title: Elevation of liver enzymes within the normal limits and metabolic syndrome.
abstract: 1. Metabolic syndrome is frequently associated with elevated liver enzymes. However, the current 'normal' limits for liver enzymes often fail to identify patients with metabolic syndrome and the associated non-alcoholic fatty liver disease (NAFLD). 2. In the present study, 1503 participants, aged between 18 and 95 years, were recruited from the physical examination centre of Shanghai Zhongshan Hospital and Shanghai Changfeng Community Health Centre. The association between liver enzymes within the 'normal' range and metabolic syndrome was investigated and optimal cut-off values for liver enzymes in metabolic syndrome were determined. We further compared the diagnostic performance of the new cut-off values for liver enzymes in metabolic syndrome and NAFLD with the traditional 'normal' range for liver enzymes. 3. Serum liver enzymes within the traditional 'normal' limits, especially alanine aminotransferase (ALT) and γ-glutamyl transpeptidase (GGT), were correlated with most of components of the metabolic syndrome, as determined by Spearman's partial correlation analysis. Logistic regression analysis revealed that within the 'normal' range of liver enzymes, the frequency of metabolic syndrome was significantly increased in the higher quintile for ALT and GGT compared with the lowest quintile. Receiver operating characteristic curve analysis revealed that the optimal cut-off values for ALT, aspartate aminotransferase and GGT to identify metabolic syndrome were 26, 25 and 29 U/L, respectively, in men and 20, 23 and 21 U/L, respectively, in women. These values were much more effective in detecting patients with potential metabolic syndrome and NAFLD than the traditional cut-off values. 4. A slight elevation of liver enzymes within the 'normal' limits, especially ALT and GGT, indicates the presence of metabolic syndrome and NAFLD. Revision of the current normal limits for liver enzymes is advisable so that patients with potential metabolic disorders can be identified.
authors.affiliation: Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, China.
authors.forename: Ming-Feng
authors.initials: MF
authors.lastname: Xia
authors.forename: Hong-Mei
authors.initials: HM
authors.lastname: Yan
authors.forename: Huan-Dong
authors.initials: HD
authors.lastname: Lin
authors.forename: Hua
authors.initials: H
authors.lastname: Bian
authors.forename: Bai-Shen
authors.initials: BS
authors.lastname: Pan
authors.forename: Xiu-Zhong
authors.initials: XZ
authors.lastname: Yao
authors.forename: Ruo-Kun
authors.initials: RK
authors.lastname: Li
authors.forename: Meng-Su
authors.initials: MS
authors.lastname: Zeng
authors.forename: Xin
authors.initials: X
authors.lastname: Gao
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000410:Alanine Transaminase / Q000097:blood*; D001219:Aspartate Aminotransferases / Q000097:blood*; D005260:Female; D006801:Humans; D008099:Liver / Q000201:enzymology*; D016015:Logistic Models; D008297:Male; D024821:Metabolic Syndrome / Q000378:metabolism*; D008875:Middle Aged; D011237:Predictive Value of Tests; D012016:Reference Values; D005723:gamma-Glutamyltransferase / Q000097:blood*
chemical_list: D005723:gamma-Glutamyltransferase; D001219:Aspartate Aminotransferases; D000410:Alanine Transaminase