European Journal of Clinical Nutritionhttps://doi.org/10.1038/s41430-017-0031-8

ARTICLE

Differential association of dietary carbohydrate intake withmetabolic syndrome in the US and Korean adults: data from the2007–2012 NHANES and KNHANES

Kyungho Ha1 ● Kijoon Kim2● Ock K. Chun 2

● Hyojee Joung1● YoonJu Song 3

Received: 21 December 2016 / Revised: 4 May 2017 / Accepted: 3 October 2017© Macmillan Publishers Limited, part of Springer Nature 2018

AbstractBackground/objectives The risk factors for metabolic syndrome may differ between Western and Asian countries due totheir distinct dietary cultures. However, few studies have directly compared macronutrient intake and its association with therisk of metabolic syndrome in the US and Korean adults using national survey data.Subject/methods Based on the data from the US and Korean versions of the 2007–2012 National Health and NutritionExamination Survey (NHANES, KNHANES), a total of 3,324 American and 20,515 Korean adults were included. In bothcountries, dietary intake was measured using a 24-h dietary recall method and metabolic syndrome was defined using theNational Cholesterol Education Program Adult Treatment Panel III criteria.Results The percentages of energy intake from carbohydrate, protein, and fat were 50:16:33 in the US adults and 66:15:19 inthe Korean adults. Regarding metabolic abnormalities, Korean adults in the highest quintile of carbohydrate intake showedan increased risk of metabolic syndrome in men and women, with abnormalities of reduced HDL cholesterol and elevatedtriglyceride levels. In contrast, the US men showed no significant association with metabolic syndrome and its abnormalities,while the US women showed an increased risk of reduced HDL cholesterol and elevated triglycerides.Conclusions A high carbohydrate intake is associated with metabolic abnormalities. As Korean adults consume morecarbohydrate than American adults, stronger associations of dietary carbohydrate with metabolic syndrome were observed.Thus, further studies are necessary to elucidate the underlying mechanisms of different contributors to developing metabolicdisease in Western and Asian populations.

Introduction

Metabolic syndrome, which represents a group of metabolicabnormalities including atherogenic dyslipidemia, raised

blood pressure, and hyperglycemia, is widely known as amultiplex risk factor of cardiovascular diseases (CVDs) [1,2]. Metabolic syndrome has become a global health issuedue to it increasing prevalence in both Western and Asiancountries. For example, the percentage of the US adults whohave metabolic syndrome was 32.9% in 2003–2004 and thisrate increased to 34.7% in 2011–2012 [3]. In Korea, theprevalence of metabolic syndrome has also shown a steadyincrease, from 24.9% in 1998 to 31.3% in 2007 [4].

Diet is one of important factors in the development andprevention of CVD, and numerous studies have investigatedthe associations of diet such as fat and carbohydrate intakewith the risk of CVD [5, 6]. However, these studies wereconducted mostly in Western populations. Due to cultural,climatic, and geographical differences, Western and Asianpopulations show distinct dietary patterns. Western popu-lations generally consume more fat than Asian populations.Reportedly, over 30% of energy has been derived from totalfat among American, British, and Australian populations

* YoonJu Songyjsong@catholic.ac.kr

1 Graduate School of Public Health, Seoul National University, 1Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea

2 Department of Nutritional Sciences, University of Connecticut,3624 Horsebarn Road Extension Unit 4017, Storrs, CT 06269-4017, USA

3 Major of Food and Nutrition, School of Human Ecology, TheCatholic University of Korea, 43 Jibong-ro, Wonmi-gu, Bucheon-si, Gyeonggi-do 14662, Republic of Korea

Electronic supplementary material The online version of this article(https://doi.org/10.1038/s41430-017-0031-8) contains supplementarymaterial, which is available to authorized users.

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[7–9]. In contrast, Korean and Japanese populationsobtained 21.6% and 26.3% of their energy from total fat,respectively [10, 11], which were much less compared withWestern populations.

The distinct dietary practice of Western and Asianpopulations may have influenced the development andprogression of metabolic disease. However, few studieshave compared nutrient intake, or its association with therisk of metabolic disease between Western and Asianpopulations. Therefore, in this study, we explored macro-nutrient profile and the associations of dietary carbohydrateintake with metabolic syndrome and its components usingnational survey data from the US and Korean versions ofthe National Health and Nutrition Examination Survey(NHANES and KNHANES, respectively).

Subjects and methods

Study design and population

This study used data from the 2007–2012 NHANES andKNHANES, which shared the same protocol that was across-sectional study with a complex, multistage, prob-ability sampling design. The original surveys were con-ducted by the Centers for Disease Control and Prevention(CDC) in each country. Each survey consisted of threesections: heath interview, health examination, and nutritionsurvey. Detailed explanations on the NHANES [12] andKNHANES [13] are available elsewhere.

The initial samples aged 19 years or more and partici-pated in 24-h dietary recall were 16,428 Americans and33,617 Koreans. Among the eligible US participants, weexcluded those with missing data on anthropometric (heightand weight) and biochemical variables related to metabolicsyndrome including waist circumference, blood pressure,high-density lipoprotein (HDL) cholesterol, triglycerides,fasting blood glucose, and total cholesterol (n= 9,470),who were pregnant or lactating (n= 109), had been diag-nosed with or were receiving medication for diabetes,hypercholesterolemia, or hypertension (n= 3,446), andreported implausible energy intake (<500 kcal/day or>5000 kcal/day) (n= 79). Similarly, for the Korean parti-cipants, we excluded those with missing data on anthro-pometric and biochemical variables related to metabolicsyndrome (n= 4,034), as well as those who were pregnantor lactating (n= 450), had been diagnosed with or werereceiving medication for diabetes, dyslipidemia, or hyper-tension (n= 8,298), and reported implausible energy intake(n= 320). Therefore, the final samples consisted of 3,324Americans (1,669 men and 1,665 women) and 20,515Koreans (8,236 men and 12,279 women) aged over 19 years(Supplementary Table 1).

NHANES was approved by the National Centers forHealth Statistics Research Ethics Review Board andKNHANES was approved by the Korea CDC InstituteReview Board. Written informed consent was obtainedfrom all participants.

Assessment of dietary intake

Dietary intake for both countries was measured by a 1-day24-h dietary recall method, administered by a trainedinterviewer at a mobile examination center (MEC)(NHANES) or at each participants’ place of residence(KNHANES). The recall data included all foods and bev-erages that were consumed by subjects within a 24-h period.For the US sample, we only included dietary recall datafrom the first of two 24-h recalls; the data for the secondrecall were obtained by a telephone interview and thesample size was smaller.

Calculation of percent energy for macronutrients wasbased on the user guide of dietary data in each country [10,14]. Both studies used the same standard conversion factorsto convert gram to kcal (4 kcal/g for protein and carbohy-drate and 9 kcal/g for fat intake), but NHNAES used theprovided energy intake in the data file, while KNHANESused the energy intake that was calculated by the standardconversion factors.

To evaluate the adequacy of energy intake, an estimatedenergy requirements (EERs) for the US and Korean parti-cipants were calculated according to the age-, sex-, andweight-specific equations published in Dietary ReferenceIntakes [15] and Dietary Reference Intakes for Koreans2015 [16], respectively. Physical activity level was assumedto be “low active” in both countries, in accordance withseveral previous studies [17, 18].

Assessment of sociodemographic variables andhealth-related behaviors

Sociodemographic variables included age, sex, ethnicity(NHANES only), family income level, and education leveland were used as confounders in this study. In NHANES,the poverty income ratio (PIR), which is the ratio of familyincome to poverty, was taken to indicate family incomelevel. The PIR of each subject was categorized into quartilesin this study. Education level was categorized into middleschool or less (≤11th grade or 12th grade with no diploma),high school (high school graduate or high school equiv-alency), and college or higher (college or associates degreeor college graduate or above). In KNHANES, householdincome level was categorized as low, lower middle, uppermiddle, and high according to the quartiles of monthlyfamily income. The education level of the Korean partici-pants was categorized into middle school or less (below

K. Ha et al.

elementary school graduate or middle school graduate),high school, and college or higher.

Health behaviors included alcohol consumption, currentsmoking, and physical activity. Alcohol consumption wasdefined by the number of alcoholic beverages consumedper day in the past 12 months and categorized as none (noconsumption of any type of alcoholic beverage), moderate(≤2 drinks per day for men and ≤1 drink per day forwomen), and high (>2 drinks per day for men and >1 drinkper day for women) according to American Heart Asso-ciation Dietary Guidelines [19]. For KNHANES, the aver-age amount of alcoholic drinks per day in the recent 1 yearwas converted into the daily number of alcoholic drinks (astandard drink: 14.0 g of pure alcohol found in 12 ounces ofbeer) [20, 21]. Current smoking status was defined those asnever (never smoked cigarettes or smoked <100 cigarettesin lifetime), former (smoked ≥100 cigarettes in lifetime butcurrent non-smoker), and current (smoked ≥100 cigarettesin lifetime and current smoker) [22]. Physical activity wasdefined as “yes” those who participated in vigorous-intensityactivities for at least 75 min, moderate-intensity activity forat least 150 min, or an equivalent combination of moderate-and vigorous- intensity activity during a typical week [23,24].

Assessment of anthropometric and biochemicalvariables

Anthropometric data including height, weight, and waistcircumference were measured at MECs by trained healthtechnicians using calibrated equipment in both NHANESand KNHANES. Body mass index (BMI) was calculatedfrom weight and height data (kg/m2). Blood pressure wasmeasured in the three consecutive times according to stan-dardized procedures in the MEC and the mean of the secondand third readings was used in this study. Fasting bloodglucose, serum total cholesterol, HDL cholesterol, and tri-glycerides were estimated by collecting and analyzing bloodspecimens of participants who fasted at least 8 h or more but<24 h. Details of laboratory procedures have been describedelsewhere [25, 26]. Low-density lipoprotein (LDL) cho-lesterol was calculated according to Friedewald’s formula:(LDL cholesterol)= (total cholesterol)−(HDL cholesterol)− (triglycerides/5). However, this calculation was validwhen the triglyceride level was ≤400 mg/dL [27].

Definition of metabolic syndrome

The definition of metabolic syndrome was based on theNational Cholesterol Education Program Adult TreatmentPanel III (NCEP-ATP III) criteria for both the US andKorea participants [28], but with a different waist cir-cumference threshold applied to the Korean population

[29]. Metabolic syndrome was diagnosed if three or more ofthe following components were existed; (1) waist cir-cumference ≥102 cm for the US men, ≥88 cm for the USwomen, ≥90 cm for Korean men, and ≥80 cm for Koreanwomen, (2) systolic blood pressure ≥130 mmHg or diastolicblood pressure ≥85 mmHg, (3) HDL cholesterol <40 mg/dLfor men and <50 mg/dL for women, (4) triglycerides ≥150mg/dL, and (5) fasting blood glucose ≥100 mg/dL.

Statistical analysis

All statistical analyses were performed using SAS software(ver. 9.4; SAS Institute, Cary, NC, USA). The complexsampling design parameters of both national surveys,including strata, cluster, and weight, were applied to PROCSURVEY procedures. All p-values were two-sided (α=0.05).

All analyses were stratified by sex in each country. Allvariables were expressed as means ± standard error forcontinuous variables and as percentages (%) for categoricalvariables. p-values were obtained from t-tests for con-tinuous variables and χ2-tests for categorical variables to testthe difference between men and women in each country.

Dietary carbohydrate intake was categorized into quin-tiles with the percent energy from carbohydrate by sex.Mean daily intake of macronutrients and metabolic syn-drome components were estimated by the quintiles of car-bohydrate and the linear trends (p for trends) of thesevariables were calculated using the Generalized LinearModel (GLM). Multiple logistic regression analysis wascarried out to estimate odds ratios (ORs) and 95% con-fidence intervals (CIs) for metabolic syndrome across thequintile groups; p for trend was also measured, with thelowest quintile set as the reference. In the GLM for esti-mating metabolic syndrome components, and in the multi-ple logistic regression model, the confounders of age,alcohol consumption, BMI (except for the model of waistcircumference), current smoking status, education level,ethnicity (NAHNES only), family income, physical activity,survey period (survey year for KNHANES), and totalenergy intake were adjusted for.

Results

The general characteristics of the study participants by sexare shown in Table 1. The mean age of the US participantswas 38.4 years for men and 39.8 years for women, and thatof Korean participants was 40.6 years for men and 41.5years for women.

In each country, higher waist circumference, bloodpressure, fasting blood glucose, and triglyceride levels wereobserved in men than women (Table 2). The prevalence of

Dietary carbohydrate and metabolic syndrome

metabolic syndrome was 18.4% for American (19.3% formen and 17.5% for women) and 16.9% for Korean (18.8%for men and 15.2% for women).

Since dietary carbohydrate was the major source ofenergy as well as the distribution of carbohydrate intake

was obviously different in two countries (SupplementaryFigure 2), macronutrient intake profiles and metabolicsyndrome components were examined by carbohydrateintake. As carbohydrate intake increased, both energy intakeand the percent of EER significantly decreased (Table 3).

Table 1 General characteristics of participants in the NHANES and KNHANES 2007–2012

NHANES KNHANES

Men (n= 1669) Women (n= 1655) P-valuea Men (n= 8236) Women (n= 12,279) P-value

n % n % n % n %

Age (years) (mean± SE) 38.4 0.6 39.8 0.5 0.0127 40.6 0.2 41.5 0.2 0.0001

Survey period 0.4204 0.8718

2007–2008 574 33.5 488 30.8 2250 26.7 3494 27.0

2009–2010 596 33.8 660 36.0 3265 36.0 4692 35.9

2011–2012 499 32.7 507 33.2 2721 37.3 4093 37.0

Ethnicity 0.0070

White 679 64.2 722 68.0

Black 304 9.8 286 9.8

Mexican–American 343 12.6 314 9.6

Others 343 13.4 333 12.7

Family income levelb 0.8817 0.0499

Low 324 14.7 349 15.2 1301 11.8 1860 12.8

Lower middle 383 18.5 360 17.1 2033 26.0 2987 26.1

Upper middle 411 28.6 402 28.6 2400 31.8 3536 30.1

High 410 38.2 410 39.1 2370 30.3 3683 31.0

Education level <0.0001 <0.0001

Middle school or less 479 20.2 360 13.7 2159 17.4 3790 24.4

High school 426 24.7 327 19.5 3051 44.4 4582 43.0

College or higher 762 55.1 966 66.8 2848 38.2 3684 32.5

Alcohol consumptionc <0.0001 <0.0001

None 291 17.3 431 23.7 1194 11.7 3666 26.4

Moderate 569 38.7 388 29.8 2614 29.0 4405 35.4

High 680 44.0 644 46.5 4263 59.3 4002 38.2

Current smokingd <0.0001 <0.0001

Never 778 51.8 1057 64.8 1914 25.9 10,959 88.9

Former 330 20.0 221 16.9 1339 14.7 226 2.4

Current 464 28.1 302 18.3 4812 59.4 878 8.7

Physical activitye <0.0001 <0.0001

No 501 26.0 776 41.0 3803 45.2 7246 60.7

Yes 1164 74.0 878 59.0 4242 54.8 4788 39.3

Bold values indicate the statistical significance

All values accounted for the complex sampling design effect of the national surveys using PROC SURVEY procedureaAll continuous variables were tested by using t-test and all categorical variables were tested by using χ2-testbPoverty income ratio for NHANES and household income for KNHANEScAlcohol consumption was defined as none (no consumption of any type of alcoholic beverage), moderate (≤2 drinks for men and ≤1 drink forwomen per day), and high (>2 drinks for men and >1 drink for women per day)dCurrent smoking was defined as never (never smoked cigarettes or smoked <100 cigarettes in lifetime), former (smoked ≥100 cigarettes inlifetime but current non-smoker), and current (smoked ≥100 cigarettes in lifetime and current smoker)ePhysical activity was defined as “yes” of vigorous-intensity activities for at least 75 min, or moderate-intensity activities for at least 150 min, or anequivalent combination of moderate- and vigorous-intensity activity during a typical week

K. Ha et al.

Moreover, protein and fat intakes decreased markedly withincreased carbohydrate intake among all study subjects (pfor trend <0.0001).

Anthropometric and biochemical measurementsaccording to dietary carbohydrate intake by sex are

presented in Table 4. Higher carbohydrate intake wassignificantly associated with lower serum total, HDL,and LDL cholesterol in both the US and Korean adults,except for the US men after adjusting for confounders.Meanwhile, high carbohydrate intake was related to

Table 2 Anthropometric and biochemical measurements and prevalence of metabolic syndrome components among participants in the NHANESand KNHANES 2007–2012

NHANES Men (n= 1669) Women (n= 1655) P-valuea

Anthropometric and biochemical variable (mean ± SE)

BMI (kg/m2) 27.2± 0.2 27.1± 0.2 0.6891

Waist circumference (cm) 96.1± 0.5 91.3± 0.5 <0.0001

SBP (mmHg) 118.3± 0.5 111.8± 0.5 <0.0001

DBP (mmHg) 70.2± 0.4 66.8± 0.5 <0.0001

Fasting glucose (mg/dL) 99.5± 0.4 95.2± 0.4 <0.0001

Total cholesterol (mg/dL) 189.8± 1.3 190.4± 1.2 0.6831

HDL cholesterol (mg/dL) 49.4± 0.5 59.2± 0.5 <0.0001

LDL cholesterol (mg/dL) 116.0± 1.1 110.8± 0.9 <0.0001

Triglyceride (mg/dL) 124.5± 2.5 102.9± 2.0 <0.0001

Prevalence (%)

Increased waist circumference 30.0 53.4 <0.0001

Elevated blood pressure 19.0 10.3 <0.0001

Reduced HDL cholesterol 23.5 27.7 0.0365

Elevated triglycerides 23.8 15.3 <0.0001

Elevated fasting glucose 44.5 26.9 <0.0001

Metabolic syndrome 19.3 17.5 0.2995

KNHANES Men (n= 8236) Women (n= 12,279) P-value

Anthropometric and biochemical variable (mean ± SE)

BMI (kg/m2) 23.8± 0.0 22.7± 0.0 <0.0001

Waist circumference (cm) 83.2± 0.1 76.3± 0.1 <0.0001

SBP (mmHg) 116.8± 0.2 110.2± 0.2 <0.0001

DBP (mmHg) 77.6± 0.2 71.8± 0.1 <0.0001

Fasting glucose (mg/dL) 94.2± 0.2 91.4± 0.2 <0.0001

Total cholesterol (mg/dL) 186.9± 0.5 184.4± 0.4 <0.0001

HDL cholesterol (mg/dL) 46.8± 0.2 52.4± 0.1 <0.0001

LDL cholesterol (mg/dL) 112.4± 0.5 111.9± 0.3 0.3714

Triglyceride (mg/dL) 146.1± 1.6 101.6± 1.0 <0.0001

Prevalence (%)

Increased waist circumference 21.4 32.2 <0.0001

Elevated blood pressure 28.0 13.7 <0.0001

Reduced HDL cholesterol 27.6 44.8 <0.0001

Elevated triglycerides 33.5 15.3 <0.0001

Elevated fasting glucose 22.1 13.7 <0.0001

Metabolic syndrome 18.8 15.2 <0.0001

Bold values indicate the statistical significance

All values accounted for the complex sampling design effect of the national surveys using PROC SURVEY procedure

BMI body mass index, DBP diastolic blood pressure, HDL high-density lipoprotein, LDL low-density lipoprotein, SBP systolic blood pressureaAll continuous variables were tested by using t-test and all categorical variables were tested by using χ2-test

Dietary carbohydrate and metabolic syndrome

increased fasting blood glucose and serum triglycerides inthe US women.

Multivariable adjusted ORs and 95% CIs of metabolicsyndrome and its components across the quintiles of percentenergy from carbohydrate were estimated for men (Table 5)and women (Table 6) in both countries. After adjustingfor confounders, higher carbohydrate intake was associatedwith increased risk of reduced HDL cholesterol (ORfor highest quintile, 1.31; 95% CI, 1.06–1.62; p for trend,0.0137), elevated triglycerides (OR for highest quin-tile, 1.32; 95% CI, 1.07–1.63; p for trend, 0.0318), andmetabolic syndrome (OR for highest quintile, 1.32; 95% CI,1.01–1.73; p for trend, 0.0118) in Korean men. However,no significant associations were observed for the US men.Greater carbohydrate intake was associated with a higherrisk of reduced HDL cholesterol in both the US (OR forhighest quintile, 2.46; 95% CI, 1.30–4.64, p for trend,0.0157) and Korean women (OR for highest quintile, 1.45;95% CI, 1.21–1.74; p for trend, 0.0001). In addition,women in the highest quintile of carbohydrate intake had asignificantly increased risk of elevated triglycerides in theUS (OR, 2.83; 95% CI, 1.51–5.30) and Korean (OR, 1.26;95% CI, 1.00–1.58) populations, but an increased risk ofmetabolic syndrome only in the Korean population (OR,1.31; 95% CI, 1.01–1.69), compared to those in the lowestquintile of carbohydrate intake.

Discussion

In this study of nationally representative data from the USand Korea, metabolic abnormalities and macronutrientintake profiles evidently differed between men and womenwithout certain disease or treatment from two countries.Significant associations between a high carbohydrate intakeand reduced HDL cholesterol were observed consistentlyamong men and women in both countries. An increased riskof metabolic syndrome was found in the highest carbohy-drate intake group of only the Korean population.

In our study, the prevalence of metabolic syndrome was18.4% in the US population and 16.9% in the Koreanpopulation; this is comparable as the Korean population hada considerably lower BMI. As our study excluded subjectswho had been diagnosed with, or were using medication fordiabetes or dyslipidemia due to dietary change, the pre-valence was lower than those reported previously (34.7% inthe US and 31.3% in Korea) [3, 4]. The prevalence ofmetabolic syndrome in the West and East is comparablewith 19.1% in Canada, 22.1% in Australia, and 21.3% inChina [30–32], despite differences in culture and environ-mental factors.

An interesting finding was that the metabolic syndromecomponents differed between the US and Korean popula-tions. Among the US adults, elevated fasting glucose in menand increased waist circumference in women were the most

Table 3 Macronutrient intake by dietary carbohydrate intake among participants in the NHANES and KNHANES 2007–2012

Quintiles of percent energy from carbohydrate

SENAHNKSENAHN

Men Q1 (n = 333)

Q2(n = 334)

Q3 (n = 334)

Q4(n = 334)

Q5 (n = 334)

P for trend

Q1(n = 1647)

Q2(n = 1647)

Q3(n = 1648)

Q4 (n = 1647)

Q5 (n = 1647)

P for trend

Median 35.0 43.9 49.0 54.7 63.6 52.1 61.5 67.4 72.8 80.4 ES±naem ES±naem

Energy (kcal) 2637.7±59.7 2693.7±65.4 2554.0±58.0 2456.1±72.2 2170.9±77.8 <0.0001 2749.9±29.8 2405.7±24.2 2253.5±19.9 2166.6±22.3 1927.6±23.3 <0.0001 EER (%) 92.3±2.2 94.2±2.2 89.7±2.4 87.5±2.7 79.4±3.2 0.0008 105.4±1.2 93.7±1.0 89.4±0.8 88.0±0.9 82.9±1.0 <0.0001 Carbohydrate(g) 223.6±5.1 295.2±7.5 314.9±7.2 336.2±10.3 348.4±12.6 <0.0001 309.8±3.4 347.9±3.4 364.8±3.1 382.3±3.9 380.8±4.7 <0.0001 Protein (g) 120.9±2.6 109.5±2.9 105.7±3.2 89.5±3.3 66.7±2.0 <0.0001 118.5±1.6 90.5±1.0 79.5±0.8 70.3±0.8 52.9±0.7 <0.0001 Fat (g) 113.9±3.4 107.6±3.0 93.8±3.4 82.7±2.5 59.2±3.1 <0.0001 86.1±1.2 57.2±0.7 43.5±0.5 32.3±0.4 17.4±0.3 <0.0001 % Energy from

Carbohydrate 34.0±0.4 43.8±0.1 49.3±0.1 54.7±0.2 64.6±0.5 <0.0001 50.1±0.2 61.4±0.1 67.3±0.0 72.8±0.1 80.7±0.1 <0.0001 Protein 18.8±0.4 16.6±0.3 16.6±0.4 14.9±0.3 12.7±0.5 <0.0001 19.2±0.2 16.2±0.1 14.7±0.1 13.5±0.1 11.2±0.1 <0.0001 Fat 38.9±0.7 35.9±0.5 32.9±0.7 29.8±0.4 23.8±0.6 <0.0001 30.7±0.2 22.5±0.1 18.0±0.1 13.7±0.1 8.1±0.1 <0.0001

Women Q1

(n = 331) Q2

(n = 331) Q3

(n = 331) Q4

(n = 331) Q5

(n = 331) P for trend

Q1(n = 2455)

Q2(n = 2456)

Q3(n = 2456)

Q4 (n = 2456)

Q5 (n = 2456)

P for trend

Median 37.0 45.5 50.9 56.7 65.0 53.7 63.5 69.5 75.0 82.1 ES±naem ES±naem

Energy (kcal) 1923.6±40.9 1957.2±36.0 1903.1±55.6 1843.6±59.6 1665.3±54.5 0.0008 1873.9±17.2 1726.7±15.3 1665.8±14.6 1586.5±14.2 1527.1±16.7 <0.0001 EER (%) 89.2±2.3 90.3±1.6 89.0±2.9 85.3±3.0 78.8±2.4 0.0055 93.5±0.9 87.6±0.8 85.7±0.8 83.5±0.8 83.3±0.9 <0.0001 Carbohydrate(g) 171.8±4.0 221.0±4.2 242.9±7.3 261.8±8.3 276.7±9.5 <0.0001 235.6±2.2 271.7±2.4 289.9±2.6 300.7±2.7 317.0±3.5 <0.0001 Protein (g) 80.1±1.8 76.9±1.5 72.4±2.0 62.7±1.6 50.6±1.9 <0.0001 81.3±0.8 65.5±0.7 58.8±0.6 51.2±0.5 40.2±0.5 <0.0001 Fat (g) 88.5±2.8 81.7±1.9 70.6±2.3 62.2±2.7 43.2±1.6 <0.0001 63.0±0.8 40.9±0.4 30.7±0.3 21.8±0.2 12.6±0.2 <0.0001 % Energy from

Carbohydrate 35.8±0.3 45.2±0.1 51.0±0.2 56.8±0.2 66.9±0.5 <0.0001 51.6±0.2 63.3±0.0 69.4±0.0 75.0±0.0 82.3±0.1 <0.0001 Protein 17.4±0.3 16.1±0.3 15.5±0.3 14.2±0.3 12.4±0.2 <0.0001 18.0±0.1 15.4±0.1 14.1±0.1 12.8±0.1 10.5±0.0 <0.0001 Fat 41.3±0.7 37.2±0.5 33.2±0.5 29.8±0.4 22.7±0.4 <0.0001 30.4±0.2 21.3±0.1 16.5±0.1 12.1±0.1 7.2±0.1 <0.0001

Bold values indicate the statistical significance

All values accounted for the complex sampling design effect of the national surveys using PROC SURVEY procedure

EER estimated energy requirement

K. Ha et al.

Table4

Anthrop

ometricandbiochemical

measurementsby

dietarycarboh

ydrate

intake

amon

gparticipantsin

theNHANESandKNHANES20

07–20

12

Quintilesof

percentenergy

from

carboh

ydrate

NHANES

KNHANES

Men

Q1(n

=33

3)Q2(n

=33

4)Q3(n

=33

4)Q4(n

=33

4)Q5(n

=33

4)pfor

trend

Q1(n

=16

47)

Q2(n

=16

47)

Q3(n

=16

48)

Q4(n

=16

47)

Q5(n

=16

47)

pfor

trend

Waistcircum

ference

(cm)

96.0±0.8

96.8±1.0

96.8±1.0

95.5±1.0

94.5±1.1

0.09

3583

.7±0.3

82.9±0.3

83.0±0.3

83.2±0.3

82.6±0.3

0.02

91

SBP(m

mHg)

118.7±1.0

120.6±1.0

118.6±0.8

118.9±0.7

118.1±1.1

0.46

9311

7.6±0.4

117.1±0.4

117.2±0.4

117.7±0.4

117.8±0.5

0.68

34

DBP(m

mHg)

70.6±1.1

70.3±0.8

69.7±0.6

69.7±0.7

70.0±0.8

0.56

2277

.3±0.4

77.2±0.3

77.1±0.3

77.4±0.3

77.2±0.4

0.93

53

Fastin

gglucose(m

g/dl)

102.1±1.1

101.1±1.0

101.8±1.0

102.9±0.9

102.8±1.0

0.32

3194

.9±0.5

93.6±0.4

94.2±0.5

95.4±0.5

94.5±0.5

0.82

00

Total

cholesterol(m

g/dl)

196.1±3.5

195.6±3.9

193.4±2.3

190.2±2.4

187.9±2.8

0.04

9319

0.1±1.2

189.0±1.1

187.2±1.1

186.2±1.1

184.8±1.2

0.00

02

HDLcholesterol(m

g/dl)

51.8±1.2

50.2±0.9

48.4±0.8

47.1±1.0

45.6±0.7

<0.00

0147

.0±0.3

46.7±0.3

45.9±0.3

46.4±0.3

45.3±0.3

0.00

14

LDLcholesterol(mg/dl)11

8.9±3.2

121.4±3.6

120.3±2.3

115.9±2.3

114.8±2.4

0.13

7111

7.0±1.1

116.4±1.0

115.2±1.0

113.1±1.0

112.0±1.1

0.00

02

Triglycerides

(mg/dl)

131.0±8.7

122.6±7.5

124.3±5.1

136.9±4.7

137.2±5.7

0.34

7213

3.5±4.0

137.0±3.3

135.2±3.2

137.8±3.4

145.7±4.1

0.07

36

Wom

enQ1(n

=33

1)Q2(n

=33

1)Q3(n

=33

1)Q4(n

=33

1)Q5(n

=33

1)pfortrend

Q1(n

=24

55)

Q2(n

=24

56)

Q3(n

=24

56)

Q4(n

=24

56)

Q5(n

=24

56)

pfortrend

Waistcircum

ference(cm)93

.9±1.2

93.7±1.2

92.0±1.1

95.3±1.3

91.3±1.2

0.21

5877

.3±0.3

77.3±0.4

77.2±0.4

77.5±0.3

77.3±0.3

0.77

36

SBP(m

mHg)

113.3±0.9

113.3±1.0

112.8±1.1

112.8±1.0

111.6±1.1

0.20

8711

0.9±0.4

110.8±0.4

111.2±0.4

111.4±0.5

111.9±0.5

0.04

31

DBP(m

mHg)

67.1±0.7

66.8±0.9

67.3±0.7

65.7±1.0

66.7±0.8

0.35

7671

.5±0.3

71.5±0.3

72.1±0.3

71.9±0.4

71.4±0.4

0.50

98

Fastin

gglucose(m

g/dl)

96.3±0.9

95.7±0.8

98.1±1.0

97.9±1.2

98.0±0.7

0.03

8292

.0±0.6

92.0±0.6

92.3±0.8

91.7±0.7

92.0±0.7

0.79

23

Total

cholesterol(m

g/dl)

195.6±2.3

189.1±2.5

191.6±2.7

186.4±3.7

185.8±2.8

0.00

5418

8.7±1.3

188.4±1.2

187.6±1.2

186.1±1.3

183.7±1.3

0.00

02

HDLcholesterol(m

g/dl)

61.7±1.2

57.6±1.0

56.7±1.0

57.3±1.1

54.6±1.3

<0.00

0153

.0±0.4

52.7±0.4

53.2±0.4

51.9±0.4

50.7±0.4

<0.00

01

LDLcholesterol(m

g/dl)

114.9±1.9

110.8±2.4

113.9±2.0

108.3±3.3

108.0±2.4

0.03

1311

3.9±1.1

113.7±1.0

113.0±1.0

112.4±1.0

110.4±1.1

0.00

24

Triglycerides

(mg/dl)

93.4±4.0

107.3±5.6

102.7±4.6

103.7±5.8

115.3±5.6

0.00

1711

1.7±3.1

111.3±2.5

108.8±2.5

111.0±2.9

114.6±3.0

0.71

85

Boldvalues

indicate

thestatistical

sign

ificance

Allvalues

accoun

tedforthecomplex

samplingdesign

effect

ofthenatio

nalsurveysusingPROCSURVEY

procedure.Allvalues

wereadjusted

mean±

SEandpfortrendwas

obtained

from

amultiv

ariate

linearregression

analysis

afteradjustingforage,

alcoho

lconsum

ption,

body

massindex(exceptforthemod

elof

waist

circum

ference),currentsm

oking,

education,

ethn

icity

(NHANESon

ly),family

income,

physical

activ

ity,survey

period

,andtotalenergy

intake

DBPdiastolic

bloo

dpressure,HDLhigh

-density

lipop

rotein;SB

Psystolic

bloo

dpressure

Dietary carbohydrate and metabolic syndrome

Table5

Multiv

ariableadjusted

odds

ratio

s(O

Rs)

and95

%confi

denceintervals(CIs)foreach

metabolic

synd

romecompo

nent

bydietarycarboh

ydrate

intake

amon

gmen

intheNHANESand

KNHANES

Men

Quintilesof

percentenergy

from

carboh

ydrate

NHANES

Q1(n

=33

3)Q2(n

=33

4)Q3(n

=33

4)Q4(n

=33

4)Q5(n

=33

4)pfortrenda

Increasedwaistcircum

ference

Prevalence(%

)22

.833

.233

.833

.826

.9

OR

(95%

CI)

1.00

1.42

(0.87–

2.32

)1.43

(0.91–

2.25

)1.41

(0.99–

2.00

)1.00

(0.67–1.50

)0.89

03

Elevatedbloo

dpressure

Prevalence(%

)18

.323

.017

.918

.816

.8

OR

(95%

CI)

1.00

1.22

(0.80–

1.87

)0.75

(0.44–

1.29

)0.82

(0.45–

1.52

)0.85

(0.44–1.62

)0.39

94

Reduced

HDLcholesterol

Prevalence(%

)18

.422

.521

.728

.428

.0

OR

(95%

CI)

1.00

1.34

(0.85–

2.11

)1.34

(0.82–

2.17

)2.01

(1.05–

3.87

)1.96

(0.98–3.93

)0.02

75

Elevatedtriglycerides

Prevalence(%

)22

.624

.220

.327

.425

.0

OR

(95%

CI)

1.00

0.92

(0.54–

1.56

)0.85

(0.53–

1.38

)1.25

(0.72–

2.15

)1.07

(0.60–1.93

)0.56

70

Elevatedfastingglucose

Prevalence(%

)42

.542

.444

.848

.045

.5

OR

(95%

CI)

1.00

0.82

(0.55–

1.24

)1.10

(0.75–

1.61

)1.18

(0.77–

1.81

)1.13

(0.68–1.87

)0.33

92

Metabolic

synd

rome

Prevalence(%

)15

.920

.717

.623

.819

.3

OR

(95%

CI)

1.00

1.21

(0.74–

1.99

)0.94

(0.46–

1.93

)1.92

(0.93–

3.95

)1.34

(0.70–2.57

)0.16

70

KNHANES

Q1(n

=16

47)

Q2(n

=16

47)

Q3(n

=16

48)

Q4(n

=16

47)

Q5(n

=16

47)

pfortrend

Increasedwaistcircum

ference

Prevalence(%

)23

.019

.120

.921

.121

.8

OR

(95%

CI)

1.00

0.78

(0.64–

0.95

)0.86

(0.70–

1.05

)0.86

(0.70–

1.06

)0.89

(0.70–

1.12

)0.29

72

Elevatedbloo

dpressure

Prevalence(%

)27

.225

.726

.628

.530

.6

OR

(95%

CI)

1.00

0.94

(0.77–

1.15

)0.93

(0.76 –

1.14

)0.99

(0.81–

1.21

)0.92

(0.73–

1.15

)0.58

05

Reduced

HDLcholesterol

Prevalence(%

)24

.025

.927

.729

.032

.5

OR

(95%

CI)

1.00

1.09

(0.90–

1.33

)1.19

(0.98–

1.45

)1.16

(0.95–

1.42

)1.31(1.06–1.62

)0.01

37

Elevatedtriglycerides

Prevalence(%

)32

.531

.632

.334

.537

.6

OR

(95%

CI)

1.00

1.02

(0.84–

1.24

)1.02

(0.84–

1.23

)1.09

(0.90–

1.33

)1.32(1.07–1.63

)0.03

18

K. Ha et al.

prevalent components, while elevated triglycerides in menand reduced HDL cholesterol in women were the mostprevalent in Korean adults. Because metabolic syndrome isa premorbid condition for CVD, determining the char-acteristics of metabolic abnormalities and the mechanismsthat underlie its progression to CVD is important.

We also found distinct differences in macronutrientcomposition of diet between the US and Korean popula-tions. Fat accounted for only 19% of energy intake by theKorean adults, compared with 33% by the US adults. Thetypical Korean diet is high in carbohydrate and low in fat. Interms of carbohydrate intake quintiles, the proportion ofenergy from carbohydrate was 80–82% in the highestquintile among the Korean adults, compared with 64–65%for the equivalent quintile in the US adults. Concerning theupper recommended limit of carbohydrate intake (65% inboth countries) [15, 16], 57% of the Korean adults wereabove this limit, compared with 8% of the US adults. Thisdifference may have contributed to the differences in theintensities of the association between carbohydrate intakeand metabolic abnormalities in the US and Koreanpopulations.

In this study, high carbohydrate intake was consistentlyrelated to a reduced HDL cholesterol level in both countries.These findings were in agreement with previous studies ofBritish [33] and American adults [34, 35]. Negative asso-ciations between carbohydrate intake and HDL cholesterollevel have also been reported in Asian populations includ-ing Korean [36], Chinese [37], and South Indian adults [38].Merchant et al. [39] reported that the HDL cholesterol levelwas reduced by increasing carbohydrate intake afteradjusting for ethnicity and other confounders in 619 mul-tiethnic participants, which included Canadians, SouthAsians, Chinese, and Europeans.

Along with reduced HDL cholesterol levels, a high car-bohydrate diet is also associated with increased plasmatriglyceride levels, due mainly to a higher triglyceridecontent in very low-density lipoprotein (VLDL) particlesand an overproduction of VLDL particles [40]. Severalcross-sectional studies reported a positive associationamong the US men and women [35, 41, 42]. For Asianpopulation, some studies showed no relationship in Koreanadults [43, 44], but significant positive associations havebeen reported among Korean men [45] and women [46]. InChinese [37] and Japanese [47] populations, who tend tohave similar dietary patterns to those of Koreans, there wasalso an association between elevated triglycerides and ahigher carbohydrate intake. This indicates that a high car-bohydrate intake may contribute to elevated triglyceridelevels. However, confounding factors such as age, sex, andother factors are also likely have a considerable impact ontriglyceride levels.

Table5(con

tinued)

KNHANES

Q1(n

=16

47)

Q2(n

=16

47)

Q3(n

=16

48)

Q4(n

=16

47)

Q5(n

=16

47)

pfortrend

Elevatedfastingglucose

Prevalence(%

)21

.018

.320

.625

.625

.6

OR

(95%

CI)

1.00

0.86

(0.70–

1.07

)0.87

(0.71–

1.07

)1.08

(0.88–

1.34

)0.87

(0.68–

1.10

)0.75

30

Metabolic

synd

rome

Prevalence(%

)17

.715

.318

.220

.222

.4

OR

(95%

CI)

1.00

0.89

(0.70–

1.13

)1.17

(0.91–

1.50

)1.21

(0.94–

1.56

)1.32(1.01–1.73

)0.01

18

Boldvalues

indicate

thestatistical

sign

ificance

Allvalues

accoun

tedforthecomplex

samplingdesign

effect

ofthenatio

nalsurveysusingPROC

SURVEY

procedure

HDLhigh

-density

lipop

rotein

a pfortrendfrom

amultiv

ariatelin

earregression

analysisafteradjustingforage,alcoho

lcon

sumption,bo

dymassindex(exceptfor

themod

elof

waistcircum

ference),current

smok

ing,education,

ethn

icity

(NHANESon

ly),family

income,

physical

activ

ity,survey

period

,andtotalenergy

intake

Dietary carbohydrate and metabolic syndrome

Table6

Multiv

ariableadjusted

odds

ratio

s(O

Rs)and95

%confi

denceintervals(CIs)foreach

metabolicsynd

romecompo

nent

bydietarycarboh

ydrateintake

amon

gwom

enin

theNHANESand

KNHANES

Wom

enQuintilesof

percentenergy

from

carboh

ydrate

NHANES

Q1(n

=33

1)Q2(n

=33

1)Q3(n

=33

1)Q4(n

=33

1)Q5(n

=33

1)pfortrenda

Increasedwaistcircum

ference

Prevalence(%

)53

.455

.550

.557

.749

.9

OR

(95%

CI)

1.00

1.13

(0.68–

1.88

)0.88

(0.54–

1.44

)1.18

(0.74–

1.89

)0.79

(0.49–1.27

)0.40

76

Elevatedbloo

dpressure

Prevalence(%

)9.5

12.1

9.2

11.3

9.5

OR

(95%

CI)

1.00

1.70

(0.80–

3.63

)1.09

(0.48–

2.45

)1.37

(0.68–

2.74

)0.82

(0.38–1.75

)0.57

74

Reduced

HDLcholesterol

Prevalence(%

)20

.332

.423

.231

.433

.7

OR

(95%

CI)

1.00

1.89

(0.97–

3.70

)1.21

(0.63–

2.32

)1.69

(0.95–

3.02

)2.46(1.30–4.64

)0.01

57

Elevatedtriglycerides

Prevalence(%

)12

.813

.115

.915

.220

.2

OR

(95%

CI)

1.00

1.50

(0.83–

2.73

)2.02

(1.21–

3.38

)1.50

(0.79–

2.84

)2.83(1.51–5.30

)0.00

58

Elevatedfastingglucose

Prevalence(%

)27

.123

.327

.330

.327

.2

OR

(95%

CI)

1.00

0.83

(0.48–

1.43

)1.14

(0.72–

1.79

)1.12

(0.63–

1.99

)1.12

(0.59–2.13

)0.55

72

Metabolic

synd

rome

Prevalence(%

)15

.516

.917

.620

.817

.4

OR

(95%

CI)

1.00

1.27

(0.68–

2.38

)1.63

(0.85–

3.13

)1.42

(0.74–

2.72

)1.53

(0.76–3.09

)0.26

13

KNHANES

Q1(n

=24

55)

Q2(n

=24

56)

Q3(n

=24

56)

Q4(n

=24

56)

Q5(n

=24

56)

pfortrend

Increasedwaistcircum

ference

Prevalence(%

)24

.327

.629

.936

.044

.2

OR

(95%

CI)

1.00

1.04

(0.89–

1.22

)0.99

(0.85–

1.16

)1.08

(0.91–

1.26

)1.07

(0.89–

1.28

)0.44

34

Elevatedbloo

dpressure

Prevalence(%

)8.1

10.0

12.4

16.3

23.2

OR

(95%

CI)

1.00

1.08

(0.85–

1.37

)1.11

(0.87–

1.40

)1.07

(0.84–

1.35

)0.98

(0.77–

1.24

)0.81

93

Reduced

HDLcholesterol

Prevalence(%

)37

.741

.141

.748

.956

.3

OR

(95%

CI)

1.00

1.09

(0.94–

1.26

)1.01

(0.87–

1.17

)1.25

(1.06–

1.46

)1.45(1.21–1.74

)0.00

01

Elevatedtriglycerides

Prevalence(%

)10

.712

.413

.916

.724

.0

OR

(95%

CI)

1.00

1.09

(0.86–

1.37

)1.08

(0.86–

1.34

)1.10

(0.88–

1.36

)1.26(1.00–1.58

)0.06

80

K. Ha et al.

We also observed that a high carbohydrate intake wasinversely associated with total cholesterol or LDL choles-terol. This result is consistent with reports that the effect ofdietary carbohydrate on HDL is quite different from that onLDL. A high carbohydrate diet, which is typically also low-fat diet, reduces the HDL cholesterol level, but also reducesthe LDL cholesterol level [48, 49].

It could also explain in part why the risks of metabolicsyndrome were found only in Korean subjects. The Koreandiet has a higher glycemic load, a measure that quantifiesthe glycemic response to carbohydrates from differentfoods. Given that the prevalence of reduced HDL choles-terol in Korean adults has continuously increased [50], avery high carbohydrate intake (more than 60% of energy) isconsidered a possible cause of reduced HDL cholesterolaccording to the Expert Panel on Detection, Evaluation, andTreatment of High Blood Cholesterol in Adults [51].Moreover, common lipid abnormalities, including increasedtriglycerides and reduced HDL cholesterol levels, are alsoclosely linked with insulin resistance [49]. Thus, the veryhigh carbohydrate intake of the Korean population shouldbe monitored carefully in the prevention and managementof metabolic syndrome.

The present study has several limitations. First, the cross-sectional design of the NHANES and KNHANES makes itdifficult to elucidate a causal relationship between diet andmetabolic syndrome. Further longitudinal studies are nee-ded to examine the effect of carbohydrate intake on variousrisk factors for metabolic syndrome. Second, it was difficultto directly compare several of the parameters between theUS and Korea, despite that fact that the studies used thesame protocol. In the NHANES, serum triglyceride andfasting blood glucose levels, metabolic syndrome compo-nents were measured only in a subsample, which resulted inexclusion of 7,236 participants. This might have weakenedthe statistical power to compare the data. Thus, the resultsshould be interpreted with caution. Finally, we used 1-day24-h dietary recall data, which might not be representativeof individuals’ usual intake due to within-person variation.However, a single 24-h recall may be adequate if the samplesize is sufficiently large; [52] moreover, we also divideddietary carbohydrate intake into quintiles and examined itsassociations with metabolic syndrome by group. Despitethese limitations, this is, to the best of our knowledge, thefirst study to compare the association of carbohydrate intakewith metabolic abnormalities in adults from two countriesusing national survey data.

In conclusion, the Korean adult population consumedsubstantially more carbohydrate, but less fat, than theUS adult population. These distinct dietary practices in theUS and Korea were linked to differences in the intensityof the association between carbohydrate intake andmetabolic abnormalities. Further studies are required toTa

ble6(con

tinued)

KNHANES

Q1(n

=24

55)

Q2(n

=24

56)

Q3(n

=24

56)

Q4(n

=24

56)

Q5(n

=24

56)

pfortrend

Elevatedfastingglucose

Prevalence(%

)10

.211

.712

.915

.819

.5

OR

(95%

CI)

1.00

1.07

(0.85–

1.35

)1.02

(0.82–

1.28

)1.05

(0.84–

1.30

)1.00

(0.79–

1.26

)0.95

73

Metabolic

synd

rome

Prevalence(%

)9.4

11.9

13.4

17.0

26.2

OR

(95%

CI)

1.00

1.24

(0.94–

1.62

)1.15

(0.90–

1.47

)1.14

(0.88–

1.46

)1.31(1.01–1.69

)0.09

11

Boldvalues

indicate

thestatistical

sign

ificance

Allvalues

accoun

tedforthecomplex

samplingdesign

effect

ofthenatio

nalsurveysusingPROC

SURVEY

procedure

HDLhigh

-density

lipop

rotein

a pfortrendfrom

amultiv

ariatelin

earregression

analysisafteradjustingforage,alcoho

lcon

sumption,bo

dymassindex(exceptfor

themod

elof

waistcircum

ference),current

smok

ing,education,

ethn

icity

(NHANESon

ly),family

income,

physical

activ

ity,survey

period

,andtotalenergy

intake

Dietary carbohydrate and metabolic syndrome

elucidate the underlying mechanisms of the factors thatcontribute the development of metabolic disease in Westernand Asian populations, and to identify country-specificstrategies for the prevention and management of metabolicdisease.

Acknowledgements This work is carried out with the support of agrant from the National Research Foundation of Korea funded by theKorean Government (NRF-2017R1A2B1008420).

Compliance with ethical standards

Competing interests The authors declare that they have no competinginterests.

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