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Combined effect of body mass index and body size perception on metabolic syndrome in South Korea: results of the fifth Korea National Health and Nutrition Examination Surveys (2010-2012)

  • Sook Hee Yoon1,
  • Kyu-Tae Han2, 3,
  • Sun Jung Kim4,
  • Tae Yong Sohn5,
  • Byungyool Jeon6,
  • Woorim Kim2, 3 and
  • Eun-Cheol Park6Email author
Contributed equally
BMC Public Health201515:554

https://doi.org/10.1186/s12889-015-1839-6

Received: 24 February 2015

Accepted: 13 May 2015

Published: 17 June 2015

Abstract

Background

Body mass index (BMI) has been used as an indirect predictor for the risk of metabolic syndrome. However, there are challenges in evaluating the risk of metabolic syndrome using BMI in certain parts of the world. Therefore, it is worth exploring additional factors that could supplement BMI to predict the risk of metabolic syndrome. In this study, we assessed the combined effect of BMI and perception for predicting metabolic syndrome.

Methods

We used the fifth Korea National Health and Nutrition Examination Surveys (KNHANES V, 2010–12, N = 16,537) in this study. Multivariable logistic regression analysis was performed to examine the association while controlling for potential confounding variables. We also performed an analysis for the combined effect of BMI and perception of body size, and subgroup analysis by age group or moderate physical activity.

Results

Data from 16,537 participants were analyzed in this study (males: 6,978, females: 9,559). Among them, metabolic syndrome was diagnosed in 1,252 (17.9 %) males and 2,445 (25.6 %) females, respectively. The combination of BMI and body size perception had a positive relation with the presence of metabolic syndrome. People who perceived themselves to be overweight for their body size had a higher risk for metabolic syndrome even if they have the same BMI.

Conclusion

Our findings suggest that the combination of body size perception and BMI is useful in predicting the risk of metabolic syndrome. The use of complementary predictors could reduce the risk for inaccurate prediction of metabolic syndrome.

Keywords

Metabolic syndrome Body mass index BMI Perception of body size Combined effect

Background

South Korea has achieved rapid socioeconomic development since the late 20th century. This fast-paced growth has led to changes in South Koreans’ daily lives, affecting lifestyle and food consumption, and contributing to improved overall health status as South Korea becomes an aging society [1, 2]. However, there has been a concomitant increase in new health problems in South Korea, such as higher rates of chronic disease. According to Statistics Korea, cardiovascular diseases were the fifth leading cause of death in South Korea (50.2 deaths per 100,000 people in 2013) [3].

In the 2013 Organization for Economic Co-operation and Development (OECD) Health at Glance report, South Korea compares poorly with other OECD countries [4]. This problem is expected to be exacerbated by an aging population. To solve those problems, many health care professionals have studied chronic diseases and identified metabolic syndrome as a major cause [5, 6]. Metabolic syndrome has rapidly increased in South Korea over the past few decades (1998 year: 24.9 %, 2007 year: 31.3 %) [7]. Problems related with metabolic syndrome are expected to continue to increase. Thus, preventing metabolic syndrome is important for managing chronic diseases.

Metabolic syndrome is generally diagnosed by five indicators: waist circumference, triglyceride level, high-density lipoprotein (HDL) cholesterol level, blood pressure, and fasting glucose level. If three indicators (including waist circumference) are met, an individual is diagnosed with metabolic syndrome [8]. Many previous studies identified obesity as the major risk factor of metabolic syndrome. Thus, body mass index (BMI) has been widely used as an indirect predictor for evaluating the risk of metabolic syndrome [9, 10]. However, the use of BMI to predict metabolic syndrome is not necessarily applicable in every country; this simple metric does not consider important factors such as racial/ethnic differences and lifestyle factors. Even in people with the same BMI, the risk of metabolic syndrome may differ, depending on whether they smoke or consume alcohol [1113]. Therefore, it is worth exploring additional predictors that could supplement BMI to assess the risk of metabolic syndrome; here, we focus on body size perception.

Although many previous studies have assessed the relationship between body size perception and obesity, few have also investigated the incidence of metabolic syndrome in South Korea [14, 15]. Perception of body size is a factor that affects peoples’ lifestyle, including food consumption. Moreover, the risk of metabolic syndrome can be changed by altering one’s lifestyle. In this study, we analyzed the relationship between the incidence of metabolic syndrome and BMI or body size perception, as well as the combined effect of BMI and the body size perception on metabolic syndrome.

Methods

Study population

This study used data from the fifth Korea National Health and Nutrition Examination Surveys (KNHANES V, 2010–12). KNHANES are cross-sectional surveys that have been conducted annually since 1998 by the Korea Centers for Disease Control and Prevention (KCDC) to assess the health and nutritional status of the Korean population. A stratified multistage cluster-sampling design was used to obtain a nationally representative sample. This survey is composed of three parts: Health Interview Survey, Health Examination, and Nutrition Survey. We used data from the Health Interview Survey, Health Examination, and Nutrition Survey. The overall response rates were 81.9 % in 2010, 80.4 % in 2011, and 80.0 % in 2012. A total of 25,967 individuals (8,958 in 2010, 8,491 in 2011, and 8,518 in 2012) completed the survey. Any respondents who did not provide BMI, perceptions of body size, five indicators for the diagnosis of metabolic syndrome, or were under the age of 19 were excluded from the study. We ultimately included 14,773 eligible participants in this study. The KNHANES was openly available in https://knhanes.cdc.go.kr/knhanes/eng/index.do after submitting e-mail address and registering short-form information. These data was approved by the KCDC Institutional Review Board, and all participants provided written informed consent (2010-02CON-21-C, 2011-02CON-06-C, 2012-01-EXP-01-2C).

Variables

The outcome variable in this study was the incidence of metabolic syndrome, which was defined by the International Diabetes Federation (IDF) criteria. It was diagnosed if two of the five indicators (including waist circumference) met the IDF criteria for waist circumference, triglyceride level, HDL cholesterol level, blood pressure, and fasting glucose level.

IDF criteria (metabolic syndrome diagnosed if two or more indicators were present)
  1. 1.

    Waist circumference (male: ≥90 cm and female: ≥80 cm for Asian subjects)

     
  2. 2.

    Triglycerides level (≥150 mg/dl)

     
  3. 3.

    HDL cholesterol level (male: <40 mg/dl, female: <50 mg/dl)

     
  4. 4.

    Blood pressure (systolic: ≥130 mmHg, diastolic: ≥85 mmHg, or treatment of diagnosed hypertension)

     
  5. 5.

    Fasting glucose level (≥100 mg/dl or type 2 diabetes)

     

The independent variables of main interest in relation to metabolic syndrome were BMI and body size perception. BMI was calculated as body weight (kg) divided into the squared height (m2). BMI was classified into three groups as follows: ≤22.9, 23.0–24.9, or ≥25. Perception of body size was defined as the answer to the question: “How do you perceive your body size?” The response to this question was classified into: underweight, normal, or overweight.

Other independent variables considered in analysis as potential confounding variables were age, sex, income, educational level, economic activity, marital status, sleep duration, smoking status, alcohol consumption, stress awareness, moderate physical activity, menopause (only female), total energy intake and survey year. Income status was classified as “low”, “mid-low”, “mid-high”, or “high”. Economic activity was defined as “yes” or “no”. Stress awareness was classified as “high”, “moderate”, or “low”. Moderate physical activity was defined as whether respondents performed moderate physical activity for 30 min per session more than 5 times per week. Total energy intake was calculated based on respondent`s self-reported for their usual food consumption.

Statistical analysis

We first examined the distribution of each categorical variable by frequency and percentages and performed χ2 tests to identify correlation with combination of BMI and body size perception by sex. Next, we performed analysis of variance (ANOVA) for continuous variables as total energy intake to identify correlation with combination of BMI and body size perception and to compare average and standard deviation of variables. In addition, these analyses were also performed to examine differences in each variable according to incidence of metabolic syndrome by sex. Multivariable logistic regression analysis was used to examine the association between BMI or body size perception and metabolic syndrome while controlling for potential confounding variables such as age, sex, income, educational level, economic activity, marital status, sleep duration, smoking status, alcohol consumption, stress awareness, moderate physical activity, menopause (only female), total energy intake, and survey year. We also included menopause status for female respondents. An additional analysis was carried out for the combined effect of BMI and body size perception, as was subgroup analysis by either age group (< vs. ≥65 years) or physical activity. Sampling weights assigned to each participant were applied in the analyses to generalize the sampled data. C-statistics were calculated to examine the predictive values for the logistics model. These values range between 0 (no predictive value) and 1, (perfect predictive value). All statistical analyses were performed using SAS statistical software (Cary, NC) version 9.2.

Results

Data from 14,773 participants were analyzed in this study (males: 5,897, females: 8,876). Tables 1 and 2 shows the association between combination of BMI and body size perception and other covariates by sex. Among them, people who rightly perceived their body size were as follows: males = BMI, ≤22.9, 52.6 %; 23.0–24.9, 64.7 %; ≥25, 78.1 %, and females = BMI, ≤22.9, 26.2 %; 23.0–24.9, 38.0 %; ≥25, 82.5 %. There were statistically significant correlations with combination of BMI and body size perception in both sex (P < .0001). By the results of association between combination of BMI and body size perception and covariates, most of covariates had statistically significant correlations with variables of interest, except to moderate physical activity and survey year in males; moderate physical activity and menopause in females.
Table 1

Association between combination of BMI and perception of body size and covariates in male

 

Males (n = 5,897)

BMI

≤22.9 (n = 2,340)

23.0–24.9 (n = 1,520)

≥25 (n = 2,037)

P-value

Perception of body size

Underweight

(52.6 %)

Normal (44.0 %)

Overweight (3.3 %)

Underweight (5.3 %)

Normal (64.7 %)

Overweight (30.0 %)

Underweight (1.1 %)

Normal (20.9 %)

Overweight (78.1 %)

Variables

N/Mean

%/SD

N/Mean

%/SD

N/Mean

%/SD

N/Mean

%/SD

N/Mean

%/SD

N/Mean

%/SD

N/Mean

%/SD

N/Mean

%/SD

N/Mean

%/SD

Age (years)

                   

 19 ~ 29

119

26.6

91

20.4

14

3.1

1

0.2

51

11.4

36

8.1

0

0.0

9

2.0

126

28.2

<.0001

 30 ~ 39

186

19.9

144

15.4

13

1.4

5

0.5

130

13.9

83

8.9

1

0.1

37

4.0

336

35.9

 

 40 ~ 49

178

17.3

136

13.2

15

1.5

10

1.0

169

16.4

79

7.7

1

0.1

72

7.0

368

35.8

 

 50 ~ 59

211

18.1

171

14.7

8

0.7

13

1.1

239

20.5

123

10.5

2

0.2

92

7.9

307

26.3

 

 60 ~ 69

247

20.4

215

17.8

13

1.1

23

1.9

216

17.9

87

7.2

10

0.8

110

9.1

289

23.9

 

 ≥70

291

26.2

273

24.6

15

1.4

28

2.5

179

16.1

48

4.3

8

0.7

105

9.5

164

14.8

 

Income

 Low

306

27.0

235

20.7

22

1.9

31

2.7

181

15.9

57

5.0

6

0.5

106

9.3

191

16.8

<.0001

 Mid-low

364

23.5

273

17.6

20

1.3

14

0.9

262

16.9

90

5.8

7

0.5

117

7.6

400

25.9

 

 Mid-high

295

18.3

283

17.6

18

1.1

21

1.3

276

17.2

135

8.4

6

0.4

98

6.1

477

29.6

 

 High

267

16.6

239

14.9

18

1.1

14

0.9

265

16.5

174

10.8

3

0.2

104

6.5

522

32.5

 

Educational level

 Below elementary school

297

24.9

262

22.0

20

1.7

34

2.9

205

17.2

59

5.0

8

0.7

125

10.5

181

15.2

<.0001

 Middle school graduated

158

20.8

116

15.2

5

0.7

16

2.1

141

18.5

55

7.2

4

0.5

78

10.2

188

24.7

 

 High school graduated

426

21.2

352

17.6

23

1.1

14

0.7

331

16.5

162

8.1

6

0.3

128

6.4

563

28.1

 

 Above University graduated

351

18.1

300

15.5

30

1.5

16

0.8

307

15.8

180

9.3

4

0.2

94

4.8

658

33.9

 

Economic activity

 Yes

870

20.0

709

16.3

49

1.1

45

1.0

716

16.5

356

8.2

13

0.3

314

7.2

1,273

29.3

<.0001

 No

362

23.3

321

20.7

29

1.9

35

2.3

268

17.3

100

6.4

9

0.6

111

7.2

317

20.4

 

Marital status

 Married

1,012

20.2

840

16.8

62

1.2

70

1.4

861

17.2

391

7.8

21

0.4

383

7.7

1,361

27.2

<.0001

 Separated/Bereavement/Divorced

64

20.9

64

20.9

3

1.0

8

2.6

57

18.6

18

5.9

1

0.3

27

8.8

64

20.9

 

 Single

156

26.4

126

21.4

13

2.2

2

0.3

66

11.2

47

8.0

0

0.0

15

2.5

165

28.0

 

Sleep duration

 Less than 6 h

497

20.5

408

16.8

35

1.4

36

1.5

401

16.5

206

8.5

14

0.6

167

6.9

660

27.2

0.0025

 7–8 h

644

21.0

520

17.0

36

1.2

39

1.3

517

16.9

222

7.2

6

0.2

230

7.5

852

27.8

 

 More than 9 h

91

22.4

102

25.1

7

1.7

5

1.2

66

16.2

28

6.9

2

0.5

28

6.9

78

19.2

 

Smoking status

 Non-smoker/Ex-smoker

704

19.2

648

17.6

49

1.3

56

1.5

635

17.3

273

7.4

17

0.5

300

8.2

992

27.0

<.0001

 Smoker

528

23.8

382

17.2

29

1.3

24

1.1

349

15.7

183

8.2

5

0.2

125

5.6

598

26.9

 

Alcohol consumption

 Never

246

23.1

219

20.5

21

2.0

26

2.4

181

17.0

59

5.5

9

0.8

92

8.6

213

20.0

<.0001

 Less than 1 time per month

259

23.4

197

17.8

10

0.9

13

1.2

197

17.8

81

7.3

4

0.4

63

5.7

284

25.6

 

 Less than 3 times per week

530

18.5

460

16.1

39

1.4

32

1.1

456

15.9

254

8.9

8

0.3

187

6.5

895

31.3

 

 More than 4 times per week

197

22.9

154

17.9

8

0.9

9

1.0

150

17.4

62

7.2

1

0.1

83

9.6

198

23.0

 

Stress awareness

 High

298

23.4

179

14.1

20

1.6

20

1.6

185

14.5

118

9.3

0

0.0

70

5.5

384

30.1

<.0001

 Moderate

724

20.8

608

17.4

45

1.3

41

1.2

580

16.6

272

7.8

15

0.4

250

7.2

950

27.3

 

 Low

210

18.5

243

21.4

13

1.1

19

1.7

219

19.2

66

5.8

7

0.6

105

9.2

256

22.5

 

Moderate physical activity

 No

1,115

20.8

924

17.3

75

1.4

70

1.3

889

16.6

413

7.7

21

0.4

383

7.2

1,460

27.3

0.4056

 Yes

117

21.4

106

19.4

3

0.5

10

1.8

95

17.4

43

7.9

1

0.2

42

7.7

130

23.8

 

Survey year

 2010

396

21.1

313

16.6

32

1.7

28

1.5

311

16.5

145

7.7

4

0.2

148

7.9

504

26.8

0.5417

 2011

456

21.4

374

17.6

25

1.2

25

1.2

341

16.0

166

7.8

8

0.4

136

6.4

595

28.0

 

 2012

380

20.1

343

18.1

21

1.1

27

1.4

332

17.6

145

7.7

10

0.5

141

7.5

491

26.0

 

Total energy intake

2,312.9

±906.0

2,252.5

±903.6

2,269.2

±1,033.1

2,073.1

±894.1

2,362.6

±895.1

2,321.0

±924.7

1,980.4

±713.5

2,360.0

±1,113.0

2,461.1

±963.6

<.0001

Total

1,232

20.9

1,030

17.5

78

1.3

80

1.4

984

16.7

456

7.7

22

0.4

425

7.2

1,590

27.0

 

BMI body mass index

Table 2

Association between combination of BMI and perception of body size and covariates in female

 

Females (n = 8,876)

BMI

≤22.9 (n = 4,219)

23.0–24.9 (n = 1,956)

≥25 (n = 2,701)

P-value

Perception of body size

Underweight (26.2 %)

Normal (57.6 %)

Overweight (16.3 %)

Underweight (5.7 %)

Normal (38.0 %)

Overweight (56.3 %)

Underweight (2.6 %)

Normal (14.9 %)

Overweight (82.5 %)

Variables

N/Mean

%/SD

N/Mean

%/SD

N/Mean

%/SD

N/Mean

%/SD

N/Mean

%/SD

N/Mean

%/SD

N/Mean

%/SD

N/Mean

%/SD

N/Mean

%/SD

Age (years)

                   

 19 ~ 29

135

18.1

293

39.3

116

15.5

0

0.0

15

2.0

67

9.0

0

0.0

1

0.1

119

16.0

<.0001

 30 ~ 39

183

10.7

659

38.5

265

15.5

0

0.0

46

2.7

231

13.5

0

0.0

12

0.7

314

18.4

 

 40 ~ 49

140

8.7

515

31.9

149

9.2

2

0.1

75

4.6

286

17.7

0

0.0

18

1.1

430

26.6

 

 50 ~ 59

163

9.1

446

24.9

104

5.8

6

0.3

153

8.5

294

16.4

6

0.3

52

2.9

569

31.7

 

 60 ~ 69

181

11.8

270

17.6

31

2.0

26

1.7

227

14.8

156

10.2

20

1.3

126

8.2

494

32.3

 

 ≥70

302

20.4

246

16.6

21

1.4

78

5.3

227

15.3

67

4.5

44

3.0

194

13.1

302

20.4

 

Income

 Low

329

17.2

364

19.0

57

3.0

70

3.7

240

12.5

128

6.7

43

2.2

195

10.2

487

25.5

<.0001

 Mid-low

252

11.0

587

25.7

164

7.2

19

0.8

202

8.8

297

13.0

12

0.5

115

5.0

640

28.0

 

 Mid-high

232

9.9

746

31.8

236

10.1

11

0.5

148

6.3

287

12.2

9

0.4

58

2.5

618

26.4

 

 High

291

12.5

732

31.4

229

9.8

12

0.5

153

6.6

389

16.7

6

0.3

35

1.5

483

20.7

 

Educational level

 Below elementary school

455

15.4

492

16.7

65

2.2

103

3.5

417

14.1

229

7.8

63

2.1

314

10.6

811

27.5

<.0001

 Middle school graduated

94

9.9

205

21.6

38

4.0

5

0.5

109

11.5

131

13.8

5

0.5

44

4.6

316

33.4

 

 High school graduated

254

9.3

833

30.4

300

11.0

4

0.1

151

5.5

420

15.3

1

0.0

33

1.2

741

27.1

 

 Above University graduated

301

13.4

899

40.1

283

12.6

0

0.0

66

2.9

321

14.3

1

0.0

12

0.5

360

16.0

 

Economic activity

 Yes

493

12.0

1,166

28.3

318

7.7

41

1.0

320

7.8

562

13.6

16

0.4

154

3.7

1,048

25.4

<.0001

 No

611

12.8

1,263

26.5

368

7.7

71

1.5

423

8.9

539

11.3

54

1.1

249

5.2

1,180

24.8

 

Marital status

 Married

708

10.8

1,832

28.1

540

8.3

50

0.8

534

8.2

894

13.7

34

0.5

232

3.6

1,704

26.1

<.0001

 Separated/Bereavement/Divorced

266

16.1

319

19.3

44

2.7

62

3.7

198

12.0

136

8.2

36

2.2

170

10.3

425

25.7

 

 Single

130

18.8

278

40.2

102

14.7

0

0.0

11

1.6

71

10.3

0

0.0

1

0.1

99

14.3

 

Sleep duration

 Less than 6 h

469

12.5

933

24.9

220

5.9

59

1.6

358

9.6

451

12.0

39

1.0

220

5.9

999

26.7

<.0001

 7–8 h

530

12.0

1,311

29.6

398

9.0

37

0.8

328

7.4

592

13.4

23

0.5

149

3.4

1,061

24.0

 

 More than 9 h

105

15.0

185

26.5

68

9.7

16

2.3

57

8.2

58

8.3

8

1.1

34

4.9

168

24.0

 

Smoking status

 Non-smoker/Ex-smoker

1,043

12.3

2,312

27.3

642

7.6

109

1.3

726

8.6

1,051

12.4

69

0.8

394

4.7

2,126

25.1

0.0019

 Smoker

61

15.1

117

29.0

44

10.9

3

0.7

17

4.2

50

12.4

1

0.2

9

2.2

102

25.2

 

Alcohol consumption

 Never

526

15.1

843

24.2

164

4.7

67

1.9

346

9.9

365

10.5

50

1.4

235

6.8

885

25.4

<.0001

 Less than 1 time per month

367

11.3

923

28.3

287

8.8

27

0.8

252

7.7

448

13.7

15

0.5

107

3.3

833

25.6

 

 Less than 3 times per week

188

9.5

622

31.5

223

11.3

15

0.8

126

6.4

270

13.7

4

0.2

54

2.7

472

23.9

 

 More than 4 times per week

23

14.2

41

25.3

12

7.4

3

1.9

19

11.7

18

11.1

1

0.6

7

4.3

38

23.5

 

Stress awareness

 High

360

14.6

637

25.8

225

9.1

40

1.6

164

6.6

287

11.6

26

1.1

84

3.4

648

26.2

<.0001

 Moderate

558

11.0

1,474

29.1

398

7.9

41

0.8

425

8.4

682

13.5

24

0.5

197

3.9

1,263

25.0

 

 Low

186

13.8

318

23.7

63

4.7

31

2.3

154

11.5

132

9.8

20

1.5

122

9.1

317

23.6

 

Moderate physical activity

 No

1,027

12.6

2,253

27.6

635

7.8

102

1.3

679

8.3

1,012

12.4

61

0.7

369

4.5

2,013

24.7

0.0755

 Yes

77

10.6

176

24.3

51

7.0

10

1.4

64

8.8

89

12.3

9

1.2

34

4.7

215

29.7

 

Survey year

 2010

330

11.8

752

26.8

222

7.9

38

1.4

227

8.1

382

13.6

21

0.7

125

4.5

708

25.2

<.0001

 2011

416

13.3

863

27.5

238

7.6

39

1.2

257

8.2

360

11.5

24

0.8

148

4.7

792

25.2

 

 2012

358

12.2

814

27.7

226

7.7

35

1.2

259

8.8

359

12.2

25

0.9

130

4.4

728

24.8

 

Menopause

 Not yet

464

11.1

1,499

35.8

534

12.8

3

0.1

152

3.6

599

14.3

0

0.0

34

0.8

898

21.5

0.7918

 Yes

640

13.6

930

19.8

152

3.2

109

2.3

591

12.6

502

10.7

70

1.5

369

7.9

1,330

28.3

 

Total energy intake

1,683.0

±649.9

1,747.8

±656.5

1,724.0

±699.4

1,447.7

±608.8

1,629.2

±576.5

1,683.4

±654.6

1,412.0

±423.2

1,610.2

±643.0

1,694.6

±650.1

<.0001

Total

1,104

12.4

2,429

27.4

686

7.7

112

1.3

743

8.4

1,101

12.4

70

0.8

403

4.5

2,228

25.1

 

BMI body mass index

Table 3 shows the univariate associations between each variable and metabolic syndrome. Among them, metabolic syndrome was noted in 1,062 (18.0 %) males and 2,304 (26.0 %) females. In both males and females, higher BMI were more frequent in those with metabolic syndrome (males: ≤22.9, 1.5 %; 23.0–24.9, 11.1 %; ≥25, 42.1 % and females: ≤22.9, 5.3 %; 23.0–24.9, 28.5 %; ≥25, 56.4 %). By body size perception, people who responded overweight were more frequently determined to have metabolic syndrome regardless of sex (males: underweight, 2.0 %; normal, 9.9 %; overweight, 37.3 % and females: underweight, 12.7 %; normal, 18.7 %; overweight, 36.7 %). In addition, males who overestimated their body size than BMI were more frequent in those with metabolic syndrome, but females who underestimated their body size were more frequent in those with metabolic syndrome.
Table 3

Demographic characteristics by metabolic syndrome (frequency, %)

  

Metabolic syndrome (n = 14,773)

Variables

 

Males (n = 5,897)

Females (n = 8,876)

 

Yes

No

P-value

Yes

No

P-value

 

N/Mean

%/SD

N/Mean

%/SD

N/Mean

%/SD

N/Mean

%/SD

BMI

           

 ≤22.9

 

36

1.5

2,304

98.5

<.0001

222

5.3

3,997

94.7

<.0001

 23.0–24.9

 

168

11.1

1,352

88.9

 

558

28.5

1,398

71.5

 

 ≥25

 

858

42.1

1,179

57.9

 

1,524

56.4

1,177

43.6

 

Perception of body size

 Underweight

 

27

2.0

1,307

98.0

<.0001

163

12.7

1,123

87.3

<.0001

 Normal

 

242

9.9

2,197

90.1

 

667

18.7

2,908

81.3

 

 Overweight

 

793

37.3

1,331

62.7

 

1,474

36.7

2,541

63.3

 

BMI

Perception of body size

          

 ≤22.9

 Underweight

11

0.9

1,221

99.1

<.0001

63

5.7

1,041

94.3

<.0001

 Normal

20

1.9

1,010

98.1

 

131

5.4

2,298

94.6

 

 Overweight

5

6.4

73

93.6

 

28

4.1

658

95.9

 

 23.0–24.9

 Underweight

10

12.5

70

87.5

 

53

47.3

59

52.7

 

 Normal

90

9.1

894

90.9

 

267

35.9

476

64.1

 

 Overweight

68

14.9

388

85.1

 

238

21.6

863

78.4

 

 ≥25

 Underweight

6

27.3

16

72.7

 

47

67.1

23

32.9

 

 Normal

132

31.1

293

68.9

 

269

66.7

134

33.3

 

 Overweight

720

45.3

870

54.7

 

1,208

54.2

1,020

45.8

 

Age (years)

           

 19 ~ 29

 

23

5.1

424

94.9

<.0001

26

3.5

720

96.5

<.0001

 30 ~ 39

 

126

13.5

809

86.5

 

113

6.6

1,597

93.4

 

 40 ~ 49

 

186

18.1

842

81.9

 

256

15.9

1,359

84.1

 

 50 ~ 59

 

229

19.6

937

80.4

 

486

27.1

1,307

72.9

 

 60 ~ 69

 

275

22.7

935

77.3

 

689

45.0

842

55.0

 

 ≥70

 

223

20.1

888

79.9

 

734

49.6

747

50.4

 

Income

 Low

 

211

18.6

924

81.4

0.7869

812

42.4

1,101

57.6

<.0001

 Mid-low

 

275

17.8

1,272

82.2

 

640

28.0

1,648

72.0

 

 Mid-high

 

279

17.3

1,330

82.7

 

471

20.1

1,874

79.9

 

 High

 

297

18.5

1,309

81.5

 

381

16.4

1,949

83.6

 

Educational level

 Below elementary school

 

212

17.8

979

82.2

<.0001

1,363

46.2

1,586

53.8

<.0001

 Middle school graduated

 

186

24.4

575

75.6

 

308

32.5

639

67.5

 

 High school graduated

 

337

16.8

1,668

83.2

 

457

16.7

2,280

83.3

 

 Above University graduated

 

327

16.9

1,613

83.1

 

176

7.8

2,067

92.2

 

Economic activity

 Yes

 

761

17.5

3,584

82.5

0.0980

905

22.0

3,213

78.0

<.0001

 No

 

301

19.4

1,251

80.6

 

1,399

29.4

3,359

70.6

 

Marital status

           

 Married

 

959

19.2

4,042

80.8

<.0001

1,544

23.7

4,984

76.3

<.0001

 Separated/Bereavement/Divorced

 

61

19.9

245

80.1

 

724

43.7

932

56.3

 

 Single

 

42

7.1

548

92.9

 

36

5.2

656

94.8

 

Sleep duration

 Less than 6 h

 

439

18.1

1,985

81.9

0.9858

1,133

30.2

2,615

69.8

<.0001

 7–8 h

 

550

17.9

2,516

82.1

 

973

22.0

3,456

78.0

 

 More than 9 h

 

73

17.9

334

82.1

 

198

28.3

501

71.7

 

Smoking status

 Non-smoker/Ex-smoker

 

683

18.6

2,991

81.4

0.1356

2,215

26.1

6,257

73.9

0.0653

 Smoker

 

379

17.0

1,844

83.0

 

89

22.0

315

78.0

 

Alcohol consumption

           

 Never

 

185

17.4

881

82.6

<.0001

1,177

33.8

2,304

66.2

<.0001

 Less than 1 time per month

 

154

13.9

954

86.1

 

721

22.1

2,538

77.9

 

 Less than 3 times per week

 

520

18.2

2,341

81.8

 

365

18.5

1,609

81.5

 

 More than 4 times per week

 

203

23.5

659

76.5

 

41

25.3

121

74.7

 

Stress awareness

 High

 

221

17.3

1,053

82.7

0.0139

622

25.2

1,849

74.8

<.0001

 Moderate

 

602

17.3

2,883

82.7

 

1,210

23.9

3,852

76.1

 

 Low

 

239

21.0

899

79.0

 

472

35.1

871

64.9

 

Moderate physical activity

 No

 

982

18.4

4,368

81.6

0.0306

2,102

25.8

6,049

74.2

0.2222

 Yes

 

80

14.6

467

85.4

 

202

27.9

523

72.1

 

Survey year

 2010

 

360

19.1

1,521

80.9

0.0240

737

26.3

2,068

73.7

0.1602

 2011

 

399

18.8

1,727

81.2

 

778

24.8

2,359

75.2

 

 2012

 

303

16.0

1,587

84.0

 

789

26.9

2,145

73.1

 

Menopause

 Not yet

 

-

-

-

-

-

417

10.0

3,766

90.0

<.0001

 Yes

 

-

-

-

-

 

1,887

40.2

2,806

59.8

 

Total energy intake

 

2,365.6

±970.4

2,346.0

±935.0

0.5391

1,619.0

±640.1

1,720.2

±651.7

<.0001

Total

 

1,062

18.0

4,835

82.0

 

2,304

26.0

6,572

74.0

 

BMI body mass index

The older age group had a higher rate of female metabolic syndrome. Notably, the distribution for metabolic syndrome had an inverse relationship with income in females (low, 42.4 %; mid-low, 28.0 %; mid-high, 20.1 %; high, 16.4 %). Similarly, subjects who were separated, widowed, or divorced were more likely to meet the criteria for metabolic syndrome compared to those with other marital statuses (males: married, 19.2 %; separated/widowed/divorced, 19.9 %; single, 7.1 % and females: married, 23.7 %; separated/widowed/divorced, 43.7 %; single, 5.2 %; Table 3).

Table 4 shows the results of logistic regression analysis for the association between BMI and metabolic syndrome adjusted for covariates by sex. In both males and females, BMI had a positive relationship with metabolic syndrome (males: ≤22.9 = ref, 23.0–24.9 odds ratio [OR]: 9.17, standard deviation [SD]: 5.81–14.50; ≥25 OR: 71.08, SD: 46.32–109.08; females: ≤22.9 = ref, 23.0–24.9 = OR: 6.79, SD: 5.57–8.28, ≥25 = OR: 27.75, SD: 22.71–33.91). Age also had a positive relationship with metabolic syndrome, whereas educational level only had an inverse relationship with metabolic syndrome in females. Both sexes who did not report economic activity had a higher risk for metabolic syndrome (males: yes = ref, no = OR: 1.50, SD = 1.10–2.05; females: no = OR: 1.27, SD = 1.08–1.48), as did smokers of males. Females who had experienced menopause had a higher risk for metabolic syndrome (not yet = ref, yes = OR: 1.46, SD = 1.09–1.94; Table 4).
Table 4

Results of multivariable logistic regression analysis for the relationship between BMI and metabolic syndrome

 

Metabolic syndrome

Variables

Males

Females

OR

SD

OR

SD

BMI

      

 ≤22.9

1.00

-

-

1.00

-

-

 23.0–24.9

9.17

5.81

14.50

6.79

5.57

8.28

 ≥25

71.08

46.32

109.08

27.75

22.71

33.91

Age (years)

      

 19 ~ 29

1.00

-

-

1.00

-

-

 30 ~ 39

2.21

1.17

4.17

2.16

1.18

3.96

 40 ~ 49

3.05

1.67

5.58

4.31

2.38

7.79

 50 ~ 59

4.20

2.19

8.04

5.02

2.58

9.77

 60 ~ 69

5.45

2.69

11.02

8.47

4.27

16.81

 ≥70

7.01

3.41

14.44

12.11

6.28

23.35

Income

 Low

1.00

-

-

1.00

-

-

 Mid-low

1.18

0.84

1.66

1.07

0.87

1.33

 Mid-high

1.12

0.80

1.58

0.98

0.77

1.23

 High

1.29

0.90

1.84

0.87

0.67

1.14

Educational level

 Below elementary school

1.00

-

-

1.00

-

-

 Middle school graduated

1.25

0.90

1.74

0.71

0.56

0.91

 High school graduated

0.98

0.72

1.33

0.59

0.46

0.76

 Above University graduated

1.03

0.73

1.43

0.51

0.37

0.72

Economic activity

 Yes

1.00

-

-

1.00

-

-

 No

1.50

1.10

2.05

1.27

1.08

1.48

Marital status

 Married

1.00

-

-

1.00

-

-

 Separated/Bereavement/Divorced

0.81

0.52

1.26

1.10

0.92

1.31

 Single

0.85

0.53

1.38

1.26

0.69

2.28

Sleep duration

 Less than 6 h

0.91

0.75

1.10

0.85

0.72

1.00

 7–8 h

1.00

-

-

1.00

-

-

 More than 9 h

0.85

0.55

1.32

1.14

0.87

1.51

Smoking status

 Non-smoker/Ex-smoker

1.00

-

-

1.00

-

-

 Smoker

1.31

1.04

1.64

1.37

0.92

2.06

Alcohol consumption

 Never

1.00

-

-

1.00

-

-

 Less than 1 time per month

1.03

0.73

1.46

0.96

0.80

1.16

 Less than 3 times per week

1.16

0.85

1.59

1.00

0.80

1.24

 More than 4 times per week

1.92

1.36

2.72

0.84

0.49

1.43

Stress awareness

 High

0.99

0.70

1.38

0.90

0.70

1.17

 Moderate

0.90

0.69

1.18

0.85

0.68

1.07

 Low

1.00

-

-

1.00

-

-

Moderate physical activity

 No

1.00

-

-

1.00

-

-

 Yes

1.29

0.89

1.87

1.09

0.84

1.41

Survey year

 2010

1.00

-

-

1.00

-

-

 2011

1.14

0.90

1.45

0.89

0.74

1.08

 2012

0.82

0.63

1.07

1.14

0.95

1.37

Menopause

 Not yet

-

-

-

1.00

-

-

 Yes

-

-

-

1.45

1.09

1.92

Total energy intake

1.00

0.99

1.01

1.01

0.99

1.02

C-statistics

0.855*

  

0.876*

  

BMI body mass index, OR odds ratio, SD, standard deviation

*P-value for likelihood ratio test <0.05

Table 5 shows the logistic regression analysis results for the association between combined effect of BMI/body size perception and metabolic syndrome adjusted for covariates by sex. The combination of BMI and body size perception had a positive relationship with metabolic syndrome. People who perceived themselves as overweight for their body size had a higher risk for metabolic syndrome, even if they had the same BMI as a person who did not consider themselves overweight. The results of other controlling variables had similar values and trends as the results listed in Table 4 (Table 5).
Table 5

Results of multivariable logistic regression analysis for the relationship between BMI/body size perception and metabolic syndrome

  

Metabolic syndrome

 

Variables

Males

Females

 

OR

SD

OR

SD

BMI

Perception of body size

      

 ≤22.9

 Underweight

0.41

0.19

0.92

0.53

0.35

0.79

 Normal

1.00

-

-

1.00

-

-

 Overweight

5.38

1.42

20.36

0.98

0.59

1.65

 23.0–24.9

 Underweight

3.23

1.32

7.91

5.79

3.18

10.54

 Normal

5.45

2.95

10.04

5.25

3.91

7.05

 Overweight

14.85

7.75

28.45

5.89

4.41

7.85

 ≥25

 Underweight

15.29

4.35

53.75

9.32

4.82

18.00

 Normal

25.63

13.87

47.35

18.89

12.92

27.63

 Overweight

78.80

44.44

139.72

24.50

19.20

31.26

Age (years)

 19 ~ 29

1.00

-

-

1.00

-

-

 30 ~ 39

2.36

1.24

4.51

2.17

1.18

3.97

 40 ~ 49

3.45

1.88

6.30

4.31

2.38

7.80

 50 ~ 59

4.97

2.60

9.50

5.03

2.58

9.81

 60 ~ 69

6.70

3.34

13.45

8.79

4.41

17.52

 ≥70

9.60

4.69

19.63

13.27

6.81

25.83

Income

 Low

1.00

-

-

1.00

-

-

 Mid-low

1.16

0.82

1.64

1.06

0.85

1.32

 Mid-high

1.09

0.77

1.54

0.95

0.76

1.21

 High

1.23

0.86

1.77

0.86

0.65

1.12

Educational level

 Below elementary school

1.00

-

-

1.00

-

-

 Middle school graduated

1.15

0.82

1.63

0.69

0.54

0.88

 High school graduated

0.86

0.62

1.18

0.57

0.44

0.73

 Above University graduated

0.84

0.60

1.19

0.49

0.35

0.69

Economic activity

 Yes

1.00

-

-

1.00

-

-

 No

1.46

1.07

2.00

1.26

1.08

1.48

Marital status

 Married

1.00

-

-

1.00

-

-

 Separated/Bereavement/Divorced

0.80

0.51

1.26

1.10

0.92

1.32

 Single

0.85

0.52

1.38

1.25

0.69

2.27

Sleep duration

 Less than 6 h

0.88

0.72

1.08

0.85

0.72

1.00

 7–8 h

1.00

-

-

1.00

-

-

 More than 9 h

0.83

0.52

1.30

1.16

0.87

1.53

Smoking status

 Non-smoker/Ex-smoker

1.00

-

-

1.00

-

-

 Smoker

1.31

1.05

1.65

1.37

0.91

2.06

Alcohol consumption

 Never

1.00

-

-

1.00

-

-

 Less than 1 time per month

1.01

0.71

1.43

0.95

0.79

1.14

 Less than 3 times per week

1.12

0.81

1.54

0.98

0.79

1.22

 More than 4 times per week

1.91

1.33

2.73

0.83

0.48

1.41

Stress awareness

 High

0.93

0.66

1.31

0.90

0.70

1.17

 Moderate

0.87

0.66

1.15

0.85

0.68

1.06

 Low

1.00

-

-

1.00

-

-

Moderate physical activity

 Yes

1.00

-

-

1.00

-

-

 No

1.25

0.87

1.80

1.09

0.84

1.42

Survey year

 2010

1.00

-

-

1.00

-

-

 2011

1.12

0.87

1.43

0.90

0.74

1.09

 2012

0.84

0.64

1.10

1.15

0.95

1.38

Menopause

 Not yet

-

-

-

1.00

-

-

 Yes

-

-

-

1.46

1.09

1.94

Total energy intake

1.00

0.99

1.01

1.01

0.99

1.02

C-statistics

0.865*

  

0.877*

  

BMI body mass index, OR odds ratio, SD standard deviation

*P-value for likelihood ratio test <0.05

We also performed subgroup analysis for the combined effect of BMI/body size perception by age group (< vs. ≥65 years) or moderate physical activity to identify possible differences in each group. In the subgroup analysis by age group, it revealed similar relationships of the combined effect of BMI and body size perception in these two groups as were observed in the overall analysis. However, there were some notable findings in non-elderly females. In the overweight group based on BMI, the risk for metabolic syndrome was inversely associated with body size perception (Table 6). In the results of subgroup analysis by moderate physical activity, overweight or obese people based on BMI who underestimated their body size had a higher trend regarding the risk of metabolic syndrome in the moderate physical activity of over 5 times per week group than the other group (data not shown). In the overall multivariable logistic regression, C-statistics were higher in the combination model of BMI and body size perception than when using only BMI models.
Table 6

Results of subgroup analysis for the relationship between combined effect of BMI/body size perception and metabolic syndrome by age group

  

Metabolic syndrome

Type of predictor for metabolic syndrome

Males

Females

Less than 64 years

More than 65 years

Less than 64 years

More than 65 years

OR

SD

OR

SD

OR

SD

OR

SD

BMI

Perception of body size

            

 ≤22.9

 -

1.00

-

-

1.00

-

-

1.00

-

-

1.00

-

-

 23.0–24.9

 -

9.13

4.87

17.12

10.74

6.00

19.21

9.13

6.46

12.88

5.32

4.04

7.02

 ≥25

 -

74.63

42.15

132.16

55.07

30.98

97.88

39.26

28.46

54.15

15.78

11.77

21.15

 ≤22.9

 Underweight

0.39

0.12

1.28

0.41

0.13

1.32

0.47

0.19

1.13

0.40

0.25

0.64

 Normal

1.00

-

-

1.00

-

-

1.00

-

-

1.00

-

-

 Overweight

5.29

1.04

26.88

2.46

0.43

14.13

1.15

0.60

2.20

1.25

0.47

3.38

 23.0-24.9

 Underweight

0.97

0.17

5.73

6.10

1.97

18.95

21.35

5.80

78.56

2.75

1.58

4.80

 Normal

4.81

1.95

11.89

6.97

3.36

14.46

7.02

4.37

11.27

3.54

2.36

5.31

 Overweight

15.32

6.06

38.74

12.20

4.77

31.22

7.96

5.13

12.37

4.44

2.59

7.60

 ≥25

 Underweight

23.41

3.74

146.52

7.82

1.38

44.43

10.18

2.27

45.72

6.52

3.14

13.53

 Normal

26.09

10.87

62.63

25.64

11.60

56.68

24.44

13.40

44.56

13.81

8.50

22.44

 Overweight

75.93

33.14

173.96

59.06

28.24

123.50

36.60

24.83

53.95

10.13

6.97

14.73

C-statistics

Only BMI Model

0.846*

  

0.856*

  

0.877*

  

0.784*

  
 

Combination Model

0.858*

  

0.864*

  

0.882*

  

0.791*

  

*P-value for likelihood ratio test <0.05

Discussion

Due to the rapidly aging population, it is expected that the prevalence of metabolic syndrome will continue to increase in South Korea [16]. It is therefore necessary to design effective strategies to prevent and manage this chronic condition. In recent years, BMI has become a widely used indicator of obesity and indirect predictor for metabolic syndrome. However, it had some limitations that were not overall considered to risk factors for metabolic syndrome [17, 18]. Thus, it is necessary to find complementary predictive factors; we focused on body size perception as a novel predictor for evaluating metabolic syndrome risk. Our results suggest that metabolic syndrome risk was positively related with BMI and were similar to previous studies that examined metabolic syndrome risk factors.

In addition, we observed a combined effect of body size perception and BMI on the risk of metabolic syndrome. Notably, the risk was clearer than that observed using BMI only, and was even observed in subjects with the same BMI but different body perceptions.

In predicting risk for chronic diseases as metabolic syndrome, using only BMI could make some misidentifications because it was calculated by just considering height and weight. If people had same BMI, the risk for metabolic syndrome could be different by major factors consisted of body constitution such as muscle mass and higher body fat [19]. Therefore, using combination of BMI and body size perception would be more helpful in predicting for risk. Based on our results, perception of body size as overweight had higher risk for metabolic syndrome. This is because that perception of body size as overweight could more reflect to risk for metabolic syndrome considering actual body image in same BMI. Perception of body size can help role of complementation of predicting for metabolic syndrome [20]. Therefore, it is suggested that people who perceive their body size as overweight are likely to be at risk of metabolic syndrome. In another point of view, people could respond as overweight for their body size due to their unhealthy behaviors such as unhealthy diet and insufficient physical activity for preventing chronic diseases even if people with same BMI and similar body constitution [21]. Therefore, perception of body size could be indirect indicators for reflecting life styles as well as actual body image.

The same phenomenon was observed when we performed a subgroup analysis by age group that excluded females who were overweight based on BMI and <65 years. This relationship was more positive in males, while the different results in females <65 years may be caused by younger females who did not exhibit health behaviors such as wrong diet and insufficient exercise due to their misperception for their body size despite being overweight or obesity based on BMI. However, in the case of elderly females, they had an effort to manage their health status due to their health concern by advanced age [22]. Based on the results of the subgroup analysis in the moderate physical activity group, people overweight or obese based on BMI tend to exhibit unhealthy behaviors by underestimating their body size and risks of gaining metabolic syndrome as they show moderate physical activity. They may be overconfident, believing in an improvement of their health status by sufficient physical activity, and could take more risky behaviors such as excessive eating. Therefore, providing correct information about preventing metabolic syndrome would be needed.

Although more detailed studies are needed, our findings suggest that inappropriate perception of their health status could be caused to unhealthy behaviors at risky population. This has been described previously; people who are borderline for chronic disease risk do not usually feel that their lives are at risk [23]. Conversely, high-risk populations were much more amenable to health behaviors to modify their risk. It is important to note that males tend to evaluate their own body status more favorably than females. Perception differences can induce people to make lifestyle changes (e.g., food or alcohol consumption, exercise, smoking status, etc.) [15, 24, 25].

In accordance with this, we found that South Korean subjects with the same BMI exhibited different behaviors based on their body size perception; therefore, predicting metabolic syndrome risk solely based on BMI did not take different behaviors into account [26, 27].

Thus, our findings suggest that the combination of body size perception and BMI could be more useful in predicting the risk of metabolic syndrome than BMI alone. The use of complementary predictors could improve prediction and prognostication.

This study has several strengths compared to previous investigations. First, we used nationally representative data, so our study results are representative and generalizable to South Korea citizens. Such data are especially helpful in establishing evidence-based health policies. To our knowledge, this is the first attempt to study the relationship between the combined effect of BMI/body size perception and metabolic syndrome in South Korea, despite numerous issues regarding the management of these health issues in the country. Therefore, our findings should be helpful in identifying ways to address these critical issues.

Our study also has some limitations. First, due to the cross-sectional nature of the KNHANES, it is not possible to identify causal relationships. Other issues must be considered to more accurately measure the relationship between the combined effect of BMI/body size perception and metabolic syndrome. Next, our findings included high OR values, not general OR values. Further studies are needed to confirm our findings, which show a combined effect for metabolic syndrome in relatively small study populations (after stratification). Nevertheless, the overall trends of our findings have serious implications for the management of metabolic syndrome. Third, body size perception was measured by the subjects’ answers to the question: “How do you perceive your body size?” The response could have been incorrectly perceived by researchers and is not a truly scientific measurement. Finally, our analysis did not include important details such as respondent food consumption. Thus, multiple variables that are not a major factor of metabolic syndrome were not considered in our findings.

Despite these limitations, our findings suggest that the combined effect of BMI and body size perception can be used to predict the presence of metabolic syndrome. Based on these findings, it is important for health policy makers to identify solutions for controlling metabolic syndrome. However, further studies of those issues are needed to establish an effective strategy.

Conclusion

The combined effects of body size perception and BMI affect the risk for metabolic syndrome in individuals with the same BMI. Our findings suggest that both variables should be used in predicting the risk of disease to reduce risk of inaccurate predictions.

Notes

Abbreviations

OECD: 

Organization for Economic Co-operation and Development

HDL: 

High-density lipoprotein

BMI: 

Body mass index

KNHANES: 

Korea National Health and Nutrition Examination Surveys

KCDC: 

Korea Centers for Disease Control and Prevention

IDF: 

International Diabetes Federation

OR: 

Odds ratio

SD: 

Standard deviation

Declarations

Authors’ Affiliations

(1)
Department of Health Policy and Management, Graduate School of Public Health, Yonsei University
(2)
Department of Public Health, Graduate School, Yonsei University
(3)
Institute of Health Services Research, Yonsei University College of Medicine
(4)
Department of Health Administration, Namseoul University
(5)
Department of Health Services Administration, Yuhan University
(6)
Department of Preventive Medicine, Yonsei University College of Medicine

References

  1. Kim NS, Moon OR, Kang JH, Lee SY, Jeong BG, Lee SJ, et al. Increasing prevalence of obesity related disease for Koreans associated with overweight and obesity. Korean J Prev Med. 2001;34(4):309–15.Google Scholar
  2. Abegunde DO, Mathers CD, Adam T, Ortegon M, Strong K. The burden and costs of chronic diseases in low-income and middle-income countries. Lancet. 2007;370(9603):1929–38.View ArticlePubMedGoogle Scholar
  3. Statistics Korea. All causes of mortality. 2013.Google Scholar
  4. Organization for Economic Cooperation and Development. Health at a Glance 2013: OECD Indicators. 2013.Google Scholar
  5. Isomaa B, Almgren P, Tuomi T, Forsén B, Lahti K, Nissen M, et al. Cardiovascular morbidity and mortality associated with the metabolic syndrome. Diabetes Care. 2001;24(4):683–9.View ArticlePubMedGoogle Scholar
  6. Kahn R, Buse J, Ferrannini E, Stern M. The metabolic syndrome: time for a critical appraisal Joint statement from the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care. 2005;28(9):2289–304.View ArticlePubMedGoogle Scholar
  7. Lim S, Shin H, Song JH, Kwak SH, Kang SM, Yoon JW, et al. Increasing prevalence of metabolic syndrome in Korea the Korean national health and nutrition examination survey for 1998–2007. Diabetes Care. 2011;34(6):1323–8.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Alberti K, Zimmet P, Shaw J. Metabolic syndrome—a new world‐wide definition. A consensus statement from the international diabetes federation. Diabet Med. 2006;23(5):469–80.View ArticlePubMedGoogle Scholar
  9. Flegal KM, Carroll MD, Kuczmarski RJ, Johnson CL. Overweight and obesity in the United States: prevalence and trends, 1960–1994. Int J Obes Relat Metab Disord. 1998;22(1):39–47.View ArticlePubMedGoogle Scholar
  10. Ervin RB. Prevalence of metabolic syndrome among adults 20 years of age and over, by sex, age, race and ethnicity, and body mass index: United States. Natl Health Stat Report. 2009;13:1–8.Google Scholar
  11. Lee CMY, Huxley RR, Wildman RP, Woodward M. Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: a meta-analysis. J Clin Epidemiol. 2008;61(7):646–53.View ArticlePubMedGoogle Scholar
  12. Dietz WH, Bellizzi MC. Introduction: the use of body mass index to assess obesity in children. Am J Clin Nutr. 1999;70(1):123s–5.PubMedGoogle Scholar
  13. Pham DD, Ku B, Shin C, Cho NH, Cha S, Kim JY. Thoracic-to-hip circumference ratio as a novel marker of type 2 diabetes, independent of body mass index and waist-to-hip ratio, in Korean adults. Diabetes Res Clin Pract. 2014;104(2):273–80.View ArticlePubMedGoogle Scholar
  14. Wright EJ, Whitehead TL. Perceptions of body size and obesity: a selected review of the literature. J Community Health. 1987;12(2–3):117–29.View ArticlePubMedGoogle Scholar
  15. KIM O, KIM K. Comparisons of body mass index, perception of body weight, body shape satisfaction, and self-esteem among Korean adolescents. Percept Mot Skills. 2003;97(3f):1339–46.View ArticlePubMedGoogle Scholar
  16. Park HS, Oh SW, Cho S-I, Choi WH, Kim YS. The metabolic syndrome and associated lifestyle factors among South Korean adults. Int J Epidemiol. 2004;33(2):328–36.View ArticlePubMedGoogle Scholar
  17. Park Y-W, Zhu S, Palaniappan L, Heshka S, Carnethon MR, Heymsfield SB. The metabolic syndrome: prevalence and associated risk factor findings in the US population from the Third National Health and Nutrition Examination Survey, 1988–1994. Arch Intern Med. 2003;163(4):427–36.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Lakka TA, Laaksonen DE, Lakka H-M, Männikkö N, Niskanen LK, Rauramaa R, et al. Sedentary lifestyle, poor cardiorespiratory fitness, and the metabolic syndrome. Med Sci Sports Exerc. 2003;35(8):1279–86.View ArticlePubMedGoogle Scholar
  19. Janssen I, Katzmarzyk PT, Ross R. Waist circumference and not body mass index explains obesity-related health risk. Am J Clin Nutr. 2004;79(3):379–84.PubMedGoogle Scholar
  20. Stice E, Shaw HE. Role of body dissatisfaction in the onset and maintenance of eating pathology: A synthesis of research findings. J Psychosom Res. 2002;53(5):985–93.View ArticlePubMedGoogle Scholar
  21. Sakamaki R, Amamoto R, Mochida Y, Shinfuku N, Toyama K. A comparative study of food habits and body shape perception of university students in Japan and Korea. Nutr J. 2005;4(1):31.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Schulken ED, Pinciaro PJ, Sawyer RG, Jensen JG, Hoban MT. Sorority women's body size perceptions and their weight-related attitudes and behaviors. J Am Coll Health. 1997;46(2):69–74.View ArticlePubMedGoogle Scholar
  23. Zhu S, St-Onge M-P, Heshka S, Heymsfield SB. Lifestyle behaviors associated with lower risk of having the metabolic syndrome. Metabolism. 2004;53(11):1503–11.View ArticlePubMedGoogle Scholar
  24. Hu FB, Manson JE, Stampfer MJ, Colditz G, Liu S, Solomon CG, et al. Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. N Engl J Med. 2001;345(11):790–7.View ArticlePubMedGoogle Scholar
  25. Paffenbarger Jr RS, Hyde RT, Wing AL, Lee I-M, Jung DL, Kampert JB. The association of changes in physical-activity level and other lifestyle characteristics with mortality among men. N Engl J Med. 1993;328(8):538–45.View ArticlePubMedGoogle Scholar
  26. Kim M, Lee H. Overestimation of own body weights in female university students: associations with lifestyles, weight control behaviors and depression. Nutr Res Pract. 2010;4(6):499–506.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Kwak H-K, Lee MY, Kim MJ. Comparisons of body image perception, health related lifestyle and dietary behavior based on the self-rated health of university students in Seoul. Korean J Community Nutr. 2011;16(6):672–82.View ArticleGoogle Scholar

Copyright

© Yoon et al. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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