, Volume 63, Issue 1, pp 52–61 | Cite as

Analysis of risk factors of metabolic syndrome using a structural equation model: a cohort study

  • Zhimin Ma
  • Ditian Li
  • Siyan Zhan
  • Feng Sun
  • Chaonan Xu
  • Yunfeng Wang
  • Xinghua YangEmail author
Original Article



We aimed to use a structural equation model (SEM) to determine the interrelations between various risk factors, including latent variables, involved in the development of metabolic syndrome(MetS).


This study used data derived from the MJ Longitudinal Health Check-up Population Database for participants aged 20 to 70 years, who were asymptomatic for MetS at enrollment and were followed up for 5 years. A SEM was applied to investigate the attributions of MetS and the interrelations between different risk factors.


Socioeconomic status (SES), living habits, components of metabolic syndrome (COMetS), and blood pressure had a diverse impact on the onset of MetS, directly and (or) indirectly. When investigating the latent risk factors and the interrelations between different risk factors. The standardized total effect (the sum of the direct and indirect effects, βt) of SES, living habits, blood pressure and COMetS on the onset of MetS was 0.084, −0.179, 0.154, and 0.353, respectively. SES, as a distal risk factor, directly influenced living habits, blood pressure, and COMetS with standardized regression coefficients (βr) of −0.079 (P < 0.001), 0.200 (P < 0.001), and −0.163 (P < 0.001) respectively. Unfavorable living habits exerted an inverse effect on blood pressure and COMetS (βr = −0.101, P < 0.001; βr = −0.463, P < 0.001), which was an important path way for developing MetS.


These results demonstrate that individuals with a higher level of SES are susceptible to high blood pressure and are at increased risk for MetS. Additionally, there is a decrease in exercise and an increase in smoking and consumption of alcohol corresponded to an increase in metabolic risk factors.


Metabolic syndrome Risk factors Structural equation model Socioeconomic status 



All authors thank MJ Health Management Institution for making their large dataset available to us. We thanks all people participating in the MJ Longitudinal Health study.

Compliance with ethical standards

Ethical approval

The Peking University Institutional Review Board approved this study. Since it eliminated all identifiable personal information, it does not belong to studies involving human beings. Consequently, this Board waived the requirement for informed consent and ethical review of the study. (Authorization Code: MJHRFB2014003C).

Informed consent

The requirement for informed consent was waived for this study.

Conflict of interest

The authors declare that no competing interests exist.

Supplementary material

12020_2018_1718_MOESM1_ESM.doc (47 kb)
Supplementary Figure 1
12020_2018_1718_MOESM2_ESM.doc (28 kb)
Supplementary Table 1


  1. 1.
    K.G. Alberti, R.H. Eckel, S.M. Grundy et al.. Harmonizing the metabolic syndrome: A joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Preventiony. Circulation 120, 1640–1645 (2009)CrossRefGoogle Scholar
  2. 2.
    J. Kang, Y.M. Song, Metabolic syndrome and its components among Korean submariners: a retrospective cross-sectional study. Endocrine 59, 614–621 (2018). CrossRefGoogle Scholar
  3. 3.
    M.Z.I. Chowdhury, A.M. Anik, Z. Farhana et al.. Prevalence of metabolic syndrome in Bangladesh: A systematic review and meta-analysis of the studies. BMC. Public. Health 18, 308 (2018). CrossRefGoogle Scholar
  4. 4.
    D. Junquero, Y. Rival, Metabolic syndrome: Which definition for what treatment(s)? Med. Sci. 21, 1045–1053 (2005). Google Scholar
  5. 5.
    N. Adler, A. Singh-Manoux, J. Schwartz, J. Stewart, K. Matthews, M.G. Marmot, Social status and health: A comparison of British civil servants in Whitehall-II with European-and African-Americans in CARDIA. Soc. Sci. Med. 66, 1034–1045 (2008). CrossRefGoogle Scholar
  6. 6.
    R. Karns, P. Succop, G. Zhang et al.. Modeling metabolic syndrome through structural equations of metabolic traits, comorbid diseases, and GWAS variants. Obesity 21, E745–E754 (2013). CrossRefGoogle Scholar
  7. 7.
    K.A. Bollen, M.D. Noble, Structural equation models and the quantification of behavior. Proc. Natl. Acad. Sci. USA 108, 15639–15646 (2011). CrossRefGoogle Scholar
  8. 8.
    M.M. Smits, P. Woudstra, K.M. Utzschneider et al.. Adipocytokines as features of the metabolic syndrome determined using confirmatory factor analysis. Ann. Epidemiol. 23, 415–421 (2013). CrossRefGoogle Scholar
  9. 9.
    C.M. Stein, Y. Song, R.C. Elston, G. Jun, H.K. Tiwari, S.K. Iyengar, Structural equation based genome scan for the metabolic syndrome. BMC Genet. 4, S99 (2003). CrossRefGoogle Scholar
  10. 10.
    J.C. Chan, J.C. Cheung, E.M. Lau, J. Wooà, A.Y. Chan, R. Swaminathan, C.S. Cockrama, The metabolic syndrome in Hong Kong Chinese. The interrelationships among its components analyzed by structural equation modeling. Diabetes Care 19, 953–959 (1996). CrossRefGoogle Scholar
  11. 11.
    C.L. Cheung, K.C. Tan, K.S. Lam, B.M. Cheung, The relationship between glucose metabolism, metabolic syndrome, and bone-specific alkaline phosphatise: A structural equation modelling approach. J. Clin. Endocrinol. Metab. 98, 3856–3863 (2013). CrossRefGoogle Scholar
  12. 12.
    J.E. Stevenson, B.R. Wright, A.S. Boydstun, The metabolic syndrome and coronary artery disease: A structural equation modeling approach suggestive of a common underlying patho-physiology. Metabolism 61, 1582 (2012). CrossRefGoogle Scholar
  13. 13.
    S. Novak, L.M. Stapleton, J.R. Litaker, K.A. Lawson, PCV18, a confirmatory factor analysis evaluation of the coronary heart disease risk factors of metabolic syndrome and the effectiveness of the current ATP III guidelines for identification. Value Health 6, 312–313 (2003). CrossRefGoogle Scholar
  14. 14.
    J.E. Given, M.J. O’Kane, V.E. Coates, A. Moore, B.P. Bunting, Comparing patient generated blood glucose diary records with meter memory in type 2 diabetes. Diabetes Res. Clin. Pract. 104, 358–362 (2014). CrossRefGoogle Scholar
  15. 15.
    R. Song, S. Ahn, H. Oh, A structural equation model of quality of life in adults with type 2 diabetes in Korea. Appl. Nurs. Res. 26, 116–120 (2013). 10.1016/j.apnr. 2013.04.001CrossRefGoogle Scholar
  16. 16.
    C. Conti, G.D. Francesco, L. Fontanella et al.. Negative affectivity predicts lower quality of life and metabolic control in type 2 diabetes patients: A structural equation modeling approach. Front. Psychol. 8, 831 (2017). CrossRefGoogle Scholar
  17. 17.
    Y.H. Shen, W.S. Yang, T.H. Lee, L.T. Lee, C.Y. Chen, K.C. Huang, Bright liver and alanine aminotransferase are associated with metabolic syndrome in adults. Obes. Res. 13, 1238–1245 (2005). CrossRefGoogle Scholar
  18. 18.
    P.F. Hsu, S.Y. Chuang, H.M. Cheng, S.T. Tsai, P. Chou, C.H. Chen, Clinical significance of the metabolic syndrome in the absence of established hypertension and diabetes: A community-based study. Diabetes Res. Clin. Pract. 79, 461–467 (2008). CrossRefGoogle Scholar
  19. 19.
    C.P. Wen, T.Y. Cheng, M.K. Tsai et al.. All-cause mortality attributable to chronic kidney disease: A prospective cohort study based on 462 293 adults in Taiwan. Lancet 371, 2173 (2008). CrossRefGoogle Scholar
  20. 20.
    C.P. Wen, P. Wai, T. Minkuang et al.. Minimum amount of physical activity for reduced mortality and extended life expectancy: A prospective cohort study. Lancet 378, 1244–1253 (2011). CrossRefGoogle Scholar
  21. 21.
    S.M. Grundy, J.I. Cleeman, S.R. Daniels et al.. Diagnosis and management of the metabolic syndrome: An AHA/NHLBI Scientific Statement. Curr. Opin. Cardiol. 21, 1–6 (2006)CrossRefGoogle Scholar
  22. 22.
    V. Edefonti, F. Bravi, W. Garavello, et al., Nutrient-based dietary patterns and laryngeal cancer: Evidence from an exploratory factor analysis. Cancer Epidemiol. Biomarkers Prev. 19, (2010).
  23. 23.
    L.A. Hayduk, Shame for disrespecting evidence: The personal consequences of insufficient respect for structural equation model testing. BMC. Med. Res. Methodol. 14, 124 (2014). CrossRefGoogle Scholar
  24. 24.
    K.R. Conner, D. Gunzler, W. Tang, X.M. Tu, S.A. Maisto, Test of a clinical model of drinkingand suicidal risk. Alcohol. Clin. Exp. Res. 35, 60 (2011). CrossRefGoogle Scholar
  25. 25.
    C.Y. Huang, C.W. Lu, Y.L. Liu, C.H. Chiang, L.T. Lee, K.C. Huang, Relationship between chr-onic hepatitis B and metabolic syndrome: A structural equation modeling approach. Obesity 24, 483 (2016). CrossRefGoogle Scholar
  26. 26.
    E. Fulu, S. Miedema, T. Roselli, et al., Pathways between childhood trauma, intimate partner violence, and harsh parenting: Findings from the UN Multi-country Study on Men and Viole-nce in Asia and the Pacific. Lancet. Health 5, e512–e522 (2017). CrossRefGoogle Scholar
  27. 27.
    E. Long, S. Xu, Z. Liu et al.. Construction and implications of structural equation modeling network for pediatric cataract: A data mining research of rare diseases. BMC Ophthalmol. 17, 74 (2017). CrossRefGoogle Scholar
  28. 28.
    S.K. Mama, P.M. Diamond, S.A. Mccurdy, A.E. Evans, L.H. Mcneill, R.E. Lee, Individual, social and environmental correlates of physical activity in overweight and obese African American and Hispanic women: A structural equation model analysis. Prev. Med. Rep. 2, 57–64 (2015). CrossRefGoogle Scholar
  29. 29.
    F. Ødegaard, P. Roos, Measuring worksite health promotion programs: An application of structural equation modeling with ordinal data. Eur. J. Health Econ. 14, 639–653 (2013). CrossRefGoogle Scholar
  30. 30.
    M.M. Belvederi, S. Mamberto, L. Briatore, C. Mazzucchelli, M. Amore, R. Cordera, The inter- play between diabetes, depression and affective temperaments: A structural equation model. J. Affect Disord. 219, 64–71 (2017). CrossRefGoogle Scholar
  31. 31.
    L. Fisher, D. Hessler, W. Polonsky et al.. Emotion regulation contributes to the development of diabetes distress among adults with type 1 diabetes. Patient Educ. Couns. 101, 124–131 (2018). CrossRefGoogle Scholar
  32. 32.
    K.J. Preacher, A.F. Hayes, Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav. Res. Methods 40, 879–891 (2008). CrossRefGoogle Scholar
  33. 33.
    A. Ala’A, S. Nicolas, L. Marie-Lise, A. Adelin, Dietary, behavioural and socio-economic determinants of the metabolic syndrome among adults in Luxembourg: Findings from the ORISCAV-LUX study. Public Health Nutr. 15, 849–859 (2012). S1368980011002278CrossRefGoogle Scholar
  34. 34.
    A. Goyal, D.L. Bhatt, P.G. Steg et al.. Attained educational level and incident atherothrombotic events in low- and middle-income compared with high-income countries. Circulation 122, 1167–1175 (2010). CrossRefGoogle Scholar
  35. 35.
    M.A. Winkleby, D.E. Jatulis, E. Frank, S.P. Fortmann, Socioeconomic status and health: How education, income, and occupation contribute to risk factors for cardiovascular disease. Am. J. Public Health 82, 816–820 (1992). CrossRefGoogle Scholar
  36. 36.
    W. Lu, K. Song, Y. Wang et al.. Relationship between serum uric acid and metabolic syndrome: An analysis by structural equation modeling. J. Clin. Lipidol. 6, 159–167 (2012). CrossRefGoogle Scholar
  37. 37.
    Lakka Hanna-Maaria, E.Laaksonen David, TimoA. Lakka et al.. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA 288, 2709–2716 (2002). CrossRefGoogle Scholar
  38. 38.
    F.Y. Shi, W.F. Gao, E.X. Tao, H.Q. Liu, S.Z. Wang, Metabolic syndrome is a risk factor for nonalcoholic fatty liver disease: Evidence from a confirmatory factor analysis and structural equation modeling. Eur. Rev. Med. Pharmacol. Sci. 20, 4313 (2016)Google Scholar
  39. 39.
    M. Santiagotorres, Y. Cui, A.K. Adams, D.B. Allen et al.. Structural equation modeling of the associations between the home environment and obesity-related cardiovascular fitness and insulin resistance among Hispanic children. Appetite 101, 23–30 (2016). CrossRefGoogle Scholar
  40. 40.
    A. Schmitt, A. Reimer, N. Hermanns et al.. Depression is linked to hyperglycaemia via subop-timal diabetes self-management: A cross-sectional mediation analysis. J. Psychosom. Res. 94, 17 (2017). CrossRefGoogle Scholar
  41. 41.
    C.M. Rebholz, M.E. Grams, Y. Chen et al.. Serum levels of 1,5-anhydroglucitol and risk of incident end-stage renal disease. Am. J. Epidemiol. 186, 952–960 (2017). CrossRefGoogle Scholar
  42. 42.
    C.I. Mercado, Q. Yang, E.S. Ford, E. Gregg, A.L. Valderrama, Gender-and race-specific metabolic score and cardiovascular disease mortality in adults: A structural equation modelling approach-United States, 1988-2006. Obesity 23, 1911–1919 (2015). CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Zhimin Ma
    • 1
    • 2
  • Ditian Li
    • 3
  • Siyan Zhan
    • 4
  • Feng Sun
    • 4
  • Chaonan Xu
    • 1
    • 2
  • Yunfeng Wang
    • 1
    • 2
  • Xinghua Yang
    • 1
    • 2
    Email author
  1. 1.School of Public HealthCapital Medical UniversityBeijingChina
  2. 2.Beijing Municipal Key Laboratory of Clinical EpidemiologyBeijingChina
  3. 3.Mailman School of Public HealthColumbia UniversityNew YorkUSA
  4. 4.Department of Epidemiology and Biostatistics, School of Public HealthPeking UniversityBeijingChina

Personalised recommendations