The Relationship between Socioeconomic Status and Metabolic Syndrome, Using Structural Equation Modelling

Sung Hi Kim

doi:10.15384/kjhp.2016.16.2.92

Korean J Health Promot > Volume 16(2); 2016 > Article

Kim: The Relationship between Socioeconomic Status and Metabolic Syndrome, Using Structural Equation Modelling

Original Article

Korean Journal of Health Promotion 2016;16(2):92-100.

Published online: January 20, 2016

DOI: https://doi.org/10.15384/kjhp.2016.16.2.92

The Relationship between Socioeconomic Status and Metabolic Syndrome, Using Structural Equation Modelling

Sung Hi Kim

Department of Family Medicine, Daegu Catholic University Hospital, Daegu, Korea

Corresponding author: Sung Hi Kim, MD, PhD Department of Family Medicine, Daegu Catholic University Hospital, 33 Duryugongwon-ro 17-gil, Nam-gu, Daegu 42472, Korea
Tel: +82-53-650-4247, Fax: +82-53-650-4122 E-mail: khmksh@cu.ac.kr

Received January 20, 2016 Accepted May 17, 2016

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

ABSTRACT

Background

This study aimed to investigate the complexity of the relationships between socioeconomic status, health behaviors, stress and risks of metabolic syndrome. By applying structural equational modelling, modified generalized conceptual model had described the associations and interactions among them.

Methods

24,210 participants (8,242 men, 15,968 women) registered with the Korean Health Examinee Cohort (KOEX) between 2004 to 2008. This study collected data on the socioeconomic status, health behaviors, and stress through individual interviews. Socioeconomic status (education, house income, occupation), health behaviors (diet, physical activity, smoking, alcohol consumption) and stress level were defined as exogenous factors. Endogenous variables were risks of metabolic syndrome based on modified National Cholesterol Education Program Adult Treatment Panel-lll (NCEP ATP-lll, 2006).

Results

According to model fitness test, these proposed model were acceptable in men, comparative fit in-dex=0.877, incremental fit Index=0.877, Tucker-Lewis index=0.757, root-mean-squared error associated=0.055. These were compatible also in women as comparative fit index=0.924, incremental fit index=0.924, Tucker-Lewis index=0.851, root-mean-squared error associated=0.050. There were sex difference related to risks of metabolic syndrome: in men socioeconomic status (β=-0.08), health behaviors (β=-0.25) and stress (β =-0.25) were relevant, but in women only socioeconomic status (β=-0.25) was relevant (P<0.05).

Conclusions

A conceptual model properly explains how Socioeconomic status may influence on health behaviors, stress, and risks of metabolic syndrome. Health behaviors in men and socioeconomic status in women had the strongest associations with risks of metabolic syndrome. In addition, socioeconomic status was strongly associated with health behaviors, stress and age.

Keywords: Socioeconomic status, Metabolic syndrome

Figure 1.

Modified generalized conceptual model of potential pathway through which social status is linked to health.

Figure 2.

Modified conceptual model; a summary of the interactions of Socioeconomic status (SES) influencing metabolic syndrome risk, in men Path coefficients are presented as standardised regression weight; dashed lines represent relationships that were non significant, model fitincluded in the exploratory analysis, but were non-significant. Model fit: X²=40.395, d.f=28, P<0.001, CFI=0.924, IFI=0.924 TLI=0.851, RMSEA=0.050.

Figure 3.

Modified conceptual model; a summary of the interactions of Socioeconomic status (SES) influencing metabolic syndrome risk, in men path coefficients are presented as standardised regression weight; dashed lines represent relationships that were non significant, model fit included in the exploratory analysis, but were non-significant. Model fit: X²=35.843, d.f=28, P<0.001, CFI=0.877, IFI=0.877, TLI=0.757, RMSEA=0.055.

Table 1.

Health behaviors, stress, and socioeconomic status of study population

Variables		Total (n=24,210)
Variables		Men (n=8,242)	Women (n=15,968)	P
General characteristics and health behaviors^a
Age, y		52.9±8.1	50.8±7.1	<0.001
Smoking, pack years		15.4±16.0	0.2±1.9	<0.001
Alcohol intake^b, g/day		25.9±36.5	2.4±8.8	<0.001
Exercise^c, Kcal/day		201.0±250.9	180.3±203.3	<0.001
Diet score^d		13.9±2.7	15.7±2.9	<0.001
Score of psychosocial wellbeing index (PWI)^e		16.0±7.8	18.5±8.2	<0.001
Socioeconomic status^e
Education, y	None	49 (0.6)	207 (1.3)	<0.001
	< 6	66 (0.8)	271 (1.7)
	6–8	626 (7.6)	2,187 (13.7)
	9–11	963 (11.7)	2,763 (17.3)
	12–13	2,840 (34.5)	6,324 (39.6)
	14	338 (4.1)	479 (3.0)
	15	247 (3.0)	335 (2.1)
	16–17	2,273 (27.6)	2,907 (18.2)
	≥ 18	840 (10.1)	495 (3.1)
House income, 10,000 won	<50 50–99	362 (4.4) 342 (4.1)	984 (6.2) 1,022 (6.4)	<0.001
	100–149	666 (8.1)	1,435 (9.0)
	150–199	812 (9.9)	1,472 (9.2)
	200–299	1,886 (22.9)	3,399 (21.3)
	300–300	1,777 (21.6)	3,109 (19.5)
	400–599	1,643 (19.9)	3,103 (19.4)
	≥600	754 (9.1)	1,444 (9.0)
Job	Managers, Professionals	1,541 (18.7)	1,319 (8.3)	<0.001
	Office workers	1,285 (15.6)	855 (5.3)
	Sales workers	729 (8.8)	1,705 (10.7)
	Service workers	651 (7.9)	917 (5.7)
	Operatives	2,724 (33.1)	1,712 (10.7)
	Soldier	82 (1.0)	0
	House wife	0	9,197 (57.6)
	Unemployed	1,183 (14.3)	137 (0.9)
	Non response	47 (0.6)	126 (0.8)

Values were presented as mean±SD or n (%).

^aAanalyzed by t-test.

^bDaily alcohol intake.

^cLeisure-time physical activity.

^dBased on daily consumption amount of 5 food groups (fruits, vegetables, crude fibers, red meats and sodium) from Food Frequency Questionnaire (FFQ).

^eAnalyzed by chi-square test.

Table 2.

Comparison of metabolic syndrome and it's components, and prevalence of diabetes and cardiovascular disease according to gender

Variables	Total (n=24,210)
Variables	Men (n=8,242)	Women (n=15,968)	P
Component of MetS, BMI and number of MetS component^a
Waist	85.9±7.3	78.9±7.8	<0.000
BP systolic, mmHg	125.9±14.8	118.9±15.2	<0.001
diastolic, mmHg	79.1±10.2	73.8±10.0	<0.001
TG, mg/dL	150.0±105.7	105.7±69.1	<0.001
FBS, mg/dL	95.4±16.6	89.6±14.1	<0.001
HDL, mg/dL	49.2±11.5	56.2±12.4	<0.001
BMI, kg/m²	24.4±2.7	23.5±2.8	<0.001
Number of MetS	1.6±1.2	1.1±1.1	<0.001
Prevalence of chronic disease and metabolic syndrome^b
CVA	140 (1.7)	128 (0.8)	<0.001
MI	297 (3.6)	287 (1.8)	<0.001
Diabetes	717 (8.7)	737 (4.6)	<0.001
MetS	2,291 (27.8)	2,650 (16.6)	<0.001

Abbreviations: Mets, metabolic syndrome; BMI, body mass index; BP, blood pressure; TG, triglyceride; FBS, fasting blood sugar; HDL, high density lipoprotein cholesterol; CVA, cerebrovasculr accident; MI, myocardial infarction. Values were presented as mean±SD or n (%).

^aAnalyzed by t-test.

^bAnalyzed by chi-square test.

Table 3.

Confirmatory factor analysis and pathway analysis: regression weight and standarized regression weight for measures of Metabolic syndrome risk

Variables	Total (n=24,210)
	Men (n=8,242)			Women (n=15,968)
	Regression	Standarized	P	Regression	Standarized	P
	weight	regression weight	P	weight	regression weight	P
Factor loading of latent variables
SES
Job	1	0.63	<0.001	1	0.35	<0.001
House income	0.14	0.71	<0.001	0.29	0.71	<0.001
Education	0.13	0.65	<0.001	0.27	0.72	<0.001
Health behavior
Diet score^a	1	0.37	<0.001	1	0.36	<0.001
LTPA, Kcal/day	75.69	0.31	<0.001	56.32	0.28	<0.001
Pack year	4.10	0.26	<0.001	0.30	0.16	<0.001
Alcohol intake^b, g/day	10.8	0.30	<0.001	1.74	0.20	<0.001
Pathway coefficients
SES
Age	–0.52	–0.45	<0.001	0.39	–0.57	<0.001
Health behavior
Age	0.05	0.37	<0.001	0.06	0.14	<0.001
SES ^c	0.04	0.37	<0.001	0.07	0.32	<0.001
Stress^c	–0.04	–0.30	<0.001	–0.04	–0.33	<0.001
Stress^c
Age	–0.15	–0.16	<0.001	–0.18	–0.16	<0.001
SES	–0.16	–0.20	<0.001	–0.37	–0.22	<0.001
MetS risk
Age	0.02	0.15	<0.001	0.03	0.19	<0.001
SES	0.01	0.07	<0.001	–0.05	–0.20	<0.001
Health behavior	–0.31	–0.25	<0.001	–0.01	–0.01	0.54
Stress^c	–0.01	–0.08	<0.001	–0.00	–0.02	0.06

Abbreviations: SES, socioeconomic status; LTPA, leisure time physical activity; MetS, metabolic syndrome.

^aBased on daily consumption amount of 5 food groups(fruits, vegetables, crude fibers, red meats, and sodium) from Food Frequency Questionnaire (FFQ).

^bDaily alcohol intake.

^cPsychosocial wellbeing index.

References

1. Tamashiro KL. Metabolic syndrome: links to social stress and socioeconomic status. Ann N Y Acad Sci 2011;1231:46-55.

2. Story M. Kaphingst KM, Robinson-O'Brien R, Glanz K. Creating healthy food and eating environments: policy and environmental approaches Annu Rev Public Health 2008;29:253-72.

3. Sallis JF, Cervero RB, Ascher W, Henderson KA, Kraft MK, Kerr J. An ecological approach to creating active living communities. Annu Rev Public Health 2006;27:297-322.

4. Gruenewald TL, Karlamangla AS, Hu P, Stein-Merkin S, Crandall C, Koretz B, et al. History of socioeconomic disadvantage and allostatic load in later life. Soc Sci Med 2012;74:75-83.

5. Kondo N, Sembajwe G, Kawachi I, van Dam RM, Subramanian SV, Yamagata Z. Income inequality, mortality, and self rated health: metaanalysis of multilevel studies. BMJ 2009;339:b447 1..

6. Steven HW, Steven J, Evonne KL. Health promotion and disease prevention in clinical practice. 2nd edition.. Philadelphia: Lippincott Williams & Wilkins; 2007. p. 5-7.

7. Braveman PA, Cubbin C, Egerter S, Chideya S, Marchi KS, Metzler M, et al. Socioeconomic status in health research: one size does not fit all. JAMA 2005;294:2879-88.

8. Wilkinson RG, Pickett KE. The problems of relative deprivation: why some societies do better than others. Soc Sci Med 2007;65:1965-78.

9. Baumann M, Spitz E, Guillemin F, Ravaud JF, Choquet M, Falissard B, et al. Association of social and material deprivation with tobacco, alcohol and psychotropic use and gender: a population study. Int J Health Geogr 2007;6:50-62.

10. Matthews KA, Räikkönen K, Gallo L, Kuller LH. Association between socioeconomic status and metabolic syndrome in women: testing the reserve capacity model. Health Psychol 2008;27:576-83.

11. Amiri P, Deihim T, Taherian R, Karimi M, Gharibzadeh S, Asghari-Jafarabadi M, et al. Factors affecting gender differences in the association between health-related quality of life and metabolic syndrome components: Tehran Lipid and Glucose Study. PLoS One 2015;10(12):): e0143167..

12. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the international Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009;120(16):);. 1640-5.

13. The Korean society for equity in health. Methods in health inequalities measurement. Paju: Hanul; 2008. p. 70-83.

14. Kim DH. Effect of Job and leisure time physical activity in the risk of colorectal cancer: a case-control study [disseration]. Seoul: Seoul National University; 1997. Korean..

15. Lee CY, Lee JY. Reliability and validity of PWI (psychosocial wellbeing index). Korean J Prev Med 1996;29(2):255-64.

16. Yu JP. Concept and understanding of structural equation modelling. 1st ed.. Seoul: Hannarae publishers; 2012. p. 360-9.

17. Lynch JW, Kaplan GA. Socioeconomic position: Social epidemiology. New York: Oxford University Press; 2000. p. 13-35.

18. Ford ES, Zhao G, Tsai J, Li C. Low-risk lifestyle behaviors and all-cause mortality: findings from the National Health and Nutrition Examination Survey III Mortality Study. Am J Public Health 2011;101(10):1922-9.

19. Elwood P, Galante J, Pickering J, Palmer S, Bayer A, Ben-Shlomo Y, et al. Healthy lifestyles reduce the incidence of chronic diseases and dementia: evidence from the Caerphilly cohort study. PLoS One 2013;8(12):): e81877..

20. Silventoinen K, Pankow J, Jousilahti P, Hu G, Tuomilehto J. Educational inequalities in the metabolic syndrome and coronary heart disease among middle-aged men and women. Int J Epidemiol 2005;34(2):327-34.

21. S⊘ltoft F, Hammer M, Kragh N. The association of body mass index and health-related quality of life in the general population: data from the 2003 Health Survey of England. Qual Life Res 2009;18(10):1293-9.

22. Chen YC, Wu HP, Hwang SJ, Li IC. Exploring the components of metabolic syndrome with respect to gender difference and its relationship to health-promoting lifestyle behaviour: a study in Taiwanese urban communities. J Clin Nurs 2010;19(21–22):3031-41.

23. Lim H, Nguyen T, Choue R, Wang Y. Sociodemographic disparities in the composition of metabolic syndrome components among adults in South Korea. Diabetes Care 2012;35(10):2028-35.

24. Park MY, Kim SH, Cho YJ, Chung RH, Lee KT, Association of leisure time physical activity and metabolic syndrome over 40 years. Korean J Fam Med 2014;35(2):65-73.

25. Kim SH, Park JY, Kim DH. Socioeconomic status and health behaviors associated with metabolic syndrome in adults over 40 years. korean J Health Promot 2013;13(4):125-32.

26. Ferguson TF, Funkhouser E, Roseman J. Factor analysis of metabolic syndrome components in the Coronary Artery Risk Development in Young Adults (CARDIA) study: examination of factors by race-sex groups and across time. Ann Epidemiol 2010;20(3):194-200.

27. Judge TA, Cable DM. When it comes to pay, do the thin win? The effect of weight on pay for men and women. J Appl Psychol 2011;96(1):95-112.

28. Bleich SN, Jarlenski MP, Bell CN, LaVeist TA. Health inequalities: trends, progress, and policy. Annu Rev Public Health 2012;33:7-40.

29. Sapolsky RM. Why Zebras Don't Get Ulcers. 3rd ed.. New York: Holt Paperbacks; 2004. p. 9-12.

30. Hendrie GA, Coveney J, Cox DN. Defining the complexity of childhood obesity and related behaviours within the family environment using structural equation modeling. Public Health Nutr 2012;15(1):48-57.