J Korean Diabetes Assoc.  2006 May;30(3):177-189. 10.4093/jkda.2006.30.3.177.

Clustering Characteristics of Risk Variables of Metabolic Syndrome in Korean Rural Populations

• Park YM
• Kwon HS
• Lim SY
• Lee JH
• Kim SR
• Yoon KH
• Cha BY
• Son HY
• Park YG
• Kim DS
• Meng KH
• Lee WC

• KMID: 2177610
• DOI: http://doi.org/10.4093/jkda.2006.30.3.177

Abstract

BACKGROUND: The risks of both type 2 diabetes mellitus and cardiovascular disease are mainly associated with the metabolic syndrome which is characterized by clustering of metabolic risk factors, including abdominal obesity, glucose intolerance, hypertension, and dyslipidemia. This study aimed to examine the relations among metabolic risk variables and the underlying structure of the metabolic syndrome that unites related components.
METHODS
Subjects were selected by stratified random cluster sampling among persons aged over 40 years from a rural area. Waist circumference, BMI, fasting glucose, fasting insulin, triglycerides, HDL cholesterol, systolic blood pressure, and diastolic blood pressure were used as risk variables of metabolic syndrome. Factor analysis, a multivariate correlation statistical technique, was performed on a dataset from nondiabetic 3,443 men and women without history of coronary heart disease.
RESULTS
Exploratory factor analysis identified three factors in both gender (obesity, hypertension, and dyslipidemia-insulin resistance in men; obesity-insulin resistance, hypertension, and dyslipidemia in women). Fasting insulin was a common contributor to the structure of metabolic syndrome in male subjects, smokers and alcohol drinking group. Confirmatory factor analysis based on the results of exploratory factor analysis revealed that metabolic syndrome was represented primarily by obesity factor in men, obesity-insulin resistance factor in women, and that dyslipidemia factor was highly correlated with obesity factor in men, with insulin resistance factor in women.
CONCLUSION
Underlying structure of metabolic syndrome was different between men and women, and obesity might be a primary target for prevention of both type 2 diabetes mellitus and cardiovascular disease in Korea.

Keyword

Confirmatory factor analysis; Exploratory factor analysis; Metabolic syndrome; Obesity

MeSH Terms

Alcohol Drinking
Blood Pressure
Cardiovascular Diseases
Cholesterol, HDL
Cluster Analysis*
Coronary Disease
Dataset
Diabetes Mellitus, Type 2
Dyslipidemias
Fasting
Female
Glucose
Glucose Intolerance
Humans
Hypertension
Insulin
Insulin Resistance
Korea
Male
Obesity
Obesity, Abdominal
Risk Factors
Rural Population*
Triglycerides
Waist Circumference
Cholesterol, HDL
Glucose
Insulin
Triglycerides

Figure

• 

  Fig. 1 Confirmatory factor analysis on three factor model of metabolic syndrome based on the results of exploratory factor analysis in male subjects, with χ2 = 58.449 (df = 12, P = 0.000), CFI = 0.983, NNFI = 0.970, and RMSEA (90%CI) = 0.053 (0.040-0.067).

• 

  Fig. 2 Confirmatory factor analysis on three factor model of metabolic syndrome based on the results of exploratory factor analysis in female subjects, with χ2 = 123.472 (df = 17, P = 0.000), CFI = 0.972, NNFI = 0.954, and RMSEA (90%CI) = 0.055 (0.046-0.065).

• 

  Fig. 3 Confirmatory factor analysis of interfactor correlation model of metabolic syndrome based on the results of exploratory factor analysis in male subjects, with χ2 = 58.449 (df = 12, P = 0.000), CFI = 0.983, NNFI = 0.970, and RMSEA (90%CI) = 0.053 (0.040-0.067).

• 

  Fig. 4 Confirmatory factor analysis of interfactor correlation model of metabolic syndrome based on the results of exploratory factor analysis in female subjects, with χ2 = 123.472 (df = 17, P = 0.000), CFI = 0.972, NNFI = 0.954, and RMSEA (90%CI) = 0.055 (0.046-0.065).

Reference

1. Reaven GM. Role of insulin resistance in human disease. Diabetes. 1988. 37:1595–1607.

2. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus-provisional report of a WHO consultation. Diabet Med. 1998. 15:539–553.

3. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of the National Cholesterol Education Program (NCEP) Adult Treatment Panel III. JAMA. 2001. 285:2486–2497.

5. Hanson RL, Imperatore G, Bennett PH, Knowler WC. Components of the "Metabolic Syndrome" and incidence of type 2 diabetes. Diabetes. 2002. 51:3120–3127.

6. Lindblad U, Langer RD, Wingard DL, Thomas RG, Barrett-Connor EL. Metabolic syndrome and ischemic heart disease in elderly men and women. Am J Epidemiol. 2001. 153:481–489.

7. Lakka HM, Laaksonen DE, Lakka TA, Niskanen LK, Kumpusalo E, Tuomilehto J, Salonen JT. The Metabolic Syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA. 2002. 288:2709–2716.

8. Anderson PJ, Critchley JA, Chan JC, Cockram CS, Lee ZS, Thomas GN, Tomlinson B. Factor analysis of the metabolic syndrome: obesity vs insulin resistance as the central abnormality. Int J Obes. 2001. 25:1782–1788.

9. Timar O, Sestier H, Levy E. Metabolic syndrome X: a review. Can J Cardiol. 2000. 16:779–789.

10. Shen BJ, Todaro JF, Niaura R, McCaffery JM, Zhang J, Spiro A 3rd, Ward KD. Are metabolic risk factors one unified syndrome? Modeling the structure of the metabolic syndrome X. Am J Epidemiol. 2003. 157:701–711.

11. Meigs JB. Invited commentary: insulin resistance syndrome? Syndrome X? multiple metabolic Syndrome? A syndrome at all? Fator analysis reveals patterns in the fabric of correlated metabolic risk factors. Am J Epidemiol. 2000. 152:908–911.

12. Edwards KL, Austin MA, Newman B, Mayer E, Krauss RM, Selby JV. Multivariate analysis of the insulin resistance syndrome in women. Arterioscler Thromb. 1994. 14:1940–1945.

13. Meigs JB, D'Agostino RB Sr, Wilson PW, Cupples LA, Nathan DM, Singer DE. Risk variable clustering in the insulin resistance syndrome. The Framingham Offspring Study. Diabetes. 1997. 46:1594–1600.

14. Sakkinen PA, Wahl P, Cushman M, Lewis MR, Tracy RP. Clustering of procoagulation, inflammation, and fibrinolysis variables with metabolic factors in insulin resistance syndrome. Am J Epidemiol. 2000. 152:897–907.

15. Hanley AJ, Karter AJ, Festa A, D'Agostino R Jr, Wagenknecht LE, Savage P, Tracy RP, Saad MF, Haffner S. Factor analysis of metabolic syndrome using directly measured insulin sensitivity: The Insulin Resistance Atherosclerosis Study. Diabetes. 2002. 51:2642–2647.

16. Lempiainen P, Mykkanen L, Pyorala K, Laakso M, Kuusisto J. Insulin resistance syndrome predicts coronary heart disease events in elderly nondiabetic men. Circulation. 1999. 100:123–128.

18. Choi KM, Lee J, Kim KB, Kim DR, Kim SK, Shin DH, Kim NH, Park IB, Choi DS, Baik SH. Factor analysis of the metabolic syndrome among elderly Koreans-the South-west Seoul Study. Diabet Med. 2003. 20:99–104.

21. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972. 18:499–502.

22. Dallongeville J, Cottel D, Arveiler D, Tauber JP, Bingham A, Wagner A, Fauvel J, Ferrieres J, Ducimetiere P, Amouyel P. The Association of metabolic disorders with the metabolic syndrome is different in men and women. Ann Nutr Metab. 2004. 48:43–50.

23. Kaiser HF. The application of electrionic computers to factor analysis. Educational and Psychological Measurement. 1960. 20:141–151.

24. Manly BFJ. Manly BFJ, editor. Factor analysis. Multivariate statistical methods. 1994. London: Chap-man & Hall;93–106.

25. Hu LT, Bentler . Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling. 1999. 6:1–55.

27. Zimmet P. The pathogenesis and prevention of diabetes in adults. Genes, autoimmunity, and demography. Diabetes Care. 1995. 18:1050–1064.

28. Laakso M. How good a marker is insulin level for insulin resistance? Am J Epidemiol. 1993. 137:959–965.

29. Pyorala M, Miettinen H, Halonen P, Laakso M, Pyorala K. Insulin resistance syndrome predicts the risk of coronary heart disease and stoke in healthy middle-aged men. Arterioscler Thromb Vasc Biol. 2000. 20:538–544.

30. Reaven GM. The metabolic syndrome or the insulin resistance syndrome? Different names, different concepts, and different goals. Endocrinol Metab Clin North Am. 2004. 33:283–303.

31. Chen CH, Lin KC, Tsai ST, Chou P. Different association of hypertension and insulin-related metabolic syndrome between men and women in 8,437 nondiabetic Chinese. Am J Hypertens. 2000. 13:846–853.

32. Oh JY, Hong YS, Sung YA, Barrett-Connor E. Prevalence and factor analysis of metabolic syndrome in an urban Korean population. Diabetes Care. 2004. 27:2027–2032.

33. Gray RS, Fabsitz RR, Cowan LD, Lee ET, Howard BV, Savage PJ. Risk factor clustering in the insulin resistance syndrome. The Strong Heart Study. Am J Epidemiol. 1998. 148:869–878.

34. Lawlor DA, Ebrahim S, May M, Davey Smith G. (Mis)use of Factor analysis in the study of insulin resistance syndrome. Am J Epidemiol. 2004. 159:1013–1018.

35. Smith U. Smoking elicits the insulin resistance syndrome: new aspects of the harmful effect of smoking. J Intern Med. 1995. 237:435–437.

36. Facchini FS, Hollenbeck CB, Jeppesen J, Chen YD, Reaven GM. Insulin resistance and cigarette smoking. Lancet. 1992. 339:1128–1130.

37. Vgontzas AN, Bixler EO, Chrousos GP. Metabolic disturbances in obesity versus sleep apnea: the importance of visceral obesity and insulin resistance. J Intern Med. 2003. 254:32–44.

38. Grundy SM. What is the contribution of obesity to the metabolic syndrome? Endocrinol Metab Clin North Am. 2004. 33:267–282.

39. Hong YS, Kim BG, Jeong BG, Park YW, Park JT, Jung KY, Kim JY. Prevalence of Metabolic Syndrome and Waist Circumference Estimation in Some Korean Adults. J Prev Med Public Health. 2003. 37:51–58.

41. Despres JP. Abdominal obesity as important component of insulin-resistance syndrome. Nutrition. 1993. 9:452–459.

42. Hair JF Jr, Anderson RE, Tatham RL, Black WC. Multivariate data analysis. 1998. 5th ed. Prentice-Hall International.

43. Michels KB, Greenland S, Rosner BA. Does body mass index adequately capture the relation of body composition and body size to health outcomes? Am J Epidemiol. 1998. 147:167–172.

44. Kronmal RA. Spurious correlation and the fallacy of the ratio standard revisited. J Roy Statist Soc A. 1993. 156:379–392.