, Volume 57, Issue 5, pp 940–949 | Cite as

Validation of metabolic syndrome score by confirmatory factor analysis in children and adults and prediction of cardiometabolic outcomes in adults

  • Anna Viitasalo
  • Timo A. LakkaEmail author
  • David E. Laaksonen
  • Kai Savonen
  • Hanna-Maaria Lakka
  • Maija Hassinen
  • Pirjo Komulainen
  • Tuomo Tompuri
  • Sudhir Kurl
  • Jari A. Laukkanen
  • Rainer Rauramaa



We validated the metabolic syndrome (MetS) score by confirmatory factor analysis (CFA) in children, middle-aged men, and older women and men and by investigating the relationships of the MetS score to incident type 2 diabetes, myocardial infarction, and cardiovascular and overall death in middle-aged men.


We assessed the core features of MetS, calculated the MetS score using z scores for waist circumference, insulin, glucose, triacylglycerols, HDL-cholesterol and blood pressure, and carried out CFA to investigate whether MetS represents a single entity in population samples of 491 children, 1,900 middle-aged men, 614 older women and 555 older men from Finland. We also followed-up incident type 2 diabetes for 11 years and other outcomes for 17–18 years in middle-aged men.


We carried out second-order CFAs in which the MetS was represented by a second-order latent variable underlying four latent variables characterised by abdominal obesity, insulin resistance, dyslipidaemia and raised blood pressure in different age groups. These second-order factors and factors derived from first-order CFA using previously proposed models were strongly associated with a composite MetS score in all age groups (r = 0.84–0.94) and similarly predicted type 2 diabetes, cardiovascular outcomes and mortality in middle-aged men. The risk of type 2 diabetes, myocardial infarction, cardiovascular death and overall death increased 3.67-, 1.38-, 1.56- and 1.44-fold, respectively, for a 1 SD increase in the MetS score.


The MetS can be described as a single entity in all age groups. The MetS score is a valid tool for research evaluating cardiometabolic risk in different age groups. Further research is needed to define cut-off points for risk estimation in clinical practice.


Cardiovascular Children Confirmatory factor analysis Metabolic syndrome Metabolic syndrome score 



Confirmatory factor analysis


Comparative fit index


Cardiovascular disease


Mean arterial pressure


Metabolic syndrome


Root mean square of approximation



We thank the personnel of the PANIC Study, The KIHD Study and the DR’s EXTRA Study for performing these population-based studies.


The PANIC Study was financially supported by the Ministry of Social Affairs and Health of Finland 1491/9.02.00/2009, the Ministry of Education and Culture of Finland 121/627/2009, the Finnish Innovation Fund Sitra, the Social Insurance Institution of Finland 22/26/2008, the Finnish Cultural Foundation, the Juho Vainio Foundation, the Foundation for Pediatric Research, and the Kuopio University Hospital EVO 5031343. The KIHD Study was supported by grants 41471, 1041086 and 2041022 from the Academy of Finland, 167/722/96, 157/722/97 and 156/722/98 from the Ministry of Education of Finland, HL44199 from the National Heart, Lung, and Blood Institute of the USA, and the City of Kuopio. The DR’s EXTRA Study was supported by grants from the Ministry of Education and Culture of Finland (722 and 627), 2004–2010, the Academy of Finland (102318, 104943, 123885 and 211119), the Social Insurance Institution of Finland (4/26/2010), the European Commission FP6 Integrated Project LSHM-CT-2004-005272, the Päivikki and Sakari Sohlberg Foundation, the Finnish Diabetes Association, the Finnish Foundation for Cardiovascular Research, the City of Kuopio, and the Kuopio University Hospital.

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

Contribution statement

AV and DEL analysed the data. AV, DEL and TAL wrote the manuscript. All authors contributed to data collection, reviewed the manuscript and approved the final version. TAL is the guarantor of the work and takes responsibility for the contents of this article.

Supplementary material

125_2014_3172_MOESM1_ESM.pdf (186 kb)
ESM Methods (PDF 185 kb)
125_2014_3172_MOESM2_ESM.pdf (99 kb)
ESM Table 1 (PDF 98 kb)
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ESM Table 2 (PDF 99 kb)
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ESM Table 3 (PDF 98 kb)
125_2014_3172_MOESM5_ESM.pdf (99 kb)
ESM Table 4 (PDF 98 kb)
125_2014_3172_MOESM6_ESM.pdf (145 kb)
ESM Fig 1 (PDF 144 kb)


  1. 1.
    Alberti KG, Eckel RH, Grundy SM et al (2009) 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 120:1640–1645PubMedCrossRefGoogle Scholar
  2. 2.
    Kassi E, Pervanidou P, Kaltsas G, Chrousos G (2011) Metabolic syndrome: definitions and controversies. BMC Med 9:48PubMedCentralPubMedCrossRefGoogle Scholar
  3. 3.
    Grundy SM (2008) Metabolic syndrome pandemic. Arterioscler Thromb Vasc Biol 28:629–636PubMedCrossRefGoogle Scholar
  4. 4.
    Flegal KM, Carroll MD, Kit BK, Ogden CL (2012) Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999–2010. JAMA 307:491–497Google Scholar
  5. 5.
    Morrison JA, Friedman LA, Gray-McGuire C (2007) Metabolic syndrome in childhood predicts adult cardiovascular disease 25 years later: the Princeton Lipid Research Clinics Follow-up Study. Pediatrics 120:340–345PubMedCrossRefGoogle Scholar
  6. 6.
    Morrison JA, Friedman LA, Wang P, Glueck CJ (2008) Metabolic syndrome in childhood predicts adult metabolic syndrome and type 2 diabetes mellitus 25 to 30 years later. J Pediatr 152:201–206PubMedCrossRefGoogle Scholar
  7. 7.
    Laaksonen DE, Lakka HM, Niskanen LK, Kaplan GA, Salonen JT, Lakka TA (2002) Metabolic syndrome and development of diabetes mellitus: application and validation of recently suggested definitions of the metabolic syndrome in a prospective cohort study. Am J Epidemiol 156:1070–1077PubMedCrossRefGoogle Scholar
  8. 8.
    McNeill AM, Rosamond WD, Girman CJ et al (2005) The metabolic syndrome and 11-year risk of incident cardiovascular disease in the atherosclerosis risk in communities study. Diabetes Care 28:385–390PubMedCrossRefGoogle Scholar
  9. 9.
    Lakka HM, Laaksonen DE, Lakka TA et al (2002) The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA 288:2709–2716PubMedCrossRefGoogle Scholar
  10. 10.
    Brambilla P, Lissau I, Flodmark CE et al (2007) Metabolic risk-factor clustering estimation in children: to draw a line across pediatric metabolic syndrome. Int J Obes (Lond) 31:591–600CrossRefGoogle Scholar
  11. 11.
    Steinberger J, Daniels SR, Eckel RH et al (2009) Progress and challenges in metabolic syndrome in children and adolescents: a scientific statement from the American Heart Association Atherosclerosis, Hypertension, and Obesity in the Young Committee of the Council on Cardiovascular Disease in the Young; Council on Cardiovascular Nursing; and Council on Nutrition, Physical Activity, and Metabolism. Circulation 119:628–647PubMedCrossRefGoogle Scholar
  12. 12.
    Andersen LB, Harro M, Sardinha LB et al (2006) Physical activity and clustered cardiovascular risk in children: a cross-sectional study (The European Youth Heart Study). Lancet 368:299–304PubMedCrossRefGoogle Scholar
  13. 13.
    Eisenmann JC, Laurson KR, DuBose KD, Smith BK, Donnelly JE (2010) Construct validity of a continuous metabolic syndrome score in children. Diabetol Metab Syndr 2:8PubMedCentralPubMedCrossRefGoogle Scholar
  14. 14.
    Pandit D, Chiplonkar S, Khadilkar A, Kinare A, Khadilkar V (2011) Efficacy of a continuous metabolic syndrome score in Indian children for detecting subclinical atherosclerotic risk. Int J Obes (Lond) 35:1318–1324CrossRefGoogle Scholar
  15. 15.
    Hanley AJ, Karter AJ, Festa A et al (2002) Factor analysis of metabolic syndrome using directly measured insulin sensitivity: The Insulin Resistance Atherosclerosis Study. Diabetes 51:2642–2647PubMedCrossRefGoogle Scholar
  16. 16.
    Martinez-Vizcaino V, Martinez MS, Aguilar FS et al (2010) Validity of a single-factor model underlying the metabolic syndrome in children: a confirmatory factor analysis. Diabetes Care 33:1370–1372PubMedCentralPubMedCrossRefGoogle Scholar
  17. 17.
    Child D (1990) The essentials of factor analysis. Cassell, LondonGoogle Scholar
  18. 18.
    Li C, Ford ES (2007) Is there a single underlying factor for the metabolic syndrome in adolescents? A confirmatory factor analysis. Diabetes Care 30:1556–1561PubMedCrossRefGoogle Scholar
  19. 19.
    Shen BJ, Todaro JF, Niaura R et al (2003) Are metabolic risk factors one unified syndrome? Modeling the structure of the metabolic syndrome X. Am J Epidemiol 157:701–711PubMedCrossRefGoogle Scholar
  20. 20.
    Pladevall M, Singal B, Williams LK et al (2006) A single factor underlies the metabolic syndrome: a confirmatory factor analysis. Diabetes Care 29:113–122PubMedCrossRefGoogle Scholar
  21. 21.
    Hillier TA, Rousseau A, Lange C et al (2006) Practical way to assess metabolic syndrome using a continuous score obtained from principal components analysis. Diabetologia 49:1528–1535PubMedCentralPubMedCrossRefGoogle Scholar
  22. 22.
    Viitasalo A, Laaksonen DE, Lindi V et al (2012) Clustering of metabolic risk factors is associated with high-normal levels of liver enzymes among 6- to 8-year-old children: the PANIC Study. Metab Syndr Relat Disord 10:337–343PubMedCrossRefGoogle Scholar
  23. 23.
    Hassinen M, Lakka TA, Savonen K et al (2008) Cardiorespiratory fitness as a feature of metabolic syndrome in older men and women: the Dose-Responses to Exercise Training Study (DR's EXTRA). Diabetes Care 31:1242–1247PubMedCrossRefGoogle Scholar
  24. 24.
    Ekelund U, Anderssen SA, Froberg K et al (2007) Independent associations of physical activity and cardiorespiratory fitness with metabolic risk factors in children: the European Youth Heart Study. Diabetologia 50:1832–1840PubMedCrossRefGoogle Scholar
  25. 25.
    Kahn R, Buse J, Ferrannini E, Stern M (2005) The metabolic syndrome: time for a critical appraisal. Joint statement from the American Diabetes Association and the European Association for the Study of Diabetes. Diabetologia 48:1684–1699PubMedCrossRefGoogle Scholar
  26. 26.
    Wijndaele K, Beunen G, Duvigneaud N et al (2006) A continuous metabolic syndrome risk score: utility for epidemiological analyses. Diabetes Care 29:2329PubMedCrossRefGoogle Scholar
  27. 27.
    Eisenmann JC (2008) On the use of a continuous metabolic syndrome score in pediatric research. Cardiovasc Diabetol 7:17PubMedCentralPubMedCrossRefGoogle Scholar
  28. 28.
    Meigs JB (2000) Invited commentary: insulin resistance syndrome? Syndrome X? Multiple metabolic syndrome? A syndrome at all? Factor analysis reveals patterns in the fabric of correlated metabolic risk factors. Am J Epidemiol 152:908–911, discussion 912PubMedCrossRefGoogle Scholar
  29. 29.
    Laaksonen DE, Niskanen L, Lakka HM, Lakka TA, Uusitupa M (2004) Epidemiology and treatment of the metabolic syndrome. Ann Med 36:332–346PubMedCrossRefGoogle Scholar
  30. 30.
    Martinez-Vizcaino V, Ortega FB, Solera-Martinez M et al (2011) Stability of the factorial structure of metabolic syndrome from childhood to adolescence: a 6-year follow-up study. Cardiovasc Diabetol 10:81PubMedCentralPubMedCrossRefGoogle Scholar
  31. 31.
    Povel C, Beulens J, van der Schouw Y et al (2012) Metabolic syndrome model definitions predicting type 2 diabetes and cardiovascular disease. Diabetes Care 36:362–368PubMedCrossRefGoogle Scholar
  32. 32.
    Siegel MJ, Hildebolt CF, Bae KT, Hong C, White NH (2007) Total and intraabdominal fat distribution in preadolescents and adolescents: measurement with MR imaging. Radiology 242:846–856PubMedCrossRefGoogle Scholar
  33. 33.
    Benfield LL, Fox KR, Peters DM et al (2008) Magnetic resonance imaging of abdominal adiposity in a large cohort of British children. Int J Obes (Lond) 32:91–99CrossRefGoogle Scholar
  34. 34.
    Cole TJ, Bellizzi MC, Flegal KM, Dietz WH (2000) Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 320:1240–1243PubMedCentralPubMedCrossRefGoogle Scholar
  35. 35.
    Cornier MA, Dabelea D, Hernandez TL et al (2008) The metabolic syndrome. Endocr Rev 29:777–822PubMedCrossRefGoogle Scholar
  36. 36.
    Widhalm K, Ghods E (2010) Nonalcoholic fatty liver disease: a challenge for pediatricians. Int J Obes (Lond) 34:1451–1467CrossRefGoogle Scholar
  37. 37.
    Bennett B, Larson-Meyer DE, Ravussin E et al (2011) Impaired insulin sensitivity and elevated ectopic fat in healthy obese vs. nonobese prepubertal children. Obesity (Silver Spring) 20:371–375CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Anna Viitasalo
    • 1
  • Timo A. Lakka
    • 1
    • 2
    • 3
    Email author
  • David E. Laaksonen
    • 1
    • 4
  • Kai Savonen
    • 2
    • 3
  • Hanna-Maaria Lakka
    • 1
  • Maija Hassinen
    • 2
  • Pirjo Komulainen
    • 2
  • Tuomo Tompuri
    • 1
    • 3
  • Sudhir Kurl
    • 5
  • Jari A. Laukkanen
    • 5
  • Rainer Rauramaa
    • 2
    • 3
  1. 1.Department of Physiology, Institute of BiomedicineUniversity of Eastern FinlandKuopioFinland
  2. 2.Kuopio Research Institute of Exercise MedicineKuopioFinland
  3. 3.Department of Clinical Physiology and Nuclear MedicineKuopio University HospitalKuopioFinland
  4. 4.Institute of Clinical MedicineKuopio University HospitalKuopioFinland
  5. 5.Department of Public Health, Institute of Public Health and Clinical NutritionUniversity of Eastern FinlandKuopioFinland

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