title: Association between γ-glutamyl transferase and metabolic syndrome: a cross-sectional study of an adult population in Beijing.
abstract: The relationship between liver enzymes and clustered components of metabolic syndrome (MetS) is explored and the predictive power of γ-glutamyl transferase (GGT) for the diagnosis of MetS in an adult population in Beijing is investigated. A total of 10,553 adults aged 20-65 years who underwent health examinations at Beijing Tongren Hospital in 2012 were enrolled in the study. Multivariate logistic regression analysis is conducted to determine the associations between the levels of various liver enzymes and clustered components of MetS. A receiver operating characteristic analysis is used to determine the optimal cut-off value of GGT for the diagnosis of MetS. A high level of GGT is found to be positively associated with clustered components of MetS in both men and women after adjusting for age, body mass index (BMI), history of alcoholic fatty liver, and the presence of taking anti-hypertensive, anti-dyslipidemic, and anti-diabetic drugs. Among all components of MetS, GGT is more predictive of triglyceride, and BMI. The area-under-the-curve values of GGT for discriminating MetS from normal metabolic status in men and women are 0.73 and 0.80, respectively. The optimal cut-off value of GGT for men is 31.50 U/L, demonstrating a sensitivity of 74.00% and specificity of 62.00%. For women, it is 19.50 U/L (sensitivity 76.00% and specificity 70.00%). GGT is therefore recommended as a useful diagnostic marker for MetS, because the test is inexpensive, highly sensitive, and frequently encountered in clinical practice.
authors.affiliation: School of Public Health, Capital Medical University, 10 Xitoutiao, Youanmen, Beijing 100069, China. statguo@ccmu.edu.cn.
authors.forename: Lixin
authors.initials: L
authors.lastname: Tao
authors.forename: Xia
authors.initials: X
authors.lastname: Li
authors.forename: Huiping
authors.initials: H
authors.lastname: Zhu
authors.forename: Yue
authors.initials: Y
authors.lastname: Gao
authors.forename: Yanxia
authors.initials: Y
authors.lastname: Luo
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.forename: Zhaoping
authors.initials: Z
authors.lastname: Wang
authors.forename: Dongning
authors.initials: D
authors.lastname: Chen
authors.forename: Lijuan
authors.initials: L
authors.lastname: Wu
authors.forename: Xiuhua
authors.initials: X
authors.lastname: Guo
mesh_terms: D000328:Adult; D015415:Biomarkers / Q000378:metabolism; D002681:China / Q000453:epidemiology; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D008099:Liver / Q000201:enzymology; D016015:Logistic Models; D008297:Male; D024821:Metabolic Syndrome / Q000175:diagnosis* / Q000201:enzymology / Q000453:epidemiology* / Q000209:etiology; D008875:Middle Aged; D015999:Multivariate Analysis; D015995:Prevalence; D012372:ROC Curve; D012307:Risk Factors; D055815:Young Adult; D005723:gamma-Glutamyltransferase / Q000378:metabolism
chemical_list: D015415:Biomarkers; D005723:gamma-Glutamyltransferase