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Is nutritional labeling associated with individual health? The effects of labeling-based awareness on dyslipidemia risk in a South Korean population

  • Jong Yeob Kim1,
  • Ki Hong Kweon1,
  • Min Jae Kim1,
  • Eun-Cheol Park2, 3,
  • Suk-Yong Jang2, 3,
  • Woorim Kim3, 4 and
  • Kyu-Tae Han3, 4Email author
Nutrition Journal201615:81

https://doi.org/10.1186/s12937-016-0200-y

Received: 25 June 2016

Accepted: 9 September 2016

Published: 15 September 2016

Abstract

Background

In 1995, the South Korean government made nutrition labeling compulsory, which has positively impacted patients with certain chronic diseases, such as dyslipidemia. We investigated the association between nutrition labeling-based awareness and the risk of dyslipidemia among individuals not yet diagnosed.

Methods

Our study used data from the fifth Korea National Health and Nutrition Examination Surveys administered during 2010–2014 (n = 17,687). We performed multiple or logistic regression analysis to examine the association between nutritional analysis and various outcome variables.

Results

Approximately 70 % of the respondents (n = 11,513) were familiar with nutrition labeling, of which 20 % (n = 3172) decided what food to buy based on that information. This awareness yielded mostly positive results on outcome indicators, such as triglyceride and high-density lipoprotein cholesterol levels. In general, individuals who used nutritional labels to make decisions regarding food purchases had a lower risk of dyslipidemia than individuals who did not (OR: 0.806, 95 % CI: 0.709–0.917).

Conclusion

Utilizing nutrition labels for making food choices correlated with a lower risk of dyslipidemia in certain subgroups. Based on our findings, we recommend that health policymakers and medical professionals consider promoting nutrition labeling as an alternative method for managing certain chronic diseases in South Korean patients.

Keywords

Nutrition labelingHealth policy perceptionDyslipidemiaHyperlipidemia

Background

During the past 30 years, South Korea has experienced evolving health care perspectives, with a recent focus on chronic diseases. Although many health care professionals have studied treatment options extensively, some chronic diseases persist in South Korean patients [1]. Therefore, developing prevention strategies for managing risk factors, such as hypertension, diabetes mellitus, and dyslipidemia, may be important for controlling these diseases [24].

Dyslipidemia is a state of abnormal amounts of lipids in the blood and is characterized by conditions such as hypercholesterolemia, hypertriglyceridemia, increased low-density lipoprotein (LDL) cholesterolemia, and decreased high-density lipoprotein (HDL) cholesterolemia [5]. Dyslipidemia can be managed by diet, exercise, and sometimes drug injections, depending on the health of the patient [6]. However, based on previous studies in South Korea, the prevalence rate of dyslipidemia has gradually increased since 2000 [7]. Although not necessarily harmful itself, the condition is a major risk factor for various cardiovascular diseases (CVD) [8]. Mortality due to CVD has also increased in recent years, making it the second most common cause of death in South Korea [9]. Therefore, it is essential to investigate alternatives for effectively preventing and/or managing dyslipidemia.

In 1995, the South Korean government made nutrition labeling compulsory. Nutrition labeling is a type of food labeling [10] that describes the nutritional properties of processed foods to help consumers make a reasonable choice in purchasing food based on its nutritional values [11]. Labeling also protects consumers from dishonest advertisement by providing exact nutrition information. Previous studies show that nutrition labeling affects food intake with respect to total fat, carbohydrates, and saturated fat and that awareness of nutrition facts and may be helpful in managing certain chronic diseases [1214].

Because nutrition labeling has since expanded in South Korea, some positive effects on patients with chronic diseases, particularly dyslipidemia, have been linked closely to dietary patterns [15, 16]. Despite increased dyslipidemia prevalence and the expansion of nutrition labeling in South Korea, few studies have investigated their relationship. As introducing the nutrition labelling system in South Korea, we expected that the health information related to food consumption would be well provided to South Korean. Therefore, South Korean would easily access to health information which might be helpful in well managing their health compared to past. Based on our hypothesis that nutrition labeling may help prevent dyslipidemia, we analyzed the potential association between nutrition labeling-based awareness and the prevalence of dyslipidemia among individuals not yet diagnosed.

Methods

Study population

This study used data from the fifth Korea National Health and Nutrition Examination Surveys (KNHANES V/VI 2010–14), which are cross-sectional questionnaires that have been administered annually since 1998 by the Korea Centers for Disease Control and Prevention (KCDC) to assess the health and nutritional status of the Korean population. This survey is composed of three parts: Health Interview Survey, Health Examination, and Nutrition Survey. The health examination survey collected the information about anthropometric index, blood pressure, blood test, urine test, dental examination, pulmonary function test, optical test, and hearing test. These tests were performed through visiting examination using vehicle for health examination. The nutrition survey was conducted through additional visiting research of investigator after Health Interview Survey and Health Examination. The nutrition survey including average amount of daily fat intake was consisted to dietary pattern, dietary supplements, nutrition knowledge, food safety, food intake of the day before survey (24 h recall method), and food frequency questionnaire. A stratified multi-stage cluster-sampling design was used to obtain a nationally representative sample from the three parts of the survey. The overall response rates were 81.9 % in 2010, 80.4 % in 2011, 80.0 % in 2012, 79.3 % in 2013, and 77.8 % in 2014 and included 41,101 total respondents. Individuals not tested for dyslipidemia indicators and those under the age of 30 were excluded from the study. In addition, we excluded respondents diagnosed with dyslipidemia before the survey. Thus, we included 17,687 eligible participants in the study.

Variables analyzed

The outcome variables analyzed in this study included four indicators of dyslipidemia: total cholesterol (TC), LDL cholesterol, HDL cholesterol, and triglyceride (TG) levels. Although TC, TG, and HDL cholesterol levels were measured on the day of investigation. This blood test was measured through fasting blood test (minimum 8 h and recommended 12 h after eating). The LDL cholesterol levels were not measured, so were instead calculated using the Friedewald formula. This methods also relatively efficient methods than the ultracentrifugal measurement of LDL cholesterol [17]. We first considered each indicator as a continuous variable and then defined dyslipidemia as the presence of at least one indicator meeting the following diagnostic criteria: TC ≥200 mg/dL, LDL cholesterol ≥130 mg/dL, HDL cholesterol ≤40 mg/dL, or TG ≥150 mg/dL [18].

The primary independent variable was the respondents’ awareness regarding nutrition labeling, which we defined as one of three levels: 1) “unaware of nutrition facts (lowest awareness)”; 2) “aware of nutrition facts but does not check them when making food purchase/checks nutrition facts but does not make labeling-dependent purchase decisions”; or 3) “checks nutrition facts and makes labeling-dependent purchase decisions (highest awareness)”.

We included other independent variables to investigate the association between labeling awareness and dyslipidemia. These additional variables were sex, age, educational level, economic activity, household income, body mass index (BMI), aerobic exercise habits, smoking status, high risk drinking, family history of hyperlipidemia, stress awareness, subjective health, average amount of daily fat intake, frequency of eating out, and survey year [1921]. Age was divided by 10-year increments or grouped as more than 60 years old. Educational level was classified as no high school graduation, bachelor’s degree, and master’s degree or above. BMI was categorized into three groups based on obesity criteria in South Korea (<23, 23–25, and >25). Aerobic exercise habits were based on the amount of aerobic exercise per week, with 150 min of exercise as the cutoff. The smoking status was defined as follows. Smoker group included the current smoker regardless the amount of smoking. Non-smoker group included the ex-smoker and people who have never smoke in their life. The high risk drinking was defined as people who consume more than seven (for males) or five (for females) drinks on a single occasion at least twice a week. The average amount of daily fat intake was calculated based on food intake of the day before survey (24 h recall method). Respondents were recorded the information about food intake of the day before survey, and investigator calculated the nutrient component based on this information. The frequency of eating out was categorized based on five times a week. Stress awareness was defined as the respondents’ daily stress awareness and was classified as “high” or “low”. Subjective health status was classified as “bad,” “normal,” or “good.”

Statistical analysis

We first examined the distribution of values by frequency and percentage for categorical variables or mean and standard deviation for continuous variables, showed the association between other independent variables and awareness of nutrition labelling. Next, we performed ANOVA for continuous variables to determine their relationship with the independent variables by comparing the means and standard deviations of the outcome variables. We also performed Chi-square tests to determine relationships with dyslipidemia diagnosis. Finally, multiple regression analysis was used to examine the association between awareness of nutrition labeling and dyslipidemia indicators while controlling for potential confounding (independent) variables described above. We then performed logistic regression analysis of dyslipidemia risk based on the four dyslipidemia indicators. In addition, we carried out subgroup multiple logistic regression analysis by sex, age, educational level, BMI, and subjective health status to examine differences in nutrition labeling-mediated awareness and dyslipidemia risk. Sampling weights assigned to each participant were applied in the analyses to generalize the sampled data.

Results

The data used in this study included 17,687 unique responses to the KNHANES V/VI from 2010 to 2014. Table 1 shows the general characteristics of our study participants by awareness of nutrition labelling. Approximately 70 % of respondents were aware of nutrition labeling, but most did not actively check nutrition labels or make food purchasing decisions based on nutrition labels. Only about 20 % of these respondents made nutrition label-dependent food purchasing decisions. Females were more frequently in higher awareness level in nutrition labelling than males. The people with younger age, higher educational level, and higher income were more recognized for nutrition labelling than others. In addition, people who had more healthy behaviors were more frequent in higher awareness of nutrition labelling.
Table 1

General characteristics of study population by awareness regarding nutrition labelling in in this study

Awareness regarding nutrition labelling

Checks nutrition facts and makes labeling-dependent purchase decisions

Checks nutrition facts but does not make labeling-dependent purchase decisions/Aware of nutrition facts but does not check them when making food purchase decisions

Unaware of nutrition facts

P-value

Variables

N/Mean

%/SD

N/Mean

%/SD

N/Mean

%/SD

Sex

 Male

645

8.76

3,739

50.78

2,979

40.46

<.0001

 Female

2,527

24.48

4,602

44.58

3,195

30.95

 

Age (years)

 30–39

1,406

34.19

2,402

58.41

304

7.39

<.0001

 40–49

1,014

25.76

2,367

60.14

555

14.10

 

 50–59

524

13.88

2,001

53.02

1,249

33.09

 

 60+

228

3.89

1,571

26.79

4,066

69.33

 

Educational level

 Under high school graduation

1,313

11.25

4,881

41.83

5,476

46.92

<.0001

 Bachelor’s degree

1,635

30.91

3,035

57.37

620

11.72

 

 Master’s degree or above

224

30.81

425

58.46

78

10.73

 

Economic activity

 Unemployed

1,389

20.23

2,706

39.41

2,772

40.37

<.0001

 Employed

1,783

16.48

5,635

52.08

3,402

31.44

 

Household income

 Low

172

5.09

869

25.70

2,340

69.21

<.0001

 Mid-low

715

15.92

2,124

47.29

1,652

36.78

 

 Mid-high

1,085

22.04

2,599

52.79

1,239

25.17

 

 High

1,200

24.53

2,749

56.19

943

19.28

 

BMI

 <23

1,629

20.62

3,814

48.28

2,456

31.09

<.0001

 23–25

687

16.21

1,951

46.04

1,600

37.75

 

 >25

856

15.42

2,576

46.41

2,118

38.16

 

Aerobic exercise habits

 Yes

937

21.97

2,115

49.59

1,213

28.44

<.0001

 No

2,235

16.65

6,226

46.39

4,961

36.96

 

Smoking status

 Non-smoker

2,821

19.73

6,536

45.71

4,943

34.57

<.0001

 Smoker

351

10.36

1,805

53.29

1,231

36.34

 

High risk drinking

 No

2,935

18.39

7,375

46.22

5,646

35.38

<.0001

 Yes

237

13.69

966

55.81

528

30.50

 

Family history for hyperlipidemia

 No

2,915

17.22

7,941

46.91

6,073

35.87

<.0001

 Yes

257

33.91

400

52.77

101

13.32

 

Survey year

 2010

725

18.28

1,721

43.39

1,520

38.33

<.0001

 2011

623

15.73

1,735

43.81

1,602

40.45

 

 2012

621

17.25

1,675

46.54

1,303

36.20

 

 2013

590

18.64

1,671

52.78

905

28.58

 

 2014

613

20.46

1,539

51.37

844

28.17

 

Stress awareness

 Low

2,348

17.35

6,369

47.05

4,820

35.61

<.0001

 High

824

19.86

1,972

47.52

1,354

32.63

 

Subjective health status

 Good

1,187

20.41

2,941

50.56

1,689

29.04

<.0001

 Normal

1,611

18.44

4,239

48.51

2,888

33.05

 

 Bad

374

11.94

1,161

37.07

1,597

50.99

 

Average amount of daily fat intake

46.39

0.77

46.34

0.51

33.16

0.55

<.0001

The frequency of eating out

 Less than four times a week

2,182

18.01

4,972

41.04

4,961

40.95

<.0001

 More than five times a week

990

17.77

3,369

60.46

1,213

21.77

 

Total

3,172

17.93

8,341

47.16

6,174

34.91

 
Table 2 shows associations between the independent and outcome variables. The average values for dyslipidemia indicators (TC, TG, HDL cholesterol, and LDL cholesterol) were 190.88, 137.42, 50.86, and 112.54 mg/dL, respectively. Individuals with higher awareness of nutrition labeling had positive association with low TC, low TG, high HDL cholesterol, low LDL cholesterol, and less diagnosis of dyslipidemia than individuals with lower awareness. Likewise, subjects with dyslipidemia were more likely to have lower awareness of nutrition labeling. In addition, older or male individuals were more frequently diagnosed with dyslipidemia, as were subjects with lower socio-economic status, educational level, or household income.
Table 2

The association between awareness on nutrition labelling and 4 indicators related to dyslipidemia or diagnosis of dyslipidemia

Variables

Total cholesterol (mg/dL)

Triglyceride (mg/dL)

HDL cholesterol (mg/dL)

LDL cholesterol (mg/dL)

Dyslipidemia

P-value

Positive

Negative

Mean

SD

P-value

Mean

SD

P-value

Mean

SD

P-value

Mean

SD

P-value

N

%

N

%

Awareness regarding nutrition labelling

 Checks nutrition facts and makes labeling-dependent purchase decisions

188.53

34.01

0.0399

111.89

79.41

<.0001

55.48

12.75

<.0001

110.68

30.10

0.0006

1,536

48.42

1,636

51.58

<.0001

 Checks nutrition facts but does not make labeling-dependent purchase decisions/Aware of nutrition facts but does not check them when making food purchase decisions

191.32

34.24

 

129.82

102.12

 

52.80

12.52

 

112.55

31.57

 

4,778

57.28

3,563

42.72

 

 Unaware of nutrition facts

192.54

36.35

 

144.53

109.29

 

50.13

12.25

 

113.50

33.74

 

4,108

66.54

2,066

33.46

 

Sex

 Male

189.06

34.48

<.0001

155.43

123.33

<.0001

48.90

11.82

<.0001

109.08

33.39

<.0001

4,777

64.88

2,586

35.12

<.0001

 Female

192.80

35.24

 

114.85

78.70

 

54.81

12.58

 

115.02

30.92

 

5,645

54.68

4,679

45.32

 

Age (years)

 30–39

183.28

33.23

<.0001

116.04

97.17

<.0001

54.78

12.67

<.0001

105.30

29.70

<.0001

1,816

44.16

2,296

55.84

<.0001

 40–49

190.31

33.50

 

130.36

113.12

 

53.24

12.41

 

110.99

31.25

 

2,151

54.65

1,785

45.35

 

 50–59

199.50

34.47

 

142.52

109.54

 

52.56

12.75

 

118.43

33.11

 

2,595

68.76

1,179

31.24

 

 60+

192.14

36.17

 

136.74

89.46

 

49.90

12.18

 

114.89

32.61

 

3,860

65.81

2,005

34.19

 

Educational level

 Under high school graduation

192.88

35.56

0.1972

136.69

104.97

0.0105

51.73

12.58

0.1640

113.82

33.02

0.0338

7,343

62.92

4,327

37.08

<.0001

 Bachelor’s degree

187.73

33.61

 

121.24

94.43

 

53.75

12.57

 

109.74

30.09

 

2,679

50.64

2,611

49.36

 

 Master’s degree or above

190.55

33.42

 

128.79

93.64

 

52.14

12.55

 

112.66

30.07

 

400

55.02

327

44.98

 

Economic activity

 Unemployed

191.26

36.26

0.5049

125.39

87.09

0.0150

52.62

12.87

0.0188

113.57

32.13

0.1766

4,024

58.60

2,843

41.40

0.4833

 Employed

191.23

34.14

 

135.77

109.81

 

52.17

12.44

 

111.90

32.08

 

6,398

59.13

4,422

40.87

 

Household income

 Low

191.86

36.26

0.5209

140.45

96.60

0.2011

50.15

12.60

0.0143

113.62

33.65

0.3923

2,249

66.52

1,132

33.48

<.0001

 Mid-low

191.04

35.62

 

133.31

110.04

 

52.20

12.58

 

112.18

32.03

 

2,634

58.65

1,857

41.35

 

 Mid-high

190.33

34.21

 

128.97

101.42

 

52.99

12.49

 

111.55

32.33

 

2,750

55.86

2,173

44.14

 

 High

191.92

34.22

 

127.06

97.02

 

53.36

12.59

 

113.15

30.80

 

2,789

57.01

2,103

42.99

 

BMI

 <23

185.60

33.75

<.0001

106.94

79.58

<.0001

55.72

13.07

<.0001

108.50

30.34

<.0001

3,666

46.41

4,233

53.59

<.0001

 23–25

192.73

34.30

 

136.59

100.29

 

51.06

11.92

 

114.35

31.64

 

2,683

63.31

1,555

36.69

 

 >25

198.14

35.85

 

163.34

119.96

 

48.53

11.11

 

116.93

34.14

 

4,073

73.39

1,477

26.61

 

Aerobic exercise habits

 Yes

190.61

33.52

0.4412

126.55

96.47

<.0001

53.42

12.90

<.0001

111.87

31.27

0.9488

2,425

56.86

1,840

43.14

0.0016

 No

191.45

35.43

 

133.39

103.28

 

52.01

12.50

 

112.76

32.36

 

7,997

59.58

5,425

40.42

 

Smoking status

 Non-smoker

191.20

34.87

<.0001

123.16

89.01

<.0001

52.98

12.51

<.0001

113.59

31.11

0.2760

8,152

57.01

6,148

42.99

<.0001

 Smoker

191.41

35.42

 

167.97

137.71

 

49.67

12.67

 

108.15

35.69

 

2,270

67.02

1,117

32.98

 

High risk drinking

 No

190.96

34.90

<.0001

125.77

89.46

<.0001

52.13

12.44

<.0001

113.68

31.34

<.0001

9,254

58.00

6,702

42.00

<.0001

 Yes

193.88

35.57

 

186.79

169.14

 

54.38

13.89

 

102.14

36.89

 

1,168

67.48

563

32.52

 

Family history for hyperlipidemia

 No

191.12

34.91

<.0001

132.05

102.08

0.1179

52.24

12.58

0.1999

112.46

32.11

0.0020

9,995

59.04

6,934

40.96

0.1381

 Yes

194.05

36.41

 

124.93

92.97

 

54.65

13.00

 

114.41

32.06

 

427

56.33

331

43.67

 

Survey year

 2010

190.41

35.86

0.0245

130.30

98.76

0.0539

52.71

12.77

<.0001

111.64

32.60

0.0032

2,294

57.84

1,672

42.16

0.0189

 2011

192.84

36.05

 

132.72

106.89

 

52.93

12.80

 

113.36

32.66

 

2,352

59.39

1,608

40.61

 

 2012

191.79

34.76

 

130.01

98.86

 

51.46

12.45

 

114.33

31.93

 

2,187

60.77

1,412

39.23

 

 2013

190.80

34.06

 

133.48

106.04

 

52.15

12.30

 

111.95

32.09

 

1,879

59.35

1,287

40.65

 

 2014

190.05

33.47

 

132.59

97.21

 

52.36

12.62

 

111.17

30.81

 

1,710

57.08

1,286

42.92

 

Stress awareness

 Low

191.18

34.87

0.1373

131.38

99.14

0.3396

52.23

12.58

0.9746

112.67

32.04

0.2953

8,020

59.25

5,517

40.75

0.1178

 High

191.46

35.33

 

132.92

109.72

 

52.73

12.69

 

112.14

32.32

 

2,402

57.88

1,748

42.12

 

Subjective health status

 Good

191.50

34.39

0.0008

126.37

95.89

0.0031

53.29

12.74

<.0001

112.93

31.31

0.0005

3,316

57.01

2,501

42.99

<.0001

 Normal

191.20

34.43

 

133.28

106.44

 

52.25

12.57

 

112.30

32.02

 

5,173

59.20

3,565

40.80

 

 Bad

190.88

37.50

 

137.42

98.28

 

50.86

12.33

 

112.54

33.77

 

1,933

61.72

1,199

38.28

 

The frequency of eating out

 Less than four times a week

191.43

35.53

0.0007

128.23

96.74

0.8791

52.55

12.76

0.9080

113.24

32.19

0.0001

7,138

58.92

4,977

41.08

0.9811

 More than five times a week

190.83

33.75

 

139.38

111.40

 

51.90

12.28

 

111.05

31.86

 

3,284

58.94

2,288

41.06

 

Total

191.243

34.976

 

131.74

101.717

 

52.347

12.611

 

112.548

32.105

 

10,422

58.92

7,265

41.08

 
Table 3 shows results of our multiple and logistic regression analysis to investigate the association between awareness of nutrition labeling and outcome variables related to dyslipidemia. Individuals with higher awareness of nutrition labeling had lower TG and higher HDL cholesterol levels than those with lower awareness, although we observed some negative associations between awareness and TC and LDL cholesterol levels. Male or older individuals generally had association with high risk levels of four indicators, while individuals with healthy behaviors had association with low risk levels of those. The results of our logistic regression analysis to examine the association between awareness of nutrition labeling and risk of dyslipidemia show that individuals with higher awareness of nutrition labelling had a lower risk of dyslipidemia than individuals who did not. Risk of dyslipidemia was also higher in males, older participants, and individuals with unhealthy behaviors.
Table 3

The results of multiple regression or logistic regression analysis to examine the association between awareness on nutrition labelling and outcome variables

Variables

Total cholesterol (mg/dL)

Triglyceride (mg/dL)

HDL cholesterol (mg/dL)

LDL cholesterol (mg/dL)

Dyslipidemia

β

SE

P-value

β

SE

P-value

β

SE

P-value

β

SE

P-value

OR

95 % CI

P-value

Awareness on nutrition labelling

 Checks nutrition facts and makes labeling-dependent purchase decisions

0.837

1.056

0.4280

−11.803

3.061

0.0001

1.259

0.357

0.0004

1.938

0.994

0.0515

0.806

0.709

0.917

0.0011

 Checks nutrition facts but does not make labeling-dependent purchase decisions/Aware of nutrition facts but does not check them when making food purchase decisions

2.350

0.783

0.0028

−7.170

2.725

0.0086

0.799

0.249

0.0014

2.985

0.774

0.0001

0.919

0.828

1.020

0.1110

 Unaware of nutrition facts

Ref

-

-

Ref

-

-

Ref

-

-

Ref

-

-

1.000

-

-

-

Sex

 Male

−5.197

0.833

<.0001

27.026

2.565

<.0001

−6.089

0.279

<.0001

−4.513

0.768

<.0001

1.395

1.265

1.537

<.0001

 Female

Ref

-

-

Ref

-

-

Ref

-

-

Ref

-

-

1.000

-

-

-

Age (years)

 30–39

−10.395

1.105

<.0001

−6.634

3.267

0.0426

1.822

0.372

<.0001

−10.890

1.056

<.0001

0.497

0.432

0.572

<.0001

 40-49

−5.299

1.035

<.0001

6.350

3.465

0.0672

0.706

0.367

0.0544

−7.275

1.001

<.0001

0.678

0.596

0.772

<.0001

 50–59

3.804

0.985

0.0001

11.906

3.065

0.0001

1.012

0.322

0.0018

0.411

0.940

0.6623

1.168

1.025

1.331

0.0198

 60+

Ref

-

-

Ref

-

-

Ref

-

-

Ref

-

-

1.000

-

-

-

Educational level

 Under high school graduation

−2.553

1.535

0.0966

1.013

4.692

0.8291

0.221

0.548

0.6871

−2.976

1.456

0.0413

0.983

0.801

1.206

0.8674

 Bachelor’s degree

−2.206

1.508

0.1438

−3.499

4.603

0.4474

0.206

0.537

0.7017

−1.712

1.417

0.2273

0.954

0.776

1.173

0.6558

 Master’s degree or above

Ref

-

-

Ref

-

-

Ref

-

-

Ref

-

-

1.000

-

-

-

Economic activity

 Unemployed

0.749

0.794

0.3461

4.398

2.187

0.0446

−0.464

0.251

0.0653

0.333

0.729

0.6480

1.153

1.049

1.267

0.0031

 Employed

Ref

-

-

Ref

-

-

Ref

-

-

Ref

-

-

1.000

-

-

-

Household income

 Low

0.258

1.039

0.8042

2.602

3.436

0.4491

−0.585

0.389

0.1331

0.323

1.031

0.7544

1.076

0.946

1.223

0.2677

 Mid-low

−0.302

0.876

0.7302

−0.916

3.057

0.7644

−0.196

0.303

0.5180

0.077

0.805

0.9237

0.926

0.829

1.035

0.1771

 Mid-high

0.064

0.848

0.9399

−2.031

2.702

0.4524

0.012

0.269

0.9648

0.458

0.808

0.5705

0.953

0.858

1.059

0.3729

 High

Ref

-

-

Ref

-

-

Ref

-

-

Ref

-

-

1.000

-

-

-

BMI

 <23

−13.918

0.749

<.0001

−55.011

2.572

<.0001

6.944

0.253

<.0001

−9.860

0.725

<.0001

0.306

0.280

0.335

<.0001

 23–25

−7.322

0.869

<.0001

−27.998

3.190

<.0001

2.754

0.265

<.0001

−4.477

0.795

<.0001

0.566

0.509

0.629

<.0001

 >25

Ref

-

-

Ref

-

-

Ref

-

-

Ref

-

-

1.000

-

-

-

Aerobic exercise habits

 Yes

Ref

-

-

Ref

-

-

Ref

-

-

Ref

-

-

1.000

-

-

-

 No

0.783

0.752

0.2981

10.527

2.478

<.0001

−1.456

0.255

<.0001

0.134

0.717

0.8517

1.090

0.992

1.199

0.0731

Smoking status

 Non-smoker

Ref

-

-

Ref

-

-

Ref

-

-

Ref

-

-

1.000

-

-

-

 Smoker

3.364

0.916

0.0003

26.004

3.516

<.0001

−1.325

0.301

<.0001

−0.512

0.910

0.5739

1.445

1.292

1.616

<.0001

High risk drinking

 No

Ref

-

-

Ref

-

-

Ref

-

-

Ref

-

-

1.000

-

-

-

 Yes

2.614

1.123

0.0202

41.059

5.895

<.0001

4.954

0.366

<.0001

−10.553

1.190

<.0001

1.229

1.066

1.416

0.0046

Family history for hyperlipidemia

 No

Ref

-

-

Ref

-

-

Ref

-

-

Ref

-

-

1.000

-

-

-

 Yes

6.016

1.501

<.0001

5.369

4.264

0.2083

0.477

0.531

0.3687

4.465

1.380

0.0013

1.307

1.096

1.560

0.0028

Survey year

 2010

0.362

1.093

0.7404

−9.251

3.187

0.0038

1.093

0.354

0.0021

1.120

1.007

0.2663

0.998

0.878

1.134

0.9711

 2011

1.526

1.090

0.1619

−6.695

3.336

0.0451

1.137

0.349

0.0012

1.728

1.003

0.0854

1.000

0.876

1.142

0.9992

 2012

1.877

1.114

0.0922

−5.973

3.523

0.0904

−0.092

0.389

0.8141

3.163

1.055

0.0028

1.117

0.975

1.278

0.1106

 2013

−0.479

1.062

0.6523

−4.680

3.494

0.1807

0.513

0.353

0.1464

−0.056

1.028

0.9567

1.030

0.900

1.179

0.6653

 2014

Ref

-

-

Ref

-

-

Ref

-

-

Ref

-

-

1.000

-

-

-

Stress awareness

 Low

Ref

-

-

Ref

-

-

Ref

-

-

Ref

-

-

1.000

-

-

-

 High

0.443

0.757

0.5589

2.219

2.994

0.4588

0.281

0.263

0.2850

−0.282

0.732

0.6998

0.994

0.907

1.089

0.8906

Subjective health status

 Good

0.979

1.014

0.3347

−8.120

3.109

0.0092

1.710

0.330

<.0001

0.894

0.853

0.2949

0.942

0.829

1.070

0.3556

 Normal

0.972

0.978

0.3209

−2.164

2.945

0.4627

0.761

0.299

0.0109

0.643

0.855

0.4521

1.056

0.937

1.190

0.3686

 Bad

Ref

-

-

Ref

-

-

Ref

-

-

Ref

-

-

1.000

-

-

-

Average amount of daily fat intake

0.040

0.011

0.0002

−0.030

0.036

0.4076

0.008

0.003

0.0139

0.038

0.010

0.0002

1.000

0.999

1.001

0.9479

The frequency of eating out

 Less than four times a week

Ref

-

-

Ref

-

-

Ref

-

-

Ref

-

-

1.000

-

-

-

 More than five times a week

1.374

0.801

0.0867

−2.755

2.835

0.3314

0.074

0.258

0.7748

1.851

0.794

0.0199

1.043

0.943

1.153

0.4132

We also performed subgroup multiple logistic regression analysis to examine possible associations between nutrition labeling awareness and the risk of dyslipidemia with respect to sex, age, educational level, BMI, subjective health status, and the frequency of eating out. Although the interactions between subgroup variables and labeling awareness were only analyzed for sex and age, we did note positive associations between low risk of dyslipidemia and higher awareness in each group. In general, these positive association were more noticeable in males, younger individuals, those with the low educational level, obese participants, and those with the less than four times a week of eating out (Figs. 1 and 2).
Fig. 1

The results of subgroup analysis for the multiple logistic regression analysis to examine the association between awareness regarding nutrition labelling and risk of dyslipidemia according to sex, age, and educational level. *Awareness regarding nutrition labelling = A1: checks nutrition facts and makes labeling-dependent purchase decisions, A2: checks nutrition facts but does not make labeling-dependent purchase decisions/aware of nutrition facts but does not check them when making food purchase decisions, and ref = unaware of nutrition facts. The OR is marked as square point; and results were statistically significant if each bar as marked to SD is not reached the cutoff line in 1.00. *UCL = 95 % upper confidence limit, LCL = 95 % lower confidence limit

Fig. 2

The results of subgroup analysis for the multiple logistic regression analysis to examine the association between awareness regarding nutrition labelling and risk of dyslipidemia according to BMI, subjective health status, and the frequency of eating out. *Awareness regarding nutrition labelling = A1: checks nutrition facts and makes labeling-dependent purchase decisions, A2: checks nutrition facts but does not make labeling-dependent purchase decisions/aware of nutrition facts but does not check them when making food purchase decisions, and ref = unaware of nutrition facts. The OR is marked as square point; and results were statistically significant if each bar as marked to SD is not reached the cutoff line in 1.00. *UCL = 95 % upper confidence limit, LCL = 95 % lower confidence limit

Discussion

After 1995, nutrition labeling was mandated by the South Korean government to improve consumer information regarding food purchases. Its expansion since then is expected to positively impact the overall health status in South Korea, especially in patients with certain chronic diseases [10]. Thus, we hypothesized that awareness of nutrition labeling significantly affects diet-related health status, particularly dyslipidemia, and explored possible associations between awareness level and risk of dyslipidemia in individuals not yet diagnosed.

Our findings indicate that a higher awareness level was inversely related to the risk of dyslipidemia, especially with respect to TG and HDL cholesterol indicators [22]. Previous studies have already shown that nutrition labeling is positively associated with patient self-management of chronic diseases, such as the changing of their dietary habits. In addition, introducing nutrition labeling may reduce obesity and promote certain healthy behaviors [10, 23]. However, simply introduction of the labeling cannot be effective without a detailed review of how people perceive and use the system [24]. Therefore, we focused on people’s self-reported awareness level of nutrition labeling rather than only examining the effects of its initial implementation. We observed similar trends to those in previous studies, but considering the poor management of dyslipidemia and mortality due to CVD in many patients, our findings could provide an effective prophylactic alternative for control of dyslipidemia.

Our subgroup analysis showed other interesting findings, such as the positive impact of higher labeling awareness in younger individuals, likely due to their general concern regarding diet choices [25]. Therefore, more public health promotion of nutrition labeling should be provided for elderly populations. Differences by sex regarding the impact of nutrition labeling were significant in only males. This also similar with reason due to age, the females had more attention for manage their health and body shape than males. In addition, there were greater impact by higher awareness of nutrition labelling than others. The nutrition labelling system in South Korea was applied into food materials for home cooking as well as meals sold by a restaurant. Based on results, the introduction of food labelling system in South Korea might be helpful in improving the health behavior of South Korean when choice the food materials for home cooking rather than eating out. Also, such results might be caused by differences of attention for health, because the people with less eating out had more attention for manage their and their family’s health. Because nutrition labeling appeared to have a greater impact in individuals with lower educational level, perhaps introduction of the system has improved accessibility of health information for economically vulnerable populations [25]. The impact was also greater in individuals with poor health, such as those with obesity [13]. These results should motivate health professionals and policymakers to consider the positive effects of nutrition labeling awareness when establishing health policies or programs for specific populations [26]. Moreover, by promoting the advantages of nutrition labeling awareness, we expected that more remarkable improvements of health status in South Korean will be observed.

Our study had several strengths compared with previous studies. First, we used nationwide sampling data during a 5-year period, so our results are helpful in establishing long-term health policy at the national level. Second, to our knowledge, our study is the first to specifically investigate the association between awareness and utilization of nutrition labeling information and the risk of dyslipidemia in South Korean individuals. Third, our results suggest that public perception of new health policies is important for determining their long-term success rather than only shortly after their introduction [24, 27]. Finally, we considered socioeconomic status and health behaviors, such as smoking, alcohol intake, fat intake, and aerobic workout habits, to minimize the effects of confounding variables on our observed results.

However, our study also has limitations. Because the data used in this study were cross-sectional, rather than longitudinal, some concerns about causal relationships between labeling awareness and outcome variables were present. To minimize these concerns, we excluded respondents who were already diagnosed with dyslipidemia and defined dyslipidemia based on their results on the day of investigation. Second, we calculated the respondents’ LDL cholesterol levels using the Friedewald formula because these data were not directly collected as part of our study [28]. The indirect measurement of LDL cholesterol may result in underestimation, so some LDL cholesterol-related results may not be accurate. Finally, the impact of labeling awareness led to some inconsistent trends with some indicators, possibly due to the method of measurement used. Therefore, further studies using data with more detailed measurements are needed.

Despite such limitations, our findings suggest that high awareness and active utilization of nutrition labeling were inversely associated with risk of dyslipidemia, especially in vulnerable populations and younger participants, as they may be more attentive to their health status than others. Based on these results, health policymakers and professionals should consider promoting nutrition labeling awareness as an alternative for managing dyslipidemia in South Korean patients.

Conclusion

The awareness of nutrition labeling had positive outcomes for TG and HDL cholesterol levels related to dyslipidemia. In addition, the active utilization of nutrition labeling was associated with a low risk of dyslipidemia. Based on our findings, health policymakers and professionals should develop effective alternatives such as promoting the use of nutrition labeling for the management of chronic diseases in South Korea.

Abbreviations

ANOVA: 

Analysis of variance

BMI: 

Body mass index

CI: 

Confidence interval

CVD: 

Cardiovascular diseases

HDL: 

High-density lipoprotein

KCDC: 

Korea Centers for disease control and prevention

KNHANES: 

Korea National Health and Nutrition Examination Surveys

LDL: 

Low-density lipoprotein

OR: 

Odds ratio

SD: 

Standard deviation

SE: 

Standard error

TC: 

Total cholesterol

TG: 

Triglyceride

Declarations

Acknowledgement

No specific funding supported this study.

Availability of data and materials

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.

Authors’ contributions

JYK, KHK, and MJK designed the study, collected data, performed statistical analyses, and wrote the manuscript. SYJ, ECP, and KTH contributed to the discussion and reviewed and edited the manuscript. KTH is the guarantor of this work and as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The text in this document has been checked by at least two professional editors who are native English speakers. In addition, WK provided re-editing services for our manuscript to improve quality of scientific writing. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

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, 2013-07CON-03-4C, and 2014-12EXP-03-5C).

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

Authors’ Affiliations

(1)
Premedical Courses, Yonsei University College of Medicine
(2)
Department of Preventive Medicine, Yonsei University Graduate School
(3)
Institute of Health Services Research, Yonsei University
(4)
Department of Public Health, Graduate School, Yonsei University

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Copyright

© The Author(s). 2016

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