European Journal of Epidemiology

, Volume 25, Issue 2, pp 115–124 | Cite as

The relative importance of modifiable potential risk factors of type 2 diabetes: a meta-analysis of two cohorts

  • Maarit A. Laaksonen
  • Paul Knekt
  • Harri Rissanen
  • Tommi Härkänen
  • Esa Virtala
  • Jukka Marniemi
  • Arpo Aromaa
  • Markku Heliövaara
  • Antti Reunanen


Lifestyle factors predict type 2 diabetes occurrence, but their effect in high- and low-risk populations is poorly known. This study determines the prediction of low-risk lifestyle on type 2 diabetes in those with and without metabolic syndrome in a pooled sample of two representative Finnish cohorts, collected in 1978–1980 and 2000–2001. Altogether 8,627 individuals, aged 40–79 years, and free of diabetes and cardiovascular disease at baseline were included in this study. A low-risk lifestyle was defined based on body mass index, exercise, alcohol consumption, smoking, and serum vitamin D concentration. The metabolic syndrome was defined according to the International Diabetes Federation including obesity, blood pressure, serum HDL cholesterol, serum triglycerides, and fasting glucose. During a 10-year follow-up, altogether 226 type 2 diabetes cases occurred. Overweight was the strongest predictor of type 2 diabetes (population attributable fraction (PAF) = 77%, 95% confidence interval (CI): 53, 88%). Together with lack of exercise, unsatisfactory alcohol consumption, smoking, and low vitamin D concentration it explained 82% of the cases. Altogether 62% (CI: 47, 73%) of the cases were attributable to the metabolic syndrome and 92% (CI: 67, 98%) to the most unfavourable combination of its components. The metabolic syndrome did not modify the prediction of lifestyle factors but persons with normal blood pressure benefited more from positive changes in exercise, alcohol consumption, and smoking than those with elevated blood pressure (P for interaction = 0.01). In conclusion, modification of lifestyle factors apparently reduces type 2 diabetes risk, especially in persons with normal blood pressure.


Type 2 diabetes Low-risk lifestyle Metabolic syndrome Cohort studies Pooling Population attributable fraction (PAF) 



Body mass index


95% Confidence interval


High-density lipoprotein

Health 2000

Health 2000 Survey


International Diabetes Federation


Mini-Finland Health Survey


Population attributable fraction


Relative risk


World Health Organisation



The financial support of the postgraduate school Doctoral Programs in Public Health (DPPH) to the first author is gratefully acknowledged. The SAS macro applied in the estimation of PAF can be obtained from the authors.


  1. 1.
    Wild S, Roglic G, Green A, Sicree R, King H. Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care. 2004;27(5):1047–53.CrossRefPubMedGoogle Scholar
  2. 2.
    van Dam RM. The epidemiology of lifestyle and risk for type 2 diabetes. Eur J Epidemiol. 2003;18(12):1115–25.PubMedGoogle Scholar
  3. 3.
    Hu FB, Manson JE, Stampfer MJ, Colditz G, Liu S, Solomon CG, et al. Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. N Engl J Med. 2001;345(11):790–7.CrossRefPubMedGoogle Scholar
  4. 4.
    Schulze MB, Hoffmann K, Boeing H, Linseisen J, Rohrmann S, Möhlig M, et al. An accurate risk score based on anthropometric, dietary, and lifestyle factors to predict the development of type 2 diabetes. Diabetes Care. 2007;30(3):510–5.CrossRefPubMedGoogle Scholar
  5. 5.
    Mozaffarian D, Kamineni A, Carnethon M, Djoussé L, Mukamal KJ, Siskovick D. Lifestyle risk factors and new-onset diabetes mellitus in older adults. Arch Intern Med. 2009;169(8):798–807.CrossRefPubMedGoogle Scholar
  6. 6.
    Knekt P, Laaksonen M, Mattila C, Härkänen T, Marniemi J, Heliövaara M, et al. Serum vitamin D and subsequent occurrence of type 2 diabetes. Epidemiology. 2008;19(5):666–71.CrossRefPubMedGoogle Scholar
  7. 7.
    Pittas AG, Lau J, Hu FB, Dawson-Hughes B. The role of vitamin D and calcium in type 2 diabetes. A systematic review and meta-analysis. J Clin Endocrinol Metab. 2007;92(3):2017–29.CrossRefPubMedGoogle Scholar
  8. 8.
    Alberti KG, Zimmet P, Shaw J. Metabolic syndrome—a new world-wide definition. A consensus statement from the International Diabetes Federation. Diabet Med. 2006;23(5):469–80.CrossRefPubMedGoogle Scholar
  9. 9.
    Cheung BM, Wat NM, Man YB, Tam S, Thomas GN, Leung GM, et al. Development of diabetes in Chinese with the metabolic syndrome: a 6-year prospective study. Diabetes Care. 2007;30(6):1430–6.CrossRefPubMedGoogle Scholar
  10. 10.
    Hanson RL, Imperatore G, Bennett PH, Knowler WC. Components of the “metabolic syndrome” and incidence of type 2 diabetes. Diabetes. 2002;51(10):3120–7.CrossRefPubMedGoogle Scholar
  11. 11.
    Stern MP, Williams K, Haffner SM. Identification of persons at high risk for type 2 diabetes mellitus: do we need the oral glucose tolerance test? Ann Intern Med. 2002;136(8):575–81.PubMedGoogle Scholar
  12. 12.
    Cameron AJ, Magliano DJ, Zimmet PZ, Welborn TA, Colagiuri S, Tonkin AM, et al. The metabolic syndrome as a tool for predicting future diabetes: the AusDiab study. J Intern Med. 2008;264(2):177–86.CrossRefPubMedGoogle Scholar
  13. 13.
    Ford ES, Li C, Sattar N. Metabolic syndrome and incident diabetes: current state of the evidence. Diabetes Care. 2008;31(9):1898–904.CrossRefPubMedGoogle Scholar
  14. 14.
    Kanaya AM, Wassel Fyr CL, de Rekeneire N, Shorr RI, Schwartz AV, Goodpaster BH, et al. Predicting the development of diabetes in older adults: the derivation and validation of a prediction rule. Diabetes Care. 2005;28(2):404–8.CrossRefPubMedGoogle Scholar
  15. 15.
    Lindström J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care. 2003;26(3):725–31.CrossRefPubMedGoogle Scholar
  16. 16.
    McNeely MJ, Boyko EJ, Leonetti DL, Kahn SE, Fujimoto WY. Comparison of a clinical model, the oral glucose tolerance test, and fasting glucose for prediction of type 2 diabetes risk in Japanese Americans. Diabetes Care. 2003;26(3):758–63.CrossRefPubMedGoogle Scholar
  17. 17.
    Norberg M, Eriksson JW, Lindahl B, Andersson C, Rolandsson O, Stenlund H, et al. A combination of HbA1c, fasting glucose and BMI is effective in screening for individuals at risk of future type 2 diabetes: OGTT is not needed. J Intern Med. 2006;260(3):263–71.CrossRefPubMedGoogle Scholar
  18. 18.
    Schmidt MI, Duncan BB, Bang H, Pankow JS, Ballantyne CM, Golden SH, et al. Identifying individuals at high risk for diabetes: the Atherosclerosis Risk in Communities study. Diabetes Care. 2005;28(8):2013–8.CrossRefPubMedGoogle Scholar
  19. 19.
    Narayan KM, Kanaya AM, Gregg EW. Lifestyle intervention for the prevention of type 2 diabetes mellitus: putting theory to practice. Treat Endocrinol. 2003;2(5):315–20.CrossRefPubMedGoogle Scholar
  20. 20.
    Hu G, Lakka TA, Lakka HM, Tuomilehto J. Lifestyle management in the metabolic syndrome. Metab Syndr Relat Disord. 2006;4(4):270–86.CrossRefPubMedGoogle Scholar
  21. 21.
    Liberopoulos EN, Tsouli S, Mikhailidis DP, Elisaf MS. Preventing type 2 diabetes in high risk patients: an overview of lifestyle and pharmacological measures. Curr Drug Targets. 2006;7(2):211–28.CrossRefPubMedGoogle Scholar
  22. 22.
    Taslim S, Tai ES. The relevance of the metabolic syndrome. Ann Acad Med Singapore. 2009;38(1):29–33.PubMedGoogle Scholar
  23. 23.
    Aromaa A, Heliövaara M, Impivaara O, Knekt P, Maatela J. Aims, methods and study population. Part 1. In: Aromaa A, Heliövaara M, Impivaara O, Knekt P, Maatela J, editors. The execution of the Mini-Finland Health Survey (in Finnish, English summary). Helsinki and Turku: Publications of the Social Insurance Institution, Finland ML:88; 1989.Google Scholar
  24. 24.
    Aromaa A, Koskinen S, editors. Health and functional capacity in Finland. Baseline results of the Health 2000 health examination survey. Helsinki: Publications of the National Public Health Institute B12; 2004.Google Scholar
  25. 25.
    Lehtonen R, Kuusela V. Statistical efficiency of the Mini-Finland Health Survey’s sampling design. Part 5. In: Aromaa A, Heliövaara M, Impivaara O, Knekt P, Maatela J, editors. The execution of the Mini-Finland Health Survey (in Finnish, English summary). Helsinki and Turku: Publications of the Social Insurance Institution, Finland ML:65; 1986.Google Scholar
  26. 26.
    Kostner GM. Enzymatic determination of cholesterol in high-density lipoprotein fractions prepared by polyanion precipitation (Letter). Clin Chem. 1976;22(5):695.PubMedGoogle Scholar
  27. 27.
    Reunanen A, Kangas T, Martikainen J, Klaukka T. Nationwide survey of comorbidity, use, and costs of all medications in Finnish diabetic individuals. Diabetes Care. 2000;23(9):1265–71.CrossRefPubMedGoogle Scholar
  28. 28.
    World Health Organization. Diabetes mellitus: report of a WHO study group. Geneva: World Health Organization; 1985.Google Scholar
  29. 29.
    Heliövaara M, Reunanen A, Aromaa A, Knekt P, Aho K, Suhonen O. Validity of hospital discharge data in a prospective epidemiological study on stroke and myocardial infarction. Acta Med Scand. 1984;216(3):309–15.PubMedGoogle Scholar
  30. 30.
    Reunanen A, Aromaa A, Pyörälä K, Punsar S, Maatela J, Knekt P. The Social Insurance Institution’s coronary heart disease study. Baseline data and 5-year mortality experience. Acta Med Scand Suppl. 1983;673:1–120.PubMedGoogle Scholar
  31. 31.
    Cox DR. Regression models and life tables (with discussion). J R Stat Soc B. 1972;34:187–220.Google Scholar
  32. 32.
    Friedman M. Piecewise constant hazards models for survival data with covariates. Ann Statist. 1982;10(1):101–13.CrossRefGoogle Scholar
  33. 33.
    Benichou J. A review of adjusted estimators of attributable risk. Stat Methods Med Res. 2001;10(3):195–216.CrossRefPubMedGoogle Scholar
  34. 34.
    Laaksonen M, Härkänen T, Knekt P, Virtala E, Oja H. Estimation of population attributable fraction (PAF) for disease occurrence in a cohort study design. Stat Med (in press).Google Scholar
  35. 35.
    Knekt P, Ritz J, Pereira MA, O’Reilly EJ, Augustsson K, Fraser GE, et al. Antioxidant vitamins and coronary heart disease risk: a pooled analysis of 9 cohorts. Am J Clin Nutr. 2004;80(6):1508–20.PubMedGoogle Scholar
  36. 36.
    DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–88.CrossRefPubMedGoogle Scholar
  37. 37.
    Stram DO. Meta-analysis of published data using a linear mixed-effects model. Biometrics. 1996;52(2):536–44.CrossRefPubMedGoogle Scholar
  38. 38.
    Schulze MB, Hu FB. Primary prevention of diabetes: what can be done and how much can be prevented? Annu Rev Public Health. 2005;26:445–67.CrossRefPubMedGoogle Scholar
  39. 39.
    Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393–403.CrossRefPubMedGoogle Scholar
  40. 40.
    Orchard TJ, Temprosa M, Goldberg R, Haffner S, Ratner R, Marcovina S, et al. The effect of metformin and intensive lifestyle intervention on the metabolic syndrome: the Diabetes Prevention Program randomized trial. Ann Intern Med. 2005;142(8):611–9.PubMedGoogle Scholar
  41. 41.
    Alberti KG, Zimmet P, Shaw J. International Diabetes Federation: a consensus on type 2 diabetes prevention. Diabet Med. 2007;24(5):451–63.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Maarit A. Laaksonen
    • 1
  • Paul Knekt
    • 1
  • Harri Rissanen
    • 1
  • Tommi Härkänen
    • 1
  • Esa Virtala
    • 1
  • Jukka Marniemi
    • 2
  • Arpo Aromaa
    • 1
  • Markku Heliövaara
    • 1
  • Antti Reunanen
    • 1
  1. 1.National Institute for Health and WelfareHelsinkiFinland
  2. 2.National Institute for Health and WelfareTurkuFinland

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