Insulin-Related Biomarkers to Predict the Risk of Metabolic Syndrome

BACKGROUND: The predictive ability of insulin resistance or insulin sensitivity, in combination with traditional cardiovascular risk factors for metabolic syndrome (MetS), has not yet been clearly evaluated in Japanese male subjects. OBJECTIVES: A one-year follow-up study was conducted to determine the ability of the insulin-related biomarkers to predict the risk of MetS development. PATIENTS AND METHODS: A total of 2642 male workers of a Japanese company free from MetS at the baseline were monitored. The homeostasis model assessment for insulin resistance (HOMA-IR), and quantitative insulin sensitivity check index (QUICKI) were selected as the insulin-related markers. RESULTS: The incidence of metabolic syndrome after one year was 8.8%. A multiple logistic regression analysis identified regular physical activity, age (≥ 45 years old), serum uric acid (≥ 7 mg/dL), serum alanine aminotransferase (≥ 45 IU/L), serum C-reactive protein (≥ 0.1 mg/L) and HOMA-IR (≥ 2.5) as significant risk factors for the development of MetS, with odds ratios (95% confidence intervals) of 0.68 (0.50 – 0.92), 2.0 (1.5 – 2.6), 2.2 (1.6 – 3.0), 1.5 (1.02 – 2.2), 1.4 (1.01 – 2.0), and 2.3 (1.6 – 3.3), respectively. When QUICKI was used instead of HOMA-IR, age (≥ 45 years old), serum uric acid (≥ 7 mg/dL), serum gamma-glutamyl transferase (≥ 50 IU/L), and QUICKI (≤ 0.33) were identified as significant contributors to the risk of MetS, with odds ratios (95% confidence intervals) of 0.68 (0.51 – 0.93), 2.0 (1.5 – 2.6), 2.2 (1.6 – 3.0), 1.4 (1.01 – 2.0), and 2.5 (1.7 – 3.6), respectively. CONCLUSIONS: The mathematical meaning of the two insulin-related biomarkers examined was the same, and the odds ratios of the two biomarkers were almost the same after adjustments for other independent variables.