Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2016 Sep 15;15(1):81.
doi: 10.1186/s12937-016-0200-y.

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

Jong Yeob Kim  1 , Ki Hong Kweon  1 , Min Jae Kim  1 , Eun-Cheol Park  2   3 , Suk-Yong Jang  2   3 , Woorim Kim  3   4 , Kyu-Tae Han  5   6
Affiliations

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

Jong Yeob Kim et al. Nutr J. .

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: Dyslipidemia; Health policy perception; Hyperlipidemia; Nutrition labeling.

PubMed Disclaimer

Figures

Fig. 1
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
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
See this image and copyright information in PMC

References

    1. Kim E-J, Yoon S-J, Jo M-W, Kim H-J. Measuring the burden of chronic diseases in Korea in 2007. Public Health. 2013;127(9):806–13. doi: 10.1016/j.puhe.2012.12.024. - DOI - PubMed
    1. Thankappan K, Shah B, Mathur P, Sarma P, Srinivas G, Mini G, Daivadanam M, Soman B, Vasan RS. Risk factor profile for chronic non-communicable diseases: results of a community-based study in Kerala, India. Indian J Med Res. 2010;131(1):53. - PubMed
    1. Egede LE. Effect of comorbid chronic diseases on prevalence and odds of depression in adults with diabetes. Psychosom Med. 2005;67(1):46–51. doi: 10.1097/01.psy.0000149260.82006.fb. - DOI - PubMed
    1. Zimmet P, King H, Björntorp S. Obesity, hypertension, carbohydrate disorders and the risk of chronic diseases. Is there any epidemiological evidence for integrated prevention programmes? Med J Aust. 1986;145(6):256–9. - PubMed
    1. Genest J, McPherson R, Frohlich J, Anderson T, Campbell N, Carpentier A, Couture P, Dufour R, Fodor G, Francis GA. 2009 Canadian Cardiovascular Society/Canadian guidelines for the diagnosis and treatment of dyslipidemia and prevention of cardiovascular disease in the adult–2009 recommendations. Can J Cardiol. 2009;25(10):567–79. doi: 10.1016/S0828-282X(09)70715-9. - DOI - PMC - PubMed

MeSH terms