Literature DB >> 20693590

Role of GGT in diagnosis of metabolic syndrome: a clinic-based cross-sectional survey.

B Kasapoglu1, C Turkay, Y Bayram, C Koca.   

Abstract

BACKGROUND &
OBJECTIVES: The aim of this study is to know if the liver function tests (LFT), especially gamma glutamyl transferase (GGT), have a predictive value in diagnosis of metabolic syndrome (MS).
METHODS: A cross-sectional, single-center study was carried out with 908 subjects. Four hundred and forty two of these subjects were diagnosed with MS with IDF criteria; while other 466 were sex and age matched healthy control subjects. Blood pressure, liver function tests, fasting blood glucose levels and lipid profile of the subjects were recorded.
RESULTS: The mean values of alanine amino transferase (ALT), aspartate aminotransferase (AST) and GGT levels were statistically significantly higher in MS group. The mean values of liver enzymes, for female/ male subjects in MS group, AST; ALT and GGT respectively, were; 20.5/19.7 U/l; 25.9/28.5 U/l; 35.9/42.1 U/l. When the sample is divided into quartiles of the GGT levels, increase in GGT is positively correlated with increased MS prevalence. In ROC analysis GGT is as strongly associated with the IDF diagnostic components as is each individual IDF component, except elevated systolic blood pressure. In covariance analysis, there was significant relationship between elevated GGT levels and MS presence after adjustment for age, sex and MS diagnostic criteria; but not AST and ALT levels. In multivariance analysis, in MS group, a high GGT was positively associated with CVD prevalance (odds ratio: 2.011, 95% CI 1.10-4.57) compared to low GGT group independent of age, sex and smoking habits. INTERPRETATION &
CONCLUSIONS: Elevated liver enzymes, although in normal ranges, especially at upper quartiles, play a central role in early diagnosis of fat overflow to the liver. Regarding the availability and simplicity of these tests in routine clinical practice, they, especially GGT, have potential to be considered in algorithms for metabolic syndrome.

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Year:  2010        PMID: 20693590

Source DB:  PubMed          Journal:  Indian J Med Res        ISSN: 0971-5916            Impact factor:   2.375


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