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Serum Gamma-Glutamyltransferase and Ferritin are Related to Insulin Resistance: A Population-Based Study by Dong Wei, Tao Chen, Yun Gao, Haoming Tian

Background: Recent studies have reported the association of gamma-glutamyltransferase (GGT) or ferritin with metabolic disorders. However, there has been no large population-based study assessing the interrelationship of these two biomarkers and their association with insulin resistance.
Methods: A population-based cross-sectional study was carried out in the Chinese Liangshan Yi ethnic group. 756 eligible subjects, aged 20 - 74 years, were included. Demographic characteristics, medical history, and lifestyle data were collected through questionnaires. An oral glucose tolerance test was performed. Laboratory tests, including GGT and ferritin measurements, were conducted. Spearman’s rank-order correlation and multiple linear regression were used to calculate the correlation of GGT with ferritin and their relationship to the insulin resistance index determined by the homeostasis model assessment (HOMA-IR).
Results: A significant correlation between serum GGT and ferritin levels was found (r = 0.393, p < 0.05). GGT was independently correlated with ferritin after adjustment for age, gender, place of residence, education, income, leisure-time physical activity, drinking, smoking, body mass index, systolic pressure, diastolic pressure, hepatitis B surface antigen, anti-hepatitis C virus, aspartate aminotransferase, alanine aminotransferase, total cholesterol, low density lipoprotein-cholesterol, triglyceride, high density lipoprotein-cholesterol, fasting plasma glucose, 2-hour postprandial plasma glucose, uric acid, and urinary albumin to creatinine ratio (p < 0.05). Positive correlations were established between HOMA-IR and GGT (standard β = 0.252) or ferritin (standard β = 0.181) after adjustment for multiple confounders (p < 0.05).
Conclusions: Insulin resistance is associated with elevated serum GGT and ferritin. A combined determination of GGT and ferritin might lead to better predictions in patients with insulin resistance.

DOI: 10.7754/Clin.Lab.2015.150227