by an elevated triglyceride level( 150 mg/dL);elevated BP as defined by systolic blood pressure (SBP) 130 mmHg or diastolic blood pressure (DBP) 85mmHg; and raised fasting glucose ( 100 mg/dL).Smokers were defined as having smoked at least 400cigarettes during their lifetime and divided into currentor ex-smokers based on their current smoking status.Drinkers were classified as drinkers or ex-drinkers interms of current alcohol intake, and nondrinker wasdefined as being unable or unwilling to drink during theirlifetime. Subjects who reported using any dietary supplements were classified as supplement users. Subjects werePage 3 of 11classified as regular exercisers or not if they answered todo exercise until sweaty regularly.Statistical analysisGeneral/metabolic characteristics and nutrients intakeare expressed as the means standard deviations (SD)for continuous variables and as frequencies or percentages for categorical variables. The effect of the riceeating patterns was assessed using analysis of variance(ANOVA) for continuous variables and chi-square testfor categorical variables. Multiple comparisons of themean were performed using Scheffe’s test. Triglyceride,HDL cholesterol, BP, and fasting serum glucose werelog-transformed to improve normality.The relationships between rice-eating patterns andMetS were examined using logistic regression. Oddsratios (ORs) and 95% confidence intervals (CIs) werecalculated. Adjustments were made for age, total energyintake, education level, supplement use, exercise, drinking, and smoking. We analyzed the effects of rice-eatingpatterns by gender because the prevalence of MetS andreported risk factors differ by gender.All statistical analyses were performed using SASv9.13 (SAS Institute Inc., Cary, NC, USA) and a P-valueof 0.05 was considered to be statistically significant.The design of study was reviewed and approved by theInstitutional Review Board of Korea Centers for DiseaseControl and Prevention.ResultsCharacteristics of the participants according to theirrice-eating patterns are shown in Table 1. The mean ageof men was highest in the rice with beans group (54.9years old). The 35 to 44 year-old participants ate morewhite rice alone and 45 to 54 year-old participants weremore distributed in mixed group. Over 55 year-old menwere more abundant in rice with beans group. Higherproportions of current smokers, drinkers, people whodid not take supplements and did not exercise wereobserved in white rice group. The habits of participantsin the rice with beans and rice with multi-grains groupswere healthier than those of participants in the whiterice group. Similar to the finding for men, the mean ageof the women in the rice with beans group was olderthan those of the other groups (51.8 years). Younger( 55 years) participants were in mixed group and olderparticipants were in rice with beans group. The proportion of participants who exercised was higher in the ricewith multi-grains groups and the proportion of dietarysupplements eaters was higher in mixed group than inthe white rice-alone group. The proportion of postmenopausal women was higher in rice with beans group.MetS component variables were compared accordingto rice-eating patterns. Means of WC and SBP in men

Ahn et al. BMC Public Health 2013, age 4 of 11Table 1 Participants’ characteristics according to rice-eating patternsTotal(n 26,006)White rice group(n 6,136)7,561Rice with other foods groupMixed group(n 4,841)PRice with beans(n 2,589)Rice with multi-grains(n 12,440)2,3866992,9911,48552.2 8.850.6 8.7 c54.9 9.1a53.3 8.8b51.1 8.0c 0.02251,728 (22.9)711 (29.8)97 (13.9)561 (18.8)359 (24.2) 0.0001MenNo. of subjectsAge (years)mean SDAge group, years, n (%)35–4445–542,997 (39.6)940 (39.4)253 (36.2)1,150 (38.4)654 (44.0)55–642,029 (26.8)545 (22.8)216 (30.9)889 (29.7)379 (25.5)64–75807 (10.7)190 (8.0)133 (19.0)391 (13.1)93 (6.3)Education levels, years, n (%) 62,186 (29.5)708 (30.2)245 (35.8)905 (30.8)328 (22.7)7 122,843 (38.4)900 (38.5)260 (37.9)1,143 (38.8)540 (37.4)12 576 (32.1)733 (31.3)180 (26.3)894 (30.4)576 (39.9) 0.0001Smoking status, n (%)Nonsmoker2,250 (29.9)674 (28.3)224 (32.2)921 (30.9)431 (29.2)Ex-smoker2,829 (37.5)825 (34.7)276 (39.7)1,151 (38.6)577 (39.0) 0.0001Current smoker2,457 (32.6)880 (37.0)195 (28.1)912 (30.5)470 (31.8)Nondrinker1,522 (20.2)443 (18.6)163 (23.4)639 (21.4)277 (18.7)Ex-drinker552 (7.3)161 (6.8)48 (6.9)235 (7.9)108 (7.3)5,456 (72.5)1,771 (74.6)486 (69.7)2,105 (70.7)1,094 (74.0)No3,648 (48.4)1,334 (56.5)319 (45.7)1,305 (43.7)680 (45.9)Yes3,895 (51.6)1,035 (43.5)379 (54.3)1.679 (56.3)802 (54.1)No5,407 (71.7)1,847 (77.8)478 (68.5)2,059 (68.9)1,023 (69.1)Yes2,132 (28.3)Drinking status, n (%)Current drinker0.0108Exercise, n (%) 0.0001Supplement use, n (%)Waist circumference (cm)Triglyceride (mg/dL)HDL cholesterol (mg/dL)85.4 7.3526 (22.2)85.1 7.4220 (31.5)b84.9 7.5929 (31.1)b457 (30.9)85.6 7.3 ab86.0 7.3a149.6 108.8150.4 105.6155.3 121.1148.4 108.3148.2 108.852.8 12.352.8 12.352.7 12.052.9 12.252.9 12.4ba 0.0001ab124.4 14.9 ab0.0004SBP (mmHg)124.8 15.3124.3 15.1DBP (mmHg)78.6 10.078.4 9.779.2 10.778.6 10.078.5 9.9Fasting glucose (mg/dL)95.0 18.994.5 18.395.4 17.695.4 18.595.0 21.2No. of subjects with MetS (%)1,486 (19.4)454 (19.0)136 (19.5)594 (19.9)284(19.1)18,4453,7501,8909,4493,35650.4 7.749.6 7.7c51.8 7.7a50.7 7.7b49.6 7.3c 0.000135–444,621 (25.1)1,089 (29.0)364 (19.2)2,248 (23.8)920 (27.4) 0.000145–548,840 (47.9)1,759 (46.9)909 (48.1)4,487 (47.5)1,685 (50.2)126.3 16.2125.1 15.40.0122WomenNo. of subjectsAge (years)mean SDAge group, years, n (%)

Ahn et al. BMC Public Health 2013, age 5 of 11Table 1 Participants’ characteristics according to rice-eating patterns (Continued)55–643,917 (21.2)693 (18.5)487 (25.8)2,121 (22.4)616 (18.4)64–751,067 (5.8)209 (5.6)130 (6.9)593 (6.3)135 (4.0) 67,549 (41.9)1,624 (44.4)867 (47.2)3,824 (41.4)1,234 (37.6)7 127,356 (40.8)1,426 (39.0)716 (39.0)3,825 (41.4)1,389 (42.3)12 658 (17.3)607 (16.6)254 (13.8)1,558 (17.2)658 (20.1)Nonsmoker17,716 (96.7)3,536 (95.2)1,830 (97.3)9,111 (96.9)3,239 (97.1)Ex-smoker243 (1.3)65 (1.7)29 (1.5)112 (1.2)37 (1.1)Current smoker371 (2.0)114 (3.1)22 (1.2)175 (1.9)60 (1.8)9,428 (63.3)2,288 (61.6)1,271 (67.5)5,923 (63.1)2,023 (60.9)Education levels, years, n (%) 0.0001Smoking status, n (%) 0.0001Drinking status, n (%)NondrinkerEx-drinker379 (2.5)105 (2.8)50 (2.7)224 (2.4)67 (2.9)5,129 (34.2)1,321 (35.6)562 (29.8)3,246 (34.5)1,207 (36.2)No8,982 (48.9)2,213 (59.4)882 (46.8)4,270 (45.4)1,617 (48.3)Yes9,383 (51.1)1,515 (40.6)1,001 (53.2)5.139 (54.6)1,728 (51.7)No10,439 (56.8)2,371 (63.7)1,061 (56.4)5,142 (54.6)1,865 (55.8)Yes7,926 (43.2)1,355 (36.3)822 (43.6)4,274 (45.4)1,475 (44.2)Post-8,856 (48.0)1,636 (43.6)1,065 (56.4)4,665 (49.4)1,490 (44.4)Pre-9,589 (52.0)2,114 (56.4)825 (43.6)4,784 (50.6)1,866 (55.6)78.0 7.878.3 7.877.7 7.578.0 7.877.8 7.9Triglyceride (mg/dL)107.7 70.7109.0 72.6108.9 69.1107.9 71.0105.2 68.3HDL cholesterol (mg/dL)58.6 12.758.4 12.858.2 12.558.8 12.858.7 12.5Current drinker 0.0001Exercise, n (%) 0.0001Supplement use, n (%)Menopause, n (%)Waist circumference (cm)SBP (mmHg)DBP (mmHg) 0.0001 0.0001118.7 15.774.0 10.0b120.2 15.6aba118.8 16.074.1 10.174.8 9.9ab118.7 15.774.1 10.0b117.6 5.4 c 0.000173.2 9.8 c 0.0001Fasting glucose (mg/dL)90.6 16.290.8 14.389.8 13.590.7 15.990.1 20.0No. of subjects with MetS (%)2,999 (16.3)625 (16.7)325 (17.2)1,572 (16.6)477 (14.2)For differences among rice-eating patterns using ANOVA with Scheffé’s test for data that are means SDs(continuous variables) and chi-square test for data thatare percentages(categorical variables).and those of SBP and DBP in women were significantlydifferent among groups.Table 2 shows nutrients consumption among the riceeating pattern groups. The dietary glycemic index washighest in the white rice group. The rice with beans groupshowed the highest consumption of protein, fiber and totalisoflavones intake. Total energy and energy from cookedrice, percentage of energy from cooked rice, carbohydrateintake, and dietary glycemic load were the highest in ricewith multi-grains group. Dietary characteristics by riceeating patterns were similar in both genders.Table 3 shows odds ratios of MetS and component riskfactors. In men, the risk for central obesity wasmaintained after adjustment for several confounding factors (adjusted OR in model 3 1.18) in mixed group. Therisk for high BP was greater in rice with beans (OR 1.27) and rice with multi-grains (OR 1.14) groupscompared with the white rice group, however, the associations were not significant after adjusting for confounding variables. No other risk factors or MetS wereassociated with rice-eating patterns. In women, the oddsratios for central obesity and high fasting glucose weresignificantly lower after adjustment in rice with beansgroup(adjusted OR in model 3 0.79, 0.83 respectively).The risk for high BP was elevated in the rice with beansand the significance was disappeared after adjustment.

Ahn et al. BMC Public Health 2013, age 6 of 11Table 2 Comparisons of nutrients intake according to gender and rice-eating patterns1Nutrient/food group intakeTotal(n 26,006)White rice group(n 6,136)Rice with other foods groupRice with beans(n 2,589)Mixed group(n 4,841)Rice with multi-grains(n 12,440)MenTotal energy (kcal)1,920 541.61,845 536.3c1,849 504.8bc2,001 547.6a1,911 532.3 bEnergy from cooked rice (kcal)1,073 313.7982 284.1c1,033 288.9 b1,161 320.6a1,059 303.7 b57.6 14.855.1 15.057.4 14.559.6 14.457.0 14.3 bProtein (g)63.5 24.060.7 24.0b65.7 21.9a65.2 24.1a63.3 24.5 aFat (g)30.6 17.030.9 17.331.9 16.130.0 16.830.8 17.2338.1 89.4 b5.8 2.8c57.0 3.1b341.0 92.1324.2c 88.9dFiber (g)6.0 2.95.3 2.8Glycemic Index56.9 3.157.2 3.2a319.3bbaEnergy % from cooked rice (%)Carbohydrate (g)cc 85.07.0 2.8a56.1 3.1d360.0ca 93.86.4 b2.956.9 3.1caGlycemic Load193.6 51.3184.8 48.9178.7 45.9204.6 52.4192.7 50.4 abTotal isoflavones (mg)11.5 9.38.6 7.3c25.5 9.7a11.0 8.3b11.0 7.91,742 522.01,637 509.4c1,694 515.0b1,796 524.2a1733 511.8 bcbabWomenTotal energy (kcal)Energy from cooked rice (kcal)944.0 320.5847.6 281.3909.9 299.9998.0 332.5919.0 307.0 bEnergy % from cooked rice (%)55.6 16.253.9 16.9c55.3 16.0b56.8 16.1a54.4 15.8 bccaaProtein (g)58.4 23.154.0 22.961.1 22.859.7 23.158.1 22.9 bFat (g)26.3 15.226.0 16.2b27.4 14.1a26.1 15.1b26.7 14.9 abccaCarbohydrate (g)313.1 91.9292.0 86.7297.3 89.9325.7 93.0310.5 89.7 bFiber (g)6.2 3.25.4 3.0d7.1 3.2a6.4 3.2b6.0 3.1cac56.2 b3.556.3 3.5b183.0 51.8a174.9 49.7 b10.8 9.2Glycemic Index56.3 3.656.7 3.855.5 3.6Glycemic Load176.0 51.0165.2 47.8 c164.5 48.3c c aTotal isoflavones (mg)11.8 10.58.48.623.911.111.0 b9.7b1. Mean SD.2. Different alphabet shows that means are significantly different among rice-eating patterns using ANOVA with Scheffé’s test for multiple comparisons (P 0.05).The ORs for central obesity(adjusted OR in model3 0.91) and hypertriglycemia (adjusted OR in model2 0.90) in the rice with multi-grains group with reference to the white rice group were significantly lower afteradjustment. The risk for BP was lowered in mixed groupand the significance was remained after adjustment(adjusted OR in model 0.86). The OR for MetS in the ricewith beans and the rice with multi-grains groups was significantly lower in model 2 (adjusted OR 0.89 and 0.82,respectively). The OR for MetS of mixed group showedsignificantly lower in all models.Table 4 shows the multivariate adjusted OR for MetSand its components in women by menopause states. Because menopause is associated with increased risk ofMetS, and waist circumference is also increased aftermenopause, we analyze the MetS risks by rice-eatingpattern before and after menopause separately. The ORsfor central obesity and abnormal glucose control weresignificantly reduced in rice with beans group, the ORfor central obesity in rice with multi-grains group, andthe ORs for central obesity, HDL abnormality and MetSin mixed group were lower in postmenopausal women.However, in premenopausal women, the OR for centralobesity was reduced only in the rice with beans group.DiscussionThe present study compared the effect of rice-eatingpatterns on MetS. We categorized the subjects into fourrice-eating groups: white rice alone, rice with beans, ricewith multi-grains, and mixed group. We analyzed theeffects of rice-eating patterns by gender because theprevalence of MetS and reported risk factors differ bygender [5,26]. We found that the risk for metabolicdisorders was lower in the rice with beans and rice withmulti-grains groups compared with the white rice group,particularly in postmenopausal women. The characteristics such as lifestyle habits, nutrient consumption,and metabolic indices differed according to rice-eatingpatterns.

Ahn et al. BMC Public Health 2013, age 7 of 11Table 3 Odds ratios (95% CIs) for metabolic syndrome and the individual components according to rice-eatingpatterns and genderMetabolicsyndrome traitsWhite rice groupRice with other foods groupRice with beansMixed groupRice with multi-grainsRefOR95% CIOR95% CIOR95% CIModel 110.970.80-1.181.070.95-1.211.261.05-1.39Model 210.930.77-1.131.000.88-1.131.181.02-1.36Model 310.910.75-1.110.980.87-1.121.181.02-1.36Model 110.920.77-1.100.930.83-1.040.980.86-1.12Model 211.000.83-1.190.960.86-1.080.980.86-1.24Model 311.060.88-1.271.010.90-1.131.020.89-1.17Model 111.150.91-1.460.950.82-1.121.000.83-1.21Model 211.120.89-1.430.940.80-1.101.000.83-1.21Model 311.130.89-1.450.950.81-1.121.020.84-1.24Model 111.271.07-1.511.141.02-1.271.040.91-1.19Model 211.150.96-1.361.060.94-1.181.030.90-1.17Model 311.160.98-1.381.090.97-1.221.080.94-1.24Model 111.030.86-1.241.070.95-1.200.990.85-1.14Model 210.980.82-1.191.030.92-1.170.980.85-1.13Model 310.960.80-1.161.020.91-1.160.990.86-1.15Model 111.030.83-1.271.060.92-1.201.010.85-1.19Model 211.010.81-1.251.020.87-1.170.990.84-1.17Model 311.010.81-1.261.030.89-1.191.030.87-1.22Model 110.940.84-1.051.000.92-1.080.940.86-1.04Model 210.780.69-0.870.880.81-0.950.920.83-1.01Model 310.790.70-0.900.910.84-0.990.970.88-1.08Model 111.100.96-1.270.980.89-1.080.910.81-1.03Model 210.970.84-1.120.900.82-0.990.900.80-1.03Model 310.980.85-1.130.920.83-1.020.930.82-1.05Model 111.020.90-1.150.950.88-1.040.940.85-1.04Model 210.960.85-1.090.920.85-1.010.940.85-1.04Model 310.980.86-1.110.960.88-1.090.980.88-1.09Model 111.151.02-1.301.020.93-1.110.840.75-0.93Model 210.980.86-1.120.930.85-1.020.830.74-0.93MenWaist circumferenceTriglycerideHDL cholesterolBlood pressureFasting glucoseMetabolic syndromeWomenWaist circumferenceTriglycerideHDL cholesterolBlood pressure

Ahn et al. BMC Public Health 2013, age 8 of 11Table 3 Odds ratios (95% CIs) for metabolic syndrome and the individual components according to rice-eatingpatterns and gender (Continued)Model 311.010.89-1.150.960.88-1.050.860.77-0.97Model 110.910.79-1.050.970.88-1.070.900.80-1.02Model 210.840.72-0.970.920.83-1.020.900.80-1.02Model 310.830.71-0.960.940.85-1.040.910.80-1.04Model 111.040.90-1.201.000.90-1.100.830.73-0.94Model 210.870.74-1.000.890.80-0.990.820.72-0.94Model 310.890.76-1.040.930.84-1.040.870.75-0.99Fasting glucoseMetabolic syndromeModel 1 : Not adjusted, Model 2 : adjusted for age and total energy intake, Model 3 : adjusted for age, total energy, education level, supplement use, exercise,drinking, and smoking.Metabolic syndrome was defined as the presence of 3 of 5 components.showed the benefits of whole grain. The third KoreaNational Health and Nutrition Examination Survey(KNHANES III) reported grains such as barely, sorghum, glutinous rice, Job’s tears, oats, millet and glutinous millet [9].We found reduced risks for central obesity, dyslipidemia, and MetS in participants in the rice with multigrains group compared with those in the white riceThe one of main characteristics of Korean diet is highproportion of carbohydrate intake because rice is thestaple food. Korean eats rice only or with other foodssuch as beans or other grains. White rice can be characterized as a refined grain because it is polished duringprocessing and the bran and germ are entirely removed.The rice with beans group showed some features of beanconsumption, and the rice with multi-grains groupTable 4 Multivariate adjusted odds ratios (95% CIs) for metabolic syndrome and individual components of metabolicsyndrome according to rice-eating patterns in womenMetabolic syndrometraitsWhite rice groupRice with other foods groupMixed groupRice with beansRice with multi-grainsRefOR95% CIOR95% CIOR95% pausal womenWaist 020.860.72-1.03HDL 7Blood sting abolic l womenWaist 120.930.77-1.13HDL 9Blood sting abolic ds ratios were adjusted for age, total energy, education level, supplement use, exercise, drinking, and smoking.The subjects with hormone replacement therapy were excluded among postmenopausal women.Metabolic syndrome was defined as the presence of 3 of 5 components.

Ahn et al. BMC Public Health 2013, roup. The rice with beans diet showed a beneficial effect on waist circumference and fasting glucose control.However, we could not find the BP lowering effect ofrice with bean diet.The most distinctive result was about central obesity.Means of WC of men in rice with multi-grains groupand mixed group were higher than that of white ricegroup and odds ratio for central obesity was also elevated. However, means of WC in women were not significantly different among rice-eating groups, ORs forWC in rice with beans group and rice with multi-grainsgroup were reduced. Especially in rice with beans group,both pre and post menopause women had low risk forcentral obesity.We believe that the reduced ORs for MetS and theMetS components observed in ri

glycemic index was calculated by dividing the average daily glycemic load by the average daily carbohydrate in-take. The glycemic index value for each food item was obtained from the International Tables of Glycemic Index [21], the online Glycemic Index Database mai

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