Elevated fasting insulin predicts the future incidence of metabolic syndrome: a 5-year follow-up study

Cardiovasc Diabetol. 2011 Nov 30;10:108. doi: 10.1186/1475-2840-10-108.


Background: There is controversy about the specific pathophysiology of metabolic syndrome (MS) but several authors have argued that hyperinsulinemia is a key feature of the cluster. We aimed to assess whether the baseline insulin levels could predict the development of MS in a well characterised cohort of otherwise healthy adults who were followed over a five year period.

Methods: We identified 2, 350 Koreans subjects who did not have MS in 2003 and who were followed up in 2008. The subjects were divided into 4 groups according to the baseline quartiles of fasting insulin, and the predictors of the incidence of MS were analyzed using multivariate regression analysis.

Results: Over the follow up period, 8.5% of the cohort developed MS. However, 16.4% of the subjects in the highest quartile of the insulin levels developed MS. In a model that included gender, age, the smoking status, the exercise level, alcohol consumption and the systolic blood pressure, the subjects in the highest quartile of the insulin levels had more than a 5 times greater risk of developing MS compared that of the subjects in the lowest quartile. This predictive importance remained significant even after correcting for all the individual features of MS.

Conclusions: These data suggest that high baseline fasting insulin levels are independent determinants for the future development of MS.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Biomarkers / blood
  • Chi-Square Distribution
  • Fasting / blood*
  • Female
  • Follow-Up Studies
  • Humans
  • Hyperinsulinism / blood*
  • Hyperinsulinism / epidemiology*
  • Incidence
  • Insulin / blood*
  • Logistic Models
  • Male
  • Metabolic Syndrome / blood*
  • Metabolic Syndrome / epidemiology*
  • Middle Aged
  • Multivariate Analysis
  • Odds Ratio
  • Republic of Korea / epidemiology
  • Risk Assessment
  • Risk Factors
  • Time Factors
  • Up-Regulation


  • Biomarkers
  • Insulin